Identifying both the commonalities and differences in brain structures among psychiatric disorders is important for understanding the pathophysiology. Recently, the ENIGMA-Schizophrenia DTI Working Group performed a large-scale meta-analysis and reported widespread white matter microstructural alterations in schizophrenia; however, no similar crossdisorder study has been carried out to date. Here, we conducted mega-analyses comparing white matter microstructural differences between healthy comparison subjects (HCS; N = 1506) and patients with schizophrenia (N = 696), bipolar disorder (N = 211), autism spectrum disorder (N = 126), or major depressive disorder (N = 398; total N = 2937 from 12 sites). In comparison with HCS, we found that schizophrenia, bipolar disorder, and autism spectrum disorder share similar white matter microstructural differences in the body of the corpus callosum; schizophrenia and bipolar disorder featured comparable changes in the limbic system, such as the fornix and cingulum. By comparison, alterations in tracts connecting neocortical areas, such as the uncinate fasciculus, were observed only in schizophrenia. No significant difference was found in major depressive disorder. In a direct comparison between schizophrenia and bipolar disorder, there were no significant differences. Significant differences between schizophrenia/bipolar disorder and major depressive disorder were found in the limbic system, which were similar to the differences in schizophrenia and bipolar disorder relative to HCS. While schizophrenia and bipolar disorder may have similar pathological characteristics, the biological characteristics of major depressive disorder may be close to those of HCS. Our findings provide insights into nosology and encourage further investigations of shared and unique pathophysiology of psychiatric disorders.

Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable marker of major depressive disorder (MDD) that would distinguish patients from healthy controls based on resting-state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD classifier. The classifier achieved an approximately 70% generalization accuracy for an independent validation dataset with 521 participants from 5 different imaging sites. The successful generalization to a perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability.

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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) 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.