Publications

Cortical circuits for perceptual inference

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.

Effective immunity and second waves: a dynamic causal modelling study [version 2; peer review: 2 approved]

This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a dynamic causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.

Anterior insula coordinates hierarchical processing of tactile mismatch responses

The body underlies our sense of self, emotion, and agency. Signals arising from the skin convey warmth, social touch, and the physical characteristics of external stimuli. Surprising or unexpected tactile sensations can herald events of motivational salience, including imminent threats (e.g., an insect bite) and hedonic rewards (e.g., a caressing touch). Awareness of such events is thought to depend upon the hierarchical integration of body-related mismatch responses by the anterior insula. To investigate this possibility, we measured brain activity using functional magnetic resonance imaging, while healthy participants performed a roving tactile oddball task. Mass-univariate analysis demonstrated robust activations in limbic, somatosensory, and prefrontal cortical areas previously implicated in tactile deviancy, body awareness, and cognitive control. Dynamic Causal Modelling revealed that unexpected stimuli increased the strength of forward connections along a caudal to rostral hierarchy—projecting from thalamic and somatosensory regions towards insula, cingulate and prefrontal cortices. Within this ascending flow of sensory information, the AIC was the only region to show increased backwards connectivity to the somatosensory cortex, augmenting a reciprocal exchange of neuronal signals. Further, participants who rated stimulus changes as easier to detect showed stronger modulation of descending PFC to AIC connections by deviance. These results suggest that the AIC coordinates hierarchical processing of tactile prediction error. They are interpreted in support of an embodied predictive coding model where AIC mediated body awareness is involved in anchoring a global neuronal workspace.

Lexical decision: differences in magnitude and onset as indexed by event-related fMRI

Inferential planning in the frontal cortex

Abstract How the brain plans and maintains sequences of future actions remains a central question in systems neuroscience. Recent studies in the frontal cortex have revealed that multiple elements of a sequence are represented simultaneously in separable neural subspaces, challenging classical serial models of sequential planning. Here, we show that these representations emerge naturally under inferential planning in which sequential actions are inferred from sensory evidence and goals. Using a hierarchical generative model, we reproduce key neural phenomena observed in primate frontal cortex, including the simultaneous activation of multiple plan elements, the emergence of (almost) orthogonal ‘memory’ subspaces, and their reuse across forward and backward sequence tasks. Our approach provides a mechanistic account of how probabilistic inference over control states gives rise to distributed and dynamic neural representations of plans. This framework not only unifies previously disparate findings on planning, working memory, and motor preparation, but also generates novel, testable predictions about the dynamics of active inference, the role of sensory subspaces, and the impact of uncertainty on sequence processing.

The Meltdown Pathway: A Multidisciplinary Account of Autistic Meltdowns

Autistic meltdowns are involuntary fits of intense frustration, rage, and often physical violence elicited by sensory and cognitive stressors easily tolerated by neurotypicals. While nearly 70% of autistic individuals display the “crisis behaviors” associated with meltdowns, the neural mechanisms that underlie this maladaptive response are not yet well understood. This has thus far hampered progress towards a dedicated therapeutic intervention–beyond traditional medications—that limits their frequency and severity. Here, we aim to initiate an interdisciplinary dialogue on the etiology of meltdowns. In doing so, we frame meltdowns as a consequence of underlying chronic hypervigilance and acute hyperreactivity to objectively benign stressors driven by differences in the insular cortex—a multimodal integration hub that adapts autonomic state and behavior to meet environmental demands. We first discuss meltdowns through the lens of neurophysiology and argue that intra-insular hypoconnectivity engenders vagal withdrawal and sympathetic hyperarousal in autism, driving chronic hypervigilance and reducing the threshold of stressors those with autism can tolerate before experiencing a meltdown. Next, we turn to neuropsychology and present evidence that meltdowns reflect an inability to properly integrate contextual evidence, particularly social cues, when acutely assessing ambiguous signs of danger in the environment—a process termed neuroception. Finally, we build on contemporary predictive coding accounts of autism to argue that meltdowns are ultimately driven by chronic failures of sensory attenuation and coherent deep inference within the interoceptive hierarchy, possibly linked to oxytocin deficiency during infancy. Throughout, we synthesize each perspective to construct a multidisciplinary, insula-based model of meltdowns.

Artificial intelligence—the death of hypothesis-led research?

Model selection for PET activation experiments

Investigating individual differences in brain abnormalities in autism

Autism is a psychiatric syndrome characterized by impairments in three domains: social interaction, communication, and restricted and repetitive behaviours and interests. Recent findings implicate the amygdala in the neurobiology of autism. In this paper, we report the results of a series of novel experimental investigations focusing on the structure and function of the amygdala in a group of children with autism. The first section attempts to determine if abnormality of the amygdala can be identified in an individual using magnetic resonance imaging in vivo . Using single–case voxel–based morphometric analyses, abnormality in the amygdala was detected in half the children with autism. Abnormalities in other regions were also found. In the second section, emotional modulation of the startle response was investigated in the group of autistic children. Surprisingly, there were no significant differences between the patterns of emotional modulation of the startle response in the autistic group compared with the controls.

