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Source space connectomics of neurodegeneration: One-metric approach does not fit all

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dc.contributor.authorPrado, Pavel
dc.contributor.authorMoguilner, Sebastian
dc.contributor.authorMejía, Jhony A.
dc.contributor.authorSainz-Ballesteros, Agustín
dc.contributor.authorOtero, Mónica
dc.contributor.authorBirba, Agustina
dc.contributor.authorSantamaria Garcia, Hernando
dc.contributor.authorLegaz, Agustina
dc.contributor.authorFittipaldi, Sol
dc.contributor.authorCruzat, Josephine
dc.contributor.authorTagliazucchi, Enzo
dc.contributor.authorParra, Mario
dc.contributor.authorHerzog, Rubén
dc.contributor.authorIbáñez, Agustín
dc.contributor.corporatenamePontificia Universidad Javeriana. Facultad de Medicina. Departamento de Psiquiatría y Salud Mentalspa
dc.contributor.javerianateacherSantamaria Garcia, Hernando
dc.coverage.spatialAmérica Latinaspa
dc.date.accessioned2023-08-31T16:28:31Z
dc.date.available2023-08-31T16:28:31Z
dc.date.created2023-02-23
dc.description.abstractenglishBrain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patients<HCs) involving convergent temporo-parieto-occipital regions in AD, and fronto-temporo-parietal areas in bvFTD. Hyperconnectivity (patients>HCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.spa
dc.description.comunidadPacientes con enfermedad de Alzheimerspa
dc.description.comunidadPacientes con Demencia frontotemporal variante conductualspa
dc.description.esciNospa
dc.description.indexingRevista Internacional - Indexadaspa
dc.description.orcidhttps://orcid.org/0000-0001-9422-3579spa
dc.description.publindexA1spa
dc.description.quartilescopusQ1spa
dc.description.quartilewosQ2spa
dc.formatPDFspa
dc.format.mimetypeapplication/pdfspa
dc.identifierhttps://www.sciencedirect.com/science/article/pii/S096999612300061X?via%3Dihubspa
dc.identifier.doihttps://doi.org/10.1016/j.nbd.2023.106047spa
dc.identifier.instnameinstname:Pontificia Universidad Javerianaspa
dc.identifier.issn0969-9961 / 1095-953X (Electrónico)spa
dc.identifier.reponamereponame:Repositorio Institucional - Pontificia Universidad Javerianaspa
dc.identifier.repourlrepourl:https://repository.javeriana.edu.cospa
dc.identifier.urihttp://hdl.handle.net/10554/65373
dc.language.isoengspa
dc.relation.citationendpage16spa
dc.relation.citationstartpage1spa
dc.relation.citationvolume179spa
dc.relation.ispartofjournalNeurobiology of Diseasespa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa
dc.rights.licenceAtribución-NoComercial 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subject.keywordComposite connectivity metricspa
dc.subject.keywordConnectomicsspa
dc.subject.keywordDementia biomarkerspa
dc.subject.keywordEEG source-spacespa
dc.subject.keywordMulti-feature machine learning classificationspa
dc.titleSource space connectomics of neurodegeneration: One-metric approach does not fit allspa
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.hasversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.localArtículo de revistaspa

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