Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders

Jonathan Repple, Marius Gruber, Marco Mauritz, Siemon C de Lange, Nils Ralf Winter, Nils Opel, Janik Goltermann, Susanne Meinert, Dominik Grotegerd, Elisabeth J Leehr, Verena Enneking, Tiana Borgers, Melissa Klug, Hannah Lemke, Lena Waltemate, Katharina Thiel, Alexandra Winter, Fabian Breuer, Pascal Grumbach, Hannes HofmannFrederike Stein, Katharina Brosch, Kai G Ringwald, Julia Pfarr, Florian Thomas-Odenthal, Tina Meller, Andreas Jansen, Igor Nenadic, Ronny Redlich, Jochen Bauer, Tilo Kircher, Tim Hahn, Martijn van den Heuvel, Udo Dannlowski

Onderzoeksoutput: Bijdrage aan wetenschappelijk tijdschrift/periodieke uitgaveArtikelWetenschappelijkpeer review

Samenvatting

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 patients with SZ, BD, or MDD and healthy control (HC) subjects.

METHODS: This study examined diffusion-weighted imaging-based structural connectome topology in 720 patients with MDD, 112 patients with BD, 69 patients with SZ, and 842 HC subjects (mean age of all subjects: 35.7 years). Graph theory-based network analysis was used 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, false discovery rate-corrected p = .028). Subnetwork analysis revealed a common core of edges that were affected across all 3 disorders, but also revealed differences between disorders. Machine learning algorithms could not discriminate between disorders but could discriminate each diagnosis from HC. Furthermore, dysconnectivity patterns were found most pronounced in patients with an early disease onset irrespective of diagnosis.

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

Originele taal-2Engels
Pagina's (van-tot)178-186
TijdschriftBiological Psychiatry
Volume93
DOI's
StatusGepubliceerd - 2023

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