Publications

Neural and phenotypic representation under the free-energy principle

The aim of this paper is to leverage the free-energy principle and its corollary process theory, active inference, to develop a generic, generalizable model of the representational capacities of living creatures; that is, a theory of phenotypic representation. Given their ubiquity, we are concerned with distributed forms of representation (e.g., population codes), whereby patterns of ensemble activity in living tissue come to represent the causes of sensory input or data. The active inference framework rests on the Markov blanket formalism, which allows us to partition systems of interest, such as biological systems, into internal states, external states, and the blanket (active and sensory) states that render internal and external states conditionally independent of each other. In this framework, the representational capacity of living creatures emerges as a consequence of their Markovian structure and nonequilibrium dynamics, which together entail a dual-aspect information geometry. This entails a modest representational capacity: internal states have an intrinsic information geometry that describes their trajectory over time in state space, as well as an extrinsic information geometry that allows internal states to encode (the parameters of) probabilistic beliefs about (fictive) external states. Building on this, we describe here how, in an automatic and emergent manner, information about stimuli can come to be encoded by groups of neurons bound by a Markov blanket; what is known as the neuronal packet hypothesis. As a concrete demonstration of this type of emergent representation, we present numerical simulations showing that self-organizing ensembles of active inference agents sharing the right kind of probabilistic generative model are able to encode recoverable information about a stimulus array.

The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing

Abstract In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales—from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data integration across multiple scales, a new culture of multidisciplinary large-scale collaboration, and translation into applications. As pioneered in Europe’s Human Brain Project (HBP), a systematic approach will be essential for meeting the coming decade’s pressing medical and technological challenges. The aims of this paper are to: develop a concept for the coming decade of digital brain research, discuss this new concept with the research community at large, identify points of convergence, and derive therefrom scientific common goals; provide a scientific framework for the current and future development of EBRAINS, a research infrastructure resulting from the HBP’s work; inform and engage stakeholders, funding organisations and research institutions regarding future digital brain research; identify and address the transformational potential of comprehensive brain models for artificial intelligence, including machine learning and deep learning; outline a collaborative approach that integrates reflection, dialogues, and societal engagement on ethical and societal opportunities and challenges as part of future neuroscience research.

Effective connectivity changes in LSD-induced altered states of consciousness in humans

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.

The many faces of action

Different activation patterns in the occipital cortex of late and congenitally blind subjects

In the Body’s Eye: The computational anatomy of interoceptive inference

A growing body of evidence highlights the intricate linkage of exteroceptive perception to the rhythmic activity of the visceral body. In parallel, interoceptive inference theories of affective perception and self-consciousness are on the rise in cognitive science. However, thus far no formal theory has emerged to integrate these twin domains; instead, most extant work is conceptual in nature. Here, we introduce a formal model of cardiac active inference, which explains how ascending cardiac signals entrain exteroceptive sensory perception and uncertainty. Through simulated psychophysics, we reproduce the defensive startle reflex and commonly reported effects linking the cardiac cycle to affective behaviour. We further show that simulated ‘interoceptive lesions’ blunt affective expectations, induce psychosomatic hallucinations, and exacerbate biases in perceptual uncertainty. Through synthetic heart-rate variability analyses, we illustrate how the balance of arousal-priors and visceral prediction errors produces idiosyncratic patterns of physiological reactivity. Our model thus offers a roadmap for computationally phenotyping disordered brain-body interaction.

The Neural Structures Expressing Perceptual Hysteresis in Visual Letter Recognition

Patterns of Cerebral Blood Flow in Schizophrenia

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.

Model-based prediction of muscarinic receptor function from auditory mismatch negativity responses

Drugs affecting neuromodulation, for example by dopamine or acetylcholine, take centre stage among therapeutic strategies in psychiatry. These neuromodulators can change both neuronal gain and synaptic plasticity and therefore affect electrophysiological measures. An important goal for clinical diagnostics is to exploit this effect in the reverse direction, i.e., to infer the status of specific neuromodulatory systems from electrophysiological measures. In this study, we provide proof-of-concept that the functional status of cholinergic (specifically muscarinic) receptors can be inferred from electrophysiological data using generative (dynamic causal) models. To this end, we used epidural EEG recordings over two auditory cortical regions during a mismatch negativity (MMN) paradigm in rats. All animals were treated, across sessions, with muscarinic receptor agonists and antagonists at different doses. Together with a placebo condition, this resulted in five levels of muscarinic receptor status. Using a dynamic causal model - embodying a small network of coupled cortical microcircuits - we estimated synaptic parameters and their change across pharmacological conditions. The ensuing parameter estimates associated with (the neuromodulation of) synaptic efficacy showed both graded muscarinic effects and predictive validity between agonistic and antagonistic pharmacological conditions. This finding illustrates the potential utility of generative models of electrophysiological data as computational assays of muscarinic function. In application to EEG data of patients from heterogeneous spectrum diseases, e.g. schizophrenia, such models might help identify subgroups of patients that respond differentially to cholinergic treatments. SIGNIFICANCE STATEMENT: In psychiatry, the vast majority of pharmacological treatments affect actions of neuromodulatory transmitters, e.g. dopamine or acetylcholine. As treatment is largely trial-and-error based, one of the goals for computational psychiatry is to construct mathematical models that can serve as "computational assays" and infer the status of specific neuromodulatory systems in individual patients. This translational neuromodeling strategy has great promise for electrophysiological data in particular but requires careful validation. The present study demonstrates that the functional status of cholinergic (muscarinic) receptors can be inferred from electrophysiological data using dynamic causal models of neural circuits. While accuracy needs to be enhanced and our results must be replicated in larger samples, our current results provide proof-of-concept for computational assays of muscarinic function using EEG.

