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The active inference framework, and in particular its recent formulation as a partially observable Markov decision process (POMDP), has gained increasing popularity in recent years as a useful approach for modeling neurocognitive processes. This framework is highly general and flexible in its ability to be customized to model any cognitive process, as well as simulate predicted neuronal responses based on its accompanying neural process theory. It also affords both simulation experiments for proof of principle and behavioral modeling for empirical studies. However, there are limited resources that explain how to build and run these models in practice, which limits their widespread use. Most introductions assume a technical background in programming, mathematics, and machine learning. In this paper we offer a step-by-step tutorial on how to build POMDPs, run simulations using standard MATLAB routines, and fit these models to empirical data. We assume a minimal background in programming and mathematics, thoroughly explain all equations, and provide exemplar scripts that can be customized for both theoretical and empirical studies. Our goal is to provide the reader with the requisite background knowledge and practical tools to apply active inference to their own research. We also provide optional technical sections and multiple appendices, which offer the interested reader additional technical details. This tutorial should provide the reader with all the tools necessary to use these models and to follow emerging advances in active inference research.

Integrating neurons into digital systems may enable performance infeasible with silicon alone. Here, we develop DishBrain, a system that harnesses the inherent adaptive computation of neurons in a structured environment. In vitro neural networks from human or rodent origins are integrated with in silico computing via a high-density multielectrode array. Through electrophysiological stimulation and recording, cultures are embedded in a simulated game-world, mimicking the arcade game "Pong." Applying implications from the theory of active inference via the free energy principle, we find apparent learning within five minutes of real-time gameplay not observed in control conditions. Further experiments demonstrate the importance of closed-loop structured feedback in eliciting learning over time. Cultures display the ability to self-organize activity in a goal-directed manner in response to sparse sensory information about the consequences of their actions, which we term synthetic biological intelligence. Future applications may provide further insights into the cellular correlates of intelligence.

The first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines. Active inference is a way of understanding sentient behavior—a theory that characterizes perception, planning, and action in terms of probabilistic inference. Developed by theoretical neuroscientist Karl Friston over years of groundbreaking research, active inference provides an integrated perspective on brain, cognition, and behavior that is increasingly used across multiple disciplines including neuroscience, psychology, and philosophy. Active inference puts the action into perception. This book offers the first comprehensive treatment of active inference, covering theory, applications, and cognitive domains. Active inference is a “first principles” approach to understanding behavior and the brain, framed in terms of a single imperative to minimize free energy. The book emphasizes the implications of the free energy principle for understanding how the brain works. It first introduces active inference both conceptually and formally, contextualizing it within current theories of cognition. It then provides specific examples of computational models that use active inference to explain such cognitive phenomena as perception, attention, memory, and planning.

The hippocampus is crucial for episodic memory, but it is also involved in online prediction. Evidence suggests that a unitary hippocampal code underlies both episodic memory and predictive processing, yet within a predictive coding framework the hippocampal-neocortical interactions that accompany these two phenomena are distinct and opposing. Namely, during episodic recall, the hippocampus is thought to exert an excitatory influence on the neocortex, to reinstate activity patterns across cortical circuits. This contrasts with empirical and theoretical work on predictive processing, where descending predictions suppress prediction errors to 'explain away' ascending inputs via cortical inhibition. In this hypothesis piece, we attempt to dissolve this previously overlooked dialectic. We consider how the hippocampus may facilitate both prediction and memory, respectively, by inhibiting neocortical prediction errors or increasing their gain. We propose that these distinct processing modes depend upon the neuromodulatory gain (or precision) ascribed to prediction error units. Within this framework, memory recall is cast as arising from fictive prediction errors that furnish training signals to optimise generative models of the world, in the absence of sensory data.

Research in clinical neuroscience is founded on the idea that a better understanding of brain (dys)function will improve our ability to diagnose and treat neurological and psychiatric disorders. In recent years, neuroscience has converged on the notion that the brain is a 'prediction machine,' in that it actively predicts the sensory input that it will receive if one or another course of action is chosen. These predictions are used to select actions that will (most often, and in the long run) maintain the body within the narrow range of physiological states consistent with survival. This insight has given rise to an area of clinical computational neuroscience research that focuses on characterizing neural circuit architectures that can accomplish these predictive functions, and on how the associated processes may break down or become aberrant within clinical conditions. Here, we provide a brief review of examples of recent work on the application of predictive processing models of brain function to study clinical (psychiatric) disorders, with the aim of highlighting current directions and their potential clinical utility. We offer examples of recent conceptual models, formal mathematical models, and applications of such models in empirical research in clinical populations, with a focus on making this material accessible to clinicians without expertise in computational neuroscience. In doing so, we aim to highlight the potential insights and opportunities that understanding the brain as a prediction machine may offer to clinical research and practice.

The positive-negative axis of emotional valence has long been recognized as fundamental to adaptive behavior, but its origin and underlying function have largely eluded formal theorizing and computational modeling. Using deep active inference, a hierarchical inference scheme that rests on inverting a model of how sensory data are generated, we develop a principled Bayesian model of emotional valence. This formulation asserts that agents infer their valence state based on the expected precision of their action model-an internal estimate of overall model fitness ("subjective fitness"). This index of subjective fitness can be estimated within any environment and exploits the domain generality of second-order beliefs (beliefs about beliefs). We show how maintaining internal valence representations allows the ensuing affective agent to optimize confidence in action selection preemptively. Valence representations can in turn be optimized by leveraging the (Bayes-optimal) updating term for subjective fitness, which we label affective charge (AC). AC tracks changes in fitness estimates and lends a sign to otherwise unsigned divergences between predictions and outcomes. We simulate the resulting affective inference by subjecting an in silico affective agent to a T-maze paradigm requiring context learning, followed by context reversal. This formulation of affective inference offers a principled account of the link between affect, (mental) action, and implicit metacognition. It characterizes how a deep biological system can infer its affective state and reduce uncertainty about such inferences through internal action (i.e., top-down modulation of priors that underwrite confidence). Thus, we demonstrate the potential of active inference to provide a formal and computationally tractable account of affect. Our demonstration of the face validity and potential utility of this formulation represents the first step within a larger research program. Next, this model can be leveraged to test the hypothesized role of valence by fitting the model to behavioral and neuronal responses.

This essay addresses Cartesian duality and how its implicit dialectic might be repaired using physics and information theory. Our agenda is to describe a key distinction in the physical sciences that may provide a foundation for the distinction between mind and matter, and between sentient and intentional systems. From this perspective, it becomes tenable to talk about the physics of sentience and ‘forces’ that underwrite our beliefs (in the sense of probability distributions represented by our internal states), which may ground our mental states and consciousness. We will refer to this view as Markovian monism, which entails two claims: (1) fundamentally, there is only one type of thing and only one type of irreducible property (hence monism). (2) All systems possessing a Markov blanket have properties that are relevant for understanding the mind and consciousness: if such systems have mental properties, then they have them partly by virtue of possessing a Markov blanket (hence Markovian). Markovian monism rests upon the information geometry of random dynamic systems. In brief, the information geometry induced in any system—whose internal states can be distinguished from external states—must acquire a dual aspect. This dual aspect concerns the (intrinsic) information geometry of the probabilistic evolution of internal states and a separate (extrinsic) information geometry of probabilistic beliefs about external states that are parameterised by internal states. We call these intrinsic (i.e., mechanical, or state-based) and extrinsic (i.e., Markovian, or belief-based) information geometries, respectively. Although these mathematical notions may sound complicated, they are fairly straightforward to handle, and may offer a means through which to frame the origins of consciousness.

Active inference is an approach to understanding behaviour that rests upon the idea that the brain uses an internal generative model to predict incoming sensory data. The fit between this model and data may be improved in two ways. The brain could optimise probabilistic beliefs about the variables in the generative model (i.e. perceptual inference). Alternatively, by acting on the world, it could change the sensory data, such that they are more consistent with the model. This implies a common objective function (variational free energy) for action and perception that scores the fit between an internal model and the world. We compare two free energy functionals for active inference in the framework of Markov decision processes. One of these is a functional of beliefs (i.e. probability distributions) about states and policies, but a function of observations, while the second is a functional of beliefs about all three. In the former (expected free energy), prior beliefs about outcomes are not part of the generative model (because they are absorbed into the prior over policies). Conversely, in the second (generalised free energy), priors over outcomes become an explicit component of the generative model. When using the free energy function, which is blind to future observations, we equip the generative model with a prior over policies that ensure preferred (i.e. priors over) outcomes are realised. In other words, if we expect to encounter a particular kind of outcome, this lends plausibility to those policies for which this outcome is a consequence. In addition, this formulation ensures that selected policies minimise uncertainty about future outcomes by minimising the free energy expected in the future. When using the free energy functional-that effectively treats future observations as hidden states-we show that policies are inferred or selected that realise prior preferences by minimising the free energy of future expectations. Interestingly, the form of posterior beliefs about policies (and associated belief updating) turns out to be identical under both formulations, but the quantities used to compute them are not.

Dynamic Causal Modelling (DCM) is the predominant method for inferring effective connectivity from neuroimaging data. In the 15 years since its introduction, the neural models and statistical routines in DCM have developed in parallel, driven by the needs of researchers in cognitive and clinical neuroscience. In this guide, we step through an exemplar fMRI analysis in detail, reviewing the current implementation of DCM and demonstrating recent developments in group-level connectivity analysis. In the appendices, we detail the theory underlying DCM and the assumptions (i.e., priors) in the models. In the first part of the guide (current paper), we focus on issues specific to DCM for fMRI. This is accompanied by all the necessary data and instructions to reproduce the analyses using the SPM software. In the second part (in a companion paper), we move from subject-level to group-level modelling using the Parametric Empirical Bayes framework, and illustrate how to test for commonalities and differences in effective connectivity across subjects, based on imaging data from any modality.

This paper provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject variability in neural circuitry (effective connectivity). It steps through an analysis in detail and provides a tutorial style explanation of the underlying theory and assumptions (i.e, priors). The analysis procedure involves specifying a hierarchical model with two or more levels. At the first level, state space models (DCMs) are used to infer the effective connectivity that best explains a subject's neuroimaging timeseries (e.g. fMRI, MEG, EEG). Subject-specific connectivity parameters are then taken to the group level, where they are modelled using a General Linear Model (GLM) that partitions between-subject variability into designed effects and additive random effects. The ensuing (Bayesian) hierarchical model conveys both the estimated connection strengths and their uncertainty (i.e., posterior covariance) from the subject to the group level; enabling hypotheses to be tested about the commonalities and differences across subjects. This approach can also finesse parameter estimation at the subject level, by using the group-level parameters as empirical priors. The preliminary first level (subject specific) DCM for fMRI analysis is covered in a companion paper. Here, we detail group-level analysis procedures that are suitable for use with data from any neuroimaging modality. This paper is accompanied by an example dataset, together with step-by-step instructions demonstrating how to reproduce the analyses.

This article presents a unifying theory of the embodied, situated human brain called the Hierarchically Mechanistic Mind (HMM). The HMM describes the brain as a complex adaptive system that actively minimises the decay of our sensory and physical states by producing self-fulfilling action-perception cycles via dynamical interactions between hierarchically organised neurocognitive mechanisms. This theory synthesises the free-energy principle (FEP) in neuroscience with an evolutionary systems theory of psychology that explains our brains, minds, and behaviour by appealing to Tinbergen's four questions: adaptation, phylogeny, ontogeny, and mechanism. After leveraging the FEP to formally define the HMM across different spatiotemporal scales, we conclude by exploring its implications for theorising and research in the sciences of the mind and behaviour.

The processes underwriting the acquisition of culture remain unclear. How are shared habits, norms, and expectations learned and maintained with precision and reliability across large-scale sociocultural ensembles? Is there a unifying account of the mechanisms involved in the acquisition of culture? Notions such as "shared expectations," the "selective patterning of attention and behaviour," "cultural evolution," "cultural inheritance," and "implicit learning" are the main candidates to underpin a unifying account of cognition and the acquisition of culture; however, their interactions require greater specification and clarification. In this article, we integrate these candidates using the variational (free-energy) approach to human cognition and culture in theoretical neuroscience. We describe the construction by humans of social niches that afford epistemic resources called cultural affordances. We argue that human agents learn the shared habits, norms, and expectations of their culture through immersive participation in patterned cultural practices that selectively pattern attention and behaviour. We call this process "thinking through other minds" (TTOM) - in effect, the process of inferring other agents' expectations about the world and how to behave in social context. We argue that for humans, information from and about other people's expectations constitutes the primary domain of statistical regularities that humans leverage to predict and organize behaviour. The integrative model we offer has implications that can advance theories of cognition, enculturation, adaptation, and psychopathology. Crucially, this formal (variational) treatment seeks to resolve key debates in current cognitive science, such as the distinction between internalist and externalist accounts of theory of mind abilities and the more fundamental distinction between dynamical and representational accounts of enactivism.

Psychedelics exert unique effects on human consciousness. The thalamic filter model suggests that core effects of psychedelics may result from gating deficits, based on a disintegration of information processing within cortico-striato-thalamo-cortical (CSTC) feedback loops. To test this hypothesis, we characterized changes in directed (effective) connectivity between selected CTSC regions after acute administration of lysergic acid diethylamide (LSD), and after pretreatment with Ketanserin (a selective serotonin 2A receptor antagonist) plus LSD in a double-blind, randomized, placebo-controlled, cross-over study in 25 healthy participants. We used spectral dynamic causal modeling (DCM) for resting-state fMRI data. Fully connected DCM models were specified for each treatment condition to investigate the connectivity between the following areas: thalamus, ventral striatum, posterior cingulate cortex, and temporal cortex. Our results confirm major predictions proposed in the CSTC model and provide evidence that LSD alters effective connectivity within CSTC pathways that have been implicated in the gating of sensory and sensorimotor information to the cortex. In particular, LSD increased effective connectivity from the thalamus to the posterior cingulate cortex in a way that depended on serotonin 2A receptor activation, and decreased effective connectivity from the ventral striatum to the thalamus independently of serotonin 2A receptor activation. Together, these results advance our mechanistic understanding of the action of psychedelics in health and disease. This is important for the development of new pharmacological therapeutics and also increases our understanding of the mechanisms underlying the potential clinical efficacy of psychedelics.

This paper formulates the action of psychedelics by integrating the free-energy principle and entropic brain hypothesis. We call this formulation relaxed beliefs under psychedelics (REBUS) and the anarchic brain, founded on the principle that-via their entropic effect on spontaneous cortical activitypsychedelics work to relax the precision of high-level priors or beliefs, thereby liberating bottom-up information flow, particularly via intrinsic sources such as the limbic system. We assemble evidence for this model and show how it can explain a broad range of phenomena associated with the psychedelic experience. With regard to their potential therapeutic use, we propose that psychedelics work to relax the precision weighting of pathologically overweighted priors underpinning various expressions of mental illness. We propose that this process entails an increased sensitization of high-level priors to bottom-up signaling (stemming from intrinsic sources), and that this

The aim of this article is to clarify how best to interpret some of the central constructs that underwrite the free-energy principle (FEP) – and its corollary, active inference – in theoretical neuroscience and biology: namely, the role that generative models and variational densities play in this theory. We argue that these constructs have been systematically misrepresented in the literature, because of the conflation between the FEP and active inference, on the one hand, and distinct (albeit closely related) Bayesian formulations, centred on the brain – variously known as predictive processing, predictive coding or the prediction error minimisation framework. More specifically, we examine two contrasting interpretations of these models: a structural representationalist interpretation and an enactive interpretation. We argue that the structural representationalist interpretation of generative and recognition models does not do justice to the role that these constructs play in active inference under the FEP. We propose an enactive interpretation of active inference – what might be called enactive inference. In active inference under the FEP, the generative and recognition models are best cast as realising inference and control – the self-organising, belief-guided selection of action policies – and do not have the properties ascribed by structural representationalists.

How do humans come to acquire shared expectations about how they ought to behave in distinct normalized social settings? This paper offers a normative framework to answer this question. We introduce the computational construct of 'deontic value' - based on active inference and Markov decision processes - to formalize conceptions of social conformity and human decision-making. Deontic value is an attribute of choices, behaviors, or action sequences that inherit directly from deontic cues in our econiche (e.g., red traffic lights); namely, cues that denote an obligatory social rule. Crucially, the prosocial aspect of deontic value rests upon a particular form of circular causality: deontic cues exist in the environment in virtue of the environment being modified by repeated actions, while action itself is contingent upon the deontic value of environmental cues. We argue that this construction of deontic cues enables the epistemic (i.e., information-seeking) and pragmatic (i.e., goal- seeking) values of any behavior to be 'cached' or 'outsourced' to the environment, where the environment effectively 'learns' about the behavior of its denizens. We describe the process whereby this particular aspect of value enables learning of habitual behavior over neurodevelopmental and transgenerational timescales.

The purpose of this review was to integrate leading paradigms in psychology and neuroscience with a theory of the embodied, situated human brain, called the Hierarchically Mechanistic Mind (HMM). The HMM describes the brain as a complex adaptive system that functions to minimize the entropy of our sensory and physical states via action-perception cycles generated by hierarchical neural dynamics. First, we review the extant literature on the hierarchical structure of the brain. Next, we derive the HMM from a broader evolutionary systems theory that explains neural structure and function in terms of dynamic interactions across four nested levels of biological causation (i.e., adaptation, phylogeny, ontogeny, and mechanism). We then describe how the HMM aligns with a global brain theory in neuroscience called the free-energy principle, leveraging this theory to mathematically formulate neural dynamics across hierarchical spatiotemporal scales. We conclude by exploring the implications of the HMM for psychological inquiry.

This monograph attempts a theory of every 'thing' that can be distinguished from other things in a statistical sense. The ensuing statistical independencies, mediated by Markov blankets, speak to a recursive composition of ensembles (of things) at increasingly higher spatiotemporal scales. This decomposition provides a description of small things; e.g., quantum mechanics - via the Schrodinger equation, ensembles of small things - via statistical mechanics and related fluctuation theorems, through to big things - via classical mechanics. These descriptions are complemented with a Bayesian mechanics for autonomous or active things. Although this work provides a formulation of every thing, its main contribution is to examine the implications of Markov blankets for self-organisation to nonequilibrium steady-state. In brief, we recover an information geometry and accompanying free energy principle that allows one to interpret the internal states of something as representing or making inferences about its external states. The ensuing Bayesian mechanics is compatible with quantum, statistical and classical mechanics and may offer a formal description of lifelike particles.

Successful behaviour depends on the right balance between maximising reward and soliciting information about the world. Here, we show how different types of information-gain emerge when casting behaviour as surprise minimisation. We present two distinct mechanisms for goal-directed exploration that express separable profiles of active sampling to reduce uncertainty. 'Hidden state' exploration motivates agents to sample unambiguous observations to accurately infer the (hidden) state of the world. Conversely, 'model parameter' exploration, compels agents to sample outcomes associated with high uncertainty, if they are informative for their representation of the task structure. We illustrate the emergence of these types of information-gain, termed active inference and active learning, and show how these forms of exploration induce distinct patterns of 'Bayes-optimal' behaviour. Our findings provide a computational framework for understanding how distinct levels of uncertainty systematically affect the exploration-exploitation trade-off in decision-making.

This paper introduces an active inference formulation of planning and navigation. It illustrates how the exploitation-exploration dilemma is dissolved by acting to minimise uncertainty (i.e. expected surprise or free energy). We use simulations of a maze problem to illustrate how agents can solve quite complicated problems using context sensitive prior preferences to form subgoals. Our focus is on how epistemic behaviour-driven by novelty and the imperative to reduce uncertainty about the world-contextualises pragmatic or goal-directed behaviour. Using simulations, we illustrate the underlying process theory with synthetic behavioural and electrophysiological responses during exploration of a maze and subsequent navigation to a target location. An interesting phenomenon that emerged from the simulations was a putative distinction between 'place cells'-that fire when a subgoal is reached-and 'path cells'-that fire until a subgoal is reached.

Motivated control refers to the coordination of behaviour to achieve affectively valenced outcomes or goals. The study of motivated control traditionally assumes a distinction between control and motivational processes, which map to distinct (dorsolateral versus ventromedial) brain systems. However, the respective roles and interactions between these processes remain controversial. We offer a novel perspective that casts control and motivational processes as complementary aspects - goal propagation and prioritization, respectively - of active inference and hierarchical goal processing under deep generative models. We propose that the control hierarchy propagates prior preferences or goals, but their precision is informed by the motivational context, inferred at different levels of the motivational hierarchy. The ensuing integration of control and motivational processes underwrites action and policy selection and, ultimately, motivated behaviour, by enabling deep inference to prioritize goals in a context-sensitive way.

The neurobiological understanding of mood, and by extension mood disorders, remains elusive despite decades of research implicating several neuromodulator systems. This review considers a new approach based on existing theories of functional brain organisation. The free energy principle (a.k.a. active inference), and its instantiation in the Bayesian brain, offers a complete and simple formulation of mood. It has been proposed that emotions reflect the precision of - or certainty about - the predicted sensorimotor/interoceptive consequences of action. By extending this reasoning, in a hierarchical setting, we suggest mood states act as (hyper) priors over uncertainty (i.e. emotions). Here, we consider the same computational pathology in the proprioceptive and interoceptive (behavioural and autonomic) domain in order to furnish an explanation for mood disorders. This formulation reconciles several strands of research at multiple levels of enquiry.

In evolutionary biology, niche construction is sometimes described as a genuine evolutionary process whereby organisms, through their activities and regulatory mechanisms, modify their environment such as to steer their own evolutionary trajectory, and that of other species. There is ongoing debate, however, on the extent to which niche construction ought to be considered a bona fide evolutionary force, on a par with natural selection. Recent formulations of the variational free-energy principle as applied to the life sciences describe the properties of living systems, and their selection in evolution, in terms of variational inference. We argue that niche construction can be described using a variational approach. We propose new arguments to support the niche construction perspective, and to extend the variational approach to niche construction to current perspectives in various scientific fields.

, in terms of the presence of Markov blankets under the active inference scheme-a corollary of the free energy principle. A Markov blanket defines the boundaries of a system in a statistical sense. Here we consider how a collective of Markov blankets can self-assemble into a global system that itself has a Markov blanket; thereby providing an illustration of how autonomous systems can be understood as having layers of nested and self-sustaining boundaries. This allows us to show that: (i) any living system is a Markov blanketed system and (ii) the boundaries of such systems need not be co-extensive with the biophysical boundaries of a living organism. In other words, autonomous systems are hierarchically composed of Markov blankets of Markov blankets-all the way down to individual cells, all the way up to you and me, and all the way out to include elements of the local environment.

Understanding the neural substrates of depression is crucial for diagnosis and treatment. Here, we review recent studies of functional and effective connectivity in depression, in terms of functional integration in the brain. Findings from these studies, including our own, point to the involvement of at least four networks in patients with depression. Elevated connectivity of a ventral limbic affective network appears to be associated with excessive negative mood (dysphoria) in the patients; decreased connectivity of a frontal-striatal reward network has been suggested to account for loss of interest, motivation, and pleasure (anhedonia); enhanced default mode network connectivity seems to be associated with depressive rumination; and diminished connectivity of a dorsal cognitive control network is thought to underlie cognitive deficits especially ineffective top-down control of negative thoughts and emotions in depressed patients. Moreover, the restoration of connectivity of these networks-and corresponding symptom improvement-following antidepressant treatment (including medication, psychotherapy, and brain stimulation techniques) serves as evidence for the crucial role of these networks in the pathophysiology of depression.

To infer the causes of its sensations, the brain must call on a generative (predictive) model. This necessitates passing local messages between populations of neurons to update beliefs about hidden variables in the world beyond its sensory samples. It also entails inferences about how we will act. Active inference is a principled framework that frames perception and action as approximate Bayesian inference. This has been successful in accounting for a wide range of physiological and behavioral phenomena. Recently, a process theory has emerged that attempts to relate inferences to their neurobiological substrates. In this paper, we review and develop the anatomical aspects of this process theory. We argue that the form of the generative models required for inference constrains the way in which brain regions connect to one another. Specifically, neuronal populations representing beliefs about a variable must receive input from populations representing the Markov blanket of that variable. We illustrate this idea in four different domains: perception, planning, attention, and movement. In doing so, we attempt to show how appealing to generative models enables us to account for anatomical brain architectures. Ultimately, committing to an anatomical theory of inference ensures we can form empirical hypotheses that can be tested using neuroimaging, neuropsychological, and electrophysiological experiments.

Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine 'prior' beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology - optimal inference with suboptimal priors - and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient's behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology.

Is self-consciousness necessary for consciousness? The answer is yes. So there you have it – the answer is yes. This was my response to a question I was asked to address in a recent AEON piece (https://aeon.co/essays/consciousness-is-not-a-thing-but-a-process-of-inference). What follows is based upon the notes for that essay, with a special focus on self-organisation, self-evidencing and self-modelling. I will try to substantiate my (polemic) answer from the perspective of a physicist. In brief, the argument goes as follows: if we want to talk about creatures, like ourselves, then we have to identify the characteristic behaviours they must exhibit. This is fairly easy to do by noting that living systems return to a set of attracting states time and time again. Mathematically, this implies the existence of a Lyapunov function that turns out to be model evidence (i.e., self-evidence) in Bayesian statistics or surprise (i.e., self-information) in information theory. This means that all biological processes can be construed as performing some form of inference, from evolution through to conscious processing. If this is the case, at what point do we invoke consciousness? The proposal on offer here is that the mind comes into being when self-evidencing has a temporal thickness or counterfactual depth, which grounds inferences about the consequences of my action. On this view, consciousness is nothing more than inference about my future; namely, the self-evidencing consequences of what I could do.

One of the central claims of the Self-model Theory of Subjectivity is that the experience of being someone - even in a minimal form - arises through a transparent phenomenal self-model, which itself can in principle be reduced to brain processes. Here, we consider whether it is possible to distinguish between phenomenally transparent and opaque states in terms of active inference. We propose a relationship of phenomenal opacity to expected uncertainty or precision; i.e., the capacity for introspective attention and implicit mental action. Thus we associate introspective attention with the deployment of 'precision' that may render the perceptual evidence (for action) opaque, while treating transparency as a necessary aspect of beliefs about action, i.e., 'what I am' doing. We conclude by proposing how we may have to nuance our conception of minimal phenomenal selfhood and agency in light of this active inference conception of transparency-opacity.

How do we navigate a deeply structured world? Why are you reading this sentence first - and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour with Bayesian belief updating - and neuronal process theories - to simulate the epistemic foraging seen in reading. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations; reproducing mismatch negativity and P300 responses respectively.

This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model of the canonical microcircuit. This provides a generative or dynamic causal model of laminar specific responses that can generate haemodynamic and electrophysiological measurements. In principle, this allows the fusion of haemodynamic and (event related or induced) electrophysiological responses. Furthermore, it enables Bayesian model comparison of competing hypotheses about physiologically plausible synaptic effects; for example, does attentional modulation act on superficial or deep pyramidal cells - or both? In this technical note, we describe the resulting dynamic causal model and provide an illustrative application to the attention to visual motion dataset used in previous papers. Our focus here is on how to answer long-standing questions in fMRI; for example, do haemodynamic responses reflect extrinsic (afferent) input from distant cortical regions, or do they reflect intrinsic (recurrent) neuronal activity? To what extent do inhibitory interneurons contribute to neurovascular coupling? What is the relationship between haemodynamic responses and the frequency of induced neuronal activity? This paper does not pretend to answer these questions; rather it shows how they can be addressed using neural mass models of fMRI timeseries.

The development of large-scale network models that infer the effective (directed) connectivity among neuronal populations from neuroimaging data represents a key challenge for computational neuroscience. Dynamic causal models (DCMs) of neuroimaging and electrophysiological data are frequently used for inferring effective connectivity but are presently restricted to small graphs (typically up to 10 regions) in order to keep model inversion computationally feasible. Here, we present a novel variant of DCM for functional magnetic resonance imaging (fMRI) data that is suited to assess effective connectivity in large (whole-brain) networks. The approach rests on translating a linear DCM into the frequency domain and reformulating it as a special case of Bayesian linear regression. This paper derives regression DCM (rDCM) in detail and presents a variational Bayesian inversion method that enables extremely fast inference and accelerates model inversion by several orders of magnitude compared to classical DCM. Using both simulated and empirical data, we demonstrate the face validity of rDCM under different settings of signal-to-noise ratio (SNR) and repetition time (TR) of fMRI data. In particular, we assess the potential utility of rDCM as a tool for whole-brain connectomics by challenging it to infer effective connection strengths in a simulated whole-brain network comprising 66 regions and 300 free parameters. Our results indicate that rDCM represents a computationally highly efficient approach with promising potential for inferring whole-brain connectivity from individual fMRI data.

Context-sensitive and activity-dependent fluctuations in connectivity underlie functional integration in the brain and have been studied widely in terms of synaptic plasticity, learning and condition-specific (e.g., attentional) modulations of synaptic efficacy. This dynamic aspect of brain connectivity has recently attracted a lot of attention in the resting state fMRI community. To explain dynamic functional connectivity in terms of directed effective connectivity among brain regions, we introduce a novel method to identify dynamic effective connectivity using spectral dynamic causal modelling (spDCM). We used parametric empirical Bayes (PEB) to model fluctuations in directed coupling over consecutive windows of resting state fMRI time series. Hierarchical PEB can model random effects on connectivity parameters at the second (between-window) level given connectivity estimates from the first (within-window) level. In this work, we used a discrete cosine transform basis set or eigenvariates (i.e., expression of principal components) to model fluctuations in effective connectivity over windows. We evaluated the ensuing dynamic effective connectivity in terms of the consistency of baseline connectivity within default mode network (DMN), using the resting state fMRI from Human Connectome Project (HCP). To model group-level baseline and dynamic effective connectivity for DMN, we extended the PEB approach by conducting a multilevel PEB analysis of between-session and between-subject group effects. Model comparison clearly spoke to dynamic fluctuations in effective connectivity - and the dynamic functional connectivity these changes explain. Furthermore, baseline effective connectivity was consistent across independent sessions - and notably more consistent than estimates based upon conventional models. This work illustrates the advantage of hierarchical modelling with spDCM, in characterizing the dynamics of effective connectivity.

