Publications

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 H 2 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.

Parkinson's disease is a common and debilitating condition, caused by aberrant activity in a complex basal ganglia-thalamocortical circuit. Therapeutic advances rely on characterising interactions in this circuit. However, recording electrophysiological responses over the entire circuit is impractical. Dynamic causal modelling offers large-scale models of predictive value based on a limited or partial sampling of complex networks. Using dynamic causal modelling, we determined the network changes underlying the pathological excess of beta oscillations that characterise the Parkinsonian state. We modelled data from five patients undergoing surgery for deep brain stimulation of more than one target. We found that connections to and from the subthalamic nucleus were strengthened and promoted beta synchrony, in the untreated compared to the treated Parkinsonian state. Dynamic causal modelling was able to replicate the effects of lesioning this nucleus and may provide a new means of directing the search for therapeutic targets.

Hallucinations, including auditory verbal hallucinations (AVH), occur in both the healthy population and in psychotic conditions such as schizophrenia (often developing after a prodromal period). In addition, hallucinations can be in-context (they can be consistent with the environment, such as when one hallucinates the end of a sentence that has been repeated many times), or out-of-context (such as the bizarre hallucinations associated with schizophrenia). In previous work, we introduced a model of hallucinations as false (positive) inferences based on a (Markov decision process) formulation of active inference. In this work, we extend this model to include content-to disclose the computational mechanisms behind in- and out-of-context hallucinations. In active inference, sensory information is used to disambiguate alternative hypotheses about the causes of sensations. Sensory information is balanced against prior beliefs, and when this balance is tipped in the favor of prior beliefs, hallucinations can occur. We show that in-context hallucinations arise when (simulated) subjects cannot use sensory information to correct prior beliefs about hearing a voice, but beliefs about content (i.e. the sequential order of a sentence) remain accurate. When hallucinating subjects also have inaccurate beliefs about state transitions, out-of-context hallucinations occur; i.e. their hallucinated speech content is disordered. Note that out-of-context hallucinations in this setting does not refer to inference about context, but rather to false perceptual inference that emerges when the confidence in-or precision of-sensory evidence is reduced. Furthermore, subjects with inaccurate beliefs about state transitions but an intact ability to use sensory information do not hallucinate and are reminiscent of prodromal patients. This work demonstrates the different computational mechanisms that may underlie the spectrum of hallucinatory experience-from the healthy population to psychotic states.

Abstract Accumulating evidence suggests that autonomic signals and their cortical representations are closely linked to emotional processes, and that related abnormalities could lead to social deficits. Although socio‐emotional impairments are a defining feature of autism spectrum disorder (ASD), empirical evidence directly supporting the link between autonomic, cortical, and socio‐emotional abnormalities in ASD is still lacking. In this study, we examined autonomic arousal indexed by skin conductance responses (SCR), concurrent cortical responses measured by functional magnetic resonance imaging, and effective brain connectivity estimated by dynamic causal modeling in seventeen unmedicated high‐functioning adults with ASD and seventeen matched controls while they performed an empathy‐for‐pain task. Compared to controls, adults with ASD showed enhanced SCR related to empathetic pain, along with increased neural activity in the anterior insular cortex, although their behavioral empathetic pain discriminability was reduced and overall SCR was decreased. ASD individuals also showed enhanced correlation between SCR and neural activities in the anterior insular cortex. Importantly, significant group differences in effective brain connectivity were limited to greater reduction in the negative intrinsic connectivity of the anterior insular cortex in the ASD group, indicating a failure in attenuating anterior insular responses to empathetic pain. These results suggest that aberrant interoceptive precision, as indexed by abnormalities in autonomic activity and its central representations, may underlie empathy deficits in ASD. Hum Brain Mapp 36:3323–3338, 2015 . © 2015 The Authors Human Brain Mapping Published byWiley Periodicals, Inc.

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.

Abstract Current whole-brain models are generally tailored to the modelling of a particular modality of data (e.g., fMRI or MEG/EEG). Although different imaging modalities reflect different aspects of neural activity, we hypothesise that this activity arises from common network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct electromagnetic and metabolic features of brain activity to the dynamics on the brain’s macroscopic structural connectome. To jointly predict dynamical and functional connectivity features of distinct signal modalities, we consider two large-scale models generating local short-lived 40 Hz oscillations with various degrees of realism - namely Stuart Landau (SL) and Wilson and Cowan (WC) models. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC) and functional connectivity dynamics (FCD) to a comparable degree, by varying global coupling and mean conduction time delay. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD, highlighting the importance of balanced dynamics for the emergence of spatiotemporal patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.45 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns beyond the anatomical framework. Finally, we show that both models can generate MOMs with empirical-like properties. Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours.

The free-energy principle is an attempt to explain the structure of the agent and its brain, starting from the fact that an agent exists (Friston and Stephan, 2007; Friston et al., 2010). More specifically, it can be regarded as a systematic attempt to understand the 'fit' between an embodied agent and its niche, where the quantity of free-energy is a measure for the 'misfit' or disattunement (Bruineberg and Rietveld, 2014) between agent and environment. This paper offers a proof-of-principle simulation of niche construction under the free-energy principle. Agent-centered treatments have so far failed to address situations where environments change alongside agents, often due to the action of agents themselves. The key point of this paper is that the minimum of free-energy is not at a point in which the agent is maximally adapted to the statistics of a static environment, but can better be conceptualized an attracting manifold within the joint agent-environment state-space as a whole, which the system tends toward through mutual interaction. We will provide a general introduction to active inference and the free-energy principle. Using Markov Decision Processes (MDPs), we then describe a canonical generative model and the ensuing update equations that minimize free-energy. We then apply these equations to simulations of foraging in an environment; in which an agent learns the most efficient path to a pre-specified location. In some of those simulations, unbeknownst to the agent, the 'desire paths' emerge as a function of the activity of the agent (i.e. niche construction occurs). We will show how, depending on the relative inertia of the environment and agent, the joint agent-environment system moves to different attracting sets of jointly minimized free-energy.