Imaging and dynamic causal modelling reveal brain-wide changes in effective connectivity and synaptic dynamics during epileptic seizures

Summary Pathophysiological explanations of epilepsy typically focus on either the micro/mesoscale (e.g. excitation-inhibition imbalance), or on the macroscale (e.g. network architecture). Linking abnormalities across spatial scales remains difficult, partly because of technical limitations in measuring neuronal signatures concurrently at the scales involved. Here we use light sheet imaging of the larval zebrafish brain during acute epileptic seizure induced with pentylenetetrazole. Empirically measured spectral changes of spontaneous neuronal activity during the seizure are then modelled using neural mass models, allowing Bayesian inference on changes in effective network connectivity and their underlying synaptic dynamics. This dynamic causal modelling of seizures in the zebrafish brain reveals concurrent changes in synaptic coupling at macro- and mesoscale. Fluctuations of synaptic connection strength and their temporal dynamics are both required to explain observed seizure patterns. These findings challenge a simple excitation-inhibition account of seizures, and highlight changes in synaptic transmission dynamics as a possible seizure generation pathomechanism. Abbreviations LFP local field potential PTZ pentylenetetrazole DCM dynamic causal modelling CSD cross spectral densities PEB Parametric Empirical Bayes

Linear models and inference

Functional anatomy of verbal fluency in people with schizophrenia and those at genetic risk

Resting-State Coupling between Core Regions within the Central-Executive and Salience Networks Contributes to Working Memory Performance

Previous studies investigated the distinct roles played by different cognitive regions and suggested that the patterns of connectivity of these regions are associated with working memory. However, the specific causal mechanism through which the neuronal circuits that involve these brain regions contribute to working memory is still unclear. Here, in a large sample of healthy young adults, we first identified the core working memory regions by linking working memory accuracy to resting-state functional connectivity with the bilateral dorsolateral prefrontal cortex (a principal region in the central-executive network). Then a spectral dynamic causal modeling analysis was performed to quantify the effective connectivity between these regions. Finally, the effective connectivity was correlated with working memory accuracy to characterize the relationship between these connections and working memory performance. We found that the functional connections between the bilateral dorsolateral prefrontal cortex and the dorsal anterior cingulate cortex and between the right dorsolateral prefrontal cortex and the left orbital fronto-insular cortex were correlated with working memory accuracy. Furthermore, the effective connectivity from the dorsal anterior cingulate cortex to the bilateral dorsolateral prefrontal cortex and from the right dorsolateral prefrontal cortex to the left orbital fronto-insular cortex could predict individual differences in working memory. Because the dorsal anterior cingulate cortex and orbital fronto-insular cortex are core regions of the salience network, we inferred that the inter- and causal-connectivity between core regions within the central-executive and salience networks is functionally relevant for working memory performance. In summary, the current study identified the dorsolateral prefrontal cortex-related resting-state effective connectivity underlying working memory and suggests that individual differences in cognitive ability could be characterized by resting-state effective connectivity.

fMRI Evidence for Default Mode Network Deactivation Associated with Rapid Eye Movements in Sleep

System-specific brain responses-time-locked to rapid eye movements (REMs) in sleep-are characteristically widespread, with robust and clear activation in the primary visual cortex and other structures involved in multisensory integration. This pattern suggests that REMs underwrite hierarchical processing of visual information in a time-locked manner, where REMs index the generation and scanning of virtual-world models, through multisensory integration in dreaming-as in awake states. Default mode network (DMN) activity increases during rest and reduces during various tasks including visual perception. The implicit anticorrelation between the DMN and task-positive network (TPN)-that persists in REM sleep-prompted us to focus on DMN responses to temporally-precise REM events. We timed REMs during sleep from the video recordings and quantified the neural correlates of REMs-using functional MRI (fMRI)-in 24 independent studies of 11 healthy participants. A reanalysis of these data revealed that the cortical areas exempt from widespread REM-locked brain activation were restricted to the DMN. Furthermore, our analysis revealed a modest temporally-precise REM-locked decrease-phasic deactivation-in key DMN nodes, in a subset of independent studies. These results are consistent with hierarchical predictive coding; namely, permissive deactivation of DMN at the top of the hierarchy (leading to the widespread cortical activation at lower levels; especially the primary visual cortex). Additional findings indicate REM-locked cerebral vasodilation and suggest putative mechanisms for dream forgetting.