The free-energy principle (FEP) is a formal model of neuronal processes that is widely recognised in neuroscience as a unifying theory of the brain and biobehaviour. More recently, however, it has been extended beyond the brain to explain the dynamics of living systems, and their unique capacity to avoid decay. The aim of this review is to synthesise these advances with a meta-theoretical ontology of biological systems called variational neuroethology, which integrates the FEP with Tinbergen's four research questions to explain biological systems across spatial and temporal scales. We exemplify this framework by applying it to Homo sapiens, before translating variational neuroethology into a systematic research heuristic that supplies the biological, cognitive, and social sciences with a computationally tractable guide to discovery.

The term 'stress' - coined in 1936 - has many definitions, but until now has lacked a theoretical foundation. Here we present an information-theoretic approach - based on the 'free energy principle' - defining the essence of stress; namely, uncertainty. We address three questions: What is uncertainty? What does it do to us? What are our resources to master it? Mathematically speaking, uncertainty is entropy or 'expected surprise'. The 'free energy principle' rests upon the fact that self-organizing biological agents resist a tendency to disorder and must therefore minimize the entropy of their sensory states. Applied to our everyday life, this means that we feel uncertain, when we anticipate that outcomes will turn out to be something other than expected - and that we are unable to avoid surprise. As all cognitive systems strive to reduce their uncertainty about future outcomes, they face a critical constraint: Reducing uncertainty requires cerebral energy. The characteristic of the vertebrate brain to prioritize its own high energy is captured by the notion of the 'selfish brain'. Accordingly, in times of uncertainty, the selfish brain demands extra energy from the body. If, despite all this, the brain cannot reduce uncertainty, a persistent cerebral energy crisis may develop, burdening the individual by 'allostatic load' that contributes to systemic and brain malfunction (impaired memory, atherogenesis, diabetes and subsequent cardio- and cerebrovascular events). Based on the basic tenet that stress originates from uncertainty, we discuss the strategies our brain uses to avoid surprise and thereby resolve uncertainty.

Research suggests that perception and imagination engage neuronal representations in the same visual areas. However, the underlying mechanisms that differentiate sensory perception from imagination remain unclear. Here, we examine the directed coupling (effective connectivity) between fronto-parietal and visual areas during perception and imagery. We found an increase in bottom-up coupling during perception relative to baseline and an increase in top-down coupling during both perception and imagery, with a much stronger increase during imagery. Modulation of the coupling from frontal to early visual areas was common to both perception and imagery. Furthermore, we show that the experienced vividness during imagery was selectively associated with increases in top-down connectivity to early visual cortex. These results highlight the importance of top-down processing in internally as well as externally driven visual experience.

The psychological concepts of working memory and attention are widely used in the cognitive and neuroscientific literatures. Perhaps because of the interdisciplinary appeal of these concepts, the same terms are often used to mean very different things. Drawing on recent advances in theoretical neurobiology, this paper tries to highlight the correspondence between these established psychological constructs and the formal processes implicit in mathematical descriptions of brain function. Here, we consider attention and salience from the perspective offered by active inference. Using variational principles and simulations, we use active inference to demonstrate how attention and salience can be disambiguated in terms of message passing between populations of neurons in cortical and subcortical structures. In brief, we suggest that salience is something that is afforded to actions that realise epistemic affordance, while attention per se is afforded to precise sensory evidence - or beliefs about the causes of sensations.

An important characteristic of spontaneous brain activity is the anticorrelation between the core default network (cDN) and the dorsal attention network (DAN) and the salience network (SN). This anticorrelation may constitute a key aspect of functional anatomy and is implicated in several brain disorders. We used dynamic causal modeling to assess the hypothesis that a causal hierarchy underlies this anticorrelation structure, using resting-state fMRI of healthy adolescent and young adults (N = 404). Our analysis revealed an asymmetric effective connectivity, such that the regions in the SN and DAN exerted an inhibitory influence on the cDN regions; whereas the cDN exerted an excitatory influence on the SN and DAN regions. The relative strength of efferent versus afferent connections places the SN at the apex of the hierarchy, suggesting that the SN modulates anticorrelated networks with descending hierarchical connections. In short, this study of directed neuronal coupling reveals a causal hierarchical architecture that generates or orchestrates anticorrelation of brain activity. These new findings shed light on functional integration of intrinsic brain networks at rest and speak to future dynamic causal modeling studies of large-scale networks.

Abstract Biological systems—like ourselves—are constantly faced with uncertainty. Despite noisy sensory data, and volatile environments, creatures appear to actively maintain their integrity. To account for this remarkable ability to make optimal decisions in the face of a capricious world, we propose a generative model that represents the beliefs an agent might possess about their own uncertainty. By simulating a noisy and volatile environment, we demonstrate how uncertainty influences optimal epistemic (visual) foraging. In our simulations, saccades were deployed less frequently to regions with a lower sensory precision, while a greater volatility led to a shorter inhibition of return. These simulations illustrate a principled explanation for some cardinal aspects of visual foraging—and allow us to propose a correspondence between the representation of uncertainty and ascending neuromodulatory systems, complementing that suggested by Yu & Dayan (Yu & Dayan 2005 Neuron46, 681–692. (doi:10.1016/j.neuron.2005.04.026)).

This article offers a formal account of curiosity and insight in terms of active (Bayesian) inference. It deals with the dual problem of inferring states of the world and learning its statistical structure. In contrast to current trends in machine learning (e.g., deep learning), we focus on how people attain insight and understanding using just a handful of observations, which are solicited through curious behavior. We use simulations of abstract rule learning and approximate Bayesian inference to show that minimizing (expected) variational free energy leads to active sampling of novel contingencies. This epistemic behavior closes explanatory gaps in generative models of the world, thereby reducing uncertainty and satisfying curiosity. We then move from epistemic learning to model selection or structure learning to show how abductive processes emerge when agents test plausible hypotheses about symmetries (i.e., invariances or rules) in their generative models. The ensuing Bayesian model reduction evinces mechanisms associated with sleep and has all the hallmarks of "aha" moments. This formulation moves toward a computational account of consciousness in the pre-Cartesian sense of sharable knowledge (i.e., con: "together"; scire: "to know").

This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of Bayesian model reduction to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM—with functional connectivity priors—is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.

This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference-and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models. Crucially, these models can entertain both discrete and continuous states, leading to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to elucidate the requisite message passing in terms of its form and scheduling. To accommodate mixed generative models (of discrete and continuous states), one also has to consider link nodes or factors that enable discrete and continuous representations to talk to each other. When mapping the implicit computational architecture onto neuronal connectivity, several interesting features emerge. For example, Bayesian model averaging and comparison, which link discrete and continuous states, may be implemented in thalamocortical loops. These and other considerations speak to a computational connectome that is inherently state dependent and self-organizing in ways that yield to a principled (variational) account. We conclude with simulations of reading that illustrate the implicit neuronal message passing, with a special focus on how discrete (semantic) representations inform, and are informed by, continuous (visual) sampling of the sensorium. AUTHOR SUMMARY: This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference-and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models that can entertain both discrete and continuous states. This leads to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to characterize the requisite message passing, and link this formal characterization to canonical microcircuits and extrinsic connectivity in the brain.

Predictive processing (PP) approaches to the mind are increasingly popular in the cognitive sciences. This surge of interest is accompanied by a proliferation of philosophical arguments, which seek to either extend or oppose various aspects of the emerging framework. In particular, the question of how to position predictive processing with respect to enactive and embodied cognition has become a topic of intense debate. While these arguments are certainly of valuable scientific and philosophical merit, they risk underestimating the variety of approaches gathered under the predictive label. Here, we first present a basic review of neuroscientific, cognitive, and philosophical approaches to PP, to illustrate how these range from solidly cognitivist applications-with a firm commitment to modular, internalistic mental representation-to more moderate views emphasizing the importance of 'body-representations', and finally to those which fit comfortably with radically enactive, embodied, and dynamic theories of mind. Any nascent predictive processing theory (e.g., of attention or consciousness) must take into account this continuum of views, and associated theoretical commitments. As a final point, we illustrate how the Free Energy Principle (FEP) attempts to dissolve tension between internalist and externalist accounts of cognition, by providing a formal synthetic account of how internal 'representations' arise from autopoietic self-organization. The FEP thus furnishes empirically productive process theories (e.g., predictive processing) by which to guide discovery through the formal modelling of the embodied mind.

This paper presents a review of theoretical and empirical work on repetition suppression in the context of predictive coding. Predictive coding is a neurobiologically plausible scheme explaining how biological systems might perform perceptual inference and learning. From this perspective, repetition suppression is a manifestation of minimising prediction error through adaptive changes in predictions about the content and precision of sensory inputs. Simulations of artificial neural hierarchies provide a principled way of understanding how repetition suppression - at different time scales - can be explained in terms of inference and learning implemented under predictive coding. This formulation of repetition suppression is supported by results of numerous empirical studies of repetition suppression and its contextual determinants.

This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity.

Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches - Bayesian model selection and generative embedding - which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning.

Twenty years have passed since the dysconnection hypothesis was first proposed (Friston and Frith, 1995; Weinberger, 1993). In that time, neuroscience has witnessed tremendous advances: we now live in a world of non-invasive neuroanatomy, computational neuroimaging and the Bayesian brain. The genomics era has come and gone. Connectomics and large-scale neuroinformatics initiatives are emerging everywhere. So where is the dysconnection hypothesis now? This article considers how the notion of schizophrenia as a dysconnection syndrome has developed - and how it has been enriched by recent advances in clinical neuroscience. In particular, we examine the dysconnection hypothesis in the context of (i) theoretical neurobiology and computational psychiatry; (ii) the empirical insights afforded by neuroimaging and associated connectomics - and (iii) how bottom-up (molecular biology and genetics) and top-down (systems biology) perspectives are converging on the mechanisms and nature of dysconnections in schizophrenia.

Tinnitus is a common disorder that often complicates hearing loss. Its mechanisms are incompletely understood. Current theories proposing pathophysiology from the ear to the cortex cannot individually - or collectively - explain the range of experimental evidence available. We propose a new framework, based on predictive coding, in which spontaneous activity in the subcortical auditory pathway constitutes a 'tinnitus precursor' which is normally ignored as imprecise evidence against the prevailing percept of 'silence'. Extant models feature as contributory mechanisms acting to increase either the intensity of the precursor or its precision. If precision (i.e., postsynaptic gain) rises sufficiently then tinnitus is perceived. Perpetuation arises through focused attention, which further increases the precision of the precursor, and resetting of the default prediction to expect tinnitus.

We use behavioral methods, magnetoencephalography, and functional MRI to investigate how human listeners discover temporal patterns and statistical regularities in complex sound sequences. Sensitivity to patterns is fundamental to sensory processing, in particular in the auditory system, because most auditory signals only have meaning as successions over time. Previous evidence suggests that the brain is tuned to the statistics of sensory stimulation. However, the process through which this arises has been elusive. We demonstrate that listeners are remarkably sensitive to the emergence of complex patterns within rapidly evolving sound sequences, performing on par with an ideal observer model. Brain responses reveal online processes of evidence accumulation--dynamic changes in tonic activity precisely correlate with the expected precision or predictability of ongoing auditory input--both in terms of deterministic (first-order) structure and the entropy of random sequences. Source analysis demonstrates an interaction between primary auditory cortex, hippocampus, and inferior frontal gyrus in the process of discovering the regularity within the ongoing sound sequence. The results are consistent with precision based predictive coding accounts of perceptual inference and provide compelling neurophysiological evidence of the brain's capacity to encode high-order temporal structure in sensory signals.

SEE MOLL AND ENGEL DOI101093/AWW308 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Brain regions dynamically engage and disengage with one another to execute everyday actions from movement to decision making. Pathologies such as Parkinson's disease and tremor emerge when brain regions controlling movement cannot readily decouple, compromising motor function. Here, we propose a novel stimulation strategy that selectively regulates neural synchrony through phase-specific stimulation. We demonstrate for the first time the therapeutic potential of such a stimulation strategy for the treatment of patients with pathological tremor. Symptom suppression is achieved by delivering stimulation to the ventrolateral thalamus, timed according to the patient's tremor rhythm. Sustained locking of deep brain stimulation to a particular phase of tremor afforded clinically significant tremor relief (up to 87% tremor suppression) in selected patients with essential tremor despite delivering less than half the energy of conventional high frequency stimulation. Phase-specific stimulation efficacy depended on the resonant characteristics of the underlying tremor network. Selective regulation of neural synchrony through phase-locked stimulation has the potential to both increase the efficiency of therapy and to minimize stimulation-induced side effects.

We review a recent shift in conceptions of interoception and its relationship to hierarchical inference in the brain. The notion of interoceptive inference means that bodily states are regulated by autonomic reflexes that are enslaved by descending predictions from deep generative models of our internal and external milieu. This re-conceptualization illuminates several issues in cognitive and clinical neuroscience with implications for experiences of selfhood and emotion. We first contextualize interoception in terms of active (Bayesian) inference in the brain, highlighting its enactivist (embodied) aspects. We then consider the key role of uncertainty or precision and how this might translate into neuromodulation. We next examine the implications for understanding the functional anatomy of the emotional brain, surveying recent observations on agranular cortex. Finally, we turn to theoretical issues, namely, the role of interoception in shaping a sense of embodied self and feelings. We will draw links between physiological homoeostasis and allostasis, early cybernetic ideas of predictive control and hierarchical generative models in predictive processing. The explanatory scope of interoceptive inference ranges from explanations for autism and depression, through to consciousness. We offer a brief survey of these exciting developments.This article is part of the themed issue 'Interoception beyond homeostasis: affect, cognition and mental health'.

This article describes a process theory based on active inference and belief propagation. Starting from the premise that all neuronal processing (and action selection) can be explained by maximizing Bayesian model evidence-or minimizing variational free energy-we ask whether neuronal responses can be described as a gradient descent on variational free energy. Using a standard (Markov decision process) generative model, we derive the neuronal dynamics implicit in this description and reproduce a remarkable range of well-characterized neuronal phenomena. These include repetition suppression, mismatch negativity, violation responses, place-cell activity, phase precession, theta sequences, theta-gamma coupling, evidence accumulation, race-to-bound dynamics, and transfer of dopamine responses. Furthermore, the (approximately Bayes' optimal) behavior prescribed by these dynamics has a degree of face validity, providing a formal explanation for reward seeking, context learning, and epistemic foraging. Technically, the fact that a gradient descent appears to be a valid description of neuronal activity means that variational free energy is a Lyapunov function for neuronal dynamics, which therefore conform to Hamilton's principle of least action.

UNLABELLED: Integrating costs and benefits is crucial for optimal decision-making. Although much is known about decisions that involve outcome-related costs (e.g., delay, risk), many of our choices are attached to actions and require an evaluation of the associated motor costs. Yet how the brain incorporates motor costs into choices remains largely unclear. We used human fMRI during choices involving monetary reward and physical effort to identify brain regions that serve as a choice comparator for effort-reward trade-offs. By independently varying both options' effort and reward levels, we were able to identify the neural signature of a comparator mechanism. A network involving supplementary motor area and the caudal portion of dorsal anterior cingulate cortex encoded the difference in reward (positively) and effort levels (negatively) between chosen and unchosen choice options. We next modeled effort-discounted subjective values using a novel behavioral model. This revealed that the same network of regions involving dorsal anterior cingulate cortex and supplementary motor area encoded the difference between the chosen and unchosen options' subjective values, and that activity was best described using a concave model of effort-discounting. In addition, this signal reflected how precisely value determined participants' choices. By contrast, separate signals in supplementary motor area and ventromedial prefrontal cortex correlated with participants' tendency to avoid effort and seek reward, respectively. This suggests that the critical neural signature of decision-making for choices involving motor costs is found in human cingulate cortex and not ventromedial prefrontal cortex as typically reported for outcome-based choice. Furthermore, distinct frontal circuits seem to drive behavior toward reward maximization and effort minimization. SIGNIFICANCE STATEMENT: The neural processes that govern the trade-off between expected benefits and motor costs remain largely unknown. This is striking because energetic requirements play an integral role in our day-to-day choices and instrumental behavior, and a diminished willingness to exert effort is a characteristic feature of a range of neurological disorders. We use a new behavioral characterization of how humans trade off reward maximization with effort minimization to examine the neural signatures that underpin such choices, using BOLD MRI neuroimaging data. We find the critical neural signature of decision-making, a signal that reflects the comparison of value between choice options, in human cingulate cortex, whereas two distinct brain circuits drive behavior toward reward maximization or effort minimization.

This paper describes an active inference scheme for visual searches and the perceptual synthesis entailed by scene construction. Active inference assumes that perception and action minimize variational free energy, where actions are selected to minimize the free energy expected in the future. This assumption generalizes risk-sensitive control and expected utility theory to include epistemic value; namely, the value (or salience) of information inherent in resolving uncertainty about the causes of ambiguous cues or outcomes. Here, we apply active inference to saccadic searches of a visual scene. We consider the (difficult) problem of categorizing a scene, based on the spatial relationship among visual objects where, crucially, visual cues are sampled myopically through a sequence of saccadic eye movements. This means that evidence for competing hypotheses about the scene has to be accumulated sequentially, calling upon both prediction (planning) and postdiction (memory). Our aim is to highlight some simple but fundamental aspects of the requisite functional anatomy; namely, the link between approximate Bayesian inference under mean field assumptions and functional segregation in the visual cortex. This link rests upon the (neurobiologically plausible) process theory that accompanies the normative formulation of active inference for Markov decision processes. In future work, we hope to use this scheme to model empirical saccadic searches and identify the prior beliefs that underwrite intersubject variability in the way people forage for information in visual scenes (e.g., in schizophrenia).

This paper considers communication in terms of inference about the behaviour of others (and our own behaviour). It is based on the premise that our sensations are largely generated by other agents like ourselves. This means, we are trying to infer how our sensations are caused by others, while they are trying to infer our behaviour: for example, in the dialogue between two speakers. We suggest that the infinite regress induced by modelling another agent - who is modelling you - can be finessed if you both possess the same model. In other words, the sensations caused by others and oneself are generated by the same process. This leads to a view of communication based upon a narrative that is shared by agents who are exchanging sensory signals. Crucially, this narrative transcends agency - and simply involves intermittently attending to and attenuating sensory input. Attending to sensations enables the shared narrative to predict the sensations generated by another (i.e. to listen), while attenuating sensory input enables one to articulate the narrative (i.e. to speak). This produces a reciprocal exchange of sensory signals that, formally, induces a generalised synchrony between internal (neuronal) brain states generating predictions in both agents. We develop the arguments behind this perspective, using an active (Bayesian) inference framework and offer some simulations (of birdsong) as proof of principle.

Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others--during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions--both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then--in principle--they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa.

This paper reports a dynamic causal modeling study of electrocorticographic (ECoG) data that addresses functional asymmetries between forward and backward connections in the visual cortical hierarchy. Specifically, we ask whether forward connections employ gamma-band frequencies, while backward connections preferentially use lower (beta-band) frequencies. We addressed this question by modeling empirical cross spectra using a neural mass model equipped with superficial and deep pyramidal cell populations-that model the source of forward and backward connections, respectively. This enabled us to reconstruct the transfer functions and associated spectra of specific subpopulations within cortical sources. We first established that Bayesian model comparison was able to discriminate between forward and backward connections, defined in terms of their cells of origin. We then confirmed that model selection was able to identify extrastriate (V4) sources as being hierarchically higher than early visual (V1) sources. Finally, an examination of the auto spectra and transfer functions associated with superficial and deep pyramidal cells confirmed that forward connections employed predominantly higher (gamma) frequencies, while backward connections were mediated by lower (alpha/beta) frequencies. We discuss these findings in relation to current views about alpha, beta, and gamma oscillations and predictive coding in the brain.

This technical note describes some Bayesian procedures for the analysis of group studies that use nonlinear models at the first (within-subject) level - e.g., dynamic causal models - and linear models at subsequent (between-subject) levels. Its focus is on using Bayesian model reduction to finesse the inversion of multiple models of a single dataset or a single (hierarchical or empirical Bayes) model of multiple datasets. These applications of Bayesian model reduction allow one to consider parametric random effects and make inferences about group effects very efficiently (in a few seconds). We provide the relatively straightforward theoretical background to these procedures and illustrate their application using a worked example. This example uses a simulated mismatch negativity study of schizophrenia. We illustrate the robustness of Bayesian model reduction to violations of the (commonly used) Laplace assumption in dynamic causal modelling and show how its recursive application can facilitate both classical and Bayesian inference about group differences. Finally, we consider the application of these empirical Bayesian procedures to classification and prediction.

We review a theory of homeostatic regulation and adaptive behavioural control within the Active Inference framework. Our aim is to connect two research streams that are usually considered independently; namely, Active Inference and associative learning theories of animal behaviour. The former uses a probabilistic (Bayesian) formulation of perception and action, while the latter calls on multiple (Pavlovian, habitual, goal-directed) processes for homeostatic and behavioural control. We offer a synthesis these classical processes and cast them as successive hierarchical contextualisations of sensorimotor constructs, using the generative models that underpin Active Inference. This dissolves any apparent mechanistic distinction between the optimization processes that mediate classical control or learning. Furthermore, we generalize the scope of Active Inference by emphasizing interoceptive inference and homeostatic regulation. The ensuing homeostatic (or allostatic) perspective provides an intuitive explanation for how priors act as drives or goals to enslave action, and emphasises the embodied nature of inference.

We offer a formal treatment of choice behavior based on the premise that agents minimize the expected free energy of future outcomes. Crucially, the negative free energy or quality of a policy can be decomposed into extrinsic and epistemic (or intrinsic) value. Minimizing expected free energy is therefore equivalent to maximizing extrinsic value or expected utility (defined in terms of prior preferences or goals), while maximizing information gain or intrinsic value (or reducing uncertainty about the causes of valuable outcomes). The resulting scheme resolves the exploration-exploitation dilemma: Epistemic value is maximized until there is no further information gain, after which exploitation is assured through maximization of extrinsic value. This is formally consistent with the Infomax principle, generalizing formulations of active vision based upon salience (Bayesian surprise) and optimal decisions based on expected utility and risk-sensitive (Kullback-Leibler) control. Furthermore, as with previous active inference formulations of discrete (Markovian) problems, ad hoc softmax parameters become the expected (Bayes-optimal) precision of beliefs about, or confidence in, policies. This article focuses on the basic theory, illustrating the ideas with simulations. A key aspect of these simulations is the similarity between precision updates and dopaminergic discharges observed in conditioning paradigms.

Despite similar behavioral effects, attention and expectation influence evoked responses differently: Attention typically enhances event-related responses, whereas expectation reduces them. This dissociation has been reconciled under predictive coding, where prediction errors are weighted by precision associated with attentional modulation. Here, we tested the predictive coding account of attention and expectation using magnetoencephalography and modeling. Temporal attention and sensory expectation were orthogonally manipulated in an auditory mismatch paradigm, revealing opposing effects on evoked response amplitude. Mismatch negativity (MMN) was enhanced by attention, speaking against its supposedly pre-attentive nature. This interaction effect was modeled in a canonical microcircuit using dynamic causal modeling, comparing models with modulation of extrinsic and intrinsic connectivity at different levels of the auditory hierarchy. While MMN was explained by recursive interplay of sensory predictions and prediction errors, attention was linked to the gain of inhibitory interneurons, consistent with its modulation of sensory precision.

Understanding how organisms establish their form during embryogenesis and regeneration represents a major knowledge gap in biological pattern formation. It has been recently suggested that morphogenesis could be understood in terms of cellular information processing and the ability of cell groups to model shape. Here, we offer a proof of principle that self-assembly is an emergent property of cells that share a common (genetic and epigenetic) model of organismal form. This behaviour is formulated in terms of variational free-energy minimization-of the sort that has been used to explain action and perception in neuroscience. In brief, casting the minimization of thermodynamic free energy in terms of variational free energy allows one to interpret (the dynamics of) a system as inferring the causes of its inputs-and acting to resolve uncertainty about those causes. This novel perspective on the coordination of migration and differentiation of cells suggests an interpretation of genetic codes as parametrizing a generative model-predicting the signals sensed by cells in the target morphology-and epigenetic processes as the subsequent inversion of that model. This theoretical formulation may complement bottom-up strategies-that currently focus on molecular pathways-with (constructivist) top-down approaches that have proved themselves in neuroscience and cybernetics.

This paper considers neuronal architectures from a computational perspective and asks what aspects of neuroanatomy and neurophysiology can be disclosed by the nature of neuronal computations? In particular, we extend current formulations of the brain as an organ of inference--based upon hierarchical predictive coding--and consider how these inferences are orchestrated. In other words, what would the brain require to dynamically coordinate and contextualize its message passing to optimize its computational goals? The answer that emerges rests on the delicate (modulatory) gain control of neuronal populations that select and coordinate (prediction error) signals that ascend cortical hierarchies. This is important because it speaks to a hierarchical anatomy of extrinsic (between region) connections that form two distinct classes, namely a class of driving (first-order) connections that are concerned with encoding the content of neuronal representations and a class of modulatory (second-order) connections that establish context-in the form of the salience or precision ascribed to content. We explore the implications of this distinction from a formal perspective (using simulations of feature-ground segregation) and consider the neurobiological substrates of the ensuing precision-engineered dynamics, with a special focus on the pulvinar and attention.

[18F]-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) is a widely used diagnostic tool that can detect and quantify pathophysiology, as assessed through changes in cerebral glucose metabolism. [18F]-FDG PET scans can be analyzed using voxel-based statistical methods such as Statistical Parametric Mapping (SPM) that provide statistical maps of brain abnormalities in single patients. In order to perform SPM, a "spatial normalization" of an individual's PET scan is required to match a reference PET template. The PET template currently used for SPM normalization is based on [15O]-H2O images and does not resemble either the specific metabolic features of [18F]-FDG brain scans or the specific morphological characteristics of individual brains affected by neurodegeneration. Thus, our aim was to create a new [18F]-FDG PET aging and dementia-specific template for spatial normalization, based on images derived from both age-matched controls and patients. We hypothesized that this template would increase spatial normalization accuracy and thereby preserve crucial information for research and diagnostic purposes. We investigated the statistical sensitivity and registration accuracy of normalization procedures based on the standard and new template-at the single-subject and group level-independently for subjects with Mild Cognitive Impairment (MCI), probable Alzheimer's Disease (AD), Frontotemporal lobar degeneration (FTLD) and dementia with Lewy bodies (DLB). We found a significant statistical effect of the population-specific FDG template-based normalisation in key anatomical regions for each dementia subtype, suggesting that spatial normalization with the new template provides more accurate estimates of metabolic abnormalities for single-subject and group analysis, and therefore, a more effective diagnostic measure.

This review surveys recent trends in the use of local field potentials-and their non-invasive counterparts-to address the principles of functional brain architectures. In particular, we treat oscillations as the (observable) signature of context-sensitive changes in synaptic efficacy that underlie coordinated dynamics and message-passing in the brain. This rich source of information is now being exploited by various procedures-like dynamic causal modelling-to test hypotheses about neuronal circuits in health and disease. Furthermore, the roles played by neuromodulatory mechanisms can be addressed directly through their effects on oscillatory phenomena. These neuromodulatory or gain control processes are central to many theories of normal brain function (e.g. attention) and the pathophysiology of several neuropsychiatric conditions (e.g. Parkinson's disease).

Autism is a pervasive developmental disorder characterized by profound social and verbal communication deficits, stereotypical motor behaviors, restricted interests, and cognitive abnormalities. Autism affects approximately 1% of children in developing countries. Given this prevalence, identifying risk factors and therapeutic interventions are pressing objectives—objectives that rest on neurobiologically grounded and psychologically informed theories about the underlying pathophysiology. In this article, we review the evidence that autism could result from a dysfunctional oxytocin system early in life. As a mediator of successful procreation, not only in the reproductive system, but also in the brain, oxytocin plays a crucial role in sculpting socio-sexual behavior. Formulated within a (Bayesian) predictive coding framework, we propose that oxytocin encodes the saliency or precision of interoceptive signals and enables the neuronal plasticity necessary for acquiring a generative model of the emotional and social 'self.' An aberrant oxytocin system in infancy could therefore help explain the marked deficits in language and social communication—as well as the sensory, autonomic, motor, behavioral, and cognitive abnormalities—seen in autism.

Recently, there has been a lot of interest in characterising the connectivity of resting state brain networks. Most of the literature uses functional connectivity to examine these intrinsic brain networks. Functional connectivity has well documented limitations because of its inherent inability to identify causal interactions. Dynamic causal modelling (DCM) is a framework that allows for the identification of the causal (directed) connections among neuronal systems--known as effective connectivity. This technical note addresses the validity of a recently proposed DCM for resting state fMRI--as measured in terms of their complex cross spectral density--referred to as spectral DCM. Spectral DCM differs from (the alternative) stochastic DCM by parameterising neuronal fluctuations using scale free (i.e., power law) forms, rendering the stochastic model of neuronal activity deterministic. Spectral DCM not only furnishes an efficient estimation of model parameters but also enables the detection of group differences in effective connectivity, the form and amplitude of the neuronal fluctuations or both. We compare and contrast spectral and stochastic DCM models with endogenous fluctuations or state noise on hidden states. We used simulated data to first establish the face validity of both schemes and show that they can recover the model (and its parameters) that generated the data. We then used Monte Carlo simulations to assess the accuracy of both schemes in terms of their root mean square error. We also simulated group differences and compared the ability of spectral and stochastic DCMs to identify these differences. We show that spectral DCM was not only more accurate but also more sensitive to group differences. Finally, we performed a comparative evaluation using real resting state fMRI data (from an open access resource) to study the functional integration within default mode network using spectral and stochastic DCMs.

Depleted of dopamine, the dynamics of the parkinsonian brain impact on both 'action' and 'resting' motor behaviour. Deep brain stimulation has become an established means of managing these symptoms, although its mechanisms of action remain unclear. Non-invasive characterizations of induced brain responses, and the effective connectivity underlying them, generally appeals to dynamic causal modelling of neuroimaging data. When the brain is at rest, however, this sort of characterization has been limited to correlations (functional connectivity). In this work, we model the 'effective' connectivity underlying low frequency blood oxygen level-dependent fluctuations in the resting Parkinsonian motor network-disclosing the distributed effects of deep brain stimulation on cortico-subcortical connections. Specifically, we show that subthalamic nucleus deep brain stimulation modulates all the major components of the motor cortico-striato-thalamo-cortical loop, including the cortico-striatal, thalamo-cortical, direct and indirect basal ganglia pathways, and the hyperdirect subthalamic nucleus projections. The strength of effective subthalamic nucleus afferents and efferents were reduced by stimulation, whereas cortico-striatal, thalamo-cortical and direct pathways were strengthened. Remarkably, regression analysis revealed that the hyperdirect, direct, and basal ganglia afferents to the subthalamic nucleus predicted clinical status and therapeutic response to deep brain stimulation; however, suppression of the sensitivity of the subthalamic nucleus to its hyperdirect afferents by deep brain stimulation may subvert the clinical efficacy of deep brain stimulation. Our findings highlight the distributed effects of stimulation on the resting motor network and provide a framework for analysing effective connectivity in resting state functional MRI with strong a priori hypotheses.

