In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 17, No. 7 ( 2021-7-14), p. e1009129-
Kurzfassung:
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.
Materialart:
Online-Ressource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1009129
DOI:
10.1371/journal.pcbi.1009129.g001
DOI:
10.1371/journal.pcbi.1009129.g002
DOI:
10.1371/journal.pcbi.1009129.g003
DOI:
10.1371/journal.pcbi.1009129.g004
DOI:
10.1371/journal.pcbi.1009129.g005
DOI:
10.1371/journal.pcbi.1009129.g006
DOI:
10.1371/journal.pcbi.1009129.g007
DOI:
10.1371/journal.pcbi.1009129.t001
DOI:
10.1371/journal.pcbi.1009129.s001
DOI:
10.1371/journal.pcbi.1009129.s002
DOI:
10.1371/journal.pcbi.1009129.s003
DOI:
10.1371/journal.pcbi.1009129.s004
DOI:
10.1371/journal.pcbi.1009129.s005
DOI:
10.1371/journal.pcbi.1009129.s006
DOI:
10.1371/journal.pcbi.1009129.s007
DOI:
10.1371/journal.pcbi.1009129.r001
DOI:
10.1371/journal.pcbi.1009129.r002
DOI:
10.1371/journal.pcbi.1009129.r003
DOI:
10.1371/journal.pcbi.1009129.r004
DOI:
10.1371/journal.pcbi.1009129.r005
DOI:
10.1371/journal.pcbi.1009129.r006
Sprache:
Englisch
Verlag:
Public Library of Science (PLoS)
Publikationsdatum:
2021
ZDB Id:
2193340-6