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Edge-centric analysis of stroke patients: an alternative approach for biomarkers of lesion recovery

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dc.contributor.author Idesis, Sebastian Ariel
dc.contributor.author Faskowitz, Joshua
dc.contributor.author Betzel, Richard F.
dc.contributor.author Corbetta, Maurizio
dc.contributor.author Sporns, Olaf
dc.contributor.author Deco, Gustavo
dc.date.accessioned 2023-02-16T07:06:23Z
dc.date.available 2023-02-16T07:06:23Z
dc.date.issued 2022
dc.identifier.citation Idesis S, Faskowitz J, Betzel RF, Corbetta M, Sporns O, Deco G. Edge-centric analysis of stroke patients: an alternative approach for biomarkers of lesion recovery. Neuroimage Clin. 2022;35:103055. DOI: 10.1016/j.nicl.2022.103055
dc.identifier.issn 2213-1582
dc.identifier.uri http://hdl.handle.net/10230/55796
dc.description.abstract Most neuroimaging studies of post-stroke recovery rely on analyses derived from standard node-centric functional connectivity to map the distributed effects in stroke patients. Here, given the importance of nonlocal and diffuse damage, we use an edge-centric approach to functional connectivity in order to provide an alternative description of the effects of this disorder. These techniques allow for the rendering of metrics such as normalized entropy, which describes the diversity of edge communities at each node. Moreover, the approach enables the identification of high amplitude co-fluctuations in fMRI time series. We found that normalized entropy is associated with stroke lesion severity and continually increases across the time of patients’ recovery. Furthermore, high amplitude co-fluctuations not only relate to the lesion severity but are also associated with patients’ level of recovery. The current study is the first edge-centric application for a clinical population in a longitudinal dataset and demonstrates how a different perspective for functional data analysis can further characterize topographic modulations of brain dynamics.
dc.description.sponsorship S.I is supported by the EU-project euSNN (MSCA-ITN-ETN. H2020-860563). G.D. is supported by the Spanish national research project (ref. PID2019-105772GB-I00/AEI/https://doi.org/10.13039/ 501100011033) funded by the Spanish Ministry of Science, Innovation and Universities (MCIU). This material is based upon work supported by the National Science Foundation under Grant No. 076059-00003C (RFB, JF, OS). MC 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 and Resilience in Cities (VARCITIES), H2020-SC5-2019-2 (Grant Agreement number 869505); Ministry of Health Italy: Eyemovement 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.language.iso eng
dc.publisher Elsevier
dc.relation.ispartof NeuroImage: Clinical. 2022;35:103055.
dc.relation.isreferencedby https://github.com/SebastianIdesis/Edge_Centric_Stroke_Recovery
dc.rights © 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.title Edge-centric analysis of stroke patients: an alternative approach for biomarkers of lesion recovery
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1016/j.nicl.2022.103055
dc.subject.keyword Stroke
dc.subject.keyword Edge-centric
dc.subject.keyword Functional connectivity
dc.subject.keyword Entropy
dc.subject.keyword Brain dynamics
dc.subject.keyword Longitudinal
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/860563
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-105772GB-I00
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/869505
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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