Functional movement disorders require attention to manifest yet patients report the abnormal movement to be out of their control. In this study we explore the phenomenon of sensory attenuation, a measure of the sense of agency for movement, in this group of patients by using a force matching task. Fourteen patients and 14 healthy control subjects were presented with forces varying from 1 to 3 N on the index finger of their left hand. Participants were required to match these forces; either by pressing directly on their own finger or by operating a robot that pressed on their finger. As expected, we found that healthy control subjects consistently overestimated the force required when pressing directly on their own finger than when operating a robot. However, patients did not, indicating a significant loss of sensory attenuation in this group of patients. These data are important because they demonstrate that a fundamental component of normal voluntary movement is impaired in patients with functional movement disorders. The loss of sensory attenuation has been correlated with the loss of sense of agency, and may help to explain why patients report that they do not experience the abnormal movement as voluntary.

Dopamine plays a key role in learning; however, its exact function in decision making and choice remains unclear. Recently, we proposed a generic model based on active (Bayesian) inference wherein dopamine encodes the precision of beliefs about optimal policies. Put simply, dopamine discharges reflect the confidence that a chosen policy will lead to desired outcomes. We designed a novel task to test this hypothesis, where subjects played a "limited offer" game in a functional magnetic resonance imaging experiment. Subjects had to decide how long to wait for a high offer before accepting a low offer, with the risk of losing everything if they waited too long. Bayesian model comparison showed that behavior strongly supported active inference, based on surprise minimization, over classical utility maximization schemes. Furthermore, midbrain activity, encompassing dopamine projection neurons, was accurately predicted by trial-by-trial variations in model-based estimates of precision. Our findings demonstrate that human subjects infer both optimal policies and the precision of those inferences, and thus support the notion that humans perform hierarchical probabilistic Bayesian inference. In other words, subjects have to infer both what they should do as well as how confident they are in their choices, where confidence may be encoded by dopaminergic firing.

This paper considers goal-directed decision-making in terms of embodied or active inference. We associate bounded rationality with approximate Bayesian inference that optimizes a free energy bound on model evidence. Several constructs such as expected utility, exploration or novelty bonuses, softmax choice rules and optimism bias emerge as natural consequences of free energy minimization. Previous accounts of active inference have focused on predictive coding. In this paper, we consider variational Bayes as a scheme that the brain might use for approximate Bayesian inference. This scheme provides formal constraints on the computational anatomy of inference and action, which appear to be remarkably consistent with neuroanatomy. Active inference contextualizes optimal decision theory within embodied inference, where goals become prior beliefs. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (associated with softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution. Crucially, this sensitivity corresponds to the precision of beliefs about behaviour. The changes in precision during variational updates are remarkably reminiscent of empirical dopaminergic responses-and they may provide a new perspective on the role of dopamine in assimilating reward prediction errors to optimize decision-making.

Over the last decade, synchronized resting-state fluctuations of blood oxygenation level-dependent (BOLD) signals between remote brain areas [so-called BOLD resting-state functional connectivity (rs-FC)] have gained enormous relevance in systems and clinical neuroscience. However, the neural underpinnings of rs-FC are still incompletely understood. Using simultaneous positron emission tomography/magnetic resonance imaging we here directly investigated the relationship between rs-FC and local neuronal activity in humans. Computational models suggest a mechanistic link between the dynamics of local neuronal activity and the functional coupling among distributed brain regions. Therefore, we hypothesized that the local activity (LA) of a region at rest determines its rs-FC. To test this hypothesis, we simultaneously measured both LA (glucose metabolism) and rs-FC (via synchronized BOLD fluctuations) during conditions of eyes closed or eyes open. During eyes open, LA increased in the visual system, and the salience network (i.e., cingulate and insular cortices) and the pattern of elevated LA coincided almost exactly with the spatial pattern of increased rs-FC. Specifically, the voxelwise regional profile of LA in these areas strongly correlated with the regional pattern of rs-FC among the same regions (e.g., LA in primary visual cortex accounts for ∼ 50%, and LA in anterior cingulate accounts for ∼ 20% of rs-FC with the visual system). These data provide the first direct evidence in humans that local neuronal activity determines BOLD FC at rest. Beyond its relevance for the neuronal basis of coherent BOLD signal fluctuations, our procedure may translate into clinical research particularly to investigate potentially aberrant links between local dynamics and remote functional coupling in patients with neuropsychiatric disorders.

The brain adapts to dynamic environments by adjusting the attentional gain or precision afforded to salient and predictable sensory input. Previous research suggests that this involves the regulation of cortical excitability (reflected in prestimulus alpha oscillations) before stimulus onset that modulates subsequent stimulus processing (reflected in stimulus-bound gamma oscillations). We present two spatial attention experiments in humans, where we first replicate the classic finding of prestimulus attentional alpha modulation and poststimulus gamma modulation. In the second experiment, the task-relevant target was a stimulus change that occurred after stimulus onset. This enabled us to show that attentional alpha modulation reflects the predictability (precision) of an upcoming sensory target, rather than an attenuation of alpha activity induced by neuronal excitation related to stimulus onset. In particular, we show that the strength of attentional alpha modulations increases with the predictability of the anticipated sensory target, regardless of current afferent drive. By contrast, we show that the poststimulus attentional gamma enhancement is stimulus-bound and decreases when the subsequent target becomes more predictable. Hence, this pattern suggests that the strength of gamma oscillations is not merely a function of cortical excitability, but also depends on the relative mismatch of predictions and sensory evidence. Together, these findings support recent theoretical proposals for distinct roles of alpha/beta and gamma oscillations in hierarchical perceptual inference and predictive coding.

Autism is a neurodevelopmental disorder characterized by problems with social-communication, restricted interests and repetitive behavior. A recent and thought-provoking article presented a normative explanation for the perceptual symptoms of autism in terms of a failure of Bayesian inference (Pellicano and Burr, 2012). In response, we suggested that when Bayesian inference is grounded in its neural instantiation-namely, predictive coding-many features of autistic perception can be attributed to aberrant precision (or beliefs about precision) within the context of hierarchical message passing in the brain (Friston et al., 2013). Here, we unpack the aberrant precision account of autism. Specifically, we consider how empirical findings-that speak directly or indirectly to neurobiological mechanisms-are consistent with the aberrant encoding of precision in autism; in particular, an imbalance of the precision ascribed to sensory evidence relative to prior beliefs.

In its full sense, perception rests on an agent's model of how its sensory input comes about and the inferences it draws based on this model. These inferences are necessarily uncertain. Here, we illustrate how the Hierarchical Gaussian Filter (HGF) offers a principled and generic way to deal with the several forms that uncertainty in perception takes. The HGF is a recent derivation of one-step update equations from Bayesian principles that rests on a hierarchical generative model of the environment and its (in)stability. It is computationally highly efficient, allows for online estimates of hidden states, and has found numerous applications to experimental data from human subjects. In this paper, we generalize previous descriptions of the HGF and its account of perceptual uncertainty. First, we explicitly formulate the extension of the HGF's hierarchy to any number of levels; second, we discuss how various forms of uncertainty are accommodated by the minimization of variational free energy as encoded in the update equations; third, we combine the HGF with decision models and demonstrate the inversion of this combination; finally, we report a simulation study that compared four optimization methods for inverting the HGF/decision model combination at different noise levels. These four methods (Nelder-Mead simplex algorithm, Gaussian process-based global optimization, variational Bayes and Markov chain Monte Carlo sampling) all performed well even under considerable noise, with variational Bayes offering the best combination of efficiency and informativeness of inference. Our results demonstrate that the HGF provides a principled, flexible, and efficient-but at the same time intuitive-framework for the resolution of perceptual uncertainty in behaving agents.

Various methods have been proposed to characterize the functional connectivity between nodes in a network measured with different modalities (electrophysiology, functional magnetic resonance imaging etc.). Since different measures of functional connectivity yield different results for the same dataset, it is important to assess when and how they can be used. In this work, we provide a systematic framework for evaluating the performance of a large range of functional connectivity measures-based upon a comprehensive portfolio of models generating measurable responses. Specifically, we benchmarked 42 methods using 10,000 simulated datasets from 5 different types of generative models with different connectivity structures. Since all functional connectivity methods require the setting of some parameters (window size and number, model order etc.), we first optimized these parameters using performance criteria based upon (threshold free) ROC analysis. We then evaluated the performance of the methods on data simulated with different types of models. Finally, we assessed the performance of the methods against different levels of signal-to-noise ratios and network configurations. A MATLAB toolbox is provided to perform such analyses using other methods and simulated datasets.

This article explores the notion that the brain is genetically endowed with an innate virtual reality generator that - through experience-dependent plasticity - becomes a generative or predictive model of the world. This model, which is most clearly revealed in rapid eye movement (REM) sleep dreaming, may provide the theater for conscious experience. Functional neuroimaging evidence for brain activations that are time-locked to rapid eye movements (REMs) endorses the view that waking consciousness emerges from REM sleep - and dreaming lays the foundations for waking perception. In this view, the brain is equipped with a virtual model of the world that generates predictions of its sensations. This model is continually updated and entrained by sensory prediction errors in wakefulness to ensure veridical perception, but not in dreaming. In contrast, dreaming plays an essential role in maintaining and enhancing the capacity to model the world by minimizing model complexity and thereby maximizing both statistical and thermodynamic efficiency. This perspective suggests that consciousness corresponds to the embodied process of inference, realized through the generation of virtual realities (in both sleep and wakefulness). In short, our premise or hypothesis is that the waking brain engages with the world to predict the causes of sensations, while in sleep the brain's generative model is actively refined so that it generates more efficient predictions during waking. We review the evidence in support of this hypothesis - evidence that grounds consciousness in biophysical computations whose neuronal and neurochemical infrastructure has been disclosed by sleep research.

This paper reviews the foundation for a role of the human anterior insular cortex (AIC) in emotional awareness, defined as the conscious experience of emotions. We first introduce the neuroanatomical features of AIC and existing findings on emotional awareness. Using empathy, the awareness and understanding of other people's emotional states, as a test case, we then present evidence to demonstrate: 1) AIC and anterior cingulate cortex (ACC) are commonly coactivated as revealed by a meta-analysis, 2) AIC is functionally dissociable from ACC, 3) AIC integrates stimulus-driven and top-down information, and 4) AIC is necessary for emotional awareness. We propose a model in which AIC serves two major functions: integrating bottom-up interoceptive signals with top-down predictions to generate a current awareness state and providing descending predictions to visceral systems that provide a point of reference for autonomic reflexes. We argue that AIC is critical and necessary for emotional awareness.

Active inference provides a simple and neurobiologically plausible account of how action and perception are coupled in producing (Bayes) optimal behaviour. This can be seen most easily as minimising prediction error: we can either change our predictions to explain sensory input through perception. Alternatively, we can actively change sensory input to fulfil our predictions. In active inference, this action is mediated by classical reflex arcs that minimise proprioceptive prediction error created by descending proprioceptive predictions. However, this creates a conflict between action and perception; in that, self-generated movements require predictions to override the sensory evidence that one is not actually moving. However, ignoring sensory evidence means that externally generated sensations will not be perceived. Conversely, attending to (proprioceptive and somatosensory) sensations enables the detection of externally generated events but precludes generation of actions. This conflict can be resolved by attenuating the precision of sensory evidence during movement or, equivalently, attending away from the consequences of self-made acts. We propose that this Bayes optimal withdrawal of precise sensory evidence during movement is the cause of psychophysical sensory attenuation. Furthermore, it explains the force-matching illusion and reproduces empirical results almost exactly. Finally, if attenuation is removed, the force-matching illusion disappears and false (delusional) inferences about agency emerge. This is important, given the negative correlation between sensory attenuation and delusional beliefs in normal subjects--and the reduction in the magnitude of the illusion in schizophrenia. Active inference therefore links the neuromodulatory optimisation of precision to sensory attenuation and illusory phenomena during the attribution of agency in normal subjects. It also provides a functional account of deficits in syndromes characterised by false inference and impaired movement--like schizophrenia and Parkinsonism--syndromes that implicate abnormal modulatory neurotransmission.

This technical note introduces a dynamic causal model (DCM) for resting state fMRI time series based upon observed functional connectivity--as measured by the cross spectra among different brain regions. This DCM is based upon a deterministic model that generates predicted crossed spectra from a biophysically plausible model of coupled neuronal fluctuations in a distributed neuronal network or graph. Effectively, the resulting scheme finds the best effective connectivity among hidden neuronal states that explains the observed functional connectivity among haemodynamic responses. This is because the cross spectra contain all the information about (second order) statistical dependencies among regional dynamics. In this note, we focus on describing the model, its relationship to existing measures of directed and undirected functional connectivity and establishing its face validity using simulations. In subsequent papers, we will evaluate its construct validity in relation to stochastic DCM and its predictive validity in Parkinson's and Huntington's disease.

The agranular architecture of motor cortex lacks a functional interpretation. Here, we consider a 'predictive coding' account of this unique feature based on asymmetries in hierarchical cortical connections. In sensory cortex, layer 4 (the granular layer) is the target of ascending pathways. We theorise that the operation of predictive coding in the motor system (a process termed 'active inference') provides a principled rationale for the apparent recession of the ascending pathway in motor cortex. The extension of this theory to interlaminar circuitry also accounts for a sub-class of 'mirror neuron' in motor cortex--whose activity is suppressed when observing an action--explaining how predictive coding can gate hierarchical processing to switch between perception and action.

BACKGROUND: In patients with chronic spinal cord injury, imaging of the spinal cord and brain above the level of the lesion provides evidence of neural degeneration; however, the spatial and temporal patterns of progression and their relation to clinical outcomes are uncertain. New interventions targeting acute spinal cord injury have entered clinical trials but neuroimaging outcomes as responsive markers of treatment have yet to be established. We aimed to use MRI to assess neuronal degeneration above the level of the lesion after acute spinal cord injury. METHODS: In our prospective longitudinal study, we enrolled patients with acute traumatic spinal cord injury and healthy controls. We assessed patients clinically and by MRI at baseline, 2 months, 6 months, and 12 months, and controls by MRI at the same timepoints. We assessed atrophy in white matter in the cranial corticospinal tracts and grey matter in sensorimotor cortices by tensor-based analyses of T1-weighted MRI data. We used cross-sectional spinal cord area measurements to assess atrophy at cervical level C2/C3. We used myelin-sensitive magnetisation transfer (MT) and longitudinal relaxation rate (R1) maps to assess microstructural changes associated with myelin. We also assessed associations between MRI parameters and clinical improvement. All analyses of brain scans done with statistical parametric mapping were corrected for family-wise error. FINDINGS: Between Sept 17, 2010, and Dec 31, 2012, we recruited 13 patients and 18 controls. In the 12 months from baseline, patients recovered by a mean of 5·27 points per log month (95% CI 1·91-8·63) on the international standards for the neurological classification of spinal cord injury (ISNCSCI) motor score (p=0·002) and by 10·93 points per log month (6·20-15·66) on the spinal cord independence measure (SCIM) score (p<0·0001). Compared with controls, patients showed a rapid decline in cross-sectional spinal cord area (patients declined by 0·46 mm per month compared with a stable cord area in controls; p<0·0001). Patients had faster rates than controls of volume decline of white matter in the cranial corticospinal tracts at the level of the internal capsule (right Z score 5·21, p=0·0081; left Z score 4·12, p=0·0004) and right cerebral peduncle (Z score 3·89, p=0·0302) and of grey matter in the left primary motor cortex (Z score 4·23, p=0·041). Volume changes were paralleled by significant reductions of MT and R1 in the same areas and beyond. Improvements in SCIM scores at 12 months were associated with a reduced loss in cross-sectional spinal cord area over 12 months (Pearson's correlation 0·77, p=0·004) and reduced white matter volume of the corticospinal tracts at the level of the right internal capsule (Z score 4·30, p=0·0021), the left internal capsule (Z score 4·27, p=0·0278), and left cerebral peduncle (Z score 4·05, p=0·0316). Improvements in ISNCSCI motor scores were associated with less white matter volume change encompassing the corticospinal tract at the level of the right internal capsule (Z score 4·01, p<0·0001). INTERPRETATION: Extensive upstream atrophic and microstructural changes of corticospinal axons and sensorimotor cortical areas occur in the first months after spinal cord injury, with faster degenerative changes relating to poorer recovery. Structural volumetric and microstructural MRI protocols remote from the site of spinal cord injury could serve as neuroimaging biomarkers in acute spinal cord injury. FUNDING: SRH Holding, Swiss National Science Foundation, Clinical Research Priority Program "NeuroRehab" University of Zurich, Wellcome Trust.

This paper presents a heuristic proof (and simulations of a primordial soup) suggesting that life-or biological self-organization-is an inevitable and emergent property of any (ergodic) random dynamical system that possesses a Markov blanket. This conclusion is based on the following arguments: if the coupling among an ensemble of dynamical systems is mediated by short-range forces, then the states of remote systems must be conditionally independent. These independencies induce a Markov blanket that separates internal and external states in a statistical sense. The existence of a Markov blanket means that internal states will appear to minimize a free energy functional of the states of their Markov blanket. Crucially, this is the same quantity that is optimized in Bayesian inference. Therefore, the internal states (and their blanket) will appear to engage in active Bayesian inference. In other words, they will appear to model-and act on-their world to preserve their functional and structural integrity, leading to homoeostasis and a simple form of autopoiesis.

How rich functionality emerges from the invariant structural architecture of the brain remains a major mystery in neuroscience. Recent applications of network theory and theoretical neuroscience to large-scale brain networks have started to dissolve this mystery. Network analyses suggest that hierarchical modular brain networks are particularly suited to facilitate local (segregated) neuronal operations and the global integration of segregated functions. Although functional networks are constrained by structural connections, context-sensitive integration during cognition tasks necessarily entails a divergence between structural and functional networks. This degenerate (many-to-one) function-structure mapping is crucial for understanding the nature of brain networks. The emergence of dynamic functional networks from static structural connections calls for a formal (computational) approach to neuronal information processing that may resolve this dialectic between structure and function.

The mismatch negativity (MMN) is a differential brain response to violations of learned regularities. It has been used to demonstrate that the brain learns the statistical structure of its environment and predicts future sensory inputs. However, the algorithmic nature of these computations and the underlying neurobiological implementation remain controversial. This article introduces a mathematical framework with which competing ideas about the computational quantities indexed by MMN responses can be formalized and tested against single-trial EEG data. This framework was applied to five major theories of the MMN, comparing their ability to explain trial-by-trial changes in MMN amplitude. Three of these theories (predictive coding, model adjustment, and novelty detection) were formalized by linking the MMN to different manifestations of the same computational mechanism: approximate Bayesian inference according to the free-energy principle. We thereby propose a unifying view on three distinct theories of the MMN. The relative plausibility of each theory was assessed against empirical single-trial MMN amplitudes acquired from eight healthy volunteers in a roving oddball experiment. Models based on the free-energy principle provided more plausible explanations of trial-by-trial changes in MMN amplitude than models representing the two more traditional theories (change detection and adaptation). Our results suggest that the MMN reflects approximate Bayesian learning of sensory regularities, and that the MMN-generating process adjusts a probabilistic model of the environment according to prediction errors.

In systems biology, questions concerning the molecular and cellular makeup of an organism are of utmost importance, especially when trying to understand how unreliable components--like genetic circuits, biochemical cascades, and ion channels, among others--enable reliable and adaptive behaviour. The repertoire and speed of biological computations are limited by thermodynamic or metabolic constraints: an example can be found in neurons, where fluctuations in biophysical states limit the information they can encode--with almost 20-60% of the total energy allocated for the brain used for signalling purposes, either via action potentials or by synaptic transmission. Here, we consider the imperatives for neurons to optimise computational and metabolic efficiency, wherein benefits and costs trade-off against each other in the context of self-organised and adaptive behaviour. In particular, we try to link information theoretic (variational) and thermodynamic (Helmholtz) free-energy formulations of neuronal processing and show how they are related in a fundamental way through a complexity minimisation lemma.

Psychedelic drugs produce profound changes in consciousness, but the underlying neurobiological mechanisms for this remain unclear. Spontaneous and induced oscillatory activity was recorded in healthy human participants with magnetoencephalography after intravenous infusion of psilocybin--prodrug of the nonselective serotonin 2A receptor agonist and classic psychedelic psilocin. Psilocybin reduced spontaneous cortical oscillatory power from 1 to 50 Hz in posterior association cortices, and from 8 to 100 Hz in frontal association cortices. Large decreases in oscillatory power were seen in areas of the default-mode network. Independent component analysis was used to identify a number of resting-state networks, and activity in these was similarly decreased after psilocybin. Psilocybin had no effect on low-level visually induced and motor-induced gamma-band oscillations, suggesting that some basic elements of oscillatory brain activity are relatively preserved during the psychedelic experience. Dynamic causal modeling revealed that posterior cingulate cortex desynchronization can be explained by increased excitability of deep-layer pyramidal neurons, which are known to be rich in 5-HT2A receptors. These findings suggest that the subjective effects of psychedelics result from a desynchronization of ongoing oscillatory rhythms in the cortex, likely triggered by 5-HT2A receptor-mediated excitation of deep pyramidal cells.

Acetylcholine (ACh) is a neuromodulatory transmitter implicated in perception and learning under uncertainty. This study combined computational simulations and pharmaco-electroencephalography in humans, to test a formulation of perceptual inference based upon the free energy principle. This formulation suggests that ACh enhances the precision of bottom-up synaptic transmission in cortical hierarchies by optimizing the gain of supragranular pyramidal cells. Simulations of a mismatch negativity paradigm predicted a rapid trial-by-trial suppression of evoked sensory prediction error (PE) responses that is attenuated by cholinergic neuromodulation. We confirmed this prediction empirically with a placebo-controlled study of cholinesterase inhibition. Furthermore, using dynamic causal modeling, we found that drug-induced differences in PE responses could be explained by gain modulation in supragranular pyramidal cells in primary sensory cortex. This suggests that ACh adaptively enhances sensory precision by boosting bottom-up signaling when stimuli are predictable, enabling the brain to respond optimally under different levels of environmental uncertainty.

Dynamic causal modeling (DCM) provides a framework for the analysis of effective connectivity among neuronal subpopulations that subtend invasive (electrocorticograms and local field potentials) and non-invasive (electroencephalography and magnetoencephalography) electrophysiological responses. This paper reviews the suite of neuronal population models including neural masses, fields and conductance-based models that are used in DCM. These models are expressed in terms of sets of differential equations that allow one to model the synaptic underpinnings of connectivity. We describe early developments using neural mass models, where convolution-based dynamics are used to generate responses in laminar-specific populations of excitatory and inhibitory cells. We show that these models, though resting on only two simple transforms, can recapitulate the characteristics of both evoked and spectral responses observed empirically. Using an identical neuronal architecture, we show that a set of conductance based models-that consider the dynamics of specific ion-channels-present a richer space of responses; owing to non-linear interactions between conductances and membrane potentials. We propose that conductance-based models may be more appropriate when spectra present with multiple resonances. Finally, we outline a third class of models, where each neuronal subpopulation is treated as a field; in other words, as a manifold on the cortical surface. By explicitly accounting for the spatial propagation of cortical activity through partial differential equations (PDEs), we show that the topology of connectivity-through local lateral interactions among cortical layers-may be inferred, even in the absence of spatially resolved data. We also show that these models allow for a detailed analysis of structure-function relationships in the cortex. Our review highlights the relationship among these models and how the hypothesis asked of empirical data suggests an appropriate model class.

This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior beliefs that action minimizes the Kullback-Leibler (KL) divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimizes a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimizing free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action-constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualizes optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution-that minimizes free energy. This sensitivity corresponds to the precision of beliefs about behavior, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behavior entails a representation of confidence about outcomes that are under an agent's control.

This paper reviews recent developments under the free energy principle that introduce a normative perspective on classical economic (utilitarian) decision-making based on (active) Bayesian inference. It has been suggested that the free energy principle precludes novelty and complexity, because it assumes that biological systems-like ourselves-try to minimize the long-term average of surprise to maintain their homeostasis. However, recent formulations show that minimizing surprise leads naturally to concepts such as exploration and novelty bonuses. In this approach, agents infer a policy that minimizes surprise by minimizing the difference (or relative entropy) between likely and desired outcomes, which involves both pursuing the goal-state that has the highest expected utility (often termed "exploitation") and visiting a number of different goal-states ("exploration"). Crucially, the opportunity to visit new states increases the value of the current state. Casting decision-making problems within a variational framework, therefore, predicts that our behavior is governed by both the entropy and expected utility of future states. This dissolves any dialectic between minimizing surprise and exploration or novelty seeking.

This paper considers psychotic symptoms in terms of false inferences or beliefs. It is based on the notion that the brain is an inference machine that actively constructs hypotheses to explain or predict its sensations. This perspective provides a normative (Bayes-optimal) account of action and perception that emphasizes probabilistic representations; in particular, the confidence or precision of beliefs about the world. We will consider hallucinosis, abnormal eye movements, sensory attenuation deficits, catatonia, and delusions as various expressions of the same core pathology: namely, an aberrant encoding of precision. From a cognitive perspective, this represents a pernicious failure of metacognition (beliefs about beliefs) that can confound perceptual inference. In the embodied setting of active (Bayesian) inference, it can lead to behaviors that are paradoxically more accurate than Bayes-optimal behavior. Crucially, this normative account is accompanied by a neuronally plausible process theory based upon hierarchical predictive coding. In predictive coding, precision is thought to be encoded by the post-synaptic gain of neurons reporting prediction error. This suggests that both pervasive trait abnormalities and florid failures of inference in the psychotic state can be linked to factors controlling post-synaptic gain - such as NMDA receptor function and (dopaminergic) neuromodulation. We illustrate these points using biologically plausible simulations of perceptual synthesis, smooth pursuit eye movements and attribution of agency - that all use the same predictive coding scheme and pathology: namely, a reduction in the precision of prior beliefs, relative to sensory evidence.

This paper describes a variational free-energy formulation of (partially observable) Markov decision problems in decision making under uncertainty. We show that optimal control can be cast as active inference. In active inference, both action and posterior beliefs about hidden states minimise a free energy bound on the negative log-likelihood of observed states, under a generative model. In this setting, reward or cost functions are absorbed into prior beliefs about state transitions and terminal states. Effectively, this converts optimal control into a pure inference problem, enabling the application of standard Bayesian filtering techniques. We then consider optimal trajectories that rest on posterior beliefs about hidden states in the future. Crucially, this entails modelling control as a hidden state that endows the generative model with a representation of agency. This leads to a distinction between models with and without inference on hidden control states; namely, agency-free and agency-based models, respectively.

The descending projections from motor cortex share many features with top-down or backward connections in visual cortex; for example, corticospinal projections originate in infragranular layers, are highly divergent and (along with descending cortico-cortical projections) target cells expressing NMDA receptors. This is somewhat paradoxical because backward modulatory characteristics would not be expected of driving motor command signals. We resolve this apparent paradox using a functional characterisation of the motor system based on Helmholtz's ideas about perception; namely, that perception is inference on the causes of visual sensations. We explain behaviour in terms of inference on the causes of proprioceptive sensations. This explanation appeals to active inference, in which higher cortical levels send descending proprioceptive predictions, rather than motor commands. This process mirrors perceptual inference in sensory cortex, where descending connections convey predictions, while ascending connections convey prediction errors. The anatomical substrate of this recurrent message passing is a hierarchical system consisting of functionally asymmetric driving (ascending) and modulatory (descending) connections: an arrangement that we show is almost exactly recapitulated in the motor system, in terms of its laminar, topographic and physiological characteristics. This perspective casts classical motor reflexes as minimising prediction errors and may provide a principled explanation for why motor cortex is agranular.

This review considers state-of-the-art analyses of functional integration in neuronal macrocircuits. We focus on detecting and estimating directed connectivity in neuronal networks using Granger causality (GC) and dynamic causal modelling (DCM). These approaches are considered in the context of functional segregation and integration and--within functional integration--the distinction between functional and effective connectivity. We review recent developments that have enjoyed a rapid uptake in the discovery and quantification of functional brain architectures. GC and DCM have distinct and complementary ambitions that are usefully considered in relation to the detection of functional connectivity and the identification of models of effective connectivity. We highlight the basic ideas upon which they are grounded, provide a comparative evaluation and point to some outstanding issues.

This paper presents a theoretical review of rapid eye movement sleep with a special focus on pontine-geniculate-occipital waves and what they might tell us about the functional anatomy of sleep and consciousness. In particular, we review established ideas about the nature and purpose of sleep in terms of protoconsciousness and free energy minimization. By combining these theoretical perspectives, we discover answers to some fundamental questions about sleep: for example, why is homeothermy suspended during sleep? Why is sleep necessary? Why are we not surprised by our dreams? What is the role of synaptic regression in sleep? The imperatives for sleep that emerge also allow us to speculate about the functional role of PGO waves and make some empirical predictions that can, in principle, be tested using recent advances in the modeling of electrophysiological data.

This article provides a neurobiological account of symptoms that have been called 'hysterical', 'psychogenic' or 'medically unexplained', which we will call functional motor and sensory symptoms. We use a neurobiologically informed model of hierarchical Bayesian inference in the brain to explain functional motor and sensory symptoms in terms of perception and action arising from inference based on prior beliefs and sensory information. This explanation exploits the key balance between prior beliefs and sensory evidence that is mediated by (body focused) attention, symptom expectations, physical and emotional experiences and beliefs about illness. Crucially, this furnishes an explanation at three different levels: (i) underlying neuromodulatory (synaptic) mechanisms; (ii) cognitive and experiential processes (attention and attribution of agency); and (iii) formal computations that underlie perceptual inference (representation of uncertainty or precision). Our explanation involves primary and secondary failures of inference; the primary failure is the (autonomous) emergence of a percept or belief that is held with undue certainty (precision) following top-down attentional modulation of synaptic gain. This belief can constitute a sensory percept (or its absence) or induce movement (or its absence). The secondary failure of inference is when the ensuing percept (and any somatosensory consequences) is falsely inferred to be a symptom to explain why its content was not predicted by the source of attentional modulation. This account accommodates several fundamental observations about functional motor and sensory symptoms, including: (i) their induction and maintenance by attention; (ii) their modification by expectation, prior experience and cultural beliefs and (iii) their involuntary and symptomatic nature.

