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Optimizing experimental design for comparing models of brain function

Daunizeau, Jean; Preuschoff, Kerstin; Friston, Karl; Stephan, Klaas E (2011). Optimizing experimental design for comparing models of brain function. PLoS Computational Biology, 7(11):e1002280.

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.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Economics
08 Research Priority Programs > Foundations of Human Social Behavior: Altruism and Egoism
Dewey Decimal Classification:170 Ethics
330 Economics
Scopus Subject Areas:Life Sciences > Ecology, Evolution, Behavior and Systematics
Physical Sciences > Modeling and Simulation
Physical Sciences > Ecology
Life Sciences > Molecular Biology
Life Sciences > Genetics
Life Sciences > Cellular and Molecular Neuroscience
Physical Sciences > Computational Theory and Mathematics
Scope:Discipline-based scholarship (basic research)
Language:English
Date:2011
Deposited On:06 Dec 2011 14:34
Last Modified:06 Jul 2025 01:36
Publisher:Public Library of Science (PLoS)
ISSN:1553-734X
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1371/journal.pcbi.1002280
PubMed ID:22125485
Other Identification Number:merlin-id:6303
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