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Shared and specific patterns of structural brain connectivity across affective and psychotic disorders

      Abstract

      Background

      Altered brain structural connectivity has been implicated in the pathophysiology of psychiatric disorders including schizophrenia (SZ), bipolar disorder (BD) and major depressive disorder (MDD). However, it is unknown which part of these connectivity abnormalities are disorder-specific and which are shared across the spectrum of psychotic and affective disorders. We investigated common and distinct brain connectivity alterations in a large sample (n= 1743) of SZ, BD, and MDD patients and healthy controls (HC).

      Methods

      This study examines diffusion-weighted-imaging-based structural connectome topology in 720 MDD patients, 112 BD patients, 69 SZ patients and 842 healthy controls (mean age of all subjects: 35.7 years). Graph theory-based network analysis was employed to investigate connectome organization. Machine learning algorithms were trained to classify groups based on their structural connectivity matrices.

      Results

      Groups differed significantly in the network metrics global efficiency, clustering, present edges, and global connectivity strength with a converging pattern of alterations between diagnoses (e.g., efficiency: HC>MDD>BD>SZ, pFDR = .028). Subnetwork analysis revealed a common core of edges that were affected across all three disorders, but also revealed differences between disorders. Machine learning algorithms could not discriminate between disorders, but could discriminate each diagnosis from healthy controls. Furthermore, dysconnectivity patterns were found most pronounced in patients with an early disease onset irrespective of diagnosis.

      Conclusion

      We demonstrate shared and specific signatures of structural white matter dysconnectivity in SZ, BD, and MDD, leading to commonly reduced network efficiency. These results demonstrate compromised brain communication across a spectrum of major psychiatric disorders.

      Key words

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