Publications

Action-Dependent Processing of Touch in the Human Parietal Operculum and Posterior Insula

Somatosensory input generated by one's actions (i.e., self-initiated body movements) is generally attenuated. Conversely, externally caused somatosensory input is enhanced, for example, during active touch and the haptic exploration of objects. Here, we used functional magnetic resonance imaging (fMRI) to ask how the brain accomplishes this delicate weighting of self-generated versus externally caused somatosensory components. Finger movements were either self-generated by our participants or induced by functional electrical stimulation (FES) of the same muscles. During half of the trials, electrotactile impulses were administered when the (actively or passively) moving finger reached a predefined flexion threshold. fMRI revealed an interaction effect in the contralateral posterior insular cortex (pIC), which responded more strongly to touch during self-generated than during FES-induced movements. A network analysis via dynamic causal modeling revealed that connectivity from the secondary somatosensory cortex via the pIC to the supplementary motor area was generally attenuated during self-generated relative to FES-induced movements-yet specifically enhanced by touch received during self-generated, but not FES-induced movements. Together, these results suggest a crucial role of the parietal operculum and the posterior insula in differentiating self-generated from externally caused somatosensory information received from one's moving limb.

Neural Mechanisms and Psychology of Psychedelic Ego Dissolution

Neuroimaging studies of psychedelics have advanced our understanding of hierarchical brain organisation and the mechanisms underlying their subjective and therapeutic effects. The primary mechanism of action of classic psychedelics is binding to serotonergic 5HT2A receptors. Agonist activity at these receptors leads to neuromodulatory changes in synaptic efficacy that can have a profound effect on hierarchical message passing in the brain. Here, we review the cognitive and neuroimaging evidence for the effects of psychedelics; in particular, their influence on selfhood and subject-object boundaries—known as ego dissolution—surmised to underwrite their subjective and therapeutic effects. Agonist of 5HT2A receptors, located at the apex of the cortical hierarchy may have a particularly powerful effect on sentience and consciousness. These effects can endure well after the pharmacological half life, suggesting that psychedelics may have long-term effects on neural plasticity – that may play a role in their therapeutic efficacy. Psychologically, this may be accompanied by a surrender of ego resistance that increases the repertoire of perceptual hypotheses, including those that undergird selfhood. We consider the interaction between serotonergic neuromodulation and sentience through the lens of hierarchical predictive coding, which speaks to the value of psychedelics in understanding how we make sense of the world—and specific predictions about effective connectivity in cortical hierarchies that can be tested using functional neuroimaging.

Neural masses and fields: modeling the dynamics of brain activity

EDITORIAL article Front. Comput. Neurosci., 17 November 2014 Volume 8 - 2014 | https://doi.org/10.3389/fncom.2014.00149

Brain in the Dark: Design Principles for Neuromimetic Inference under the Free Energy Principle

Deep learning has revolutionised artificial intelligence (AI) by enabling automatic feature extraction and function approximation from raw data. However, it faces challenges such as a lack of out-of-distribution generalisation, catastrophic forgetting and poor interpretability. In contrast, biological neural networks, such as those in the human brain, do not suffer from these issues, inspiring AI researchers to explore neuromimetic deep learning, which aims to replicate brain mechanisms within AI models. A foundational theory for this approach is the Free Energy Principle (FEP), which despite its potential, is often considered too complex to understand and implement in AI as it requires an interdisciplinary understanding across a variety of fields. This paper seeks to demystify the FEP and provide a comprehensive framework for designing neuromimetic models with human-like perception capabilities. We present a roadmap for implementing these models and a Pytorch code repository for applying FEP in a predictive coding network.

Resilience and active inference

In this article, we aim to conceptualize and formalize the construct of resilience using the tools of active inference, a new physics-based modeling approach apt for the description and analysis of complex adaptive systems. We intend this as a first step toward a computational model of resilient systems. We begin by offering a conceptual analysis of resilience, to clarify its meaning, as established in the literature. We examine an orthogonal, threefold distinction between meanings of the word “resilience”: (i) inertia, or the ability to resist change (ii) elasticity, or the ability to bounce back from a perturbation, and (iii) plasticity, or the ability to flexibly expand the repertoire of adaptive states. We then situate all three senses of resilience within active inference. We map resilience as inertia onto high precision beliefs, resilience as elasticity onto relaxation back to characteristic (i.e., attracting) states, and resilience as plasticity onto functional redundancy and structural degeneracy.