The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal) cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a (Bayes-optimal) model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors) to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level.

Attentional orientation to a spatial cue and reorientation-after invalid cueing-are mediated by two distinct networks in the human brain. A bilateral dorsal frontoparietal network, comprising the intraparietal sulcus (IPS) and the frontal eye fields (FEF), controls the voluntary deployment of attention and may modulate visual cortex in preparation for upcoming stimulation. In contrast, reorienting attention to invalidly cued targets engages a right-lateralized ventral frontoparietal network comprising the temporoparietal junction (TPJ) and ventral frontal cortex. The present fMRI study investigated the functional architecture of these two attentional systems by characterizing effective connectivity during lateralized orienting and reorienting of attention, respectively. Subjects performed a modified version of Posner's location-cueing paradigm. Dynamic causal modeling (DCM) of regional responses in the dorsal and ventral network, identified in a conventional (SPM) whole-brain analysis, was used to compare different functional architectures. Bayesian model selection showed that top-down connections from left and right IPS to left and right visual cortex, respectively, were modulated by the direction of attention. Moreover, model evidence was highest for a model with directed influences from bilateral IPS to FEF, and reciprocal coupling between right and left FEF. Invalid cueing enhanced forward connections from visual areas to right TPJ, and directed influences from right TPJ to right IPS and IFG (inferior frontal gyrus). These findings shed further light on the functional organization of the dorsal and ventral attentional network and support a context-sensitive lateralization in the top-down (backward) mediation of attentional orienting and the bottom-up (forward) effects of invalid cueing.

Functional neurosurgery has afforded the opportunity to assess interactions between populations of neurons in the human cerebral cortex and basal ganglia in patients with Parkinson's disease (PD). Interactions occur over a wide range of frequencies, and the functional significance of those >30 Hz is particularly unclear. Do they improve movement, and, if so, in what way? We acquired simultaneously magnetoencephalography and direct recordings from the subthalamic nucleus (STN) in 17 PD patients. We examined the effect of synchronous and sequential finger movements and of the dopamine prodrug levodopa on induced power in the contralateral primary motor cortex (M1) and STN and on the coherence between the two structures. We observed discrete peaks in M1 and STN power at 60-90 Hz and at 300-400 Hz. All these power peaks increased with movement and levodopa treatment. Only STN activity at 60-90 Hz was coherent with activity in M1. Directionality analysis showed that STN gamma activity at 60-90 Hz tended to drive gamma activity in M1. The effects of levodopa on both local and distant synchronization at 60-90 Hz correlated with the degree of improvement in bradykinesia-rigidity as did local STN activity at 300-400 Hz. Despite this, there were no effects of movement type, nor interactions between movement type and levodopa in the STN, nor in the coherence between STN and M1. We conclude that synchronization at 60-90 Hz in the basal ganglia cortical network is prokinetic but likely through a modulatory effect rather than any involvement in explicit motor processing.

This study addresses the neuronal representation of aversive sounds that are perceived as unpleasant. Functional magnetic resonance imaging in humans demonstrated responses in the amygdala and auditory cortex to aversive sounds. We show that the amygdala encodes both the acoustic features of a stimulus and its valence (perceived unpleasantness). Dynamic causal modeling of this system revealed that evoked responses to sounds are relayed to the amygdala via auditory cortex. While acoustic features modulate effective connectivity from auditory cortex to the amygdala, the valence modulates the effective connectivity from amygdala to the auditory cortex. These results support a complex (recurrent) interaction between the auditory cortex and amygdala based on object-level analysis in the auditory cortex that portends the assignment of emotional valence in amygdala that in turn influences the representation of salient information in auditory cortex.

The mechanisms underlying anesthesia-induced loss of consciousness remain a matter of debate. Recent electrophysiological reports suggest that while initial propofol infusion provokes an increase in fast rhythms (from beta to gamma range), slow activity (from delta to alpha range) rises selectively during loss of consciousness. Dynamic causal modeling was used to investigate the neural mechanisms mediating these changes in spectral power in humans. We analyzed source-reconstructed data from frontal and parietal cortices during normal wakefulness, propofol-induced mild sedation, and loss of consciousness. Bayesian model selection revealed that the best model for explaining spectral changes across the three states involved changes in corticothalamic interactions. Compared with wakefulness, mild sedation was accounted for by an increase in thalamic excitability, which did not further increase during loss of consciousness. In contrast, loss of consciousness per se was accompanied by a decrease in backward corticocortical connectivity from frontal to parietal cortices, while thalamocortical connectivity remained unchanged. These results emphasize the importance of recurrent corticocortical communication in the maintenance of consciousness and suggest a direct effect of propofol on cortical dynamics.

This paper considers state-dependent dynamics that mediate perception in the brain. In particular, it considers the formal basis of self-organized instabilities that enable perceptual transitions during Bayes-optimal perception. The basic phenomena we consider are perceptual transitions that lead to conscious ignition (Dehaene and Changeux, 2011) and how they depend on dynamical instabilities that underlie chaotic itinerancy (Breakspear, 2001; Tsuda, 2001) and self-organized criticality (Beggs and Plenz, 2003; Plenz and Thiagarajan, 2007; Shew et al., 2011). Our approach is based on a dynamical formulation of perception as approximate Bayesian inference, in terms of variational free energy minimization. This formulation suggests that perception has an inherent tendency to induce dynamical instabilities (critical slowing) that enable the brain to respond sensitively to sensory perturbations. We briefly review the dynamics of perception, in terms of generalized Bayesian filtering and free energy minimization, present a formal conjecture about self-organized instability and then test this conjecture, using neuronal (numerical) simulations of perceptual categorization.

Recent years have seen the emergence of an important new fundamental theory of brain function. This theory brings information-theoretic, Bayesian, neuroscientific, and machine learning approaches into a single framework whose overarching principle is the minimization of surprise (or, equivalently, the maximization of expectation). The most comprehensive such treatment is the "free-energy minimization" formulation due to Karl Friston (see e.g., Friston and Stephan, 2007; Friston, 2010a,b - see also Fiorillo, 2010; Thornton, 2010). A recurrent puzzle raised by critics of these models is that biological systems do not seem to avoid surprises. We do not simply seek a dark, unchanging chamber, and stay there. This is the "Dark-Room Problem." Here, we describe the problem and further unpack the issues to which it speaks. Using the same format as the prolog of Eddington's Space, Time, and Gravitation (Eddington, 1920) we present our discussion as a conversation between: an information theorist (Thornton), a physicist (Friston), and a philosopher (Clark).

If perception corresponds to hypothesis testing (Gregory, 1980); then visual searches might be construed as experiments that generate sensory data. In this work, we explore the idea that saccadic eye movements are optimal experiments, in which data are gathered to test hypotheses or beliefs about how those data are caused. This provides a plausible model of visual search that can be motivated from the basic principles of self-organized behavior: namely, the imperative to minimize the entropy of hidden states of the world and their sensory consequences. This imperative is met if agents sample hidden states of the world efficiently. This efficient sampling of salient information can be derived in a fairly straightforward way, using approximate Bayesian inference and variational free-energy minimization. Simulations of the resulting active inference scheme reproduce sequential eye movements that are reminiscent of empirically observed saccades and provide some counterintuitive insights into the way that sensory evidence is accumulated or assimilated into beliefs about the world.

This paper describes a free energy principle that tries to explain the ability of biological systems to resist a natural tendency to disorder. It appeals to circular causality of the sort found in synergetic formulations of self-organization (e.g., the slaving principle) and models of coupled dynamical systems, using nonlinear Fokker Planck equations. Here, circular causality is induced by separating the states of a random dynamical system into external and internal states, where external states are subject to random fluctuations and internal states are not. This reduces the problem to finding some (deterministic) dynamics of the internal states that ensure the system visits a limited number of external states; in other words, the measure of its (random) attracting set, or the Shannon entropy of the external states is small. We motivate a solution using a principle of least action based on variational free energy (from statistical physics) and establish the conditions under which it is formally equivalent to the information bottleneck method. This approach has proved useful in understanding the functional architecture of the brain. The generality of variational free energy minimisation and corresponding information theoretic formulations may speak to interesting applications beyond the neurosciences; e.g., in molecular or evolutionary biology.

The articles in this special issue provide a rich and thoughtful perspective on the brain as an inference machine. They illuminate key aspects of the internal or generative models the brain might use for perception. Furthermore, they explore the implications for a sense of agency and the nature of false inference in neuropsychiatric syndromes. In this review, I try to gather together some of the themes that emerge in this special issue and use them to illustrate how far one can take the notion of predictive coding in understanding behaviour and agency.

This paper presents a nonlinear image registration algorithm based on the setting of Large Deformation Diffeomorphic Metric Mapping (LDDMM), but with a more efficient optimisation scheme--both in terms of memory required and the number of iterations required to reach convergence. Rather than perform a variational optimisation on a series of velocity fields, the algorithm is formulated to use a geodesic shooting procedure, so that only an initial velocity is estimated. A Gauss-Newton optimisation strategy is used to achieve faster convergence. The algorithm was evaluated using freely available manually labelled datasets, and found to compare favourably with other inter-subject registration algorithms evaluated using the same data.

This is the final paper in a Comments and Controversies series dedicated to "The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution". We argue that discovering effective connectivity depends critically on state-space models with biophysically informed observation and state equations. These models have to be endowed with priors on unknown parameters and afford checks for model Identifiability. We consider the similarities and differences among Dynamic Causal Modeling, Granger Causal Modeling and other approaches. We establish links between past and current statistical causal modeling, in terms of Bayesian dependency graphs and Wiener-Akaike-Granger-Schweder influence measures. We show that some of the challenges faced in this field have promising solutions and speculate on future developments.

This note describes a Bayesian model selection or optimization procedure for post hoc inferences about reduced versions of a full model. The scheme provides the evidence (marginal likelihood) for any reduced model as a function of the posterior density over the parameters of the full model. It rests upon specifying models through priors on their parameters, under the assumption that the likelihood remains the same for all models considered. This provides a quick and efficient scheme for scoring arbitrarily large numbers of models, after inverting a single (full) model. In turn, this enables the selection among discrete models that are distinguished by the presence or absence of free parameters, where free parameters are effectively removed from the model using very precise shrinkage priors. An alternative application of this post hoc model selection considers continuous model spaces, defined in terms of hyperparameters (sufficient statistics) of the prior density over model parameters. In this instance, the prior (model) can be optimized with respect to its evidence. The expressions for model evidence become remarkably simple under the Laplace (Gaussian) approximation to the posterior density. Special cases of this scheme include Savage-Dickey density ratio tests for reduced models and automatic relevance determination in model optimization. We illustrate the approach using general linear models and a more complicated nonlinear state-space model.

Exact timing is essential for functional MRI data analysis. Datasets are commonly measured using repeated 2D imaging methods, resulting in a temporal offset between slices. To compensate for this timing difference, slice-timing correction (i.e. temporal data interpolation) has been used as an fMRI pre-processing step for more than fifteen years. However, there has been an ongoing debate about the effectiveness and applicability of this method. This paper presents the first elaborated analysis of the impact of the slice-timing effect on simulated data for different fMRI paradigms and measurement parameters, taking into account data noise and smoothing effects. Here we show, depending on repetition time and paradigm design, slice-timing effects can significantly impair fMRI results and slice-timing correction methods can successfully compensate for these effects and therefore increase the robustness of the data analysis. In addition, our results from simulated data were supported by empirical in vivo datasets. Our findings suggest that slice-timing correction should be included in the fMRI pre-processing pipeline.

The slight perversion of the original title of this piece (The Future of the Bayesian Brain) reflects my attempt to write prospectively about 'Science and Stories' over the past 20 years. I will meet this challenge by dealing with the future and then turning to its history. The future of the Bayesian brain (in neuroimaging) is clear: it is the application of dynamic causal modeling to understand how the brain conforms to the free energy principle. In this context, the Bayesian brain is a corollary of the free energy principle, which says that any self organizing system (like a brain or neuroimaging community) must maximize the evidence for its own existence, which means it must minimize its free energy using a model of its world. Dynamic causal modeling involves finding models of the brain that have the greatest evidence or the lowest free energy. In short, the future of imaging neuroscience is to refine models of the brain to minimize free energy, where the brain refines models of the world to minimize free energy. This endeavor itself minimizes free energy because our community is itself a self organizing system. I cannot imagine an alternative future that has the same beautiful self consistency as mine. Having dispensed with the future, we can now focus on the past, which is much more interesting:

Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience. One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain's functional architecture and operational principles. The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. I accepted the invitation to write this review with great pleasure and hope to celebrate and critique the achievements to date, while addressing the challenges ahead.

The impact of traumatic spinal cord injury on structural integrity, cortical reorganization and ensuing disability is variable and may depend on a dynamic interaction between the severity of local damage and the capacity of the brain for plastic reorganization. We investigated trauma-induced anatomical changes in the spinal cord and brain, and explored their relationship to functional changes in sensorimotor cortex. Structural changes were assessed using cross-sectional cord area, voxel-based morphometry and voxel-based cortical thickness of T1-weighted images in 10 subjects with cervical spinal cord injury and 16 controls. Cortical activation in response to right-sided (i) handgrip; and (ii) median and tibial nerve stimulation were assessed using functional magnetic resonance imaging. Regression analyses explored associations between cord area, grey and white matter volume, cortical activations and thickness, and disability. Subjects with spinal cord injury had impaired upper and lower limb function bilaterally, a 30% reduced cord area, smaller white matter volume in the pyramids and left cerebellar peduncle, and smaller grey matter volume and cortical thinning in the leg area of the primary motor and sensory cortex compared with controls. Functional magnetic resonance imaging revealed increased activation in the left primary motor cortex leg area during handgrip and the left primary sensory cortex face area during median nerve stimulation in subjects with spinal cord injury compared with controls, but no increased activation following tibial nerve stimulation. A smaller cervical cord area was associated with impaired upper limb function and increased activations with handgrip and median nerve stimulation, but reduced activations with tibial nerve stimulation. Increased sensory deficits were associated with increased activations in the left primary sensory cortex face area due to median nerve stimulation. In conclusion, spinal cord injury leads to cord atrophy, cortical atrophy of primary motor and sensory cortex, and cortical reorganization of the sensorimotor system. The degree of cortical reorganization is predicted by spinal atrophy and is associated with significant disability.

Frontoparietal cortex is involved in the explicit processing (awareness) of stimuli. Frontoparietal activation has also been found in studies of subliminal stimulus processing. We hypothesized that an impairment of top-down processes, involved in recurrent neuronal message-passing and the generation of long-latency electrophysiological responses, might provide a more reliable correlate of consciousness in severely brain-damaged patients, than frontoparietal responses. We measured effective connectivity during a mismatch negativity paradigm and found that the only significant difference between patients in a vegetative state and controls was an impairment of backward connectivity from frontal to temporal cortices. This result emphasizes the importance of top-down projections in recurrent processing that involve high-order associative cortices for conscious perception.

SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools.

Cortico-basal ganglia-thalamocortical circuits are severely disrupted by the dopamine depletion of Parkinson's disease (PD), leading to pathologically exaggerated beta oscillations. Abnormal rhythms, found in several circuit nodes are correlated with movement impairments but their neural basis remains unclear. Here, we used dynamic causal modelling (DCM) and the 6-hydroxydopamine-lesioned rat model of PD to examine the effective connectivity underlying these spectral abnormalities. We acquired auto-spectral and cross-spectral measures of beta oscillations (10-35 Hz) from local field potential recordings made simultaneously in the frontal cortex, striatum, external globus pallidus (GPe) and subthalamic nucleus (STN), and used these data to optimise neurobiologically plausible models. Chronic dopamine depletion reorganised the cortico-basal ganglia-thalamocortical circuit, with increased effective connectivity in the pathway from cortex to STN and decreased connectivity from STN to GPe. Moreover, a contribution analysis of the Parkinsonian circuit distinguished between pathogenic and compensatory processes and revealed how effective connectivity along the indirect pathway acquired a strategic importance that underpins beta oscillations. In modelling excessive beta synchrony in PD, these findings provide a novel perspective on how altered connectivity in basal ganglia-thalamocortical circuits reflects a balance between pathogenesis and compensation, and predicts potential new therapeutic targets to overcome dysfunctional oscillations.

Perception is the foundation of cognition and is fundamental to our beliefs and consequent action planning. The Editorial (this issue) asks: "what mechanisms, if any, mediate between perceptual and cognitive processes?" It has recently been argued that attention might furnish such a mechanism. In this paper, we pursue the idea that action planning (motor preparation) is an attentional phenomenon directed toward kinesthetic signals. This rests on a view of motor control as active inference, where predictions of proprioceptive signals are fulfilled by peripheral motor reflexes. If valid, active inference suggests that attention should not be limited to the optimal biasing of perceptual signals in the exteroceptive (e.g., visual) domain but should also bias proprioceptive signals during movement. Here, we investigate this idea using a classical attention (Posner) paradigm cast in a motor setting. Specially, we looked for decreases in reaction times when movements were preceded by valid relative to invalid cues. Furthermore, we addressed the hierarchical level at which putative attentional effects were expressed by independently cueing the nature of the movement and the hand used to execute it. We found a significant interaction between the validity of movement and effector cues on reaction times. This suggests that attentional bias might be mediated at a low level in the motor hierarchy, in an intrinsic frame of reference. This finding is consistent with attentional enabling of top-down predictions of proprioceptive input and may rely upon the same synaptic mechanisms that mediate directed spatial attention in the visual system.

Functional neuroimaging techniques are used widely in cognitive neuroscience to investigate aspects of functional specialization and functional integration in the human brain. Functional integration can be characterized in two ways, functional connectivity and effective connectivity. While functional connectivity describes statistical dependencies between data, effective connectivity rests on a mechanistic model of the causal effects that generated the data. This review addresses the conceptual and methodological basis of established techniques for characterizing effective connectivity using functional magnetic resonance imaging (fMRI) data. In particular, we focus on dynamic causal modeling (DCM) of fMRI data and emphasize the importance of model selection procedures and nonlinear mechanisms for context-dependent changes in connection strengths. Copyright © 2010 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website.

We have previously tried to explain perceptual inference and learning under a free-energy principle that pursues Helmholtz's agenda to understand the brain in terms of energy minimization. It is fairly easy to show that making inferences about the causes of sensory data can be cast as the minimization of a free-energy bound on the likelihood of sensory inputs, given an internal model of how they were caused. In this article, we consider what would happen if the data themselves were sampled to minimize this bound. It transpires that the ensuing active sampling or inference is mandated by ergodic arguments based on the very existence of adaptive agents. Furthermore, it accounts for many aspects of motor behavior; from retinal stabilization to goal-seeking. In particular, it suggests that motor control can be understood as fulfilling prior expectations about proprioceptive sensations. This formulation can explain why adaptive behavior emerges in biological agents and suggests a simple alternative to optimal control theory. We illustrate these points using simulations of oculomotor control and then apply to same principles to cued and goal-directed movements. In short, the free-energy formulation may provide an alternative perspective on the motor control that places it in an intimate relationship with perception.

Analytic tools for psychophysiological signals often make implicit assumptions that are unspecified. In developing a mathematical framework for analysis of skin conductance responses [SCRs], we formalise our assumptions by positing that SCRs can be regarded as the output of a linear time-invariant filter. Here, we provide an empirical test of these assumptions. Our findings indicate that a large component of the variance in SCRs can be explained by one response function per individual. We note that baseline variance (i.e. variance in the absence of evoked responses) is higher than variance that could not be explained by a linear time-invariant model of evoked responses. Furthermore, there was no evidence for nonlinear interactions among evoked responses that depended on their temporal overlap. We develop a canonical response function and show that it can be used for signals from different recording sites. We discuss the implications of these observations for model-based analysis of SCRs.

Autonomic arousal is often indexed by spontaneous fluctuations in skin conductance. Here, we derive a simple measure of sympathetic arousal, using a convolution model of how sudomotor bursting causes fluctuations in skin conductivity. Under this model, the time-integral of measured conductance is proportional to the frequency and amplitude of sudomotor bursts. We demonstrate the validity of this measure in relation to finite impulse response models, and show that it is a better predictor of autonomic arousal, relative to conventional measures.

This paper is about inferring or discovering the functional architecture of distributed systems using Dynamic Causal Modelling (DCM). We describe a scheme that recovers the (dynamic) Bayesian dependency graph (connections in a network) using observed network activity. This network discovery uses Bayesian model selection to identify the sparsity structure (absence of edges or connections) in a graph that best explains observed time-series. The implicit adjacency matrix specifies the form of the network (e.g., cyclic or acyclic) and its graph-theoretical attributes (e.g., degree distribution). The scheme is illustrated using functional magnetic resonance imaging (fMRI) time series to discover functional brain networks. Crucially, it can be applied to experimentally evoked responses (activation studies) or endogenous activity in task-free (resting state) fMRI studies. Unlike conventional approaches to network discovery, DCM permits the analysis of directed and cyclic graphs. Furthermore, it eschews (implausible) Markovian assumptions about the serial independence of random fluctuations. The scheme furnishes a network description of distributed activity in the brain that is optimal in the sense of having the greatest conditional probability, relative to other networks. The networks are characterised in terms of their connectivity or adjacency matrices and conditional distributions over the directed (and reciprocal) effective connectivity between connected nodes or regions. We envisage that this approach will provide a useful complement to current analyses of functional connectivity for both activation and resting-state studies.

This article explores the notion that Freudian constructs may have neurobiological substrates. Specifically, we propose that Freud's descriptions of the primary and secondary processes are consistent with self-organized activity in hierarchical cortical systems and that his descriptions of the ego are consistent with the functions of the default-mode and its reciprocal exchanges with subordinate brain systems. This neurobiological account rests on a view of the brain as a hierarchical inference or Helmholtz machine. In this view, large-scale intrinsic networks occupy supraordinate levels of hierarchical brain systems that try to optimize their representation of the sensorium. This optimization has been formulated as minimizing a free-energy; a process that is formally similar to the treatment of energy in Freudian formulations. We substantiate this synthesis by showing that Freud's descriptions of the primary process are consistent with the phenomenology and neurophysiology of rapid eye movement sleep, the early and acute psychotic state, the aura of temporal lobe epilepsy and hallucinogenic drug states.

Both phenotype and treatment response vary in patients with Parkinson's disease. Anatomical and functional imaging studies suggest that individual symptoms may represent malfunction of different segregated networks running in parallel through the basal ganglia. In this study, we use a newly described, electrophysiological method to describe cortico-subthalamic networks in humans. We performed combined magnetoencephalographic and subthalamic local field potential recordings in thirteen patients with Parkinson's disease at rest. Two spatially and spectrally separated networks were identified. A temporoparietal-brainstem network was coherent with the subthalamic nucleus in the alpha (7-13 Hz) band, whilst a predominantly frontal network was coherent in the beta (15-35 Hz) band. Dopaminergic medication modulated the resting beta network, by increasing beta coherence between the subthalamic region and prefrontal cortex. Subthalamic activity was predominantly led by activity in the cortex in both frequency bands. The cortical topography and frequencies involved in the alpha and beta networks suggest that these networks may be involved in attentional and executive, particularly motor planning, processes, respectively.

We describe a Bayesian filtering scheme for nonlinear state‐space models in continuous time. This scheme is called Generalised Filtering and furnishes posterior (conditional) densities on hidden states and unknown parameters generating observed data. Crucially, the scheme operates online, assimilating data to optimize the conditional density on time‐varying states and time‐invariant parameters. In contrast to Kalman and Particle smoothing, Generalised Filtering does not require a backwards pass. In contrast to variational schemes, it does not assume conditional independence between the states and parameters. Generalised Filtering optimises the conditional density with respect to a free‐energy bound on the model′s log‐evidence. This optimisation uses the generalised motion of hidden states and parameters, under the prior assumption that the motion of the parameters is small. We describe the scheme, present comparative evaluations with a fixed‐form variational version, and conclude with an illustrative application to a nonlinear state‐space model of brain imaging time‐series.

Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This "best model" approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data.

In this paper, we present a generic approach that can be used to infer how subjects make optimal decisions under uncertainty. This approach induces a distinction between a subject's perceptual model, which underlies the representation of a hidden "state of affairs" and a response model, which predicts the ensuing behavioural (or neurophysiological) responses to those inputs. We start with the premise that subjects continuously update a probabilistic representation of the causes of their sensory inputs to optimise their behaviour. In addition, subjects have preferences or goals that guide decisions about actions given the above uncertain representation of these hidden causes or state of affairs. From a Bayesian decision theoretic perspective, uncertain representations are so-called "posterior" beliefs, which are influenced by subjective "prior" beliefs. Preferences and goals are encoded through a "loss" (or "utility") function, which measures the cost incurred by making any admissible decision for any given (hidden) state of affair. By assuming that subjects make optimal decisions on the basis of updated (posterior) beliefs and utility (loss) functions, one can evaluate the likelihood of observed behaviour. Critically, this enables one to "observe the observer", i.e. identify (context- or subject-dependent) prior beliefs and utility-functions using psychophysical or neurophysiological measures. In this paper, we describe the main theoretical components of this meta-Bayesian approach (i.e. a Bayesian treatment of Bayesian decision theoretic predictions). In a companion paper ('Observing the observer (II): deciding when to decide'), we describe a concrete implementation of it and demonstrate its utility by applying it to simulated and real reaction time data from an associative learning task.

Both perceptual inference and motor responses are shaped by learned probabilities. For example, stimulus-induced responses in sensory cortices and preparatory activity in premotor cortex reflect how (un)expected a stimulus is. This is in accordance with predictive coding accounts of brain function, which posit a fundamental role of prediction errors for learning and adaptive behavior. We used functional magnetic resonance imaging and recent advances in computational modeling to investigate how (failures of) learned predictions about visual stimuli influence subsequent motor responses. Healthy volunteers discriminated visual stimuli that were differentially predicted by auditory cues. Critically, the predictive strengths of cues varied over time, requiring subjects to continuously update estimates of stimulus probabilities. This online inference, modeled using a hierarchical Bayesian learner, was reflected behaviorally: speed and accuracy of motor responses increased significantly with predictability of the stimuli. We used nonlinear dynamic causal modeling to demonstrate that striatal prediction errors are used to tune functional coupling in cortical networks during learning. Specifically, the degree of striatal trial-by-trial prediction error activity controls the efficacy of visuomotor connections and thus the influence of surprising stimuli on premotor activity. This finding substantially advances our understanding of striatal function and provides direct empirical evidence for formal learning theories that posit a central role for prediction error-dependent plasticity.

Humans have the arguably unique ability to understand the mental representations of others. For success in both competitive and cooperative interactions, however, this ability must be extended to include representations of others' belief about our intentions, their model about our belief about their intentions, and so on. We developed a "stag hunt" game in which human subjects interacted with a computerized agent using different degrees of sophistication (recursive inferences) and applied an ecologically valid computational model of dynamic belief inference. We show that rostral medial prefrontal (paracingulate) cortex, a brain region consistently identified in psychological tasks requiring mentalizing, has a specific role in encoding the uncertainty of inference about the other's strategy. In contrast, dorsolateral prefrontal cortex encodes the depth of recursion of the strategy being used, an index of executive sophistication. These findings reveal putative computational representations within prefrontal cortex regions, supporting the maintenance of cooperation in complex social decision making.

We suggested recently that attention can be understood as inferring the level of uncertainty or precision during hierarchical perception. In this paper, we try to substantiate this claim using neuronal simulations of directed spatial attention and biased competition. These simulations assume that neuronal activity encodes a probabilistic representation of the world that optimizes free-energy in a Bayesian fashion. Because free-energy bounds surprise or the (negative) log-evidence for internal models of the world, this optimization can be regarded as evidence accumulation or (generalized) predictive coding. Crucially, both predictions about the state of the world generating sensory data and the precision of those data have to be optimized. Here, we show that if the precision depends on the states, one can explain many aspects of attention. We illustrate this in the context of the Posner paradigm, using the simulations to generate both psychophysical and electrophysiological responses. These simulated responses are consistent with attentional bias or gating, competition for attentional resources, attentional capture and associated speed-accuracy trade-offs. Furthermore, if we present both attended and non-attended stimuli simultaneously, biased competition for neuronal representation emerges as a principled and straightforward property of Bayes-optimal perception.

We present a review of dynamic causal modeling (DCM) for magneto- and electroencephalography (M/EEG) data. DCM is based on a spatiotemporal model, where the temporal component is formulated in terms of neurobiologically plausible dynamics. Following an intuitive description of the model, we discuss six recent studies, which use DCM to analyze M/EEG and local field potentials. These studies illustrate how DCM can be used to analyze evoked responses (average response in time), induced responses (average response in time-frequency), and steady-state responses (average response in frequency). Bayesian model comparison plays a critical role in these analyses, by allowing one to compare equally plausible models in terms of their model evidence. This approach might be very useful in M/EEG research; where correlations among spatial and neuronal model parameter estimates can cause uncertainty about which model best explains the data. Bayesian model comparison resolves these uncertainties in a principled and formal way. We suggest that DCM and Bayesian model comparison provides a useful way to test hypotheses about distributed processing in the brain, using electromagnetic data.

The mismatch negativity (MMN) is a brain response to violations of a rule, established by a sequence of sensory stimuli (typically in the auditory domain) [Näätänen R. Attention and brain function. Hillsdale, NJ: Lawrence Erlbaum; 1992]. The MMN reflects the brain's ability to perform automatic comparisons between consecutive stimuli and provides an electrophysiological index of sensory learning and perceptual accuracy. Although the MMN has been studied extensively, the neurophysiological mechanisms underlying the MMN are not well understood. Several hypotheses have been put forward to explain the generation of the MMN; amongst these accounts, the "adaptation hypothesis" and the "model adjustment hypothesis" have received the most attention. This paper presents a review of studies that focus on neuronal mechanisms underlying the MMN generation, discusses the two major explanatory hypotheses, and proposes predictive coding as a general framework that attempts to unify both.

