Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/47464
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dc.contributor.authorBajouco, Miguel-
dc.contributor.authorMota, David-
dc.contributor.authorCoroa, Manuel-
dc.contributor.authorCaldeira, Salomé-
dc.contributor.authorSantos, Vítor-
dc.contributor.authorMadeira, Nuno-
dc.date.accessioned2018-02-06T00:42:15Z-
dc.date.available2018-02-06T00:42:15Z-
dc.date.issued2017-11-15-
dc.identifier.urihttp://hdl.handle.net/10316/47464-
dc.description.abstractSchizophrenia is a severe mental disorder and one of the leading causes of disease burden worldwide. It represents a source of signi cant su ering and disability to the a ected individuals, and is associated with substantial societal and economical costs. The diagnosis of schizophrenia still depends exclusively on the detection of symptoms that are also present in other mental disorders. This situation causes overlapping of the boundaries of the diagnostic categories and constitutes a source of diagnostic errors. Moreover, current treatment algorithms do not take into account the substantial interindi- vidual variability in response to antipsychotic drugs. As a result, around one-third of patients are treatment-resistant to rst line antipsychotic drugs. This deleterious consequence is associated with poor individual outcomes and elevated healthcare costs. Neuroimaging research in schizophrenia has shed some light in a vast array of structural and functional connectivity abnormalities and neurochemical (dopamine and glutamate) imbalances, which may constitute ‘organic surrogates’ of this disorder. However, the neuroimaging eld, so far, has not been able to identify biomarkers that could facilitate early detection and allow individualised treatment management. This paper reviews neuroimaging studies from di erent modalities that may provide relevant biomarkers for schizo- phrenia. We discuss how the current application of novel Machine Learning methods to the analyses of imaging data is allowing the translation of such ndings into potential biomarkers enabling the prediction of clinical outcomes at the individual level, towards the development of innovative and personalised treatment strategies.por
dc.language.isoengpor
dc.publisherARC Publishingpor
dc.rightsopenAccesspor
dc.subjectSchizophreniapor
dc.subjectMachine-Learningpor
dc.subjectBiomarkerspor
dc.subjectNeuroimagingpor
dc.titleThe quest for biomarkers in Schizophrenia: from neuroimaging to machine learningpor
dc.typearticle-
degois.publication.issue4(Suppl.3)por
degois.publication.locationPortopor
degois.publication.titleClinical Neurosciences and Mental Healthpor
dc.peerreviewedyespor
dc.identifier.doi10.21035/ijcnmh.2017.4(Suppl.3).S03por
degois.publication.volumeS03por
item.languageiso639-1en-
item.fulltextCom Texto completo-
item.grantfulltextopen-
crisitem.author.researchunitCenter for Research in Neuropsychology and Cognitive Behavioral Intervention-
crisitem.author.orcid0000-0002-5218-0600-
crisitem.author.orcid0000-0001-5009-8841-
Appears in Collections:I&D CINEICC - Artigos em Revistas Nacionais
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