Analysis of functional MRI time‐series

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.

Dynamic causal models for EEG

Protocol for measuring the effects of cortical scotoma on visual space perception

We aim to investigate the spatial experience of patients with chronic scotomas caused by lesion to early visual cortex, including primary visual cortex (V1) and adjacent extrastriate visual areas. These experiments are conducted as part of an adversarial collaboration testing contrasting theories of consciousness: Integrated Information Theory (IIT) and two Predictive Processing accounts, Active Inference (AI) and Neurorepresentationalism (NREP). The central question is whether lesions to early visual cortex alter the experienced extent of visual space itself, or instead primarily disrupt stimulus content within an otherwise preserved visual space. To address this, we use paradigms in which patients estimate distances or spatial extents that either span a scotomatous region or fall entirely within intact visual field locations. Psychometric functions relating perceived and physical extent are modeled to estimate shifts in the point of subjective equality (PSE). According to IIT, lesions to early visual cortex, including V1 and occipital exstrastriate cortex, should lead to systematic reductions in perceived spatial extent across the scotoma (negative PSE shifts), reflecting a contraction of experienced space. In contrast, Predictive Processing accounts posit that higher-level predictive mechanisms preserve spatial structure despite loss of early input, predicting little or no systematic contraction (with NREP allowing limited context-dependent effects). By quantifying distortions in perceived spatial extent, this protocol aims to distinguish between these competing theoretical predictions.

Linking fast and slow: The case for generative models

Abstract A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days, or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multiscale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature.

Grey matter correlates of syndromes in schizophrenia

Background Symptoms in schizophrenia duster into syndromes, each of which may be associated with a particular pattern of cerebral blood flow. We sought to investigate whether these syndromes are also related to neuroanatomical changes. Method A semi-automated method was used to examine structural magnetic resonance images in 12 patients with schizophrenia. The relationship between the relative regional grey matter volume and ratings of the syndromes of psychomotor poverty, disorganisation and reality distortion was investigated. Results There was a significant negative correlation between psychomotor poverty score and the relative volume of the left ventro-medial prefrontal grey matter, and a significant positive correlation between disorganisation and the relative volumes of the hippocampus, and the parahippocampal/fusiform gyrus bilaterally. Conclusion The correlation between psychomotor poverty and left prefrontal grey matter volume resembles that previously seen with prefrontal blood flow in the same patients, suggesting that this functional abnormality is related to an underlying anatomical change.

P329: Functional connectivity of predictive coding in schizophrenia

Revealing the Transmission Dynamics of COVID-19: A Bayesian Framework for Rt Estimation

<title>Abstract</title> In epidemiological modelling, the instantaneous reproduction number, R_t, is important to understand the transmission dynamics of infectious diseases. Current R_t estimates often suffer from problems such as lagging, averaging and uncertainties demoting the usefulness of R_t. To address these problems, we propose a new method in the framework of sequential Bayesian inference where a Data Assimilation approach is taken for R_t estimation, resulting in the state-of-the-art ‘DARt’ system for R_t estimation. With DARt, the problem of time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and R_t; the drawback of averaging is improved by instantaneous updating upon new observations and a model selection mechanism capturing abrupt changes caused by interventions; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt through simulations and demonstrate its power in revealing the transmission dynamics of COVID-19.

Estimating required 'lockdown' cycles before immunity to SARS-CoV-2: Model-based analyses of susceptible population sizes, 'S0', in seven European countries including the UK and Ireland