A primer on Variational Laplace (VL)

This article details a scheme for approximate Bayesian inference, which has underpinned thousands of neuroimaging studies since its introduction 15 years ago. Variational Laplace (VL) provides a generic approach to fitting linear or non-linear models, which may be static or dynamic, returning a posterior probability density over the model parameters and an approximation of log model evidence, which enables Bayesian model comparison. VL applies variational Bayesian inference in conjunction with quadratic or Laplace approximations of the evidence lower bound (free energy). Importantly, update equations do not need to be derived for each model under consideration, providing a general method for fitting a broad class of models. This primer is intended for experimenters and modellers who may wish to fit models to data using variational Bayesian methods, without assuming previous experience of variational Bayes or machine learning. Accompanying code demonstrates how to fit different kinds of model using the reference implementation of the VL scheme in the open-source Statistical Parametric Mapping (SPM) software package. In addition, we provide a standalone software function that does not require SPM, in order to ease translation to other fields, together with detailed pseudocode. Finally, the supplementary materials provide worked derivations of the key equations.

Active inference under visuo-proprioceptive conflict: Simulation and empirical results

Abstract It has been suggested that the brain controls hand movements via internal models that rely on visual and proprioceptive cues about the state of the hand. In active inference formulations of such models, the relative influence of each modality on action and perception is determined by how precise (reliable) it is expected to be. The ‘top-down’ affordance of expected precision to a particular sensory modality is associated with attention. Here, we asked whether increasing attention to (i.e., the precision of) vision or proprioception would enhance performance in a hand-target phase matching task, in which visual and proprioceptive cues about hand posture were incongruent. We show that in a simple simulated agent—based on predictive coding formulations of active inference—increasing the expected precision of vision or proprioception improved task performance (target matching with the seen or felt hand, respectively) under visuo-proprioceptive conflict. Moreover, we show that this formulation captured the behaviour and self-reported attentional allocation of human participants performing the same task in a virtual reality environment. Together, our results show that selective attention can balance the impact of (conflicting) visual and proprioceptive cues on action—rendering attention a key mechanism for a flexible body representation for action.

Stimulating at the right time: phase-specific deep brain stimulation

See Moll and Engel (doi:10.1093/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.

Neural correlates of hand-object congruency effects during action planning

Abstract Selecting hand actions to manipulate an object is affected both by perceptual factors and by action goals. Affordances are associated with the automatic potentiation of motor representations to an object, independent of the goal of the actor. In previous studies, we have demonstrated an influence of the congruency between hand and object orientations on response times when reaching to turn an object, such as a cup. In this study, we investigated how the representation of hand postures triggered by planning to turn a cup were influenced by this congruency effect, in an fMRI scanning environment. Healthy participants were asked to reach and turn a real cup that was placed in front of them either in an upright orientation or upside down. They were instructed to use a hand orientation that was either congruent or incongruent with the cup orientation. As expected, the motor responses were faster when the hand and cup orientations were congruent. There was increased activity in a network of brain regions involving object-directed actions during action planning, which included bilateral primary and extrastriate visual, medial and superior temporal areas, as well as superior parietal, primary motor and premotor areas in the left hemisphere. Specific activation of the dorsal premotor cortex (PMd) was associated with hand-object orientation congruency during planning, and prior to any action taking place. Activity in that area and its connectivity with the lateral occipito-temporal cortex (LOTC) increased when planning incongruent actions. The increased activity in premotor areas in trials where the orientation of the hand was incongruent to that of the object suggests a role in eliciting competing representations specified by hand postures in LOTC.

An Active Inference Account of Touch and Verbal Communication in Therapy

This paper offers theoretical explanations for why “guided touch” or manual touch with verbal communication can be an effective way of treating the body (e.g., chronic pain) and the mind (e.g., emotional disorders). The active inference theory suggests that chronic pain and emotional disorders can be attributed to distorted and exaggerated patterns of interoceptive and proprioceptive inference. We propose that the nature of active inference is abductive. As such, to rectify aberrant active inference processes, we should change the “Rule” of abduction, or the “prior beliefs” entailed by a patient’s generative model. This means pre-existing generative models should be replaced with new models. To facilitate such replacement—or updating—the present treatment proposes that we should weaken prior beliefs, especially the one at the top level of hierarchical generative models, thereby altering the sense of agency, and redeploying attention. Then, a new prior belief can be installed through inner communication along with manual touch. The present paper proposes several hypotheses for possible experimental studies. If touch with verbal guidance is proven to be effective, this would demonstrate the relevance of active inference and the implicit prediction model at a behavioral level. Furthermore, it would open new possibilities of employing inner communication interventions, including self-talk training, for a wide range of psychological and physical therapies.

Computational modeling and autonomic control

Dynamic causal modelling of induced responses

The free-energy principle: a rough guide to the brain?

This article reviews a free-energy formulation that advances Helmholtz's agenda to find principles of brain function based on conservation laws and neuronal energy. It rests on advances in statistical physics, theoretical biology and machine learning to explain a remarkable range of facts about brain structure and function. We could have just scratched the surface of what this formulation offers; for example, it is becoming clear that the Bayesian brain is just one facet of the free-energy principle and that perception is an inevitable consequence of active exchange with the environment. Furthermore, one can see easily how constructs like memory, attention, value, reinforcement and salience might disclose their simple relationships within this framework.

Magnitudes on Neuronal Activity in Primate Striatum Effects of Expectations for Different Reward

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