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Normative modelling detects abnormal functional connectivity in schizophrenia

dc.contributor.authorSaraiva, Duarte
dc.contributor.authorFerreira, Hugo
dc.date.accessioned2025-03-13T18:04:07Z
dc.date.available2025-03-13T18:04:07Z
dc.date.issued2022
dc.description.abstractMachine learning (ML) applications on the diagnosis of neuropsychiatric disorders (NPD) have not reached clinical practice yet, as the continuous spectrum of NPD demands more complex, non-binary classification approaches. Herein, a ML-based normative model was created from healthy subjects, which “fails” when tested on schizophrenia patients. In particular, abnormal functional connectivity patterns were found in such patients, in agreement to what has been described in the literature. Moreover, a clustering method and analysis at the individual level indicate that subgroups may exist within the schizophrenia spectrum, suggesting that a personalized and precision-based diagnosis is within reach for such NPD.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.58530/2022/0262pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.5/99319
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationFundação para a Ciência e Tecnologia (FCT) UID/BIO/00645/2017pt_PT
dc.relationFundação para a Ciência e Tecnologia (FCT) DSAIPA/DS/0065/2018pt_PT
dc.titleNormative modelling detects abnormal functional connectivity in schizophreniapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceLondon, United Kingdompt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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