Event-related skin conductance responses (SCRs) are traditionally analysed by comparing the amplitude of individual peaks against a pre-stimulus baseline. Many experimental manipulations in cognitive neuroscience dictate paradigms with short inter trial intervals, precluding accurate baseline estimation for SCR measurements. Here, we present a novel and general approach to SCR analysis, derived from methods used in neuroimaging that estimate responses using a linear convolution model. In effect, the method obviates peak-scoring and makes use of the full SCR. We demonstrate, across three experiments, that the method has face validity in analysing reactions to a loud white noise and emotional pictures, can be generalised to paradigms where the shape of the response function is unknown and can account for parametric trial-by-trial effects. We suggest our approach provides greater flexibility in analysing SCRs than existing methods.

This paper assumes that cortical circuits have evolved to enable inference about the causes of sensory input received by the brain. This provides a principled specification of what neural circuits have to achieve. Here, we attempt to address how the brain makes inferences by casting inference as an optimisation problem. We look at how the ensuing recognition dynamics could be supported by directed connections and message-passing among neuronal populations, given our knowledge of intrinsic and extrinsic neuronal connections. We assume that the brain models the world as a dynamic system, which imposes causal structure on the sensorium. Perception is equated with the optimisation or inversion of this internal model, to explain sensory input. Given a model of how sensory data are generated, we use a generic variational approach to model inversion to furnish equations that prescribe recognition; i.e., the dynamics of neuronal activity that represents the causes of sensory input. Here, we focus on a model whose hierarchical and dynamical structure enables simulated brains to recognise and predict sequences of sensory states. We first review these models and their inversion under a variational free-energy formulation. We then show that the brain has the necessary infrastructure to implement this inversion and present stimulations using synthetic birds that generate and recognise birdsongs.

In this technical note, we describe and validate a topological false discovery rate (FDR) procedure for statistical parametric mapping. This procedure is designed to deal with signal that is continuous and has, in principle, unbounded spatial support. We therefore infer on topological features of the signal, such as the existence of local maxima or peaks above some threshold. Using results from random field theory, we assign a p-value to each maximum in an SPM and identify an adaptive threshold that controls false discovery rate, using the Benjamini and Hochberg (BH) procedure (1995). This provides a natural complement to conventional family wise error (FWE) control on local maxima. We use simulations to contrast these procedures; both in terms of their relative number of discoveries and their spatial accuracy (via the distribution of the Euclidian distance between true and discovered activations). We also assessed two other procedures: cluster-wise and voxel-wise FDR procedures. Our results suggest that (a) FDR control of maxima or peaks is more sensitive than FWE control of peaks with minimal cost in terms of false-positives, (b) voxel-wise FDR is substantially less accurate than topological FWE or FDR control. Finally, we present an illustrative application using an fMRI study of visual attention.

Dynamic causal modeling (DCM) is a generic Bayesian framework for inferring hidden neuronal states from measurements of brain activity. It provides posterior estimates of neurobiologically interpretable quantities such as the effective strength of synaptic connections among neuronal populations and their context-dependent modulation. DCM is increasingly used in the analysis of a wide range of neuroimaging and electrophysiological data. Given the relative complexity of DCM, compared to conventional analysis techniques, a good knowledge of its theoretical foundations is needed to avoid pitfalls in its application and interpretation of results. By providing good practice recommendations for DCM, in the form of ten simple rules, we hope that this article serves as a helpful tutorial for the growing community of DCM users.

In this paper, we describe a general variational Bayesian approach for approximate inference on nonlinear stochastic dynamic models. This scheme extends established approximate inference on hidden-states to cover: (i) nonlinear evolution and observation functions, (ii) unknown parameters and (precision) hyperparameters and (iii) model comparison and prediction under uncertainty. Model identification or inversion entails the estimation of the marginal likelihood or evidence of a model. This difficult integration problem can be finessed by optimising a free-energy bound on the evidence using results from variational calculus. This yields a deterministic update scheme that optimises an approximation to the posterior density on the unknown model variables. We derive such a variational Bayesian scheme in the context of nonlinear stochastic dynamic hierarchical models, for both model identification and time-series prediction. The computational complexity of the scheme is comparable to that of an extended Kalman filter, which is critical when inverting high dimensional models or long time-series. Using Monte-Carlo simulations, we assess the estimation efficiency of this variational Bayesian approach using three stochastic variants of chaotic dynamic systems. We also demonstrate the model comparison capabilities of the method, its self-consistency and its predictive power.

Over the last 2 decades, a large number of neurophysiological and neuroimaging studies of patients with schizophrenia have furnished in vivo evidence for dysconnectivity, ie, abnormal functional integration of brain processes. While the evidence for dysconnectivity in schizophrenia is strong, its etiology, pathophysiological mechanisms, and significance for clinical symptoms are unclear. First, dysconnectivity could result from aberrant wiring of connections during development, from aberrant synaptic plasticity, or from both. Second, it is not clear how schizophrenic symptoms can be understood mechanistically as a consequence of dysconnectivity. Third, if dysconnectivity is the primary pathophysiology, and not just an epiphenomenon, then it should provide a mechanistic explanation for known empirical facts about schizophrenia. This article addresses these 3 issues in the framework of the dysconnection hypothesis. This theory postulates that the core pathology in schizophrenia resides in aberrant N-methyl-D-aspartate receptor (NMDAR)-mediated synaptic plasticity due to abnormal regulation of NMDARs by neuromodulatory transmitters like dopamine, serotonin, or acetylcholine. We argue that this neurobiological mechanism can explain failures of self-monitoring, leading to a mechanistic explanation for first-rank symptoms as pathognomonic features of schizophrenia, and may provide a basis for future diagnostic classifications with physiologically defined patient subgroups. Finally, we test the explanatory power of our theory against a list of empirical facts about schizophrenia.

This paper considers prediction and perceptual categorization as an inference problem that is solved by the brain. We assume that the brain models the world as a hierarchy or cascade of dynamical systems that encode causal structure in the sensorium. Perception is equated with the optimization or inversion of these internal models, to explain sensory data. Given a model of how sensory data are generated, we can invoke a generic approach to model inversion, based on a free energy bound on the model's evidence. The ensuing free-energy formulation furnishes equations that prescribe the process of recognition, i.e. the dynamics of neuronal activity that represent the causes of sensory input. Here, we focus on a very general model, whose hierarchical and dynamical structure enables simulated brains to recognize and predict trajectories or sequences of sensory states. We first review hierarchical dynamical models and their inversion. We then show that the brain has the necessary infrastructure to implement this inversion and illustrate this point using synthetic birds that can recognize and categorize birdsongs.

In this, the 21st century, human-brain mapping celebrates 21 years of cognitive activation studies. This review looks at imaging neuroscience and key ideas it has pursued; some ideas portend exciting developments, and others have failed gloriously. In terms of achievements, there is much to celebrate, in the sense that it is difficult to imagine modern neuroscience without brain imaging. I will look at recent advances from the perspectives of functional segregation and integration in the brain, paying special attention to approaches that deal with the distributed and integrated nature of neuronal processing and the questions they address.

This article describes the use of dynamic causal modeling to test hypotheses about the genesis of evoked responses. Specifically, we consider the mismatch negativity (MMN), a well-characterized response to deviant sounds and one of the most widely studied evoked responses. There have been several mechanistic accounts of how the MMN might arise. It has been suggested that the MMN results from a comparison between sensory input and a memory trace of previous input, although others have argued that local adaptation, due to stimulus repetition, is sufficient to explain the MMN. Thus the precise mechanisms underlying the generation of the MMN remain unclear. This study tests some biologically plausible spatiotemporal dipole models that rest on changes in extrinsic top-down connections (that enable comparison) and intrinsic changes (that model adaptation). Dynamic causal modeling suggested that responses to deviants are best explained by changes in effective connectivity both within and between cortical sources in a hierarchical network of distributed sources. Our model comparison suggests that both adaptation and memory comparison operate in concert to produce the early (N1 enhancement) and late (MMN) parts of the response to frequency deviants. We consider these mechanisms in the light of predictive coding and hierarchical inference in the brain.

Recent advances in data analysis and modeling allow the use of fMRI data to ask not just which brain regions are involved in various cognitive and perceptual tasks, but also how they communicate with each other. Karl Friston examines two different state-of-the-art approaches to modeling brain connectivity using neuroimaging.

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

Marginal utility theory prescribes the relationship between the objective property of the magnitude of rewards and their subjective value. Despite its pervasive influence, however, there is remarkably little direct empirical evidence for such a theory of value, let alone of its neurobiological basis. We show that human preferences in an intertemporal choice task are best described by a model that integrates marginally diminishing utility with temporal discounting. Using functional magnetic resonance imaging, we show that activity in the dorsal striatum encodes both the marginal utility of rewards, over and above that which can be described by their magnitude alone, and the discounting associated with increasing time. In addition, our data show that dorsal striatum may be involved in integrating subjective valuation systems inherent to time and magnitude, thereby providing an overall metric of value used to guide choice behavior. Furthermore, during choice, we show that anterior cingulate activity correlates with the degree of difficulty associated with dissonance between value and time. Our data support an integrative architecture for decision making, revealing the neural representation of distinct subcomponents of value that may contribute to impulsivity and decisiveness.

There is much current debate about the existence of mirror neurons in humans. To identify mirror neurons in the inferior frontal gyrus (IFG) of humans, we used a repetition suppression paradigm while measuring neural activity with functional magnetic resonance imaging. Subjects either executed or observed a series of actions. Here we show that in the IFG, responses were suppressed both when an executed action was followed by the same rather than a different observed action and when an observed action was followed by the same rather than a different executed action. This pattern of responses is consistent with that predicted by mirror neurons and is evidence of mirror neurons in the human IFG.

Dynamic Causal Modelling (DCM) is an approach first introduced for the analysis of functional magnetic resonance imaging (fMRI) to quantify effective connectivity between brain areas. Recently, this framework has been extended and established in the magneto/encephalography (M/EEG) domain. DCM for M/EEG entails the inversion a full spatiotemporal model of evoked responses, over multiple conditions. This model rests on a biophysical and neurobiological generative model for electrophysiological data. A generative model is a prescription of how data are generated. The inversion of a DCM provides conditional densities on the model parameters and, indeed on the model itself. These densities enable one to answer key questions about the underlying system. A DCM comprises two parts; one part describes the dynamics within and among neuronal sources, and the second describes how source dynamics generate data in the sensors, using the lead-field. The parameters of this spatiotemporal model are estimated using a single (iterative) Bayesian procedure. In this paper, we will motivate and describe the current DCM framework. Two examples show how the approach can be applied to M/EEG experiments.

Actions are guided by prior sensory information [1-10], which is inherently uncertain. However, how the motor system is sculpted by trial-by-trial content of current sensory information remains largely unexplored. Previous work suggests that conditional probabilities, learned under a particular context, can be used preemptively to influence the output of the motor system [11-14]. To test this we used transcranial magnetic stimulation (TMS) to read out corticospinal excitability (CSE) during preparation for action in an instructed delay task [15, 16]. We systematically varied the uncertainty about an impending action by changing the validity of the instructive visual cue. We used two information-theoretic quantities to predict changes in CSE, prior to action, on a trial-by-trial basis: entropy (average uncertainty) and surprise (the stimulus-bound information conveyed by a visual cue) [17-19]. Our data show that during preparation for action, human CSE varies according to the entropy and surprise conveyed by visual events guiding action. CSE increases on trials with low entropy about the impending action and low surprise conveyed by an event. Commensurate effects were observed in reaction times. We suggest that motor output is biased according to contextual probabilities that are represented dynamically in the brain.

The aim of this paper is to describe a simple procedure for electromagnetic (EEG or MEG) source reconstruction, in the context of group studies. This entails a simple extension of existing source reconstruction techniques based upon the inversion of hierarchical models. The extension ensures that evoked or induced responses are reconstructed in the same subset of sources, over subjects. Effectively, the procedure aligns the deployment of reconstructed activity over subjects and increases, substantially, the detection of differences between evoked or induced responses at the group or between-subject level.

In this paper, we describe a dynamic causal model (DCM) of steady-state responses in electrophysiological data that are summarised in terms of their cross-spectral density. These spectral data-features are generated by a biologically plausible, neural-mass model of coupled electromagnetic sources; where each source comprises three sub-populations. Under linearity and stationarity assumptions, the model's biophysical parameters (e.g., post-synaptic receptor density and time constants) prescribe the cross-spectral density of responses measured directly (e.g., local field potentials) or indirectly through some lead-field (e.g., electroencephalographic and magnetoencephalographic data). Inversion of the ensuing DCM provides conditional probabilities on the synaptic parameters of intrinsic and extrinsic connections in the underlying neuronal network. This means we can make inferences about synaptic physiology, as well as changes induced by pharmacological or behavioural manipulations, using the cross-spectral density of invasive or non-invasive electrophysiological recordings. In this paper, we focus on the form of the model, its inversion and validation using synthetic and real data. We conclude with an illustrative application to multi-channel local field potential data acquired during a learning experiment in mice.

BACKGROUND: It has been suggested that some psychotic symptoms reflect 'aberrant salience', related to dysfunctional reward learning. To test this hypothesis we investigated whether patients with schizophrenia showed impaired learning of task-relevant stimulus-reinforcement associations in the presence of distracting task-irrelevant cues. METHOD: We tested 20 medicated patients with schizophrenia and 17 controls on a reaction time game, the Salience Attribution Test. In this game, participants made a speeded response to earn money in the presence of conditioned stimuli (CSs). Each CS comprised two visual dimensions, colour and form. Probability of reinforcement varied over one of these dimensions (task-relevant), but not the other (task-irrelevant). Measures of adaptive and aberrant motivational salience were calculated on the basis of latency and subjective reinforcement probability rating differences over the task-relevant and task-irrelevant dimensions respectively. RESULTS: Participants rated reinforcement significantly more likely and responded significantly faster on high-probability-reinforced relative to low-probability-reinforced trials, representing adaptive motivational salience. Patients exhibited reduced adaptive salience relative to controls, but the two groups did not differ in terms of aberrant salience. Patients with delusions exhibited significantly greater aberrant salience than those without delusions, and aberrant salience also correlated with negative symptoms. In the controls, aberrant salience correlated significantly with 'introvertive anhedonia' schizotypy. CONCLUSIONS: These data support the hypothesis that aberrant salience is related to the presence of delusions in medicated patients with schizophrenia, but are also suggestive of a link with negative symptoms. The relationship between aberrant salience and psychotic symptoms warrants further investigation in unmedicated patients.

Confronted with a rich sensory environment, the brain must learn statistical regularities across sensory domains to construct causal models of the world. Here, we used functional magnetic resonance imaging and dynamic causal modeling (DCM) to furnish neurophysiological evidence that statistical associations are learnt, even when task-irrelevant. Subjects performed an audio-visual target-detection task while being exposed to distractor stimuli. Unknown to them, auditory distractors predicted the presence or absence of subsequent visual distractors. We modeled incidental learning of these associations using a Rescorla-Wagner (RW) model. Activity in primary visual cortex and putamen reflected learning-dependent surprise: these areas responded progressively more to unpredicted, and progressively less to predicted visual stimuli. Critically, this prediction-error response was observed even when the absence of a visual stimulus was surprising. We investigated the underlying mechanism by embedding the RW model into a DCM to show that auditory to visual connectivity changed significantly over time as a function of prediction error. Thus, consistent with predictive coding models of perception, associative learning is mediated by prediction-error dependent changes in connectivity. These results posit a dual role for prediction-error in encoding surprise and driving associative plasticity.

The cortex is a complex system, characterized by its dynamics and architecture, which underlie many functions such as action, perception, learning, language, and cognition. Its structural architecture has been studied for more than a hundred years; however, its dynamics have been addressed much less thoroughly. In this paper, we review and integrate, in a unifying framework, a variety of computational approaches that have been used to characterize the dynamics of the cortex, as evidenced at different levels of measurement. Computational models at different space-time scales help us understand the fundamental mechanisms that underpin neural processes and relate these processes to neuroscience data. Modeling at the single neuron level is necessary because this is the level at which information is exchanged between the computing elements of the brain; the neurons. Mesoscopic models tell us how neural elements interact to yield emergent behavior at the level of microcolumns and cortical columns. Macroscopic models can inform us about whole brain dynamics and interactions between large-scale neural systems such as cortical regions, the thalamus, and brain stem. Each level of description relates uniquely to neuroscience data, from single-unit recordings, through local field potentials to functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), and magnetoencephalogram (MEG). Models of the cortex can establish which types of large-scale neuronal networks can perform computations and characterize their emergent properties. Mean-field and related formulations of dynamics also play an essential and complementary role as forward models that can be inverted given empirical data. This makes dynamic models critical in integrating theory and experiments. We argue that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the physical sciences.

In this paper, we suggest that cortical anatomy recapitulates the temporal hierarchy that is inherent in the dynamics of environmental states. Many aspects of brain function can be understood in terms of a hierarchy of temporal scales at which representations of the environment evolve. The lowest level of this hierarchy corresponds to fast fluctuations associated with sensory processing, whereas the highest levels encode slow contextual changes in the environment, under which faster representations unfold. First, we describe a mathematical model that exploits the temporal structure of fast sensory input to track the slower trajectories of their underlying causes. This model of sensory encoding or perceptual inference establishes a proof of concept that slowly changing neuronal states can encode the paths or trajectories of faster sensory states. We then review empirical evidence that suggests that a temporal hierarchy is recapitulated in the macroscopic organization of the cortex. This anatomic-temporal hierarchy provides a comprehensive framework for understanding cortical function: the specific time-scale that engages a cortical area can be inferred by its location along a rostro-caudal gradient, which reflects the anatomical distance from primary sensory areas. This is most evident in the prefrontal cortex, where complex functions can be explained as operations on representations of the environment that change slowly. The framework provides predictions about, and principled constraints on, cortical structure-function relationships, which can be tested by manipulating the time-scales of sensory input.

This paper describes a general model that subsumes many parametric models for continuous data. The model comprises hidden layers of state-space or dynamic causal models, arranged so that the output of one provides input to another. The ensuing hierarchy furnishes a model for many types of data, of arbitrary complexity. Special cases range from the general linear model for static data to generalised convolution models, with system noise, for nonlinear time-series analysis. Crucially, all of these models can be inverted using exactly the same scheme, namely, dynamic expectation maximization. This means that a single model and optimisation scheme can be used to invert a wide range of models. We present the model and a brief review of its inversion to disclose the relationships among, apparently, diverse generative models of empirical data. We then show that this inversion can be formulated as a simple neural network and may provide a useful metaphor for inference and learning in the brain.

This paper introduces a model of 'theory of mind', namely, how we represent the intentions and goals of others to optimise our mutual interactions. We draw on ideas from optimum control and game theory to provide a 'game theory of mind'. First, we consider the representations of goals in terms of value functions that are prescribed by utility or rewards. Critically, the joint value functions and ensuing behaviour are optimised recursively, under the assumption that I represent your value function, your representation of mine, your representation of my representation of yours, and so on ad infinitum. However, if we assume that the degree of recursion is bounded, then players need to estimate the opponent's degree of recursion (i.e., sophistication) to respond optimally. This induces a problem of inferring the opponent's sophistication, given behavioural exchanges. We show it is possible to deduce whether players make inferences about each other and quantify their sophistication on the basis of choices in sequential games. This rests on comparing generative models of choices with, and without, inference. Model comparison is demonstrated using simulated and real data from a 'stag-hunt'. Finally, we note that exactly the same sophisticated behaviour can be achieved by optimising the utility function itself (through prosocial utility), producing unsophisticated but apparently altruistic agents. This may be relevant ethologically in hierarchal game theory and coevolution.

Statistical parametric mapping is the application of Random Field Theory to make inferences about the topological features of statistical processes that are continuous functions of space or time. It is usually used to identify regionally specific effects (e.g., brain activations) in neuroimaging data to characterize functional anatomy and disease-related changes.

If one formulates Helmholtz's ideas about perception in terms of modern-day theories one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory input and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain's organisation and responses.In this paper, we suggest that these perceptual processes are just one emergent property of systems that conform to a free-energy principle. The free-energy considered here represents a bound on the surprise inherent in any exchange with the environment, under expectations encoded by its state or configuration. A system can minimise free-energy by changing its configuration to change the way it samples the environment, or to change its expectations. These changes correspond to action and perception respectively and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment implies that the system's state and structure encode an implicit and probabilistic model of the environment. We will look at models entailed by the brain and how minimisation of free-energy can explain its dynamics and structure.

Dynamic causal modelling (DCM) has been applied recently to event-related responses (ERPs) measured with EEG/MEG. DCM attempts to explain ERPs using a network of interacting cortical sources and waveform differences in terms of coupling changes among sources. The aim of this work was to establish the validity of DCM by assessing its reproducibility across subjects. We used an oddball paradigm to elicit mismatch responses. Sources of cortical activity were modelled as equivalent current dipoles, using a biophysical informed spatiotemporal forward model that included connections among neuronal subpopulations in each source. Bayesian inversion provided estimates of changes in coupling among sources and the marginal likelihood of each model. By specifying different connectivity models we were able to evaluate three different hypotheses: differences in the ERPs to rare and frequent events are mediated by changes in forward connections (F-model), backward connections (B-model) or both (FB-model). The results were remarkably consistent over subjects. In all but one subject, the forward model was better than the backward model. This is an important result because these models have the same number of parameters (i.e., the complexity). Furthermore, the FB-model was significantly better than both, in 7 out of 11 subjects. This is another important result because it shows that a more complex model (that can fit the data more accurately) is not necessarily the most likely model. At the group level the FB-model supervened. We discuss these findings in terms of the validity and usefulness of DCM in characterising EEG/MEG data and its ability to model ERPs in a mechanistic fashion.

A key component of group analyses of neuroimaging data is precise and valid spatial normalization (i.e., inter-subject image registration). When patients have structural brain lesions, such as a stroke, this process can be confounded by the lack of correspondence between the subject and standardized template images. Current procedures for dealing with this problem include regularizing the estimate of warping parameters used to match lesioned brains to the template, or "cost function masking"; both these solutions have significant drawbacks. We report three experiments that identify the best spatial normalization for structurally damaged brains and establish whether differences among normalizations have a significant effect on inferences about functional activations. Our novel protocols evaluate the effects of different normalization solutions and can be applied easily to any neuroimaging study. This has important implications for users of both structural and functional imaging techniques in the study of patients with structural brain damage.

We present a neural mass model of steady-state membrane potentials measured with local field potentials or electroencephalography in the frequency domain. This model is an extended version of previous dynamic causal models for investigating event-related potentials in the time-domain. In this paper, we augment the previous formulation with parameters that mediate spike-rate adaptation and recurrent intrinsic inhibitory connections. We then use linear systems analysis to show how the model's spectral response changes with its neurophysiological parameters. We demonstrate that much of the interesting behaviour depends on the non-linearity which couples mean membrane potential to mean spiking rate. This non-linearity is analogous, at the population level, to the firing rate-input curves often used to characterize single-cell responses. This function depends on the model's gain and adaptation currents which, neurobiologically, are influenced by the activity of modulatory neurotransmitters. The key contribution of this paper is to show how neuromodulatory effects can be modelled by adding adaptation currents to a simple phenomenological model of EEG. Critically, we show that these effects are expressed in a systematic way in the spectral density of EEG recordings. Inversion of the model, given such non-invasive recordings, should allow one to quantify pharmacologically induced changes in adaptation currents. In short, this work establishes a forward or generative model of electrophysiological recordings for psychopharmacological studies.

The classical model of blood oxygen level-dependent (BOLD) responses by Buxton et al. [Buxton, R.B., Wong, E.C., Frank, L.R., 1998. Dynamics of blood flow and oxygenation changes during brain activation: the Balloon model. Magn. Reson. Med. 39, 855-864] has been very important in providing a biophysically plausible framework for explaining different aspects of hemodynamic responses. It also plays an important role in the hemodynamic forward model for dynamic causal modeling (DCM) of fMRI data. A recent study by Obata et al. [Obata, T., Liu, T.T., Miller, K.L., Luh, W.M., Wong, E.C., Frank, L.R., Buxton, R.B., 2004. Discrepancies between BOLD and flow dynamics in primary and supplementary motor areas: application of the Balloon model to the interpretation of BOLD transients. NeuroImage 21, 144-153] linearized the BOLD signal equation and suggested a revised form for the model coefficients. In this paper, we show that the classical and revised models are special cases of a generalized model. The BOLD signal equation of this generalized model can be reduced to that of the classical Buxton model by simplifying the coefficients or can be linearized to give the Obata model. Given the importance of hemodynamic models for investigating BOLD responses and analyses of effective connectivity with DCM, the question arises which formulation is the best model for empirically measured BOLD responses. In this article, we address this question by embedding different variants of the BOLD signal equation in a well-established DCM of functional interactions among visual areas. This allows us to compare the ensuing models using Bayesian model selection. Our model comparison approach had a factorial structure, comparing eight different hemodynamic models based on (i) classical vs. revised forms for the coefficients, (ii) linear vs. non-linear output equations, and (iii) fixed vs. free parameters, epsilon, for region-specific ratios of intra- and extravascular signals. Using fMRI data from a group of twelve subjects, we demonstrate that the best model is a non-linear model with a revised form for the coefficients, in which epsilon is treated as a free parameter.

Neuronal responses to stimuli, measured electrophysiologically, unfold over several hundred milliseconds. Typically, they show characteristic waveforms with early and late components. It is thought that early or exogenous components reflect a perturbation of neuronal dynamics by sensory input bottom-up processing. Conversely, later, endogenous components have been ascribed to recurrent dynamics among hierarchically disposed cortical processing levels, top-down effects. Here, we show that evoked brain responses are generated by recurrent dynamics in cortical networks, and late components of event-related responses are mediated by backward connections. This evidence is furnished by dynamic causal modeling of mismatch responses, elicited in an oddball paradigm. We used the evidence for models with and without backward connections to assess their likelihood as a function of peristimulus time and show that backward connections are necessary to explain late components. Furthermore, we were able to quantify the contribution of backward connections to evoked responses and to source activity, again as a function of peristimulus time. These results link a generic feature of brain responses to changes in the sensorium and a key architectural component of functional anatomy; namely, backward connections are necessary for recurrent interactions among levels of cortical hierarchies. This is the theoretical cornerstone of most modern theories of perceptual inference and learning.

To identify and categorize complex stimuli such as familiar objects or speech, the human brain integrates information that is abstracted at multiple levels from its sensory inputs. Using cross-modal priming for spoken words and sounds, this functional magnetic resonance imaging study identified 3 distinct classes of visuoauditory incongruency effects: visuoauditory incongruency effects were selective for 1) spoken words in the left superior temporal sulcus (STS), 2) environmental sounds in the left angular gyrus (AG), and 3) both words and sounds in the lateral and medial prefrontal cortices (IFS/mPFC). From a cognitive perspective, these incongruency effects suggest that prior visual information influences the neural processes underlying speech and sound recognition at multiple levels, with the STS being involved in phonological, AG in semantic, and mPFC/IFS in higher conceptual processing. In terms of neural mechanisms, effective connectivity analyses (dynamic causal modeling) suggest that these incongruency effects may emerge via greater bottom-up effects from early auditory regions to intermediate multisensory integration areas (i.e., STS and AG). This is consistent with a predictive coding perspective on hierarchical Bayesian inference in the cortex where the domain of the prediction error (phonological vs. semantic) determines its regional expression (middle temporal gyrus/STS vs. AG/intraparietal sulcus).

Is it possible to understand the intentions of other people by simply observing their movements? Many neuroscientists believe that this ability depends on the brain's mirror-neuron system, which provides a direct link between action and observation. Precisely how intentions can be inferred through movement-observation, however, has provoked much debate. One problem in inferring the cause of an observed action, is that the problem is ill-posed because identical movements can be made when performing different actions with different goals. Here we suggest that this problem is solved by the mirror-neuron system using predictive coding on the basis of a statistical approach known as empirical Bayesian inference. This means that the most likely cause of an observed movement can be inferred by minimizing the prediction error at all cortical levels that are engaged during movement observation. This account identifies a precise role for the mirror-neuron system in our ability to infer intentions from observed movement and outlines possible computational mechanisms.

We describe a simple and efficient solution to the problem of reconstructing electromagnetic sources into a canonical or standard anatomical space. Its simplicity rests upon incorporating subject-specific anatomy into the forward model in a way that eschews the need for cortical surface extraction. The forward model starts with a canonical cortical mesh, defined in a standard stereotactic space. The mesh is warped, in a nonlinear fashion, to match the subject's anatomy. This warping is the inverse of the transformation derived from spatial normalization of the subject's structural MRI image, using fully automated procedures that have been established for other imaging modalities. Electromagnetic lead fields are computed using the warped mesh, in conjunction with a spherical head model (which does not rely on individual anatomy). The ensuing forward model is inverted using an empirical Bayesian scheme that we have described previously in several publications. Critically, because anatomical information enters the forward model, there is no need to spatially normalize the reconstructed source activity. In other words, each source, comprising the mesh, has a predetermined and unique anatomical attribution within standard stereotactic space. This enables the pooling of data from multiple subjects and the reporting of results in stereotactic coordinates. Furthermore, it allows the graceful fusion of fMRI and MEG data within the same anatomical framework.

The mechanisms underlying interhemispheric integration (IHI) remain poorly understood, particularly for lateralized cognitive processes. To test competing theories of IHI, we constructed and fitted dynamic causal models to functional magnetic resonance data from two visual tasks that operated on identical stimuli but showed opposite hemispheric dominance. Using a systematic Bayesian model selection procedure, we found that, in the ventral visual stream, which was activated by letter judgments, interhemispheric connections mediated asymmetric information transfer from the nonspecialized right to the specialized left hemisphere when the latter did not have direct access to stimulus information. Notably, this form of IHI did not engage all areas activated by the task but was specific for areas in the lingual and fusiform gyri. In the dorsal stream, activated by spatial judgments, it did not matter which hemisphere received the stimulus: interhemispheric coupling increased bidirectionally, reflecting recruitment of the nonspecialized left hemisphere. Again, not all areas activated by the task were involved in this form of IHI; instead, it was restricted to interactions between areas in the superior parietal gyrus. Overall, our results provide direct neurophysiological evidence, in terms of effective connectivity, for the existence of context-dependent mechanisms of IHI that are implemented by specific visual areas during task-driven lateralization.

In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically,Statistical Parametric Mappingprovides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain's functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis. * An essential reference and companion for users of the SPM software * Provides a complete description of the concepts and procedures entailed by the analysis of brain images * Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data * Stands as a compendium of all the advances in neuroimaging data analysis over the past decade * Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes * Structured treatment of data analysis issues that links different modalities and models * Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible. © 2007 Elsevier Ltd All rights reserved.

