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 Hofmann; Frederike 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

doi:10.1016/j.biopsych.2022.05.031

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

Netherlands Institute for Neuroscience (NIN)

Research output: Contribution to journal/periodical › Article › Scientific › peer-review

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 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.

Original language	English
Pages (from-to)	178-186
Journal	Biological Psychiatry
Volume	93
DOIs	https://doi.org/10.1016/j.biopsych.2022.05.031
Publication status	Published - 2023

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10.1016/j.biopsych.2022.05.031

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Repple, J., Gruber, M., Mauritz, M., de Lange, S. C., Winter, N. R., Opel, N., Goltermann, J., Meinert, S., Grotegerd, D., Leehr, E. J., Enneking, V., Borgers, T., Klug, M., Lemke, H., Waltemate, L., Thiel, K., Winter, A., Breuer, F., Grumbach, P., ... Dannlowski, U. (2023). Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders. Biological Psychiatry, 93, 178-186. https://doi.org/10.1016/j.biopsych.2022.05.031

Repple, Jonathan ; Gruber, Marius ; Mauritz, Marco ; de Lange, Siemon C ; Winter, Nils Ralf ; Opel, Nils ; Goltermann, Janik ; Meinert, Susanne ; Grotegerd, Dominik ; Leehr, Elisabeth J ; Enneking, Verena ; Borgers, Tiana ; Klug, Melissa ; Lemke, Hannah ; Waltemate, Lena ; Thiel, Katharina ; Winter, Alexandra ; Breuer, Fabian ; Grumbach, Pascal ; Hofmann, Hannes ; Stein, Frederike ; Brosch, Katharina ; Ringwald, Kai G ; Pfarr, Julia ; Thomas-Odenthal, Florian ; Meller, Tina ; Jansen, Andreas ; Nenadic, Igor ; Redlich, Ronny ; Bauer, Jochen ; Kircher, Tilo ; Hahn, Tim ; van den Heuvel, Martijn ; Dannlowski, Udo. / Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders. In: Biological Psychiatry. 2023 ; Vol. 93. pp. 178-186.

@article{756df357af734397a15da62ece824ec9,

title = "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 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.",

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

year = "2023",

doi = "10.1016/j.biopsych.2022.05.031",

language = "English",

volume = "93",

pages = "178--186",

journal = "Biological Psychiatry",

issn = "0006-3223",

publisher = "Elsevier USA",

}

Repple, J, Gruber, M, Mauritz, M, de Lange, SC, Winter, NR, Opel, N, Goltermann, J, Meinert, S, Grotegerd, D, Leehr, EJ, Enneking, V, Borgers, T, Klug, M, Lemke, H, Waltemate, L, Thiel, K, Winter, A, Breuer, F, Grumbach, P, Hofmann, H, Stein, F, Brosch, K, Ringwald, KG, Pfarr, J, Thomas-Odenthal, F, Meller, T, Jansen, A, Nenadic, I, Redlich, R, Bauer, J, Kircher, T, Hahn, T, van den Heuvel, M & Dannlowski, U 2023, 'Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders', Biological Psychiatry, vol. 93, pp. 178-186. https://doi.org/10.1016/j.biopsych.2022.05.031

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

In: Biological Psychiatry, Vol. 93, 2023, p. 178-186.

Research output: Contribution to journal/periodical › Article › Scientific › peer-review

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

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

VL - 93

SP - 178

EP - 186

JO - Biological Psychiatry

JF - Biological Psychiatry

SN - 0006-3223

ER -

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

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