TY - JOUR
T1 - Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders
AU - Repple, Jonathan
AU - Gruber, Marius
AU - Mauritz, Marco
AU - de Lange, Siemon C
AU - Winter, Nils Ralf
AU - Opel, Nils
AU - Goltermann, Janik
AU - Meinert, Susanne
AU - Grotegerd, Dominik
AU - Leehr, Elisabeth J
AU - Enneking, Verena
AU - Borgers, Tiana
AU - Klug, Melissa
AU - Lemke, Hannah
AU - Waltemate, Lena
AU - Thiel, Katharina
AU - Winter, Alexandra
AU - Breuer, Fabian
AU - Grumbach, Pascal
AU - Hofmann, Hannes
AU - Stein, Frederike
AU - Brosch, Katharina
AU - Ringwald, Kai G
AU - Pfarr, Julia
AU - Thomas-Odenthal, Florian
AU - Meller, Tina
AU - Jansen, Andreas
AU - Nenadic, Igor
AU - Redlich, Ronny
AU - Bauer, Jochen
AU - Kircher, Tilo
AU - Hahn, Tim
AU - van den Heuvel, Martijn
AU - Dannlowski, Udo
N1 - Copyright © 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
U2 - 10.1016/j.biopsych.2022.05.031
DO - 10.1016/j.biopsych.2022.05.031
M3 - Article
C2 - 36114041
SN - 0006-3223
VL - 93
SP - 178
EP - 186
JO - Biological Psychiatry
JF - Biological Psychiatry
ER -