A World Unto Itself: Human Communication as Active Inference

Recent theoretical work in developmental psychology suggests that humans are predisposed to align their mental states with those of other individuals. One way this manifests is in cooperative communication; that is, intentional communication aimed at aligning individuals' mental states with respect to events in their shared environment. This idea has received strong empirical support. The purpose of this paper is to extend this account by proposing an integrative model of the biobehavioral dynamics of cooperative communication. Our formulation is based on active inference. Active inference suggests that action-perception cycles operate to minimize uncertainty and optimize an individual's internal model of the world. We propose that humans are characterized by an evolved adaptive prior belief that their mental states are aligned with, or similar to, those of conspecifics (i.e., that 'we are the same sort of creature, inhabiting the same sort of niche'). The use of cooperative communication emerges as the principal means to gather evidence for this belief, allowing for the development of a shared narrative that is used to disambiguate interactants' (hidden and inferred) mental states. Thus, by using cooperative communication, individuals effectively attune to a hermeneutic niche composed, in part, of others' mental states; and, reciprocally, attune the niche to their own ends via epistemic niche construction. This means that niche construction enables features of the niche to encode precise, reliable cues about the deontic or shared value of certain action policies (e.g., the utility of using communicative constructions to disambiguate mental states, given expectations about shared prior beliefs). In turn, the alignment of mental states (prior beliefs) enables the emergence of a novel, contextualizing scale of cultural dynamics that encompasses the actions and mental states of the ensemble of interactants and their shared environment. The dynamics of this contextualizing layer of cultural organization feedback, across scales, to constrain the variability of the prior expectations of the individuals who constitute it. Our theory additionally builds upon the active inference literature by introducing a new set of neurobiologically plausible computational hypotheses for cooperative communication. We conclude with directions for future research.

Interictal discharges spread along local recurrent networks between tubers and surrounding cortex

Abstract The presence of interictal epileptiform discharges on electroencephalography (EEG) may indicate increased epileptic seizure risk and on invasive EEG are the signature of the irritative zone. In highly epileptogenic lesions – such as cortical tubers in tuberous sclerosis – these discharges can be recorded with intracranial stereotactic EEG as part of the evaluation for epilepsy surgery. Yet the network mechanisms that underwrite the generation and spread of these discharges remain poorly understood, limiting their current diagnostic use. Here, we investigate the dynamics of interictal epileptiform discharges using a combination of quantitative analysis of invasive EEG recordings and mesoscale neural mass modelling of cortical dynamics. We first characterise spatially organised local dynamics of discharges recorded from 36 separate tubers in 8 patients with tuberous sclerosis. We characterise these dynamics with a set of competing explanatory network models using dynamic causal modelling. Bayesian model comparison of plausible network architectures suggests that the recurrent coupling between neuronal populations within – and adjacent to – the tuber core explains the travelling wave dynamics observed in these patient recordings. Our results – based on interictal activity – unify competing theories about the pathological organisation of epileptic foci and surrounding cortex in patients with tuberous sclerosis. Coupled oscillator dynamics have previously been used to describe ictal activity, where fast travelling ictal discharges are commonly observed within the recruited seizure network. The interictal data analysed here add the insight that this functional architecture is already established in the interictal state. This links observations of interictal EEG abnormalities directly to pathological network coupling in epilepsy, with possible implications for epilepsy surgery approaches in tuberous sclerosis. Significance Statement Interictal epileptiform discharges (IEDs) are clinically important markers of an epileptic brain. Here we link local IED spread to network coupling through a combination of clinical recordings in paediatric patients with tuberous sclerosis complex, quantitative EEG analysis of interictal discharges spread, and Bayesian inference on coupled neural mass model parameters. We show that the kinds of interictal discharges seen in our patients require recurrent local network coupling extending beyond the putative seizure focus and that in fact only those recurrent coupled networks can support seizure-like and interictal dynamics when run in simulation. Our findings provide a novel integrated perspective on emergent epileptic dynamics in human patients.

PsPM-trSP2: SCR measurement in response to aversive/arousing/neutral IAPS pictures while subjected to auditory distractors

This dataset includes skin conductance response (SCR) measurements for each of 40 healthy unmedicated participants (20 males and 20 females aged 21.9 +/- 3.8 years) in response to the 16 most arousing negative, the 16 most arousing positive (excluding explicit nude), and the 16 least arousing neutral IAPS pictures, presented for 1 s each in 1 block, while listening to regular or random distractor sounds, as described in Bach et al. (2015). ITI was 4.4 s.

Analysing connectivity with Granger causality and dynamic causal modelling

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.