Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.4/2175
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dc.contributor.authorBajouco, M-
dc.contributor.authorMota, D-
dc.contributor.authorCoroa, M-
dc.contributor.authorCaldeira, S-
dc.contributor.authorSantos, V-
dc.contributor.authorMadeira, N-
dc.date.accessioned2018-11-08T17:06:48Z-
dc.date.available2018-11-08T17:06:48Z-
dc.date.issued2017-
dc.identifier.citationInt J Clin Neurosci Mental Health. 2017; 4(Suppl. 3):S03.pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.4/2175-
dc.description.abstractSchizophrenia is a severe mental disorder and one of the leading causes of disease burden worldwide. It represents a source of significant suffering and disability to the affected 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 interindividual variability in response to antipsychotic drugs. As a result, around one-third of patients are treatment-resistant to first 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 field, 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 different modalities that may provide relevant biomarkers for schizophrenia. We discuss how the current application of novel Machine Learning methods to the analyses of imaging data is allowing the translation of such findings into potential biomarkers enabling the prediction of clinical outcomes at the individual level, towards the development of innovative and personalised treatment strategies.pt_PT
dc.language.isoengpt_PT
dc.rightsopenAccesspt_PT
dc.subjectBiomarcadorespt_PT
dc.subjectEsquizofreniapt_PT
dc.titleThe quest for biomarkers in schizophrenia: from neuroimaging to machine learning.pt_PT
dc.typearticlept_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.peerreviewedyespt_PT
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