We used Bayesian model inversion to estimate epidemic parameters from the reported case and death rates from seven countries using data from late January 2020 to April 5th 2020. Two distinct generative model types were employed: first a continuous time dynamical-systems implementation of a Susceptible-Exposed-Infectious-Recovered (SEIR) model and second: a partially observable Markov Decision Process (MDP) or hidden Markov model (HMM) implementation of an SEIR model. Both models parameterise the size of the initial susceptible population (S0), as well as epidemic parameters. Parameter estimation (data fitting) was performed using a standard Bayesian scheme (variational Laplace) designed to allow for latent unobservable states and uncertainty in model parameters. Both models recapitulated the dynamics of transmissions and disease as given by case and death rates. The peaks of the current waves were predicted to be in the past for four countries (Italy, Spain, Germany and Switzerland) and to emerge in 0.5-2 weeks in Ireland and 1-3 weeks in the UK. For France one model estimated the peak within the past week and the other in the future in two weeks. Crucially, Maximum a posteriori (MAP) estimates of S0 for each country indicated effective population sizes of below 20% (of total population size), under both the continuous time and HMM models. With a Bayesian weighted average across all seven countries and both models, we estimated that 6.4% of the total population would be immune. From the two models the maximum percentage of the effective population was estimated at 19.6% of the total population for the UK, 16.7% for Ireland, 11.4% for Italy, 12.8% for Spain, 18.8% for France, 4.7% for Germany and 12.9% for Switzerland. Our results indicate that after the current wave, a large proportion of the total population will remain without immunity.

Author response: Progressive neurodegeneration following spinal cord injury: Implications for clinical trials

We agree with Dr. Domingue's observation that the trajectory of recovery after spinal cord injury (SCI) is complex and difficult to predict. We should clarify that in the present study, we investigated the effects of traumatic SCI on neurodegeneration across the neuroaxis.1 However, we and others have shown that in cervical spondolytic myelopathy (i.e., nontraumatic SCI) remote tissue specific cord pathology is also evident.2,3 Surprisingly, the extent of neurodegeneration is similar to traumatic SCI, although these patients with nontraumatic SCI had only mild clinical symptoms.2 This suggests that, in a slow progressive disease (e.g., cervical spondolytic myelopathy), the CNS can compensate for neurodegenerative processes for much longer; however, with time, the competition between processes of reorganization and neurodegeneration is lost in favor of the latter. Nevertheless, the clinical viability of MRI-based structural measures for monitoring and predicting recovery after nontraumatic and traumatic SCI is feasible and will provide a tool to improve our understanding of the disease mechanism, which affects not only the spinal cord but also the brain after SCI.3–5 These new insights will enable us to better predict individual recovery trajectories and identify patients who could profit from further interventions to delay, or even prevent, further clinical deterioration.

Computational Modelling of EEG and fMRI Paradigms Reveals a Consistent Loss of Pyramidal Cell Synaptic Gain in Schizophrenia

Expectation maximization

Cerebellar state estimation enables resilient coupling across behavioural domains

Abstract Cerebellar computations are necessary for fine behavioural control and are thought to rely on internal probabilistic models performing state estimation. We propose that the cerebellum infers how states contextualise (i.e., interact with) each other, and coordinates extra-cerebellar neuronal dynamics underpinning a range of behaviours. To support this claim, we describe a cerebellar model for state estimation that includes states interactions, and link the underlying inference with the neuronal architecture and dynamics observed empirically. This is formalised using the free energy principle, which provides a dual perspective on a system in terms of both the dynamics of its physical – in this case neuronal – states, and the inference process they entail. As a proof of principle, we simulate cerebellar-dependent synchronisation of whisking and respiration, which are known to be tightly coupled in rodents. In summary, we suggest that cerebellar-dependent contextualisation of behaviour can explain its ubiquitous involvement in most aspects of behaviour.

Sophisticated Inference

Active inference offers a first principle account of sentient behavior, from which special and important cases—for example, reinforcement learning, active learning, Bayes optimal inference, Bayes optimal design—can be derived. Active inference finesses the exploitation-exploration dilemma in relation to prior preferences by placing information gain on the same footing as reward or value. In brief, active inference replaces value functions with functionals of (Bayesian) beliefs, in the form of an expected (variational) free energy. In this letter, we consider a sophisticated kind of active inference using a recursive form of expected free energy. Sophistication describes the degree to which an agent has beliefs about beliefs. We consider agents with beliefs about the counterfactual consequences of action for states of affairs and beliefs about those latent states. In other words, we move from simply considering beliefs about “what would happen if I did that” to “what I would believe about what would happen if I did that.” The recursive form of the free energy functional effectively implements a deep tree search over actions and outcomes in the future. Crucially, this search is over sequences of belief states as opposed to states per se. We illustrate the competence of this scheme using numerical simulations of deep decision problems.