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Implications of hierarchical Bayesian models of the brain for the understanding of psychiatric disorders

Adams, RA; (2014) Implications of hierarchical Bayesian models of the brain for the understanding of psychiatric disorders. Doctoral thesis , UCL (University College London).

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Abstract

This thesis explores how a hierarchical Bayesian model of the brain can explain aspects of schizophrenia and ‘functional’ motor and sensory symptoms (FMS). Bayesian computations prescribe the optimal integration of prior expectations with sensory evidence using the relative uncertainty (precision) of each source of information. The accurate representation of precision is crucial; its loss can lead to false inference by overweighting prior expectations or sensory evidence. Numerous phenomena in schizophrenia have been thought of as due to a loss of the brain’s predictive ability. This could be the result of a loss of precision of prior beliefs, through a reduction in synaptic gain at higher hierarchical levels, and I use this alteration to model the reduced mismatch negativity and three characteristic smooth pursuit eye movement (SPEM) abnormalities found in schizophrenia. My ultimate goal is to use the pursuit (and other) models to make inferences about the cortical encoding of precision. To establish the face validity of this approach, I tried to manipulate the precision estimates in normal subjects’ internal models, using a target moving with ‘smooth’ or ‘noisy’ (imprecise) velocity. I used subjects’ eye movements and DCM to invert the pursuit model and estimate the parameters of subjects’ internal models. I showed that noisy target velocity caused subjects to attend more to the sensory aspects of the stimulus (i.e. increased sensory precision). To demonstrate the construct validity of this pursuit DCM, I used magnetoencephalography (MEG) and DCM to test its prediction that noisy target motion increases sensory precision: corresponding to a decrease in the self-inhibition of superficial pyramidal cells in early visual cortex. Noisy motion decreased self-inhibition in central V1 at the group level, and in V2, changes in sensory precision in the pursuit DCM correlated with changes in V2 self-inhibition in the MEG DCM on an individual subject basis.

Type: Thesis (Doctoral)
Title: Implications of hierarchical Bayesian models of the brain for the understanding of psychiatric disorders
Language: English
Additional information: Permission for digitisation not received.
Keywords: schizophrenia, precision, smooth pursuit, Bayesian brain, active inference, functional symptoms
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1429291
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