PT  - JOURNAL ARTICLE
AU  - Tomas Hajek
AU  - Christopher Cooke
AU  - Miloslav Kopecek
AU  - Tomas Novak
AU  - Cyril Hoschl
AU  - Martin Alda
TI  - Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study
AID  - 10.1503/jpn.140142
DP  - 2015 Sep 01
TA  - Journal of Psychiatry and Neuroscience
PG  - 316--324
VI  - 40
IP  - 5
4099  - http://jpn.ca/content/40/5/316.short
4100  - http://jpn.ca/content/40/5/316.full
SO  - J Psychiatry Neurosci2015 Sep 01; 40
AB  - Background: 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.Methods: We studied unaffected and affected relatives of BD probands recruited from 2 sites (Halifax, Canada, and Prague, Czech Republic). Each participant was individually matched by age and sex to controls without personal or family history of psychiatric disorders. We applied support vector machines (SVM) and Gaussian process classifiers (GPC) to structural MRI.Results: We included 45 unaffected and 36 affected relatives of BD probands matched by age and sex on an individual basis to healthy controls. The SVM of white matter distinguished unaffected HR from control participants (accuracy = 68.9%, p = 0.001), with similar accuracy for the GPC (65.6%, p = 0.002) or when analyzing data from each site separately. Differentiation of the more clinically heterogeneous affected familiar group from healthy controls was less accurate (accuracy = 59.7%, p = 0.05). Machine learning applied to grey matter did not distinguish either the unaffected HR or affected familial groups from controls. The regions that most contributed to between-group discrimination included white matter of the inferior/middle frontal gyrus, inferior/middle temporal gyrus and precuneus.Limitations: Although we recruited 126 participants, ML benefits from even larger samples.Conclusions: Machine learning applied to white but not grey matter distinguished unaffected participants at high and low genetic risk for BD based on regions previously implicated in the pathophysiology of BD.