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Optimizing experimental design for comparing models of brain function.

Daunizeau, J; Preuschoff, K; Friston, K; Stephan, K; (2011) Optimizing experimental design for comparing models of brain function. PLoS Computational Biology , 7 (11) , Article e1002280. 10.1371/journal.pcbi.1002280. Green open access

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Abstract

This article presents the first attempt to formalize the optimization of experimental design with the aim of comparing models of brain function based on neuroimaging data. We demonstrate our approach in the context of Dynamic Causal Modelling (DCM), which relates experimental manipulations to observed network dynamics (via hidden neuronal states) and provides an inference framework for selecting among candidate models. Here, we show how to optimize the sensitivity of model selection by choosing among experimental designs according to their respective model selection accuracy. Using Bayesian decision theory, we (i) derive the Laplace-Chernoff risk for model selection, (ii) disclose its relationship with classical design optimality criteria and (iii) assess its sensitivity to basic modelling assumptions. We then evaluate the approach when identifying brain networks using DCM. Monte-Carlo simulations and empirical analyses of fMRI data from a simple bimanual motor task in humans serve to demonstrate the relationship between network identification and the optimal experimental design. For example, we show that deciding whether there is a feedback connection requires shorter epoch durations, relative to asking whether there is experimentally induced change in a connection that is known to be present. Finally, we discuss limitations and potential extensions of this work.

Type: Article
Title: Optimizing experimental design for comparing models of brain function.
Location: US
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pcbi.1002280
Publisher version: http://dx.doi.org/10.1371/journal.pcbi.1002280
Language: English
Additional information: © 2011 Daunizeau et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMCID: PMC3219623
Keywords: Bayes Theorem, Brain, Brain Mapping, Computational Biology, Computer Simulation, Feedback, Physiological, Humans, Magnetic Resonance Imaging, Models, Neurological, Monte Carlo Method, Psychomotor Performance, Reproducibility of Results, Research Design
UCL classification: UCL
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
URI: https://discovery.ucl.ac.uk/id/eprint/1332721
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