Recovery of motor function after subcortical stroke appears to be related to the integrity of descending connections from the ipsilesional cortical motor system, a view supported by the observation of greater than normal movement-related activation in ipsilesional motor regions in chronic subcortical stroke patients. This suggests that damage to the descending output fibres from one region of the cortical motor system may be compensated by activity in areas that retain corticofugal outputs. Though the trajectories of corticofugal fibres from each major component of the motor system through the corona radiata and internal capsule are well described in non-human primates, they have not been described fully in humans. Our study set out to map the trajectories of these connections in a group of healthy volunteers (8 male, 4 female; age range = 31-68 years, median = 48.5 years) and establish whether this knowledge can be used to assess stroke-induced disconnection of the cortical motor system and better interpret functional reorganization of the cortical motor system. We describe the trajectories of the connections from each major component of the motor system to the cerebral peduncle using diffusion-weighted imaging and probabilistic tractography in normal subjects. We observed good reproducibility of these connections over subjects. The comparative topography of these connections revealed many similarities between humans and other primates. We then inferred damage to corticofugal pathways in stroke patients (n = 3) by comparing the overlap between regions of subcortical white matter damage with the trajectories of the connections to each motor area. In a small series of case studies, we found that inferred disconnections could explain enhanced hand-grip-related responses, as assessed with functional MRI, in the ipsilesional motor system. These results confirm that selective disruption of motor corticofugal fibres influences functional reorganization and outcome in individual patients.

BACKGROUND: Functional brain imaging in acute migraine has proved challenging because of the logistic problems associated with an episodic condition. Since the seminal observation of brainstem activation in migraine, there has been only a single case substantiating this finding. OBJECTIVE: To test the hypothesis that brainstem activation could be detected in migraine and to refine the anatomic localization with higher-resolution positron emission tomography than previously used. DESIGN: Using positron emission tomography with radioactive water (H(2)15O), we studied acute migraine attacks occurring spontaneously. Five patients underwent imaging in ictal and interictal states, and the differences were analyzed by means of statistical parametric mapping. SETTING: Tertiary referral center. PATIENTS: Six volunteers with episodic migraine were recruited from advertisements in migraine newsletters. One patient was excluded because of use of preventive medication. MAIN OUTCOME MEASURE: Brainstem activation during migraine state vs interictal state. RESULTS: Two patients had a typical migrainous aura before the onset of the headache. All of the attacks studied fulfilled standard diagnostic criteria for migraine. Comparing the migraine scans with interictal scans, there was significant activation in the dorsal pons, lateralized to the left (small volume correction, P = .003). Activation was also seen in the right anterior cingulate, posterior cingulate, cerebellum, thalamus, insula, prefrontal cortex, and temporal lobes. There was an area of deactivation in the migraine phase also located in the pons, lateralized to the right. CONCLUSIONS: Our findings provide clear evidence of dorsal pontine activation in migraine and reinforce the view that migraine is a subcortical disorder modulating afferent neural traffic.

This commentary takes a closer look at how “constructive models of subjective perception,” referred to by Collerton et al. (sect. 2), might contribute to the Perception and Attention Deficit (PAD) model. It focuses on the neuronal mechanisms that could mediate hallucinations, or false inference – in particular, the role of cholinergic systems in encoding uncertainty in the context of hierarchical Bayesian models of perceptual inference (Friston 2002b; Yu &amp; Dayan 2002).

The cognitive and neural mechanisms mediating category-selective responses in the human brain remain controversial. Using functional magnetic resonance imaging and effective connectivity analyses (Dynamic Causal Modelling), we investigated animal- and tool-selective responses by manipulating stimulus modality (pictures versus words) and task (implicit versus explicit semantic). We dissociated two distinct mechanisms that engender category selectivity: in the ventral occipito-temporal cortex, tool-selective responses were observed irrespective of task, greater for pictures and mediated by bottom-up effects. In a left temporo-parietal action system, tool-selective responses were observed irrespective of modality, greater for explicit semantic tasks and mediated by top-down modulation from the left prefrontal cortex. These distinct activation and connectivity patterns suggest that the two systems support different cognitive operations, with the ventral occipito-temporal regions engaged in structural processing and the dorsal visuo-motor system in strategic semantic processing. Consistent with current semantic theories, explicit semantic processing of tools might thus rely on reactivating their associated action representations via top-down modulation. In terms of neuronal mechanisms, the category selectivity may be mediated by distinct top-down (task-dependent) and bottom-up (stimulus-dependent) mechanisms.

This article concerns the nature of evoked brain responses and the principles underlying their generation. We start with the premise that the sensory brain has evolved to represent or infer the causes of changes in its sensory inputs. The problem of inference is well formulated in statistical terms. The statistical fundaments of inference may therefore afford important constraints on neuronal implementation. By formulating the original ideas of Helmholtz on perception, in terms of modern-day statistical theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts.It turns out that the problems of inferring the causes of sensory input (perceptual inference) and learning the relationship between input and cause (perceptual learning) can be resolved using exactly the same principle. Specifically, both inference and learning rest on minimizing the brain's free energy, as defined in statistical physics. Furthermore, inference and learning can proceed in a biologically plausible fashion. Cortical responses can be seen as the brain's attempt to minimize the free energy induced by a stimulus and thereby encode the most likely cause of that stimulus. Similarly, learning emerges from changes in synaptic efficacy that minimize the free energy, averaged over all stimuli encountered. The underlying scheme rests on empirical Bayes and hierarchical models of how sensory input is caused. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of cortical organization and responses. The aim of this article is to encompass many apparently unrelated anatomical, physiological and psychophysical attributes of the brain within a single theoretical perspective. In terms of cortical architectures, the theoretical treatment predicts that sensory cortex should be arranged hierarchically, that connections should be reciprocal and that forward and backward connections should show a functional asymmetry (forward connections are driving, whereas backward connections are both driving and modulatory). In terms of synaptic physiology, it predicts associative plasticity and, for dynamic models, spike-timing-dependent plasticity. In terms of electrophysiology, it accounts for classical and extra classical receptive field effects and long-latency or endogenous components of evoked cortical responses. It predicts the attenuation of responses encoding prediction error with perceptual learning and explains many phenomena such as repetition suppression, mismatch negativity (MMN) and the P300 in electroencephalography. In psychophysical terms, it accounts for the behavioural correlates of these physiological phenomena, for example, priming and global precedence. The final focus of this article is on perceptual learning as measured with the MMN and the implications for empirical studies of coupling among cortical areas using evoked sensory responses.

Voxel-based-morphometry (VBM) is a whole-brain, unbiased technique for characterizing regional cerebral volume and tissue concentration differences in structural magnetic resonance images. We describe an optimized method of VBM to examine the effects of age on grey and white matter and CSF in 465 normal adults. Global grey matter volume decreased linearly with age, with a significantly steeper decline in males. Local areas of accelerated loss were observed bilaterally in the insula, superior parietal gyri, central sulci, and cingulate sulci. Areas exhibiting little or no age effect (relative preservation) were noted in the amygdala, hippocampi, and entorhinal cortex. Global white matter did not decline with age, but local areas of relative accelerated loss and preservation were seen. There was no interaction of age with sex for regionally specific effects. These results corroborate previous reports and indicate that VBM is a useful technique for studying structural brain correlates of ageing through life in humans.

Functional imaging studies have identified a matrix of structures in the brain that respond to noxious stimuli. Within this matrix, a division of function between sensory-discriminative and affective responses has so far been demonstrated by manipulating either pain intensity or unpleasantness under hypnosis in two different normal volunteer groups studied on separate occasions. Our study used positron emission tomography (PET) to demonstrate this division of function under more natural conditions in a healthy group of volunteers, using a CO(2) laser to provide nociceptive stimuli that selectively activate A-delta and C-fibres without contamination by touch sensations. We measured the differential cerebral responses to noxious and innocuous laser stimuli during conditions of selective attention to either the unpleasantness or location of the stimuli. Attention to location increased responses in the contralateral (right) primary somatosensory and inferior parietal cortices. This result implies that these components of the lateral pain system are concerned mainly with the localization of pain. In contrast, attention to unpleasantness increased responses in bilateral perigenual cingulate and orbitofrontal cortices, contralateral (right) amygdala, ipsilateral (left) hypothalamus, posterior insula, M1 and frontal pole. These areas comprise key components of the medial pain and neuroendocrine systems and the results suggest that they have a role in the affective response to pain. Our results indicate the importance of attentional effects on the pattern of nociceptive processing in the brain. They also provide the first clear demonstration, within a single experiment, of a major division of function within the neural pain matrix.

Previous studies of patients with phonological and surface alexia have demonstrated a double dissociation between the reading of pseudo words and words with atypical spelling-to-sound relationships. A corresponding double dissociation in the neuronal activation patterns for pseudo words and exception words has not, however, been consistently demonstrated in normal subjects. Motivated by the literature on acquired alexia, the present study contrasted pseudo words to exception words and explored how neuronal interactions within the reading system are influenced by word type. Functional magnetic resonance imaging was used to measure neuronal responses during reading in 22 healthy volunteers. The direct comparison of reading pseudo words and exception words revealed a double dissociation within the left frontal cortex. Pseudo words preferentially increased left dorsal premotor activation, whereas exception words preferentially increased left pars triangularis activation. Critically, these areas correspond to those previously associated with phonological and semantic processing, respectively. Word-type dependent interactions between brain areas were then investigated using dynamic causal modeling. This revealed that increased activation in the dorsal premotor cortex for pseudo words was associated with a selective increase in effective connectivity from the posterior fusiform gyrus. In contrast, increased activation in the pars triangularis for exception words was associated with a selective increase in effective connectivity from the anterior fusiform gyrus. The present investigation is the first to identify distinct neuronal mechanisms for semantic and phonological contributions to reading.

The morphology of the human cortex varies remarkably across individuals, regardless of overall brain size. It is currently unclear whether related cortical regions covary in gray matter density, as a result of mutually trophic influences or common experience-related plasticity. We acquired a structural magnetic resonance imaging scan from 172 subjects and extracted the regional gray matter densities from 12 readily identifiable regions of interest involved in sensorimotor or higher-order cognitive functions. We then used these values to predict regional densities in the remaining areas of the cortex, using voxel-based morphometry. This revealed patterns of positive and negative covariance that provide insight into the topographical organization of multiple cortical regions. We report that the gray matter density of a region is a good predictor of the density of the homotopic region in the contralateral hemisphere, with the striking exception of primary visual cortex. Whereas some regions express patterns of regional covariance that are mirror symmetrical relative to the interhemispheric fissure, other regions express asymmetric patterns of regional covariance. Finally, patterns of covariance are remarkably consistent between males and females, with the exception of the left amygdala, which is positively associated with the left and right anterior inferior temporal cortex in males and with the right angular gyrus in females. Our study establishes that the density of different cortical regions is coordinated within an individual. The coordinated variations we report are likely to be determined by both genetic and environmental factors and may be the basis for differences in individual behavior.

In recent years, a whole-brain unbiased objective technique, known as voxel-based morphometry (VBM), has been developed to characterise brain differences in vivo using structural magnetic resonance images. The present review provides a brief description of VBM and then focuses on exemplar applications in healthy and diseased subjects. The procedure involves normalising high-resolution structural magnetic resonance images to a standard template in stereotactic space. Normalised images are then segmented into gray and white matter and smoothed using an isotropic Gaussian kernel. Finally, a series of voxel-wise comparisons of gray and white matter in different groups of subjects are performed, using Random Field theory to correct for multiple comparisons. VBM has been useful in characterizing subtle changes in brain structure in a variety of diseases associated with neurological and psychiatric dysfunction. These include schizophrenia, developmental and congenital disorders, temporal lobe epilepsy and even cluster headache. In addition, VBM has been successful in identifying gross structural abnormalities, such as those observed in herpes simplex encephalitis, multiple sclerosis and Alzheimers disease. Studies of normal subjects, on the other hand, have focussed on the impact of learning and practice on brain structure. These studies have led to the finding that environmental demands may be associated with changes in gray and white matter. For instance, it has been reported that the structure of the brain alters when human beings learn to navigate, read music, speak a second language and even perform a complex motor task such as juggling. We conclude the present review by discussing the potential limitations of the technique.

Cognitive scientists have traditionally specified the functional components of cognitive skills on the basis of behavioural studies of normal and neurologically impaired subjects. The results of functional imaging studies are challenging these classical models because there is a high degree of overlap among the neural systems activated by tasks that share no cognitive components. This suggests that a given neuronal structure can perform multiple functions that depend on the areas with which it interacts. However, there will be a limited range of functions that an area can perform given that its anatomical (intrinsic and extrinsic) connectivity is fixed. Assigning labels that encompass the operations that each area performs should enable a task to be re-described in terms of the functions of the areas activated. In other words, function should predict the structure and conversely structure should predict function. These systematic descriptions are referred to as ontologies. We argue that a systematic ontology for cognition would facilitate the integration of cognitive and anatomical models and organise the cognitive components of diverse tasks into a single framework. These points are illustrated with cognitive and anatomical models of reading and object recognition.

Instrumental conditioning studies how animals and humans choose actions appropriate to the affective structure of an environment. According to recent reinforcement learning models, two distinct components are involved: a "critic," which learns to predict future reward, and an "actor," which maintains information about the rewarding outcomes of actions to enable better ones to be chosen more frequently. We scanned human participants with functional magnetic resonance imaging while they engaged in instrumental conditioning. Our results suggest partly dissociable contributions of the ventral and dorsal striatum, with the former corresponding to the critic and the latter corresponding to the actor.

Inferences about brain function, using neuroimaging data, rest on models of how the data were caused. These models can be quite diverse, ranging from conceptual models of functional anatomy to nonlinear mathematical models of hemodynamics. However, they all have to be internally consistent because they model the same thing. This consistency encompasses many levels of description and places constraints on the statistical models, adopted for data analysis, and the experimental designs they embody. The aim of this review is to introduce the key models used in imaging neuroscience and how they relate to each other. We start with anatomical models of functional brain architectures, which motivate some of the fundaments of neuroimaging. We then turn to basic statistical models (e.g., the general linear model) used for making classical and Bayesian inferences about where neuronal responses are expressed. By incorporating biophysical constraints, these basic models can be finessed and, in a dynamic setting, rendered causal. This allows us to infer how interactions among brain regions are mediated.

Two identical stimuli, such as a pair of electrical shocks to the skin, are readily perceived as two separate events in time provided the interval between them is sufficiently long. However, as they are presented progressively closer together, there comes a point when the two separate stimuli are perceived as a single stimulus. Damage to posterior parietal cortex, peri-supplementary motor area (peri-SMA), and basal ganglia can disturb this form of temporal discrimination. Our aim was to establish, in healthy subjects, the brain areas that are involved in this process. During functional magnetic resonance imaging scanning, paired electrical pulses, separated by variable inter-stimulus intervals (5-110 msec), were delivered to different sites on one forearm (8-64 mm from the midline). Subjects were required to simply detect the stimulus (control task) or to identify a stimulus property. For temporal discrimination (TD), subjects reported whether they felt one or two stimuli. For spatial discrimination, they reported whether the stimuli were located on the right or left side of the forearm. Subjects reported their choice by pressing a button with the opposite hand. Our results showed that discrimination, as opposed to simply detection, activated several brain areas. Most were common to both discrimination tasks. These included regions of prefrontal cortex, right postcentral gyrus and inferior parietal lobule, basal ganglia, and cerebellum. However, activation of pre-SMA and anterior cingulate was found to be specific to the TD task. This suggests that these two frontal regions may play a role in the temporal processing of somatosensory events.

We studied a patient with idiopathic generalized epilepsy and frequent absences, using electroencephalogram-correlated functional magnetic resonance imaging. Four prolonged runs of generalized spike-wave discharge occurred during a 35-minute experiment. Time-locked activation was observed bilaterally within the thalami in conjunction with widespread but symmetrical cortical deactivation with a frontal maximum. We demonstrate the reciprocal participation of focal thalamic and widespread cortical networks during human absence seizures and suggest reductions in cortical blood flow, in response to synchronized electroencephalogram activity.

The amygdala, which plays a critical role in emotional learning and social cognition, is structurally and functionally sexually dimorphic in humans. We used magnetic neuroimaging and molecular genetic analyses with healthy subjects and patients possessing X-chromosome anomalies to find dosage-sensitive genes that might influence amygdala development. If such X-linked genes lacked a homologue on the Y-chromosome they would be expressed in one copy in normal 46,XY males and two copies in normal 46,XX females. We showed by means of magnetic neuroimaging that 46,XY males possess significantly increased amygdala volumes relative to normal 46,XX females. However, females with Turner syndrome (45,X) have even larger amygdalae than 46,XY males. This finding implies that haploinsufficiency for one or more X-linked genes influences amygdala development irrespective of a direct or indirect (endocrinological) mechanism involving the Y-chromosome. 45,X females also have increased grey matter volume in the orbitofrontal cortex bilaterally, close to a region implicated in emotional learning. They are as poor as patients with bilateral amygdalectomies in the recognition of fear from facial expressions. We attempted to localize the gene(s) responsible for these deficits in X-monosomy by means of a deletion mapping strategy. We studied female patients possessing structural X-anomalies of the short arm. A genetic locus (no greater than 4.96 Mb in size) at Xp11.3 appears to play a key role in amygdala and orbitofrontal structural and (by implication) functional development. Females with partial X-chromosome deletions, in whom this critical locus is deleted, have normal intelligence. Their fear recognition is as poor as that of 45,X females and their amygdalae are correspondingly enlarged. This 4.96 Mb region contains, among others, the genes for monoamine oxidase A (MAOA) and B (MAOB), which are involved in the oxidative deamination of several neurotransmitters, including dopamine and serotonin. Abnormal activity of these neurotransmitters has been implicated in the aetiology of several neurodevelopmental disorders in which social cognitive deficits are prominent. These associated deficits include a specific lack of fear recognition from facial expressions. We show that the thrombocytic activity of MAOB is proportionate to the number of X-chromosomes, and hypothesize that haploinsufficiency of this enzyme in 45,X females predisposes to their deficits in social cognition.

The principles underlying human hemispheric specialization are poorly understood. We used functional magnetic resonance imaging of letter and visuospatial decision tasks with identical word stimuli to address two unresolved problems. First, hemispheric specialization depended on the nature of the task rather than on the nature of the stimulus. Second, analysis of frontal candidate regions for cognitive control showed increased coupling between left anterior cingulate cortex (ACC) and left inferior frontal gyrus during letter decisions, whereas right ACC showed enhanced coupling with right parietal areas during visuospatial decisions. Cognitive control is thus localized in the same hemisphere as task execution.

Repetitive transcranial magnetic stimulation (rTMS) of human primary motor cortex (M1) changes cortical excitability at the site of stimulation and at distant sites without affecting simple motor performance. The aim of this study was to explore how rTMS changes regional excitability and how the motor system compensates for these changes. Using functional brain imaging, activation was mapped at rest and during freely selected finger movements after 30 min of 1 Hz rTMS. rTMS increased synaptic activity in the stimulated left M1 and induced widespread changes in activity throughout areas engaged by the task. In particular, movement-related activity in the premotor cortex of the nonstimulated hemisphere increased after 1 Hz rTMS. Analyses of effective connectivity confirmed that the stimulated part of M1 became less responsive to input from premotor and mesial motor areas. Conversely, after rTMS our results were consistent with increased coupling between an inferomedial portion of left M1 and anterior motor areas. These results are important for three reasons. First, they show changes in motor excitability to central inputs from other cortical areas (as opposed to peripheral or exogenous inputs used in previous studies). Second, they suggest that maintenance of task performance may involve activation of premotor areas contralateral to the site of rTMS, similar to that seen in stroke patients. Third, changes in motor activations at the site of rTMS suggest an rTMS-induced remodeling of motor representations during movement. This remapping may provide a neural substrate for acute compensatory plasticity of the motor system in response to focal lesions such as stroke.

The neural basis of attention to action was studied in 12 patients with Parkinson's disease (Hoehn and Yahr grades II and III) and 12 healthy age-matched controls. The subjects were studied by functional MRI (fMRI) during performance of a simple paced overlearned motor sequence task, with and without an additional attentional task. For the attentional task, subjects were instructed to attend either to their actions or to a visual distractor task. Statistical parametric mapping was used to implement a random effects analysis of the regional task-related activations in patient and control populations. Structural equation modelling of fMRI time series was used to measure effective connectivity among prefrontal and premotor areas. In both groups, the motor task was associated with activation of a distributed network including the premotor, motor and parietal cortex, striatum and cerebellum. In control subjects, but not patients, attention to action (relative to execution of an overlearned sequence) was associated with further activation of prefrontal, parietal and paracingulate cortex, and the supplementary motor area (SMA). Patients with Parkinson's disease showed greater than normal activation of the SMA during execution of the simple overlearned motor sequence, but less augmentation when attending to their actions. In control subjects, attention to action, but not attention to the visual distractor task, increased the effective connectivity between prefrontal cortex and both the lateral premotor cortex and the SMA. This represents a specific increase in effective connectivity. Attentional modulation of effective connectivity between the prefrontal, premotor cortex and SMA was not observed in patients. This deficit indicates a context-specific functional disconnection between the prefrontal cortex and the supplementary and premotor cortex in Parkinson's disease.

Analyses of brain structure in genetic speech and language disorders provide an opportunity to identify neurobiological phenotypes and further elucidate the neural bases of language and its development. Here we report such investigations in a large family, known as the KE family, half the members of which are affected by a severe disorder of speech and language, which is transmitted as an autosomal-dominant monogenic trait. The structural brain abnormalities associated with this disorder were investigated using two morphometric methods of MRI analysis. A voxel-based morphometric method was used to compare the amounts of grey matter in the brains of three groups of subjects: the affected members of the KE family, the unaffected members and a group of age-matched controls. This method revealed a number of mainly motor- and speech-related brain regions in which the affected family members had significantly different amounts of grey matter compared with the unaffected and control groups, who did not differ from each other. Several of these regions were abnormal bilaterally, including the caudate nucleus, which was of particular interest because this structure was also found to show functional abnormality in a related PET study. We performed a more detailed volumetric analysis of this structure. The results confirmed that the volume of this nucleus was reduced bilaterally in the affected family members compared with both the unaffected members and the group of age-matched controls. This reduction in volume was most evident in the superior portion of the nucleus. The volume of the caudate nucleus was significantly correlated with the performance of affected family members on a test of oral praxis, a test of non-word repetition and the coding subtest of the Wechsler Intelligence Scale. These results thus provide further evidence of a relationship between the abnormal development of this nucleus and the impairments in oromotor control and articulation reported in the KE family.

Unsupervised models of how the brain identifies and categorizes the causes of its sensory input can be divided into two classes: those that minimize the mutual information (i.e., redundancy) among evoked responses and those that minimize the prediction error. Although these models have the same goal, the way that goal is attained, and the functional architectures required, are fundamentally different. This review describes the differences, in the functional anatomy of sensory cortical hierarchies, implied by the two models. We then consider how neuroimaging can be used to disambiguate between them. The key distinction reduces to whether backward connections are employed by the brain to generate a prediction of sensory inputs. To ascertain whether backward influences are evident empirically requires a characterization of functional integration among brain systems. This review summarizes the approaches to measuring functional integration in terms of effective connectivity and proceeds to address the question posed by the theoretical considerations. In short, it will be shown that the conjoint manipulation of bottom-up and top-down inputs to an area can be used to test for interactions between them, in elaborating cortical responses. The conclusion, from these sorts of neuroimaging studies, points to the prevalence of top-down influences and the plausibility of generative models of sensory brain function.

Functional neuroimaging was used to investigate how lesions to the Broca's area impair neuronal responses in remote undamaged cortical regions. Four patients with speech output problems, but relatively preserved comprehension, were scanned while viewing words relative to consonant letter strings. In normal subjects, this results in left lateralized activation in the posterior inferior frontal, middle temporal, and posterior inferior temporal cortices. Each patient activated normally in the middle temporal region but abnormally in the damaged posterior inferior frontal cortex and the undamaged posterior inferior temporal cortex. In the damaged frontal region, activity was insensitive to the presence of words but in the undamaged posterior inferior temporal region, activity decreased in the presence of words rather than increasing as it did in the normal individuals. The reversal of responses in the left posterior inferior temporal region illustrate the context-sensitive nature of the abnormality and that failure to activate the left posterior temporal region could not simply be accounted for by insufficient demands on the underlying function. We propose that, in normal individuals, visual word presentation changes the effective connectivity among reading areas and, in patients, posterior temporal responses are abnormal when they depend upon inputs from the damaged inferior frontal cortex. Our results serve to introduce the concept of dynamic diaschisis; the anatomically remote and context-sensitive effects of focal brain lesions. Dynamic diaschisis reveals abnormalities of functional integration that may have profound implications for neuropsychological inference, functional anatomy and, vicariously, cognitive rehabilitation.

This paper describes a Bayesian method for three-dimensional registration of brain images. A finite element approach is used to obtain a maximum a posteriori estimate of the deformation field at every voxel of a template volume. The priors used by the MAP estimate penalize unlikely deformations and enforce a continuous one-to-one mapping. The deformations are assumed to have some form of symmetry, in that priors describing the probability distribution of the deformations should be identical to those for the inverses (i.e., warping brain A to brain B should not be different probablistically from warping B to A). A gradient descent algorithm is presented for estimating the optimum deformations.

The nonlinear nature of integration among cortical brain areas renders the effective connectivity between them inherently dynamic and context-sensitive. One emerging architectural principle of functional brain organization, which rests explicitly on these nonlinear interactions, is that neuronal responses expressed at any level in a sensory hierarchy reflect an interaction between (i) bottom up "driving" afferents from lower cortical areas and (ii) backwards "modulatory" inputs from higher areas that mediate top-down contextual effects. A compelling example is attentional modulation of responses in functionally specialized sensory areas. The aim of this work was to demonstrate that parietal regions may mediate selective attention to motion by modulating the effective connectivity from early visual cortex to the motion-sensitive area V5/MT. Using functional magnetic resonance imaging, and an analysis of effective connectivity based on nonlinear system identification, we found that backwards modulatory influences from the posterior parietal cortex are sufficient to account for a significant component of attentional modulation of V5/MT responses to "driving" inputs from V2. By explicitly modeling interactions among inputs to V5/MT, we were able to make inferences about the influences of V2 inputs and their concomitant activity-dependent modulation by parietal afferents. The latter effects embody dynamic changes in effective connectivity that may underlie attentional mechanisms. These results speak to the context-sensitive nature of functional integration in the brain and provide empirical evidence that attentional effects may be mediated by backwards connections, of a modulatory sort, in humans.

In this, the first of three papers, the nature of, and motivation for, neuronal transients is described in relation to characterizing brain dynamics. This paper deals with some basic aspects of neuronal dynamics, interactions, coupling and implicit neuronal codes. The second paper develops neuronal transients and nonlinear coupling in the context of dynamic instability and complexity, and suggests that instability or lability is necessary for adaptive self-organization. The final paper addresses the role of neuronal transients through information theory and the emergence of spatio-temporal receptive fields and functional specialization. By considering the brain as an ensemble of connected dynamic systems one can show that a sufficient description of neuronal dynamics comprises neuronal activity at a particular time and its recent history This history constitutes a neuronal transient. As such, transients represent a fundamental metric of neuronal interactions and, implicitly, a code employed in the functional integration of brain systems. The nature of transients, expressed conjointly in distinct neuronal populations, reflects the underlying coupling among populations. This coupling may be synchronous (and possibly oscillatory) or asynchronous. A critical distinction between synchronous and asynchronous coupling is that the former is essentially linear and the latter is nonlinear. The nonlinear nature of asynchronous coupling enables the rich, context-sensitive interactions that characterize real brain dynamics, suggesting that it plays a role in functional integration that may be as important as synchronous interactions. The distinction between linear and nonlinear coupling has fundamental implications for the analysis and characterization of neuronal interactions, most of which are predicated on linear (synchronous) coupling (e.g. cross-correlograms and coherence). Using neuromagnetic data it is shown that nonlinear (asynchronous) coupling is, in fact, more abundant and can be more significant than synchronous coupling.

BACKGROUND: PET studies of verbal fluency in schizophrenia report a failure of 'deactivation' of left superior temporal gyrus (STG) in the presence of activation of left dorsolateral prefrontal cortex (DLPFC), which deficit has been attributed to underlying 'functional disconnectivity'. AIM: To test whether these findings provide trait-markers for schizophrenia. METHOD: We used H2(15)O PET to examine verbal fluency in 10 obligate carriers of the predisposition to schizophrenia, 10 stable patients and 10 normal controls. RESULTS: We found no evidence of a failure of left STG deactivation in carriers or patients. Instead, patients failed to deactivate the precuneus relative to other groups. We found no differences in functional connectivity between left DLPFC and left STG but patients exhibited significant disconnectivity between left DLPFC and anterior cingulate cortex. CONCLUSIONS: Failure of left STG 'deactivation' and left fronto-temporal disconnectivity are not consistent findings in schizophrenia; neither are they trait-markers for genetic risk. Prefrontal functional disconnectivity here may characterise the schizophrenic phenotype.

Reward is one of the most important influences shaping behavior. Single-unit recording and lesion studies in experimental animals have implicated a number of regions in response to reinforcing stimuli, in particular regions of the extended limbic system and the ventral striatum. In this experiment, functional neuroimaging was used to assess neural response within human reward systems under different psychological contexts. Nine healthy volunteers were scanned using functional magnetic resonance imaging during the performance of a gambling task with financial rewards and penalties. We demonstrated neural sensitivity of midbrain and ventral striatal regions to financial rewards and hippocampal sensitivity to financial penalties. Furthermore, we show that neural responses in globus pallidus, thalamus, and subgenual cingulate were specific to high reward levels occurring in the context of increasing reward. Responses to both reward level in the context of increasing reward and penalty level in the context of increasing penalty were seen in caudate, insula, and ventral prefrontal cortex. These results demonstrate dissociable neural responses to rewards and penalties that are dependent on the psychological context in which they are experienced.

