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@ARTICLE{Hashemi:903833,
      author       = {Hashemi, Meysam and Vattikonda, Anirudh N. and Sip, Viktor
                      and Diaz-Pier, Sandra and Peyser, Alexander and Wang,
                      Huifang and Guye, Maxime and Bartolomei, Fabrice and
                      Woodman, Marmaduke M. and Jirsa, Viktor K.},
      title        = {{O}n the influence of prior information evaluated by fully
                      {B}ayesian criteria in a personalized whole-brain model of
                      epilepsy spread},
      journal      = {PLoS Computational Biology},
      volume       = {17},
      number       = {7},
      issn         = {1553-734X},
      address      = {San Francisco, Calif.},
      publisher    = {Public Library of Science},
      reportid     = {FZJ-2021-05466},
      pages        = {e1009129 -},
      year         = {2021},
      abstract     = {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.},
      cin          = {JSC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS) / JL SMHB - Joint Lab
                      Supercomputing and Modeling for the Human Brain (JL
                      SMHB-2021-2027) / HBP SGA2 - Human Brain Project Specific
                      Grant Agreement 2 (785907) / HBP SGA3 - Human Brain Project
                      Specific Grant Agreement 3 (945539) / VirtualBrainCloud -
                      Personalized Recommendations for Neurodegenerative Disease
                      (826421) / PhD no Grant - Doktorand ohne besondere
                      Förderung (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel1)Helmholtz-SLNS /
                      G:(DE-Juel1)JL SMHB-2021-2027 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(EU-Grant)826421 /
                      G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:34260596},
      UT           = {WOS:000677707000002},
      doi          = {10.1371/journal.pcbi.1009129},
      url          = {https://juser.fz-juelich.de/record/903833},
}