Optimizing experimental design for comparing models of brain function.

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 observ...

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Autores principales:	Jean Daunizeau, Kerstin Preuschoff, Karl Friston, Klaas Stephan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/e4d226ef10d24a4db28d9ce3afc88369
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spelling	oai:doaj.org-article:e4d226ef10d24a4db28d9ce3afc883692021-11-18T05:51:47ZOptimizing experimental design for comparing models of brain function.1553-734X1553-735810.1371/journal.pcbi.1002280https://doaj.org/article/e4d226ef10d24a4db28d9ce3afc883692011-11-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22125485/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358This 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.Jean DaunizeauKerstin PreuschoffKarl FristonKlaas StephanPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 7, Iss 11, p e1002280 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Jean Daunizeau Kerstin Preuschoff Karl Friston Klaas Stephan Optimizing experimental design for comparing models of brain function.
description	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.
format	article
author	Jean Daunizeau Kerstin Preuschoff Karl Friston Klaas Stephan
author_facet	Jean Daunizeau Kerstin Preuschoff Karl Friston Klaas Stephan
author_sort	Jean Daunizeau
title	Optimizing experimental design for comparing models of brain function.
title_short	Optimizing experimental design for comparing models of brain function.
title_full	Optimizing experimental design for comparing models of brain function.
title_fullStr	Optimizing experimental design for comparing models of brain function.
title_full_unstemmed	Optimizing experimental design for comparing models of brain function.
title_sort	optimizing experimental design for comparing models of brain function.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/e4d226ef10d24a4db28d9ce3afc88369
work_keys_str_mv	AT jeandaunizeau optimizingexperimentaldesignforcomparingmodelsofbrainfunction AT kerstinpreuschoff optimizingexperimentaldesignforcomparingmodelsofbrainfunction AT karlfriston optimizingexperimentaldesignforcomparingmodelsofbrainfunction AT klaasstephan optimizingexperimentaldesignforcomparingmodelsofbrainfunction
_version_	1718424761587990528

Optimizing experimental design for comparing models of brain function.

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