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Weighted Symbolic Dependence Metric (wSDM) for fMRI restingstate connectivity : A multicentric validation for frontotemporal dementia

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dc.contributor.authorMoguilne, Sebastian
dc.contributor.authorGarcía, Adolfo M.
dc.contributor.authorMikulan, Ezequiel
dc.contributor.authorHesse, Eugenia
dc.contributor.authorGarcía-Cordero, Indira
dc.contributor.authorMelloni, Margherita
dc.contributor.authorCervetto, Sabrina
dc.contributor.authorSerrano, Cecilia
dc.contributor.authorHerrera, Eduar
dc.contributor.authorReyes, Pablo
dc.contributor.authorManes, Facundo
dc.contributor.authorIbáñez, Agustín
dc.contributor.authorSedeño, Lucas
dc.contributor.authorMatallana Eslava, Diana
dc.contributor.corporatenamePontificia Universidad Javeriana. Facultad de Medicina. Instituto de Envejecimiento
dc.date.accessioned2020-04-28T20:35:31Z
dc.date.accessioned2020-05-08T15:24:10Z
dc.date.available2020-04-28T20:35:31Z
dc.date.available2020-05-08T15:24:10Z
dc.date.created2018-07-25
dc.description.abstractenglishThe search for biomarkers of neurodegenerative diseases via fMRI functional connectivity (FC) research has yielded inconsistent results. Yet, most FC studies are blind to non-linear brain dynamics. To circumvent this limitation, we developed a “weighted Symbolic Dependence Metric” (wSDM) measure. Using symbolic transforms, we factor in local and global temporal features of the BOLD signal to weigh a robust copula-based dependence measure by symbolic similarity, capturing both linear and non-linear associations. We compared this measure with a linear connectivity metric (Pearson’s R) in its capacity to identify patients with behavioral variant frontotemporal dementia (bvFTD) and controls based on resting-state data. We recruited participants from two international centers with different MRI recordings to assess the consistency of our measure across heterogeneous conditions. First, a seed-analysis comparison of the salience network (a specific target of bvFTD) and the default-mode network (as a complementary control) between patients and controls showed that wSDM yields better identification of resting-state networks. Moreover, machine learning analysis revealed that wSDM yielded higher classification accuracy. These results were consistent across centers, highlighting their robustness despite heterogeneous conditions. Our findings underscore the potential of wSDM to assess fMRI-derived FC data, and to identify sensitive biomarkers in bvFTD.spa
dc.description.paginas1-15spa
dc.description.quartilescopusQ1spa
dc.description.tipoarticuloReporte Científicospa
dc.formatPDFspa
dc.format.mimetypeapplication/pdfspa
dc.format.soportePapel / Electrónicospa
dc.identifierhttps://www.nature.com/articles/s41598-018-29538-9#Abs1spa
dc.identifier.doihttps://doi.org/10.1038/s41598-018-29538-9spa
dc.identifier.issn2045-2322 (Electrónico)spa
dc.identifier.urihttp://hdl.handle.net/10554/48487
dc.languagespaspa
dc.rights.licenceAtribución-NoComercial 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.sourceScientific Reports; Vol. 8 (2018)spa
dc.subject.keywordNeural circuitsspa
dc.subject.keywordDementiaspa
dc.titleWeighted Symbolic Dependence Metric (wSDM) for fMRI restingstate connectivity : A multicentric validation for frontotemporal dementiaspa
dc.title.englishWeighted Symbolic Dependence Metric (wSDM) for fMRI restingstate connectivity : A multicentric validation for frontotemporal dementiaspa
dc.typeinfo:eu-repo/semantics/article
dc.type.hasversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.localArtículo de revistaspa

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