Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study | Zendy

Tomáš Hájek | Zendy; Christopher Cooke | Zendy; Miloslav Kopeček | Zendy; Tomáš Novák | Zendy; Cyril Höschl | Zendy; Martin Alda | Zendy

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Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study

Author(s) -

Tomáš Hájek,

Christopher Cooke,

Miloslav Kopeček,

Tomáš Novák,

Cyril Höschl,

Martin Alda

Publication year - 2015

Publication title -

journal of psychiatry and neuroscience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.767

H-Index - 99

eISSN - 1488-2434

pISSN - 1180-4882

DOI - 10.1503/jpn.140142

Subject(s) - neuroimaging , proband , grey matter , precuneus , white matter , inferior frontal gyrus , psychology , bipolar disorder , cohort , medicine , magnetic resonance imaging , clinical psychology , psychiatry , cognition , biology , genetics , mutation , gene , radiology

Brain imaging is of limited diagnostic use in psychiatry owing to clinical heterogeneity and low sensitivity/specificity of between-group neuroimaging differences. Machine learning (ML) may better translate neuroimaging to the level of individual participants. Studying unaffected offspring of parents with bipolar disorders (BD) decreases clinical heterogeneity and thus increases sensitivity for detection of biomarkers. The present study used ML to identify individuals at genetic high risk (HR) for BD based on brain structure.

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