Huifang E Wang1, Julia Scholly2, Paul Triebkorn3, Viktor Sip3, Samuel Medina Villalon2, Marmaduke M Woodman3, Arnaud Le Troter4, Maxime Guye4, Fabrice Bartolomei2, Viktor Jirsa5. 1. Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France. Electronic address: huyfang.wang@univ-amu.fr. 2. Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France; Epileptology Department, and Clinical Neurophysiology Department, Assistance Publique des Hôpitaux de Marseille, Marseille, France. 3. Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France. 4. Aix Marseille Univ, CNRS, CRMBM, Marseille, France. 5. Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France. Electronic address: viktor.jirsa@univ-amu.fr.
Abstract
BACKGROUND: Several automated parcellation atlases of the human brain have been developed over the past decades, based on various criteria, and have been applied in basic and clinical research. NEW METHOD: Here we present the Virtual Epileptic Patient (VEP) atlas that offers a new automated brain region parcellation and labeling, which has been developed for the specific use in the domains of epileptology and functional neurosurgery and is able to apply at individual patient's level. RESULTS: It comprises 162 brain regions, including 73 cortical and 8 subcortical regions per hemisphere. We demonstrate the successful application of the VEP atlas in a cohort of 50 retrospective patients. The structural organization is complemented by the functional variation of stereotactic intracerebral EEG (SEEG) signal data features establishing brain region-specific 3d-maps. COMPARISON WITH EXISTING METHODS: The VEP atlas integrates both anatomical and functional definitions in the same atlas, adapted to applications for epilepsy patients and individualizable. CONCLUSION: The covariation of structural and functional organization is the basis for current efforts of patient-specific large-scale brain network modeling exploiting virtual brain technologies for the identification of the epileptogenic regions in an ongoing prospective clinical trial EPINOV.
BACKGROUND: Several automated parcellation atlases of the human brain have been developed over the past decades, based on various criteria, and have been applied in basic and clinical research. NEW METHOD: Here we present the Virtual EpilepticPatient (VEP) atlas that offers a new automated brain region parcellation and labeling, which has been developed for the specific use in the domains of epileptology and functional neurosurgery and is able to apply at individual patient's level. RESULTS: It comprises 162 brain regions, including 73 cortical and 8 subcortical regions per hemisphere. We demonstrate the successful application of the VEP atlas in a cohort of 50 retrospective patients. The structural organization is complemented by the functional variation of stereotactic intracerebral EEG (SEEG) signal data features establishing brain region-specific 3d-maps. COMPARISON WITH EXISTING METHODS: The VEP atlas integrates both anatomical and functional definitions in the same atlas, adapted to applications for epilepsypatients and individualizable. CONCLUSION: The covariation of structural and functional organization is the basis for current efforts of patient-specific large-scale brain network modeling exploiting virtual brain technologies for the identification of the epileptogenic regions in an ongoing prospective clinical trial EPINOV.
Authors: Viktor Sip; Meysam Hashemi; Anirudh N Vattikonda; Marmaduke M Woodman; Huifang Wang; Julia Scholly; Samuel Medina Villalon; Maxime Guye; Fabrice Bartolomei; Viktor K Jirsa Journal: PLoS Comput Biol Date: 2021-02-17 Impact factor: 4.475
Authors: Julia Makhalova; Samuel Medina Villalon; Huifang Wang; Bernard Giusiano; Marmaduke Woodman; Christian Bénar; Maxime Guye; Viktor Jirsa; Fabrice Bartolomei Journal: Epilepsia Date: 2022-06-06 Impact factor: 6.740
Authors: Andrew Y Revell; Alexander B Silva; T Campbell Arnold; Joel M Stein; Sandhitsu R Das; Russell T Shinohara; Dani S Bassett; Brian Litt; Kathryn A Davis Journal: Neuroimage Date: 2022-03-23 Impact factor: 7.400