Publications

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.

Abstract This review considers computational psychiatry from a particular viewpoint: namely, a commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion of a generative model as underwriting (i) sentient processing in the brain, and (ii) the scientific process in psychiatry. The story starts with a view of the brain—from cognitive and computational neuroscience—as an organ of inference and prediction. This offers a formal description of neuronal message passing, distributed processing and belief propagation in neuronal networks; and how certain kinds of dysconnection lead to aberrant belief updating and false inference. The dysconnections in question can be read as a pernicious synaptopathy that fits comfortably with formal notions of how we—or our brains—encode uncertainty or its complement, precision . It then considers how the ensuing process theories are tested empirically, with an emphasis on the computational modelling of neuronal circuits and synaptic gain control that mediates attentional set, active inference, learning and planning. The opportunities afforded by this sort of modelling are considered in light of in silico experiments; namely, computational neuropsychology, computational phenotyping and the promises of a computational nosology for psychiatry. The resulting survey of computational approaches is not scholarly or exhaustive. Rather, its aim is to review a theoretical narrative that is emerging across subdisciplines within psychiatry and empirical scales of investigation. These range from epilepsy research to neurodegenerative disorders; from post-traumatic stress disorder to the management of chronic pain, from schizophrenia to functional medical symptoms.

Abstract All organisms must integrate cognition, emotion, and motivation to guide action toward valuable (goal) states, as described by active inference. Within this framework, cognition, emotion, and motivation interact through the (Bayesian) fusion of exteroceptive, proprioceptive, and interoceptive signals, the precision-weighting of prediction errors, and the “affective tuning” of neuronal representations. Crucially, misregulation of these processes may have profound psychopathological consequences.

Abstract Reversible cooling experiments in monkey visual cortex have demonstrated that visually driven neuronal activity in V2 depends on feedforward projections from V1, whereas neuronal activity in V1 is modulated by feedback, or reentrant, projections from V2. We present evidence for a homologous asymmetry in reciprocal connections between V1 and V2 in human cortex using physiological measurements obtained with functional MRI. The analysis was based on a nonlinear model of effective connectivity that partitioned the influence that one region exerted over another into an obligatory effect (an effect that depended only on the input) and a modulatory effect (an effect that represented an interaction between input and activity intrinsic to the target region). Using estimates of the modulatory effect we tested two related hypotheses: (1) that V2 would be a major source of modulatory influences on V1; and (2) that the modulatory effects of V2 on V1 would be greater than those of V1 on V2. The first constitutes a hypothesis about the regional or topographic organization of (modulatory) effective connectivity and the second hypothesis directly addresses the functional asymmetry suggested by reversible cooling experiments. The results confirmed that the origins of feedback modulatory effects on V1 were regionally specific and most pronounced in V2. In contrast, feedforward modulatory influences on V1 on V2 were negligible. This apparent asymmetry between feedforward and feedback modulatory interactions was evident in both hemispheres and appears to be a fairly robust feature of nonlinear interactions between striate and extrastriate cortex. © 1995 Wiley‐Liss, Inc.

This dataset includes skin conductance response (SCR) measurements, CS and US information, keypress responses, keypress response times, key correctness and shock ratings for each of 20 healthy unmedicated participants (10 males and 10 females aged 22.2+/-4.0 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. CS is a visual stimulus appearing in the middle of the screen with variation in color. US is an electric shock as a 500 Hz current pulses train (individual pulse width: 0.5ms, varying current amplitudes (0.90+/-0.63 mA) train width:500 ms). SOA between the CS and US is 3.5 s. The ITI is randomly determined on each trial to be 7, 8, 9, 10 or 11 s. (This was correctly stated in Staib et al. (2015) but wrongly described in Bach et al. (2010).)

Survival requires the implementation of adaptive changes that demand energy resources. The efficient regulation of energetic resources thus plays a critical role in enabling systems to adapt to the demands of their internal and external environments. The framework of active inference explains how living organisms can build probabilistic models that enable them to predict, track, and regulate energy expenditure in the short and long run. The aim of the paper is to characterize the physiological changes that accompany stress, and the relationship between these changes and the loss of confidence in a system's predictions about its internal and external milieu-ultimately manifesting as depressive symptomatology. We identify the systems that underwrite goal-directed behavior, and the neuroendocrine and immunological systems, as the hierarchical controller that regulates energy resources. In doing so, we establish an etiological pathway from allostatic overload to depression via active inference.

This chapter 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 exchanges with subordinate brain systems. This neurobiological account rests on a view of the brain as a hierarchical inference machine. In this view, large-scale intrinsic networks occupy supraordinate levels of hierarchically organized brain that tries to optimize its representation of its 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 certain altered states of consciousness.

We characterise the dynamics of neuronal activity, in terms of field theory, using neural units placed on a 2D-lattice modelling the cortical surface. The electrical activity of neuronal units was analysed with the aim of deriving a neural field model with a simple functional form that still able to predict or reproduce empirical findings. Each neural unit was modelled using a neural mass and the accompanying field theory was derived in the continuum limit. The field theory comprised coupled (real) Klein-Gordon fields, where predictions of the model fall within the range of experimental findings. These predictions included the frequency spectrum of electric activity measured from the cortex, which was derived using an equipartition of energy over eigenfunctions of the neural fields. Moreover, the neural field model was invariant, within a set of parameters, to the dynamical system used to model each neuronal mass. Specifically, topologically equivalent dynamical systems resulted in the same neural field model when connected in a lattice; indicating that the fields derived could be read as a canonical cortical field theory. We specifically investigated non-dispersive fields that provide a structure for the coding (or representation) of afferent information. Further elaboration of the ensuing neural field theory, including the effect of dispersive forces, could be of importance in the understanding of the cortical processing of information.

Model-based analysis of psychophysiological signals is more robust to noise - compared to standard approaches - and may furnish better predictors of psychological state, given a physiological signal. We have previously established the improved predictive validity of model-based analysis of evoked skin conductance responses to brief stimuli, relative to standard approaches. Here, we consider some technical aspects of the underlying generative model and demonstrate further improvements. Most importantly, harvesting between-subject variability in response shape can improve predictive validity, but only under constraints on plausible response forms. A further improvement is achieved by conditioning the physiological signal with high pass filtering. A general conclusion is that precise modelling of physiological time series does not markedly increase predictive validity; instead, it appears that a more constrained model and optimised data features provide better results, probably through a suppression of physiological fluctuation that is not caused by the experiment.