Inference of Brain Networks with Approximate Bayesian Computation– assessing face validity with an example application in Parkinsonism

West, Timothy O, Berthouze, Luc, Farmer, Simon F and Litvak, Vladimir (2021) Inference of Brain Networks with Approximate Bayesian Computation– assessing face validity with an example application in Parkinsonism. NeuroImage, 236. a118020. ISSN 1053-8119

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Abstract

This paper describes and validates a novel framework using the Approximate Bayesian Computation (ABC) algorithm for parameter estimation and model selection in models of mesoscale brain network activity. We provide a proof of principle, first pass validation of this framework using a set of neural mass models of the cortico-basal ganglia thalamic circuit inverted upon spectral features from experimental in vivo recordings. This optimization scheme relaxes an assumption of fixed-form posteriors (i.e. the Laplace approximation) taken in previous approaches to inverse modelling of spectral features. This enables the exploration of model dynamics beyond that approximated from local linearity assumptions and so fit to explicit, numerical solutions of the underlying non-linear system of equations. In this first paper, we establish a face validation of the optimization procedures in terms of: (i) the ability to approximate posterior densities over parameters that are plausible given the known causes of the data; (ii) the ability of the model comparison procedures to yield posterior model probabilities that can identify the model structure known to generate the data; and (iii) the robustness of these procedures to local minima in the face of different starting conditions. Finally, as an illustrative application we show (iv) that model comparison can yield plausible conclusions given the known neurobiology of the cortico-basal ganglia-thalamic circuit in Parkinsonism. These results lay the groundwork for future studies utilizing highly nonlinear or brittle models that can explain time dependent dynamics, such as oscillatory bursts, in terms of the underlying neural circuits.

Item Type: Article
Keywords: Inverse modelling, networks, brain dynamics, oscillations, circuits, Parkinsonism
Schools and Departments: School of Engineering and Informatics > Informatics
Research Centres and Groups: Centre for Computational Neuroscience and Robotics
Evolutionary and Adaptive Systems Research Group
Sussex Neuroscience
Subjects: Q Science > QA Mathematics > QA0273 Probabilities. Mathematical statistics
Q Science > QP Physiology > QP0351 Neurophysiology and neuropsychology
R Medicine > RC Internal medicine > RC0321 Neurosciences. Biological psychiatry. Neuropsychiatry
Depositing User: Luc Berthouze
Date Deposited: 12 Apr 2021 09:08
Last Modified: 26 Apr 2021 15:00
URI: http://sro.sussex.ac.uk/id/eprint/98337

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Project NameSussex Project NumberFunderFunder Ref
Confronting High Dimensional Network Models With Data: Low Dimensional Forward Approximation and Bayesian Parameter LearningG2299LEVERHULME TRUSTUnset