We describe a comprehensive framework for performing rapid and automatic nonlabel-based nonlinear spatial normalizations. The approach adopted minimizes the residual squared difference between an image and a template of the same modality. In order to reduce the number of parameters to be fitted, the nonlinear warps are described by a linear combination of low spatial frequency basis functions. The objective is to determine the optimum coefficients for each of the bases by minimizing the sum of squared differences between the image and template, while simultaneously maximizing the smoothness of the transformation using a maximum a posteriori (MAP) approach. Most MAP approaches assume that the variance associated with each voxel is already known and that there is no covariance between neighboring voxels. The approach described here attempts to estimate this variance from the data, and also corrects for the correlations between neighboring voxels. This makes the same approach suitable for the spatial normalization of both high-quality magnetic resonance images, and low-resolution noisy positron emission tomography images. A fast algorithm has been developed that utilizes Taylor's theorem and the separable nature of the basis functions, meaning that most of the nonlinear spatial variability between images can be automatically corrected within a few minutes.

We present an overview of the types of imaging experiments that can be performed on psychologically impaired patients. The critical observation from such studies is a differential pattern of activation in the patients and normals. Underactivity is interpretable only when the patients make normal responses. In this context, a failure to activate a component region of the normal system implies that this region was not necessary for task performance. Overactivity indicates either cognitive or neuronal reorganisation. Neuronal reorganisation is indicated only if the patient performs the task using the same set of cognitive operations as normal subjects. Cognitive reorganisation can be demonstrated if the same activation pattern is elicited by normals when they are co-erced into using the same cognitive implementation as the patient. We conclude that the interpretation of neuroimaging studies of psychologically impaired patients depends on intact task performance and a detailed task analysis. When these criteria are met, patient studies can be used to identify: (1) necessary and sufficient brain systems, (2) dysfunction at sites distant to damage, (3) peri-damage activation, and (4) compensation either at a neuronal level when pre-existing cognitive strategies are re-instantiated using duplicated neuronal systems (degeneracy), or at a cognitive level when alternative cognitive strategies (and their corresponding brain systems) are adopted.

It is now accepted that hippocampal lesions impair episodic memory. However, the precise functional role of the hippocampus in episodic memory remains elusive. Recent functional imaging data implicate the hippocampus in processing novelty, a finding supported by human in vivo recordings and event-related potential studies. Here we measure hippocampal responses to novelty, using functional MRI (fMRI), during an item-learning paradigm generated from an artificial grammar system. During learning, two distinct types of novelty were periodically introduced: perceptual novelty, pertaining to the physical characteristics of stimuli (in this case visual characteristics), and exemplar novelty, reflecting semantic characteristics of stimuli (in this case grammatical status within a rule system). We demonstrate a left anterior hippocampal response to both types of novelty and adaptation of these responses with stimulus familiarity. By contrast to these novelty effects, we also show bilateral posterior hippocampal responses with increasing exemplar familiarity. These results suggest a functional dissociation within the hippocampus with respect to the relative familiarity of study items. Neural responses in anterior hippocampus index generic novelty, whereas posterior hippocampal responses index familiarity to stimuli that have behavioral relevance (i.e., only exemplar familiarity). These findings add to recent evidence for functional segregation within the human hippocampus during learning.

We have studied focal changes in dopaminergic function throughout the brain volume in early and advanced Parkinson's disease by applying statistical parametric mapping (SPM) to 3D [(18)F]dopa-PET. Data from seven early hemi-Parkinson's disease and seven advanced bilateral Parkinson's disease patients were compared with that from 12 normal controls. Parametric images of [(18)F]dopa influx rate constant (K(i)(o)) were generated for each subject from dynamic 3D [(18)F]dopa datasets and transformed into standard stereotactic space. Significant changes in mean voxel [(18)F]dopa K(i)(o) values between the normal control group and each Parkinson's disease group were localized with SPM. Conventional region of interest analysis was also applied to comparable regions on the untransformed image datasets. In early left hemi-Parkinson's disease, significant extrastriatal increases in [(18)F]dopa K(i)(o) were observed in the left anterior cingulate gyrus and the dorsal midbrain region (P < 0.05, corrected) along with decreases in striatal [(18)F]dopa K(i)(o). In advanced Parkinson's disease, significant extrastriatal decreases in [(18)F]dopa K(i)(o) were observed in the ventral and dorsal midbrain regions (P < 0.05, corrected). No significant changes in [(18)F]dopa K(i)(o) were observed in the anterior cingulate region. In a direct comparison between the early and late Parkinson's disease groups, we observed relative [(18)F]dopa K(i)(o) reductions in ventral and dorsal midbrain, and dorsal pontine regions along with striatal [(18)F]dopa K(i)(o) reductions. Similiar results were found with a region of interest approach, on non-transformed data, except for the focal midbrain [(18)F]dopa K(i)(o) increase seen in early Parkinson's disease. In conclusion, using SPM with [(18)F]dopa-PET, we have objectively localized changes in extrastriatal, pre-synaptic dopaminergic function in Parkinson's disease. The significance of the increased dopaminergic activity of anterior cingulate in early Parkinson's disease remains unclear, but may be compensatory. The [(18)F]dopa signal in dorsal midbrain and pontine regions suggests that [(18)F]dopa is taken up by serotonergic and noradrenergic neurons which also degenerate in advanced Parkinson's disease. This suggests, therefore, that Parkinson's disease is a monoaminergic neurodegenerative disorder.

Autism is a biological disorder which affects social cognition, and understanding brain abnormalities of the former will elucidate the brain basis of the latter. We report structural MRI data on 15 high-functioning individuals with autistic disorder. A voxel-based whole brain analysis identified grey matter differences in an amygdala centered system relative to 15 age- and IQ-matched controls. Decreases of grey matter were found in anterior parts of this system (right paracingulate sulcus, left inferior frontal gyrus). Increases were found in posterior parts (amygdala/peri-amygdaloid cortex, middle temporal gyrus, inferior temporal gyrus), and in regions of the cerebellum. These structures are implicated in social cognition by animal, imaging and histopathological studies. This study therefore provides converging evidence of the physiological basis of social cognition.

This article reviews the disconnection hypothesis of schizophrenia and presents a mechanistic account of how dysfunctional integration among neuronal systems might arise. This neurobiological account is based on some simple observations about schizophrenia and the central role that neuronal plasticity plays in shaping the connections, and the ensuing dynamics, that underlie brain function. The particular hypothesis put forward here is that the pathophysiology of schizophrenia is expressed at the level of modulation of associative changes in synaptic efficacy, specifically the modulation of plasticity in those brain systems that are responsible for learning, memory and emotion. This modulation is mediated by ascending neurotransmitter systems that (i) have been implicated in schizophrenia and (ii) are known to be involved in consolidating synaptic connections during learning. The proposed pathophysiology would translate, in functional terms, into a disruption of the reinforcement of adaptive behaviour that is consistent with the disintegrative aspects of schizophrenic neuropsychology.

During learning, neural responses decrease over repeated exposure to identical stimuli. This repetition suppression is thought to reflect a progressive optimization of neuronal responses elicited by the task. Functional magnetic resonance imaging was used to study the neural basis of associative learning of visual objects and their locations. As expected, activation in specialized cortical areas decreased with time. However, with path analysis it was shown that, in parallel to this adaptation, increases in effective connectivity occurred between distinct cortical systems specialized for spatial and object processing. The time course of these plastic changes was highly correlated with individual learning performance, suggesting that interactions between brain areas underlie associative learning.

This paper demonstrates how functional imaging studies of neuropsychological patients can provide a way of determining which areas in a cognitive network are jointly necessary and sufficient. The approach is illustrated with an investigation of the neural system underlying semantic similarity judgments. Functional neuroimaging demonstrates that normal subjects activate left temporal, parietal, and inferior frontal cortices during this task relative to physical size judgments. Neuropsychology demonstrates that damage to the temporal and parietal regions results in semantic deficits, indicating that these areas are necessary for task performance. In contrast, damage to the inferior frontal cortex does not impair task performance, indicating that the inferior frontal cortex might not be necessary. However, there are two other possible accounts of intact performance following frontal lobe damage: (1) there is functional reorganization involving the right frontal cortex and (2) there is peri-infarct activity around the damaged left-hemisphere tissue. Functional imaging of the patient is required to discount these possibilities. We investigated a patient (SW), who was able to associate words and pictures on the basis of semantic relationships despite extensive damage to the left frontal, inferior parietal, and superior temporal cortices. Although SW showed peri-infarct activation in left extrasylvian temporal cortices, no activity was observed in either left or right inferior frontal cortices. These findings demonstrate that activity in extrasylvian temporo-parietal and medial superior frontal regions is sufficient to perform semantic similarity judgments. In contrast, the left inferior frontal activations detected in each control subject appear not to be necessary for task performance. In conclusion, necessary and sufficient brain systems can be delineated by functional imaging or brain-damaged patients who are not functionally impaired.

Previous functional neuroimaging studies have characterized brain systems mediating associative learning using classical delay conditioning paradigms. In the present study, we used event-related functional magnetic resonance imaging to characterize neuronal responses mediating aversive trace conditioning. During conditioning, neutral auditory tones were paired with an aversive sound [unconditioned stimulus (US)]. We compared neuronal responses evoked by conditioned (CS+) and nonconditioned (CS-) stimuli in which a 50% pairing of CS+ and the US enabled us to limit our analysis to responses evoked by the CS+ alone. Differential responses (CS+ vs CS-), related to conditioning, were observed in anterior cingulate and anterior insula, regions previously implicated in delay fear conditioning. Differential responses were also observed in the amygdala and hippocampus that were best characterized with a time x stimulus interaction, indicating rapid adaptation of CS+-specific responses in medial temporal lobe. These results are strikingly similar to those obtained with a previous delay conditioning experiment and are in accord with a preferential role for medial temporal lobe structures during the early phase of conditioning. However, an additional activation of anterior hippocampus in the present experiment supports a view that its role in trace conditioning is to maintain a memory trace between the offset of the CS+ and the delayed onset of the US to enable associative learning in trace conditioning.

The desiderata for functional neuroimaging are high spatial and high temporal resolution. Recent developments in rapid echo-planar functional resonance imaging (fMRI) and event-related analyses [1,2] combine a spatial resolution of millimetres with a temporal resolution of seconds. A fundamental trade-off remains however: large brain coverage at high spatial resolution requires many, thin image planes (slices), resulting in a longer interscan interval (TR) and hence poorer temporal resolution. Though staggering stimulus times relative to scan times allows a higher effective sampling rate, existing volume-based analysis techniques such as SPM [2] assume simultaneous sampling of all slices. We show that this assumption can result in poor model fits and hence type II errors. Two popular solutions are compared: the use of multiple regressors and the interpolation of data in time.

BACKGROUND: Functional neuroimaging experiments have implicated prefrontal cortex (PFC) in memory processes. Several studies of schizophrenic patients have shown failure of activation in the dorsolateral region of PFC (DLPFC). We used a graded memory challenge to characterize functional neuroanatomical differences between schizophrenic and control subjects. The graded manipulation of task demands enabled us to assess group differences in the context of normal and abnormal psychological task performance. METHODS: Memory-related activity was assessed using positron emission tomography in schizophrenic patients and age-matched controls during performance of a graded memory task. Subjects underwent scanning while learning and recalling word lists of variable length. RESULTS: We used a model that assessed linear and nonlinear effects of memory load. Nonlinear group differences in DLPFC activation were observed. Controls showed a steepening slope of DLPFC increase as task demands increased. By contrast, schizophrenic subjects showed initial DLPFC increases that fell away with increasing memory load. The DLPFC response in schizophrenic subjects was closely related to measured task performance. In addition, schizophrenic subjects failed to show task-related decreases in activity in the left superior temporal and inferior parietal gyrus. CONCLUSIONS: Patients with schizophrenia showed a failure in DLPFC activation only in the face of diminished performance measures, suggesting that a full characterization of task-related changes in DLPFC activation must consider performance levels. However, striking failures of deactivation in superior temporal and inferior parietal regions were independent of task performance, possibly reflecting a core abnormality of the condition.

The aim of this paper is to illustrate a method for identifying macroscopic anatomical differences among the brains of different populations of subjects. The method involves spatially normalizing the structural MR images of a number of subjects so that they all conform to the same stereotactic space. Multivariate statistics are then applied to the parameters describing the estimated nonlinear deformations that ensue. To illustrate the method, we compared the gross morphometry of male and female subjects. We also assessed brain asymmetry, the effect of handedness, and interactions among these effects.

This paper presents an approach to characterizing evoked hemodynamic responses in fMRI based on nonlinear system identification, in particular the use of Volterra series. The approach employed enables one to estimate Volterra kernels that describe the relationship between stimulus presentation and the hemodynamic responses that ensue. Volterra series are essentially high-order extensions of linear convolution or "smoothing." These kernels, therefore, represent a nonlinear characterization of the hemodynamic response function that can model the responses to stimuli in different contexts (in this work, different rates of word presentation) and interactions among stimuli. The nonlinear components of the responses were shown to be statistically significant, and the kernel estimates were validated using an independent event-related fMRI experiment. One important manifestation of these nonlinear effects is a modulation of stimulus-specific responses by preceding stimuli that are proximate in time. This means that responses at high-stimulus presentation rates saturate and, in some instances, show an inverted U behavior. This behavior appears to be specific to BOLD effects (as distinct from evoked changes in cerebral blood flow) and may represent a hemodynamic "refractoriness." The aim of this paper is to describe the theory and techniques upon which these conclusions were based and to discuss the implications for experimental design and analysis.

Investigation of the three-generation KE family, half of whose members are affected by a pronounced verbal dyspraxia, has led to identification of their core deficit as one involving sequential articulation and orofacial praxis. A positron emission tomography activation study revealed functional abnormalities in both cortical and subcortical motor-related areas of the frontal lobe, while quantitative analyses of magnetic resonance imaging scans revealed structural abnormalities in several of these same areas, particularly the caudate nucleus, which was found to be abnormally small bilaterally. A recent linkage study [Fisher, S., Vargha-Khadem, F., Watkins, K. E., Monaco, A. P. & Pembry, M. E. (1998) Nat. Genet. 18, 168-170] localized the abnormal gene (SPCH1) to a 5. 6-centiMorgan interval in the chromosomal band 7q31. The genetic mutation or deletion in this region has resulted in the abnormal development of several brain areas that appear to be critical for both orofacial movements and sequential articulation, leading to marked disruption of speech and expressive language.

When dissimilar images are presented to the two eyes, perception alternates spontaneously between each monocular view, a phenomenon called binocular rivalry. Functional brain imaging in humans was used to study the neural basis of these subjective perceptual changes. Cortical regions whose activity reflected perceptual transitions included extrastriate areas of the ventral visual pathway, and parietal and frontal regions that have been implicated in spatial attention; whereas the extrastriate areas were also engaged by nonrivalrous perceptual changes, activity in the frontoparietal cortex was specifically associated with perceptual alternation only during rivalry. These results suggest that frontoparietal areas play a central role in conscious perception, biasing the content of visual awareness toward abstract internal representations of visual scenes, rather than simply toward space.

The aim of this study was to investigate the functional anatomy of distributed cortical and subcortical motor areas in the human brain that participate in the central control of overlearned complex sequential unimanual finger movements. On the basis of previous research in nonhuman primates, a principal involvement of basal ganglia medial premotor loops [corrected] was predicted for central control of finger sequences performed automatically. In pertinent areas, a correlation of activation levels with the complexity of a motor sequence was hypothesized. H2 15O positron emission tomography (PET) was used in a group of seven healthy male volunteers [mean age 32.0 +/- 10.4 yr] to determine brain regions where levels of regional cerebral blood flow (rCBF) correlated with graded complexity levels of five different key-press sequences. All sequences were overlearned before PET and involved key-presses of fingers II-V of the right hand. Movements of individual fingers were kept constant throughout all five conditions by external pacing at 1-Hz intervals. Positive correlations of rCBF with increasing sequence complexity were identified in the contralateral rostral supplementary motor area (pre-SMA) and the associated pallido-thalamic loop, as well as in right parietal area 7 and ipsilateral primary motor cortex (M1). In contrast, while rCBF in contralateral M1 and [corrected] extensive parts of caudal SMA was increased compared with rest during task performance, significant correlated increases of rCBF with sequence complexity were not observed. Inverse correlations of rCBF with increasing sequence complexity were identified in mesial prefrontal-, medial temporal-, and anterior cingulate areas. The findings provide further evidence in humans supporting the notion of a segregation of SMA into functionally distinct subcomponents: although pre-SMA was differentially activated depending on the complexity of a sequence of learned finger movements, such modulation was not detectable in caudal SMA (except the most antero-superior part), implicating a motor executive role. Our observations of complexity-correlated rCBF increases in anterior globus pallidus suggest a specific role for the basal ganglia in the process of sequence facilitation and control. They may act to filter and focus input from motor cortical areas as patterns of action become increasingly complex.

We present a simple method that allows statistical inferences to be made about the significance of regional effects in statistical parametric maps (SPMs) when the approximate location of the effect is specified in advance. The test can be thought of as analogous to assessing activations with uncorrected P values based on the height of SPMs but, in this instance, using the spatial extent or volume of the nearest activated region. The advantage of the current test is that it eschews a correction for multiple comparisons even though the exact location of the expected activation may not be known.

We present a method for detecting event-related responses in functional magnetic resonance imaging (fMRI). The occurrence of time-locked activations is formulated in terms of the general linear model, i.e., multiple linear regression. This permits the use of established statistical techniques that correct for multiple comparisons in the context of spatially smooth and serially correlated data. Responses are modelled using event-related temporal basis functions. Inferences are then made about all components of the model, using the F-ratio at all voxels in the image, to produce a statistical parametric map (SPM{F}). This method allows for the experimental design to relate the timing of events to the acquisition of data to give a temporal resolution (with respect to the event-related response) far better than the scanning repeat time.

The neural mechanisms involved in the selective processing of salient or behaviourally important stimuli are uncertain. We used an aversive conditioning paradigm in human volunteer subjects to manipulate the salience of visual stimuli (emotionally expressive faces) presented during positron emission tomography (PET) neuroimaging. Increases in salience, and conflicts between the innate and acquired value of the stimuli, produced augmented activation of the pulvinar nucleus of the right thalamus. Furthermore, this pulvinar activity correlated positively with responses in structures hypothesized to mediate value in the brain right amygdala and basal forebrain (including the cholinergic nucleus basalis of Meynert). The results provide evidence that the pulvinar nucleus of the thalamus plays a crucial modulatory role in selective visual processing, and that changes in perceptual salience are mediated by value-dependent plasticity in pulvinar responses.

OBJECTIVE: Positron emission tomography was used to investigate the neural substrates of normal human emotional and their dependence on the types of emotional stimulus. METHOD: Twelve healthy female subjects underwent 12 measurements of regional brain activity following the intravenous bolus administration of [15O]H2O as they alternated between emotion-generating and control film and recall tasks. Automated image analysis techniques were used to characterize and compare the increases in regional brain activity associated with the emotional response to complex visual (film) and cognitive (recall) stimuli. RESULTS: Film- and recall-generated emotion were each associated with significantly increased activity in the vicinity of the medial prefrontal cortex and thalamus, suggesting that these regions participate in aspects of emotion that do not depend on the nature of the emotional stimulus. Film-generated emotion was associated with significantly greater increases in activity bilaterally in the occipitotemporparietal cortex, lateral cerebellum, hypothalamus, and a region that includes the anterior temporal cortex, amygdala, and hippocampal formation, suggesting that these regions participate in the emotional response to certain exteroceptive sensory stimuli. Recall-generated sadness was associated with significantly greater increases in activity in the vicinity of the anterior insular cortex, suggesting that this region participates in the emotional response to potentially distressing cognitive or interoceptive sensory stimuli. CONCLUSIONS: While this study should be considered preliminary, it identified brain regions that participate in externally and internally generated human emotion.

We present a unified statistical theory for assessing the significance of apparent signal observed in noisy difference images. The results are usable in a wide range of applications, including fMRI, but are discussed with particular reference to PET images which represent changes in cerebral blood flow elicited by a specific cognitive or sensorimotor task. Our main result is an estimate of the P-value for local maxima of Gaussian, t, chi(2) and F fields over search regions of any shape or size in any number of dimensions. This unifies the P-values for large search areas in 2-D (Friston et al. [1991]: J Cereb Blood Flow Metab 11:690-699) large search regions in 3-D (Worsley et al. [1992]: J Cereb Blood Flow Metab 12:900-918) and the usual uncorrected P-value at a single pixel or voxel.

We present a unified statistical theory for assessing the significance of apparent signal observed in noisy difference images. The results are usable in a wide range of applications, including fMRI, but are discussed with particular reference to PET images which represent changes in cerebral blood flow elicited by a specific cognitive or sensorimotor task. Our main result is an estimate of the P-value for local maxima of Gaussian, t, χ2 and F fields over search regions of any shape or size in any number of dimensions. This unifies the P-values for large search areas in 2-D (Friston et al. [1991]: J Cereb Blood Flow Metab 11:690–699) large search regions in 3-D (Worsley et al. [1992]: J Cereb Blood Flow Metab 12:900–918) and the usual uncorrected P-value at a single pixel or voxel. © 1996 Wiley-Liss, Inc.

In this paper we present a general multivariate approach to the analysis of functional imaging studies. This analysis uses standard multivariate techniques to make statistical inferences about activation effects and to describe the important features of these effects. More specifically, the proposed analysis uses multivariate analysis of covariance (ManCova) with Wilk's lambda to test for specific effects of interest (e.g., differences among activation conditions), and canonical variates analysis (CVA) to characterize differential responses in terms of distributed brain systems. The data are subject to ManCova after transformation using their principal components or eigenimages. After significance of the activation effect has been assessed, underlying changes are described in terms of canonical images. Canonical images are like eigenimages but take explicit account of the effects of error or noise. The generality of this approach is assured by the general linear model used in the ManCova. The design and inferences sought are embodied in the design matrix and can, in principle, accommodate most parametric statistical analyses. This multivariate analysis may provide a statistical approach to PET activation studies that 1) complements univariate approaches like statistical parametric mapping, and 2) may facilitate the extension of existing multivariate techniques, like the scaled subprofile model and eigenimage analysis, to include hypothesis testing and statistical inference.

This paper concerns the spatial and intensity transformations that are required to adjust for the confounding effects of subject movement during functional MRI (fMRI) activation studies. An approach is presented that models, and removes, movement-related artifacts from fMRI time-series. This approach is predicated on the observation that movement-related effects are extant even after perfect realignment. Movement-related effects can be divided into those that are a function of position of the object in the frame of reference of the scanner and those that are due to movement in previous scans. This second component depends on the history of excitation experienced by spins in a small volume and consequent differences in local saturation. The spin excitation history thus will itself be a function of previous positions, suggesting an autoregression-moving average model for the effects of previous displacements on the current signal. A model is described as well as the adjustments for movement-related components that ensue. The empirical analyses suggest that (in extreme situations) over 90% of fMRI signal can be attributed to movement, and that this artifactual component can be successfully removed.

The neural systems involved in hearing and repeating single words were investigated in a series of experiments using PET. Neuropsychological and psycholinguistic studies implicate the involvement of posterior and anterior left perisylvian regions (Wernicke's and Broca's areas). Although previous functional neuroimaging studies have consistently shown activation of Wernicke's area, there has been only variable implication of Broca's area. This study demonstrates that Broca's area is involved in both auditory word perception and repetition but activation is dependent on task (greater during repetition than hearing) and stimulus presentation (greater when hearing words at a slow rate). The peak of frontal activation in response to hearing words is anterior to that associated with repeating words; the former is probably located in Brodmann's area 45, the latter in Brodmann's area 44 and the adjacent precentral sulcus. As Broca's area activation is more subtle and complex than that in Wernicke's area during these tasks, the likelihood of observing it is influenced by both the study design and the image analysis technique employed. As a secondary outcome from the study, the response of bilateral auditory association cortex to 'own voice' during repetition was shown to be the same as when listening to "other voice' from a prerecorded tape.

In neuroimaging, functional mapping usually implies mapping function into an anatomical space, for example, using statistical parametric mapping to identify activation foci, or the characterization of distributed changes with spatial modes (eigenimages or principal components) (Friston et al., 1993a). This article is about a complementary approach, namely, mapping anatomy into a functional space. We describe a simple variant of multidimensional scaling (principal coordinates analysis; Gower, 1966) that uses functional connectivity as its metric. The scaling transformation maps anatomy into a functional space. The topography, or proximity relationships, in this space embody the functional connectivity among brain regions. The higher the functional connectivity, the closer the regions. Functional connectivity is defined here as the correlation between remote neurophysiological events. The technique represents a descriptive characterization of anatomically distributed changes in the brain that reveals the structure of corticocortical interactions in terms of functional correlations. To illustrate the approach we have analyzed data from normal subjects and schizophrenic patients obtained with PET during the performance of word generation tasks. In particular, we focus on prefrontotemporal integration in normal subjects and show that, in schizophrenia, the left temporal regions and prefrontal cortex evidence abnormal functional connectivity.

This positron emission tomography study dissociates the neural correlates of object recognition and naming. Stimuli comprised coloured outline drawings of objects and coloured nonsense shapes. Subjects either viewed or explicitly named objects, similarly they viewed or named the colour of the shapes. Activations common to object and colour naming were identified by contrasting the explicit naming conditions (objects and colours) with the control (viewing) conditions. Activations associated with object recognition were identified by contrasting both object conditions (naming and viewing) with both shape conditions and activations specific to object or colour naming were identified by contrasting object naming (relative to object viewing) with colour naming (relative to shape viewing). The results associate: (i) object recognition with left middle occipital and bilateral anterior temporal cortices; (ii) modality independent naming with left posterior basal temporal lobe and the left prefrontal cortex; (iii) areas specific to object naming with left temporal extrasylvian regions, left anterior insula and right cerebellum; and (iv) areas specific to colour naming with left posterior lingual and fusiform gyri and midline cerebellum. These results are discussed in relation to previous neuroimaging and neuropsychological findings.

We used positron emission tomography (PET) to measure brain activity in healthy control subjects and unmedicated patients with schizophrenia. Subjects were scanned in the context of a combined psychological and pharmacological challenge, and we examined the effects of apomorphine, a drug acting on dopamine receptors, on brain systems engaged by a paced verbal fluency task. This factorial design enabled comparison of control subjects and schizophrenics in terms of the activations engendered by the cognitive task and the pharmacological challenge and the interaction of the two. We report a failure of cognitive task-related activation in anterior cingulate cortex and of task-related deactivation in the left superior temporal gyrus in the schizophrenic subjects. Compared with controls, the impaired cingulate activation was significantly reversed by apomorphine. Additionally, there was a trend for the abnormal fronto-temporal pattern of activation in schizophrenic subjects to be normalized by the drug. Overall, in schizophrenic subjects the effect of apomorphine, which we interpret in terms of a net dopaminergic antagonism, was to modify the brain activity, making the pattern more akin to that seen in control subjects. The results indicate both a regionally specific abnormality of brain function in schizophrenia and an abnormal pattern of fronto-temporal interactions.

Abstract This paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This technique minimizes the sum of squares between two images following nonlinear spatial deformations and transformations of the voxel (intensity) values. The spatial and intensity transformations are obtained simultaneously, and explicitly, using a least squares solution and a series of linearising devices. The approach is completely noninteractive (automatic), nonlinear, and noniterative. It can be applied in any number of dimensions. Various applications are considered, including the realignment of functional magnetic resonance imaging (MRI) time‐series, the linear (affine) and nonlinear spatial normalization of positron emission tomography (PET) and structural MRI images, the coregistration of PET to structural MRI, and, implicitly, the conjoining of PET and MRI to obtain high resolution functional images. © 1995 Wiley‐Liss, Inc.

This article was written at the invitation of the Editor, in response to the preceding article by Strother et aI. (1995). I have taken this opportunity to comment on several issues, some of which have been the subject of recent exchanges (e.g. , Taylor et aI., 1993; Worsley et aI. , 1993). Others deserve mention because they have become of special cur­ rent interest. An overview of the development of statistical analysis in functional imaging is pre­ sented, followed by a discussion of some key areas.

BACKGROUND: This study examined the pattern of cerebral blood flow observed in chronic schizophrenic patients while they performed a paced verbal fluency task. Such tasks engage a distributed brain system associated with willed action. Since willed action is impaired in many chronic schizophrenic patients we hypothesised that task performance would be associated with an abnormal pattern of blood flow. METHOD: Positron emission tomography (PET) was applied to 18 chronic schizophrenic patients stratified into three groups on the basis of verbal fluency performance and current symptoms. Regional cerebral blood flow (rCBF) was measured while the patients performed (a) verbal fluency, (b) word categorisation, and (c) word repetition. Results were compared with six normal controls matched for age, sex and premorbid IQ. Analysis was restricted to six brain regions previously identified in studies of normal volunteers. RESULTS: In five brain areas, including the left dorsolateral prefrontal cortex, the patients showed the same pattern of activation as control subjects. However, in the left superior temporal cortex, all patient groups failed to show the normal decrease in blood flow when verbal fluency was compared with word repetition. CONCLUSION: These observations suggest that (a) chronic schizophrenic patients can show a normal magnitude of frontal activation when matched for performance with controls, and (b) they fail to show the expected reductions of activity in the superior temporal cortex. This latter result may reflect abnormal functional connectivity between frontal and temporal cortex.

We review the evidence of pathophysiological changes in the prefrontal and temporal cortices of schizophrenic subjects and of abnormal integration of the physiological dynamics in these two regions. The argument we develop is that some schizophrenic phenomena are best understood in terms of abnormal interactions between different areas, not only at the levels of physiology and functional anatomy, but at the level of cognitive and sensorimotor functioning. We discuss recent functional imaging evidence suggesting abnormal prefronto-temporal interactions in relation to a psychological analysis of experiential symptoms in schizophrenia. Cortico-cortical interactions have been assessed in terms of functional connectivity and eigenimages, using time series of neurophysiological data obtained with positron emission tomography. The results of these analyses suggest that there is a profound disruption of large-scale prefronto-temporal interactions in schizophrenia. These disruptions are particularly relevant if one considers that many positive symptoms of schizophrenia reflect a failure to integrate intrinsically generated behaviour and concurrent perception.

Abstract A method for detecting significant and regionally specific correlations between sensory input and the brain's physiological response, as measured with functional magnetic resonance imaging (MRI), is presented in this paper. The method involves testing for correlations between sensory input and the hemodynamic response after convolving the sensory input with an estimate of the hemodynamic response function. This estimate is obtained without reference to any assumed input. To lend the approach statistical validity, it is brought into the framework of statistical parametric mapping by using a measure of cross‐correlations between sensory input and hemodynamic response that is valid in the presence of intrinsic autocorrelations. These autocorrelations are necessarily present, due to the hemodynamic response function or temporal point spread function. © 1994 Wiley‐Liss, Inc.

