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Distinctive Neuroanatomical Substrates for Depression in Bipolar Disorder versus Major Depressive Disorder.


ABSTRACT: No neuroanatomical substrates for distinguishing between depression of bipolar disorder (dBD) and major depressive disorder (dMDD) are currently known. The aim of the current multicenter study was to identify neuroanatomical patterns distinct to depressed patients with the two disorders. Further analysis was conducted on an independent sample to enable generalization of results. We directly compared MR images of these subjects using voxel-based morphometry (VBM) and a support vector machine (SVM) algorithm using 1531 participants. The VBM analysis showed significantly reduced gray matter volumes in the bilateral dorsolateral prefrontal (DLPFC) and anterior cingulate cortices (ACC) in patients with dBD compared with those with dMDD. Patients with the two disorders shared small gray matter volumes for the right ACC and left inferior frontal gyrus when compared with healthy subjects. Voxel signals in these regions during SVM analysis contributed to an accurate classification of the two diagnoses. The VBM and SVM results in the second cohort also supported these results. The current findings provide new evidence that gray matter volumes in the DLPFC and ACC are core regions in displaying shared and distinct neuroanatomical substrates and can shed light on elucidation of neural mechanism for depression within the bipolar/major depressive disorder continuum.

SUBMITTER: Matsuo K 

PROVIDER: S-EPMC6294408 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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No neuroanatomical substrates for distinguishing between depression of bipolar disorder (dBD) and major depressive disorder (dMDD) are currently known. The aim of the current multicenter study was to identify neuroanatomical patterns distinct to depressed patients with the two disorders. Further analysis was conducted on an independent sample to enable generalization of results. We directly compared MR images of these subjects using voxel-based morphometry (VBM) and a support vector machine (SVM  ...[more]

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