Abstract
This work is in line with an on-going effort tending toward a computational (quantitative and refutable) understanding of human neuro-cognitive processes. Many sophisticated models for behavioural and neurobiological data have flourished during the past decade. Most of these models are partly unspecified (i.e. they have unknown parameters) and nonlinear. This makes them difficult to peer with a formal statistical data analysis framework. In turn, this compromises the reproducibility of model-based empirical studies. This work exposes a software toolbox that provides generic, efficient and robust probabilistic solutions to the three problems of model-based analysis of empirical data: (i) data simulation, (ii) parameter estimation/model selection, and (iii) experimental design optimization.
Highlights
Spectrum diseases in psychiatry, such as schizophrenia or depression, display profound heterogeneity with regard to the underlying pathophysiological mechanisms, requiring the development of models that can infer subject-specific mechanisms from neurophysiological and/or behavioural data [1,2,3]
Imaging neuroscience or neuroimaging is capable of observing certain biophysical characteristics of this biological substrate. These typically map to two classes of models, i.e. (i) formal models of perception, learning and decision making that predict behavioural responses, and (ii) biophysically realistic models that describe how electrophysiological activity propagate through neural networks
N options: an optional structure containing specific information regarding the model, i.e.: prior sufficient statistics on model parameters, mircrotime resolution, additional information that may have to be passed to evolution/ observation functions, lag k for the variational Bayesian approach (VBA)-Kalman forward pass, variational Bayesian (VB) convergence variables, delay matrix, flag for continuous/categorical data, etc
Summary
Spectrum diseases in psychiatry, such as schizophrenia or depression, display profound heterogeneity with regard to the underlying pathophysiological mechanisms, requiring the development of models that can infer subject-specific mechanisms from neurophysiological and/or behavioural data [1,2,3]. (i) formal models of perception, learning and decision making that predict behavioural responses, and (ii) biophysically realistic models that describe how electrophysiological activity propagate through neural networks The issue with such models is that they are based upon mechanisms that are usually both hidden (they are not directly accessible from experimental data) and nonlinear (this is the curse of realism). One requires sophisticated statistical approaches that can deal efficiently with parameter estimation and model selection (given experimental data) If only, these are necessary to capture the inter-individual variability of neurophysiological and behavioural responses. This article describes a (matlab) software toolbox that is designed to perform such model-based analyses of neuroimaging and behavioural data It consists of a probabilistic model inversion scheme that borrows from disciplines such as inverse problems, statistical physics and machine learning. In section ‘‘Availability and future directions’’, we will discuss limitations and on-going developments of this work
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call