Current approaches to detecting significantly activated regions of cerebral tissue use statistical parametric maps, which are thresholded to render the probability of one or more activated regions of one voxel, or larger, suitably small (e. g., 0.05). We present an approximate analysis giving the probability that one or more activated regions of a specified volume, or larger, could have occurred by chance. These results mean that detecting significant activations no longer depends on a fixed (and high) threshold, but can be effected at any (lower) threshold, in terms of the spatial extent of the activated region. The substantial improvement in sensitivity that ensues is illustrated using a power analysis and a simulated phantom activation study. © 1994 Wiley-Liss, Inc.

Abstract The brain appears to adhere to two principles of functional organization; functional segregation and functional integration . The integration within and between functionally specialized areas is mediated by functional or effective connectivity . The characterization of this sort of connectivity is an important theme in many areas of neuroscience. This article presents one approach that has been used in functional imaging. This article reviews the basic distinction between functional and effective connectivity (as the terms are used in neuroimaging) and their role in addressing several aspects of functional organization (e.g. the topography of distributed system, integration between cortical areas, time‐dependent changes in connectivity and nonlinear interactions). Emphasis is placed on the points of contact between the apparently diverse applications of these concepts and in particular the central role of eigenimages or spatial modes. Although the framework that has been developed is inherently linear, it has been extended to assess nonlinear interactions among cortical areas. ©1994 Wiley‐Liss, Inc.

Abstract Statistical parametric maps are spatially extended statistical processes that are used to test hypotheses about regionally specific effects in neuroimaging data. The most established sorts of statistical parametric maps (e.g., Friston et al. [1991]: J Cereb Blood Flow Metab 11:690–699; Worsley et al. [1992]: J Cereb Blood Flow Metab 12:900–918) are based on linear models, for example ANCOVA, correlation coefficients and t tests. In the sense that these examples are all special cases of the general linear model it should be possible to implement them (and many others) within a unified framework. We present here a general approach that accomodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors). This approach brings together two well established bodies of theory (the general linear model and the theory of Gaussian fields) to provide a complete and simple framework for the analysis of imaging data. The importance of this framework is twofold: (i) Conceptual and mathematical simplicity, in that the same small number of operational equations is used irrespective of the complexity of the experiment or nature of the statistical model and (ii) the generality of the framework provides for great latitude in experimental design and analysis. © 1995 Wiley‐Liss, Inc.

Positron emission tomography measurements of regional cerebral blood flow (rCBF) were performed in normal volunteers during a graded auditory-verbal memory task. Subjects were required to remember and then immediately, and freely, recall a series of auditorily presented word lists varying from two to 13 words in length. Significant regional correlations between rCBF and memory load (word list length) were identified using statistical parametric mapping. Increasing memory load correlated with increasing rCBF in the cerebellar vermis and hemispheres, thalamus bilaterally, the superior and middle frontal gyri bilaterally, anterior insular regions bilaterally, anterior cingulate, precuneus and left and right lateral premotor areas. Increasing memory load also correlated with decreasing rCBF in the left and right superior temporal/insular regions, medial frontal gyrus, Brodmann's area 37 bilaterally, cuneus, inferior parietal lobule bilaterally and the mid-portion of the cingulate cortex. The pattern of rCBF change closely resembled that identified in a previously reported study using a cognitive subtraction paradigm and provides further evidence for a widespread neural system subserving auditory-verbal memory. The patterns of rCBF response suggest that the areas identified are associated with limited capacity processes for encoding and retrieval.

The localised PET cerebral correlates of the painful experience in the normal human brain have previously been demonstrated. This study examined whether these responses are different in patients with chronic atypical facial pain. The regional cerebral responses to non-painful and painful thermal stimuli in six female patients with atypical facial pain and six matched female controls were studied by taking serial measurements of regional blood flow by PET. Both groups displayed highly significant differences in responses to painful heat compared with non-painful heat in the thalamus, anterior cingulate cortex (area 24), lentiform nucleus, insula, and prefrontal cortex. These structures are closely related to the "medial pain system". The atypical facial pain group had increased blood flow in the anterior cingulate cortex and decreased blood flow in the prefrontal cortex. These findings show the importance of the anterior cingulate cortex and the reciprocal (possibly inhibitory) connections with the prefrontal cortex in the processing of pain in patients with this disorder. A hypothesis is proposed to explain the mechanisms of cognitive and pharmacological manipulation of these pain processes.

We have previously shown bilateral activation of motor pathways and the recruitment of additional motor areas in studies of groups of patients with recovery from motor stroke. We have now developed a new positron emission tomographic technique to measure the changes in regional cerebral blood flow elicited during a motor task in individual patients, relative to the cerebral activation found in normal subjects. The patterns of cerebral activation in each of 8 individual patients with capsular lesions of the pyramidal tract and complete recovery from hemiplegia are described by comparison with the pattern found in a representative sample of 10 normal subjects. We found a large ventral extension of the hand field of the contralateral (sensori)motor cortex in all patients with lesions of the posterior limb of the internal capsule. Greater activation than in normal subjects was found in variable combinations of the supplementary motor areas, the insula, the frontal operculum, and the parietal cortex. Structures belonging to motor pathways ipsilateral to the recovered limb were also more activated in the patients than in normal subjects. However, additional activation of the ipsilateral (sensori)motor cortex was only found in the 4 patients who exhibited associated movements of the unaffected hand when the recovered hand performed the motor task. We conclude that recovery from motor stroke due to striatocapsular damage is associated with individually different patterns of functional reorganization of the brain. These patterns are dependent on the site of the subcortical lesion and the somatotopic organization of the pyramidal tract, both of which may determine the precise potential for recovery of limb function following this type of brain injury.

Abstract A voxel‐based method of measuring the effective connectivity between brain regions is presented. The approach is based on electrophysiological concepts and is applied to neurophysiological data obtained with [positron emission tomography (PET)] functional imaging. Time‐dependent changes in effective connectivity were assessed during a verbal fluency activation paradigm using 12 consecutive measurements of regional cerebral blood flow (rCBF). The changes observed were predicted by an associative (Hebbian) model of plasticity, applied to cortical activity, measured in terms of rCBF. The main finding was, in most regions, a selective enhancement of effective connections from the distributed brain regions exhibiting the dominant pattern of correlated activity. © 1993 Wiley‐Liss, Inc.

We have previously reported focal abnormalities of regional cerebral blood flow (rCBF) in a group of 33 patients with major depression. This report, on an extended sample of 40 patients who demonstrated identical regional deficits to those previously described, examines the relationships between depressive symptoms and patterns of rCBF. Patients' symptom ratings were subjected to factor analysis, producing a three-factor solution. The scores for these three factors, which corresponded to recognizable dimensions of depressive illness, were then correlated with rCBF. The first factor had high loadings for anxiety and correlated positively with rCBF in the posterior cingulate cortex and inferior parietal lobule bilaterally. The second factor had high loadings for psychomotor retardation and depressed mood and correlated negatively with rCBF in the left dorsolateral prefrontal cortex and left angular gyrus. The third factor had a high loading for cognitive performance and correlated positively with rCBF in the left medial prefrontal cortex. These data indicate that symptomatic specificity may be ascribed to regional functional deficits in major depressive illness.

The distributed brain systems associated with performance of a verbal fluency task were identified in a nondirected correlational analysis of neurophysiological data obtained with positron tomography. This analysis used a recursive principal-component analysis developed specifically for large data sets. This analysis is interpreted in terms of functional connectivity, defined as the temporal correlation of a neurophysiological index measured in different brain areas. The results suggest that the variance in neurophysiological measurements, introduced experimentally, was accounted for by two independent principal components. The first, and considerably larger, highlighted an intentional brain system seen in previous studies of verbal fluency. The second identified a distributed brain system including the anterior cingulate and Wernicke's area that reflected monotonic time effects. We propose that this system has an attentional bias.

Positron emission tomography measurements of regional cerebral blood flow (rCBF) were performed in normal volunteers during two auditory--verbal memory tasks: a subspan and supraspan task. The difference in rCBF between tasks was used to identify brain areas/systems involved in auditory--verbal long-term memory. Increases in rCBF were observed in the left and right prefrontal cortex, precuneus and the retrosplenial area of the cingulate gyrus. Decreases in blood flow were centred in the superior temporal gyrus bilaterally. Separate comparisons were also made between each span task and a resting state. Brain regions showing increases in rCBF in these comparisons included the thalamus, left anterior cingulate, right parahippocampal gyrus, cerebellum and the superior temporal gyrus. The brain areas identified in these comparison define a number of the neuroanatomical components of a distributed system for signal processing and storage relevant to auditory--verbal memory function.

Parkinsonian tremor can be abolished by chronic high frequency thalamic stimulation of the ventral intermediate nucleus. We have studied six patients with unilateral Parkinson's disease. The patients had an electrode chronically implanted in the ventral intermediate nucleus of the thalamus. We measured changes in cerebral activity by positron emission tomography using an index of regional cerebral blood flow (rCBF). Each patient was scanned in three states: (i) tremor without stimulation (condition A); (ii) tremor with ineffective stimulation (condition B); (iii) tremor abolished by effective stimulation (condition C). The suppression of tremor (C compared with B) was specifically associated with a decrease of rCBF in the cerebellum, whereas the ineffective stimulation (B compared with A) induced a decrease of rCBF in homolateral cerebral cortex. The results give evidence for different contributions from cortex and cerebellum to the generation of parkinsonian tremor and suggest that tremor suppression is mainly associated with a decrease of synaptic activity in the cerebellum.

Neurophysiological deficits in the left dorsolateral prefrontal cortex (DLPFC) have been described in positron emission tomography studies of schizophrenia and depression. In schizophrenia and depression this deficit has been associated with the syndromes of psychomotor poverty and psychomotor retardation, respectively. Such findings lead to a prediction that DLPFC dysfunction is symptom rather than disease related. This prediction was empirically tested in a retrospective study that pooled data from 40 patients meeting research diagnostic criteria for depression and 30 patients meeting DSM-III R criteria for schizophrenia. The patients were categorised into those with and without poverty of speech, a symptom that is an observable manifestation of psychomotor impairment. The profile of regional cerebral blood flow (rCBF), measured in all subjects under resting conditions, was subsequently compared in these two groups. Patients with poverty of speech had significantly lower rCBF in the left DLFPC. This reduction of rCBF was independent of diagnosis. The findings support the view that the study of symptoms, or symptom clusters, can provide information additional to that of traditional diagnostic systems in the study of the major psychoses.

We used positron emission tomography (PET) to study organizational changes in the functional anatomy of the brain in 10 patients following recovery from striatocapsular motor strokes. Comparisons of regional cerebral blood flow maps at rest between the patients and 10 normal subjects revealed significantly lower regional cerebral blood flow in the basal ganglia, thalamus, sensorimotor, insular, and dorsolateral prefrontal cortices, in the brainstem, and in the ipsilateral cerebellum in patients, contralateral to the side of the recovered hand. These deficits reflect the distribution of dysfunction caused by the ischemic lesion. Regional cerebral blood flow was significantly increased in the contralateral posterior cingulate and premotor cortices, and in the caudate nucleus ipsilateral to the recovered hand. During the performance of a motor task by the recovered hand, patients activated the contralateral cortical motor areas and ipsilateral cerebellum to the same extent as did normal subjects. However, activation was greater than in normal subjects in both insulae; in the inferior parietal (area 40), prefrontal and anterior cingulate cortices; in the ipsilateral premotor cortex and basal ganglia; and in the contralateral cerebellum. The pattern of cortical activation was also abnormal when the unaffected hand, contralateral to the hemiplegia, performed the task. We showed that bilateral activation of motor pathways and the recruitment of additional sensorimotor areas and of other specific cortical areas are associated with recovery from motor stroke due to striatocapsular infarction. Activation of anterior and posterior cingulate and prefrontal cortices suggests that selective attentional and intentional mechanisms may be important in the recovery process. Our findings suggest that there is considerable scope for functional plasticity in the adult human cerebral cortex.

Using positron emission tomography (PET) and 15Oxygen, regional cerebral blood flow (rCBF) was measured in 33 patients with primary depression, 10 of whom had an associated severe cognitive impairment, and 23 age-matched controls. PET scans from these groups were analysed on a pixel-by-pixel basis and significant differences between the groups were identified on Statistical Parametric Maps (SPMs). In the depressed group as a whole rCBF was decreased in the left anterior cingulate and the left dorsolateral prefrontal cortex (P less than 0.05 Bonferroni-corrected for multiple comparisons). Comparing patients with and without depression-related cognitive impairment, in the impaired group there were significant decreases in rCBF in the left medial frontal gyrus and increased rCBF in the cerebellar vermis (P less than 0.05 Bonferroni-corrected). Therefore an anatomical dissociation has been described between the rCBF profiles associated with depressed mood and depression-related cognitive impairment. The pre-frontal and limbic areas identified in this study constitute a distributed anatomical network that may be functionally abnormal in major depressive disorder.

Regional cerebral blood flow (rCBF) was measured in 30 schizophrenic patients with severe, persistent and stable symptoms using positron emission tomography (PET). Directed and non-directed correlational analysis of the relationship between psychopathology and rCBF was used to identify brain structures implicated in three behavioural subsyndromes of schizophrenia. Psychopathology and neurophysiology (rCBF) exhibited high correlations in the left medial temporal region, mesencephalic, thalamic and left striatal structures. The highest correlation was in the left parahippocampal region. A canonical analysis of the same data highlighted the left parahippocampal region and left striatum (globus pallidus) as sites which linked the behavioural subsyndromes in terms of shared rCBF correlates. Increasing severity of psychopathology was associated with increased rCBF in these regions. Disinhibition of left medial temporal lobe activity mediated by fronto-limbic connections is a possible explanation for these findings; however, the prefrontal component appears to be critically dependent on the behavioural subsyndrome.

Positron emission tomography was used to investigate changes in regional cerebral blood flow (rCBF) in neurologically normal subjects during word reading and word repetition. The blood flow in these conditions was compared with control conditions where subjects were presented with stimuli of comparable auditory and visual complexity to real words and said the same word on presentation of each stimulus. The control condition for word repetition (hearing spoken words presented backwards) resulted in bilateral activation of the superior temporal gyrus. Word repetition caused a significant increase in rCBF over this control condition in the left superior and middle temporal gyri. The control condition for word reading (seeing stimuli written in 'false fonts', i.e. non-existent letter-like forms) resulted in significant changes in rCBF bilaterally in the striate and extrastriate cortex. Word reading caused a significant increase in blood flow relative to this control in the posterior part of the left middle temporal gyrus. The implications of these results are discussed, and it is argued that they are consistent with localization of a lexicon for spoken word recognition in the middle part of the left superior and middle temporal gyri, and a lexicon for written word recognition in the posterior part of the left middle temporal gyrus.

We have used positron tomography (PET) to demonstrate that some parts of the motor system exhibit physiological adaptation during the repeated performance of a simple motor task, but others do not. In contrast to the primary sensori-motor cortex, the cerebellum exhibits a decrease in physiological activation (increases in regional blood flow during performance) with practice. A new application of factorial experimental design to PET activation studies was used to make these measurements in four normal males. This design allowed adaptation to be examined by testing for an interaction between regional cerebral blood flow (rCBF) increases brought about by a motor task and the number of trials (time). These findings are interpreted as the neurophysiological correlates of synaptic changes in the cerebellum associated with motor learning in man.

Depression with cognitive impairment, so called depressive pseudodementia, is commonly mistaken for a neurodegenerative dementia. Using positron emission tomography (PET) derived measures of regional cerebral blood flow (rCBF) a cohort of 33 patients with major depression was studied. Ten patients displayed significant and reversible cognitive impairment. The patterns of rCBF of these patients were compared with a cohort of equally depressed non-cognitively impaired depressed patients. In the depressed cognitively impaired patients a profile of rCBF abnormalities was identified consisting of decreases in the left anterior medial prefrontal cortex and increases in the cerebellar vermis. These changes were additional to those seen in depression alone and are distinct from those described in neurodegenerative dementia. The cognitive impairment seen in a proportion of depressed patients would seem to be associated with dysfunction of neural systems distinct from those implicated in depression alone or the neurodegenerative dementias.

Positron emission tomography was used to study the relationship between rCBF and symptom profiles in 30 schizophrenic patients. Factor analysis confirmed that the symptoms segregated into three syndromes--psychomotor poverty, disorganisation, and reality distortion--described previously. Analysis of the correlations between syndrome scores and rCBF revealed that each syndrome was associated with a specific pattern of perfusion in paralimbic and association cortex, and in related subcortical nuclei. The study confirmed predictions that psychomotor poverty and disorganisation are associated with altered perfusion at different loci in the pre-frontal cortex, and reality distortion with altered perfusion in the medial temporal lobe. The perfusion patterns suggest that the abnormalities of brain function underlying each of the three syndromes are not confined to single loci, but involve distributed neuronal networks.

The functional anatomy of motor skill acquisition was investigated in six normal human subjects who learned to perform a pursuit rotor task with their dominant right hand during serial positron emission tomography (PET) imaging of relative cerebral blood flow (relCBF). The effect of motor execution, rather than learning, was identified by a comparison of four motor performance scans with two control scans (eye movements only). Motor execution was associated with activation of a distributed network involving cortical, striatonigral, and cerebellar sites. Second, the effect of early motor learning was examined. Performance improved from 17% to 66% mean time on target across the four PET scans obtained during pursuit rotor performance. Across the same scans, significant longitudinal increases of relCBF were located in the left primary motor cortex, the left supplementary motor area, and the left pulvinar thalamus. The results demonstrate that changes of regional cerebral activity associated with early learning of skilled movements occur in sites that are a subset of a more widely distributed network that is active during motor execution.

Positron emission tomographic (PET) images of regional cerebral blood flow (rCBF) from 30 normal, resting volunteers aged 30 to 85 years were analysed to identify areas where rCBF fell with age. Images were anatomically normalised, and a pixel-by-pixel linear regression was performed to remove differences in global CBF between subjects. Pixels at which rCBF then showed a significant (p less than 0.01) negative correlation with age were identified. They were displayed as a statistical parametric map (SPM) of correlations. We demonstrate an age-related decrease in adjusted rCBF in the cingulate, parahippocampal, superior temporal, medial frontal, and posterior parietal cortices bilaterally, and in the left insular and left posterior prefrontal cortices (omnibus significance, chi 2 = 2,291, p less than 0.0001, df = 1). Decreases in rCBF suggest a regionally specific loss of cerebral function with age. The affected areas were all limbic, or association, cortices. Therefore, these decreases may constitute the cerebral substrate of the cognitive changes that occur during normal aging.

Statistical parametric maps (SPMs) are potentially powerful ways of localizing differences in regional cerebral activity. This potential is limited by uncertainties in assessing the significance of these maps. In this report, we describe an approach that may partially resolve this issue. A distinction is made between using SPMs as images of change significance and using them to identify foci of significant change. In the first case, the SPM can be reported nonselectively as a single mathematical object with its omnibus significance. Alternatively, the SPM constitutes a large number of repeated measures over the brain. To reject the null hypothesis, that no change has occurred at a specific location, a threshold adjustment must be made that accounts for the large number of comparisons made. This adjustment is shown to depend on the SPM's smoothness. Smoothness can be determined empirically and be used to calculate a threshold required to identify significant foci. The approach models the SPM as a stationary stochastic process. The theory and applications are illustrated using uniform phantom images and data from a verbal fluency activation study of four normal subjects.

Six normal volunteers were studied with positron emission tomography to identify the cortical neural networks that participate in the processing of single words. Activity-related changes in regional cerebral blood flow were measured consecutively on 6 occasions in each subject, 2 while the subject was at rest and 4 while single word language tasks were being performed. The data from each subject were standardized for brain shape and size, reconstructed parallel to the intercommissural line, normalized for global flow differences, and then averaged for each activation condition across the 6 subjects. Significant areas of change in rCBF (P less than 0.05, with appropriate Bonferroni corrections) between task and rest conditions were displayed with reference to the coordinates of a standard neuroanatomical atlas. We have demonstrated that categorical judgements on heard pairs of real words activate neural networks along both superior temporal gyri, but with an anatomical distribution no different from that seen when the subjects listened to nonwords: the tasks would appear to be very different in cognitive demands but not in terms of the distribution of activation. However, during a verb generation task that involved thinking of verbs appropriate to heard nouns presented at a slow rate, the only temporal region activated was the left posterior superior temporal association cortex (Wernicke's area). Further analysis showed that whereas activation in other superior temporal regions, both left and right, correlated with rates of word presentation during the 4 tasks, there was no such correlation in Wernicke's area; evidence that this site is responsible for more than early acoustic processing. During verb generation there was also activation of left premotor and prefrontal cortex (including Broca's area and the supplementary motor area). The supplementary motor area is thought to be involved in the motor planning of speech. The subjects did not vocalize during the task, and therefore it would appear that the act of retrieving words from semantic memory activates networks concerned with the production of speech sounds. We conclude that single word comprehension and retrieval activate very different distributed regions of cerebral cortex, with Wernicke's area the only region engaged by both processes and with participation during silent word generation of networks involved in vocalization.

A theory and technique are presented that allow nonlinear resampling of positron emission tomography data to remove nonlinear differences in brain shape. The resampling is determined empirically by a function relating the observed image and a desired template. The validity, reliability, and precision of the plastic transformation are compared with linear rescaling alone, within, and between subjects.

A quantitative study of the regional cerebral responses to non-painful and painful thermal stimuli in six normal volunteers has been done by monitoring serial measurements of regional blood flow measured by positron emission tomography (PET). In comparison to a baseline of warm stimulation no statistically significant changes in blood flow were seen in relation to increasing non-painful heat. However, highly significant increases in blood flow were seen in response to painful heat in comparison to non-painful heat. These changes were in the contralateral cingulate cortex, thalamus and lenticular nucleus. These findings are discussed in relation to previous physiological observations of responses to nociceptive stimuli in man and primates.

By using positron emission tomography (PET) we examined the biological validity of a network model describing changes in cerebral activity associated with intrinsic and extrinsic word generation. The production of words not specified by an extrinsic stimulus constitutes willed or intrinsic generation. Perceiving a heard word is an example of extrinsic generation. The model incorporates three neuronal systems: a pool that stores word representations in a distributed fashion, an afferent system conveying sensory input to the pool and a modulating system that alters the responsivity of neurons in the pool. Simulations based on the model suggested that intrinsic generation would be associated with low activity in the pool, consequent on reduced modulation, and extrinsic generation with high activity. We measured cerebral activity with PET during intrinsic (verbal fluency) and extrinsic (responding to heard words) word generation and found this pattern of changes in the left superior temporal region. We were able to designate this region the site of the distributed word store and implicate the left dorsolateral prefrontal cortex (DLPFC) as the source of modulation. The relation between the superior temporal gyrus and DLPFC was shown by examining the correlation between the two regions in terms of cerebral activity. We conclude that the left DLPFC is responsible for modulating the responsivity of a neural system in the superior temporal gyrus and is the probable mediator of changes in attentional and intentional states that underlies the intrinsic generation of words.

We used positron emission tomography to contrast changes in cerebral blood flow associated with willed and routine acts. In the six tasks used, volunteers had to make a series of responses to a sequence of stimuli. For the routine acts, each response was completely specified by the stimulus. For the willed acts, the response was open-ended and therefore volunteers had to make a deliberate choice. Willed acts in the two response modalities studied (speaking a word, or lifting a finger) were associated with increased blood flow in the dorsolateral prefrontal cortex (Brodmann area 46). Willed acts were also associated with decreases in blood flow, but the location of these decreases was modality dependent.

1. Positron emission tomographic imaging of brain blood flow was used to identify areas of motor activation associated with volitional inspiration in six normal male subjects. 2. Scans were performed using intravenous infusion of H2(15)O during voluntary targeted breathing and positive pressure passive ventilation at the same level. 3. Regional increases in brain blood flow, due to active inspiration, were derived using a pixel by pixel comparison of images obtained during the voluntary and passive ventilation phases. 4. Pooling data from all subjects revealed statistically significant increases in blood flow bilaterally in the primary motor cortex (left, 5.4%; right, 4.3%), in the right pre-motor cortex (7.6%), in the supplementary motor area (SMA; 3.1%) and in the cerebellum (4.9%). 5. The site of increased neural activation in the motor cortex, associated with volitional inspiration, is consistent with an area which when stimulated, either directly during neurosurgery or transcranially with a magnetic stimulus, results in activation of the diaphragm. 6. The presence of additional sites of neural activation in the pre-motor cortex and SMA appears analogous to the results of studies on voluntary limb movement. The site of the increase in the SMA was posterior to that previously reported for arm movements. These areas are believed to have a role 'upstream' of the motor cortex in the planning and organization of movement. 7. This technique provides a means of studying the volitional motor control of respiratory related tasks in man.

1. Regional cerebral blood flow (rCBF) was measured using positron emission tomography in six normal volunteers while at rest and while performing four different repetitive movements of the right arm. 2. The four movements were performed in random order and consisted of abduction of the index finger, making a fist, sequential thumb to digit opposition, and shoulder flexion. All the movements were done at the same rate, using an auditory cue and involved displacements through similar amounts of the physiological range at each joint. 3. Increases in rCBF were interpreted as evidence of local neural activation and all four movements were associated with significant increases in CBF in the contralateral sensorimotor and premotor areas and in the supplementary motor area (SMA). 4. The average increase in blood flow in the contralateral sensorimotor cortex was significantly greater for the shoulder movement (31%) than for the three other movements. The increases with finger opposition (21%) and fist-making (24%) were not significantly different, and both were significantly greater than with index finger movement (13%). These data indicate that neither "fractionation" nor distal movement per se cause selective activation of sensorimotor cortex. 5. Significantly greater increases in blood flow in both the contralateral premotor cortex and the SMA ("nonprimary motor areas") occurred with shoulder movement than with the other movements. Because this difference may be related to the significantly greater activation occurring concurrently in the sensorimotor cortex, this finding does not prove unequivocally a "selective" role of the nonprimary motor areas in proximal movement. 6. Neither of the two nonprimary motor areas showed selective activation when a simple sequence of finger movements was performed compared with repetitive contractions of the same fingers. 7. Shoulder movement alone was associated with significant increases in rCBF in the ipsilateral sensorimotor cortex (10%), the superior vermis of the cerebellum (19%), and Brodmann areas 5 and 40 in the contralateral hemisphere. 8. The average location of the center of excitation in the sensorimotor cortex and SMA differed for the four movements and was interpreted as evidence of within-limb somatotopy. The shoulder focus lay highest in the sensorimotor cortex and lowest in the SMA.

We have used positron emission tomography (PET), which measures regional cerebral blood flow (rCBF), to demonstrate directly the specialization of function in the normal human visual cortex. A novel technique, statistical parametric mapping, was used to detect foci of significant change in cerebral blood flow within the prestriate cortex, in order to localize those parts involved in the perception of color and visual motion. For color, we stimulated the subjects with a multicolored abstract display containing no recognizable objects (Land color Mondrian) and contrasted the resulting blood flow maps with those obtained when subjects viewed an identical display consisting of equiluminous shades of gray. The comparison identified a unique area (area V4) located in the lingual and fusiform gyri of the prestriate cortex. For motion, blood flow maps when subjects viewed moving or stationary black and white random-square patterns were contrasted. The comparison identified a unique area located in the region of the temporo-parieto-occipital junction (area V5). We thus provide direct evidence to show that, just as in the macaque monkey, different areas of the human prestriate visual cortex are specialized for different attributes of vision. The striate cortex (V1) and the contiguous visual area (V2), which in the monkey brain feed both the homologous areas, were active in all 4 conditions. This pattern of activity allowed us to use an extension of the approach to assess the functional relationship between the 3 areas during color and motion stimulation. This is based on an hypothesis-led analysis of the covariance structure of the blood flow maps and promises to be a powerful tool for inferring anatomical pathways in the normal human brain.(ABSTRACT TRUNCATED AT 250 WORDS)

In order to localize cerebral cognitive or sensorimotor function, activation paradigms are being used in conjunction with PET measures of cerebral activity (e.g., rCBF). The changes in local cerebral activity have two components: a global, region independent change and a local or regional change. As the first step in localizing the regional effects of an activation, global variance must be removed by a normalization procedure. A simple normalization procedure is division of regional values by the whole brain mean. This requires the dependence of local activity on global activity to be one of simple proportionality. This is shown not to be the case. Furthermore, a systematic deviation from a proportional relationship across brain regions is demonstrated. Consequently, any normalization must be approached on a pixel-by-pixel basis by measuring the change in local activity and change in global activity. The changes associated with an activation can be partitioned into global and local effects according to two models: one assumes that the increase in local activity depends on global values and the other assumes independence. It is shown that the increase in activity due to a cognitive activation is independent of global activity. This independence of the (activation) condition effect and the confounding linear effect of global activity on observed local activity meet the requirements for an analysis of covariance, with the “nuisance” variable as global activity and the activation condition as the categorical independent variable. These conclusions are based on analysis of data from 24 scans: six conditions over four normal subjects using a verbal fluency paradigm. A technique is described based on ANCOVA and using statistical parametric mapping to localize foci in the brain that have been significantly perturbed by the cognitive tasks. This technique represents a fundamental and necessary departure from ROI-based approaches allowing the separation of global and local effects pixel by pixel, and provides an image of affected regions whose significance can be quantified. The specificity and sensitivity of the described method of change detection is assessed.

A technique is described for estimating the position of the intercommisural line (AC-PC line) directly from landmarks on positron emission tomographic (PET) images, namely the ventral aspects of the anterior and posterior corpus callosum, the thalamus, and occipital pole. The relationship of this estimate to the true AC-PC line, fitted through the centres of the anterior and posterior commissures, showed minimal vertical and angular displacement when measured on magnetic resonance imaging (MRI) scans. Using regression analysis, the ease and reliability of fitting to these points was found to be high. This directly derived AC-PC line estimate was validated in terms of the assumptions used in the method of Fox et al. The ratio of distance between the AC-PC line and a line passing through the base of the inion (GI line) to total brain height was 0.21, as predicted. The technique has been further validated by localizing focal activation of the sensorimotor cortex. The technique is discussed in terms of absolute limits to localization of structures in the brain using noninvasive tomographic techniques in general and PET in particular.