On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread.

Individualized anatomical information has been used as prior knowledge in Bayesian inference paradigms of whole-brain network models. However, the actual sensitivity to such personalized information in priors is still unknown. In this study, we introduce the use of fully Bayesian information criteri...

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Autores principales:	Meysam Hashemi, Anirudh N Vattikonda, Viktor Sip, Sandra Diaz-Pier, Alexander Peyser, Huifang Wang, Maxime Guye, Fabrice Bartolomei, Marmaduke M Woodman, Viktor K Jirsa
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f18937fa6a4141e2811371d370af1a29
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spelling	oai:doaj.org-article:f18937fa6a4141e2811371d370af1a292021-12-02T19:57:32ZOn the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread.1553-734X1553-735810.1371/journal.pcbi.1009129https://doaj.org/article/f18937fa6a4141e2811371d370af1a292021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009129https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Individualized anatomical information has been used as prior knowledge in Bayesian inference paradigms of whole-brain network models. However, the actual sensitivity to such personalized information in priors is still unknown. In this study, we introduce the use of fully Bayesian information criteria and leave-one-out cross-validation technique on the subject-specific information to assess different epileptogenicity hypotheses regarding the location of pathological brain areas based on a priori knowledge from dynamical system properties. The Bayesian Virtual Epileptic Patient (BVEP) model, which relies on the fusion of structural data of individuals, a generative model of epileptiform discharges, and a self-tuning Monte Carlo sampling algorithm, is used to infer the spatial map of epileptogenicity across different brain areas. Our results indicate that measuring the out-of-sample prediction accuracy of the BVEP model with informative priors enables reliable and efficient evaluation of potential hypotheses regarding the degree of epileptogenicity across different brain regions. In contrast, while using uninformative priors, the information criteria are unable to provide strong evidence about the epileptogenicity of brain areas. We also show that the fully Bayesian criteria correctly assess different hypotheses about both structural and functional components of whole-brain models that differ across individuals. The fully Bayesian information-theory based approach used in this study suggests a patient-specific strategy for epileptogenicity hypothesis testing in generative brain network models of epilepsy to improve surgical outcomes.Meysam HashemiAnirudh N VattikondaViktor SipSandra Diaz-PierAlexander PeyserHuifang WangMaxime GuyeFabrice BartolomeiMarmaduke M WoodmanViktor K JirsaPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 7, p e1009129 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Meysam Hashemi Anirudh N Vattikonda Viktor Sip Sandra Diaz-Pier Alexander Peyser Huifang Wang Maxime Guye Fabrice Bartolomei Marmaduke M Woodman Viktor K Jirsa On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread.
description	Individualized anatomical information has been used as prior knowledge in Bayesian inference paradigms of whole-brain network models. However, the actual sensitivity to such personalized information in priors is still unknown. In this study, we introduce the use of fully Bayesian information criteria and leave-one-out cross-validation technique on the subject-specific information to assess different epileptogenicity hypotheses regarding the location of pathological brain areas based on a priori knowledge from dynamical system properties. The Bayesian Virtual Epileptic Patient (BVEP) model, which relies on the fusion of structural data of individuals, a generative model of epileptiform discharges, and a self-tuning Monte Carlo sampling algorithm, is used to infer the spatial map of epileptogenicity across different brain areas. Our results indicate that measuring the out-of-sample prediction accuracy of the BVEP model with informative priors enables reliable and efficient evaluation of potential hypotheses regarding the degree of epileptogenicity across different brain regions. In contrast, while using uninformative priors, the information criteria are unable to provide strong evidence about the epileptogenicity of brain areas. We also show that the fully Bayesian criteria correctly assess different hypotheses about both structural and functional components of whole-brain models that differ across individuals. The fully Bayesian information-theory based approach used in this study suggests a patient-specific strategy for epileptogenicity hypothesis testing in generative brain network models of epilepsy to improve surgical outcomes.
format	article
author	Meysam Hashemi Anirudh N Vattikonda Viktor Sip Sandra Diaz-Pier Alexander Peyser Huifang Wang Maxime Guye Fabrice Bartolomei Marmaduke M Woodman Viktor K Jirsa
author_facet	Meysam Hashemi Anirudh N Vattikonda Viktor Sip Sandra Diaz-Pier Alexander Peyser Huifang Wang Maxime Guye Fabrice Bartolomei Marmaduke M Woodman Viktor K Jirsa
author_sort	Meysam Hashemi
title	On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread.
title_short	On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread.
title_full	On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread.
title_fullStr	On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread.
title_full_unstemmed	On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread.
title_sort	on the influence of prior information evaluated by fully bayesian criteria in a personalized whole-brain model of epilepsy spread.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f18937fa6a4141e2811371d370af1a29
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On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread.

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