Generative whole-brain dynamics models from healthy subjects predict functional alterations in stroke at the level of individual patients

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• dc.contributor.author Idesis, Sebastian
• dc.contributor.author Allegra, Michele
• dc.contributor.author Vohryzek, Jakub
• dc.contributor.author Sanz Perl, Yonatan
• dc.contributor.author Metcalf, Nicholas V.
• dc.contributor.author Griffis, Joseph C.
• dc.contributor.author Corbetta, Maurizio
• dc.contributor.author Shulman, Gordon L.
• dc.contributor.author Deco, Gustavo
• dc.date.accessioned 2025-05-19T06:57:21Z
• dc.date.available 2025-05-19T06:57:21Z
• dc.date.issued 2024
• dc.description.abstract Computational whole-brain models describe the resting activity of each brain region based on a local model, inter-regional functional interactions, and a structural connectome that specifies the strength of inter-regional connections. Strokes damage the healthy structural connectome that forms the backbone of these models and produce large alterations in inter-regional functional interactions. These interactions are typically measured by correlating the time series of the activity between two brain regions in a process, called resting functional connectivity. We show that adding information about the structural disconnections produced by a patient’s lesion to a whole-brain model previously trained on structural and functional data from a large cohort of healthy subjects enables the prediction of the resting functional connectivity of the patient and fits the model directly to the patient’s data (Pearson correlation = 0.37; mean square error = 0.005). Furthermore, the model dynamics reproduce functional connectivity-based measures that are typically abnormal in stroke patients and measures that specifically isolate these abnormalities. Therefore, although whole-brain models typically involve a large number of free parameters, the results show that, even after fixing those parameters, the model reproduces results from a population very different than that on which the model was trained. In addition to validating the model, these results show that the model mechanistically captures the relationships between the anatomical structure and the functional activity of the human brain.
• dc.description.sponsorship S.I. is supported by the project Neurological Mechanisms of Injury and Sleep-like Cellular Dynamics (NEMESIS) (ref. 101071900) funded by the EU ERC Synergy Horizon Europe. G.D. is supported by Horizon EU ERC Synergy Grant Project ID: 101071900, Grant PID2022-136216NB-I00 funded by MICIU/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, “ERDF, EU”. M.C. was supported by FLAG-ERA JTC 2017 (grant ANR-17-HBPR-0001), MIUR—Departments of Excellence Italian Ministry of Research (MART_ECCELLENZA18_01), Fondazione Cassa di Risparmio di Padova e Rovigo (CARIPARO)—Ricerca Scientifica di Eccellenza 2018—(grant agreement number 55403); Ministry of Health Italy: Brain connectivity measured with high-density electroencephalography: a novel neurodiagnostic tool for stroke — NEUROCONN (RF-2008 -12366899); Celeghin Foundation Padova (CUP C94I20000420007); BIAL foundation grant (No. 361/18); H2020 European School of Network Neuroscience (euSNN), H2020-SC5-2019-2 (grant agreement number 869505); H2020 Visionary Nature Based Actions For Heath, Wellbeing & Resilience in Cities (VARCITIES), H2020-SC5-2019-2 (grant agreement number 869505); Ministry of Health Italy: Eye-movement dynamics during free viewing as biomarker for assessment of visuospatial functions and for closed-loop rehabilitation in stroke—EYEMOVINSTROKE (RF-2019-12369300).
• dc.format.mimetype application/pdf
• dc.identifier.citation Idesis S, Allegra M, Vohryzek J, Sanz Perl Y, Metcalf NV, Griffis JC, et al. Generative whole-brain dynamics models from healthy subjects predict functional alterations in stroke at the level of individual patients. Brain Commun. 2024;6(4):fcae237. DOI: 10.1093/braincomms/fcae237
• dc.identifier.doi http://dx.doi.org/10.1093/braincomms/fcae237
• dc.identifier.issn 2632-1297
• dc.identifier.uri http://hdl.handle.net/10230/70431
• dc.language.iso eng
• dc.publisher Oxford University Press
• dc.relation.ispartof Brain Communications. 2024;6(4):fcae237
• dc.relation.projectID info:eu-repo/grantAgreement/EC/HE/101071900
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/869505
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2022-136216NB-I00
• dc.rights © The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Whole-brain models
• dc.subject.keyword Predictive
• dc.subject.keyword Stroke
• dc.subject.keyword (f)MRI
• dc.subject.keyword Dynamics
• dc.title Generative whole-brain dynamics models from healthy subjects predict functional alterations in stroke at the level of individual patients
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion