TY - JOUR
T1 - Genetic, individual, and familial risk correlates of brain network controllability in major depressive disorder
AU - Hahn, Tim
AU - Winter, Nils R.
AU - Ernsting, Jan
AU - Gruber, Marius
AU - Mauritz, Marco J.
AU - Fisch, Lukas
AU - Leenings, Ramona
AU - Sarink, Kelvin
AU - Blanke, Julian
AU - Holstein, Vincent
AU - Emden, Daniel
AU - Beisemann, Marie
AU - Opel, Nils
AU - Grotegerd, Dominik
AU - Meinert, Susanne
AU - Heindel, Walter
AU - Witt, Stephanie
AU - Rietschel, Marcella
AU - Nöthen, Markus M.
AU - Forstner, Andreas J.
AU - Kircher, Tilo
AU - Nenadic, Igor
AU - Jansen, Andreas
AU - Müller-Myhsok, Bertram
AU - Andlauer, Till F.M.
AU - Walter, Martin
AU - van den Heuvel, Martijn P.
AU - Jamalabadi, Hamidreza
AU - Dannlowski, Udo
AU - Repple, Jonathan
AU - N?then, Markus M.
AU - M?ller-Myhsok, Bertram
N1 - Funding Information: This work was funded by the German Research Foundation (DFG grants HA7070/2-2, HA7070/3, HA7070/4 to TH) and the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Münster (grants Dan3/012/17 to UD, SEED 11/19 to NO, and MzH 3/020/20 to TH). HJ was supported by body jgrant of Medical Faculty of University of Tübingen (No. 2487‐1‐0). The MACS dataset used in this work is part of the German multicenter consortium “Neurobiology of Affective Disorders. A translational perspective on brain structure and function“, funded by the German Research Foundation (Deutsche Forschungsgemeinschaft DFG; Forschungsgruppe/Research Unit FOR2107). Principal investigators (PIs) with respective areas of responsibility in the FOR2107 consortium are: Work Package WP1, FOR2107/MACS cohort and brainimaging: TK (speaker FOR2107; DFG grant numbers KI 588/14-1, KI 588/14-2), UD (co-speaker FOR2107; DA 1151/5-1, DA 1151/5-2), Axel Krug (KR 3822/5-1, KR 3822/7-2), IN (NE 2254/1-2), Carsten Konrad (KO 4291/3-1). WP2, animal phenotyping: Markus Wöhr (WO 1732/4-1, WO 1732/4-2), Rainer Schwarting (SCHW 559/14-1, SCHW 559/14-2). WP3, miRNA: Gerhard Schratt (SCHR 1136/3-1, 1136/3-2). WP4, immunology, mitochondriae: Judith Alferink (AL 1145/5-2), Carsten Culmsee (CU 43/9-1, CU 43/9-2), Holger Garn (GA 545/5-1, GA 545/7-2). WP5, genetics: Marcella Rietschel (RI 908/11-1, RI 908/11-2), MN (NO 246/10-1, NO 246/10-2), Stephanie Witt (WI 3439/3-1, WI 3439/3-2). WP6, multi method data analytics: Andreas Jansen (JA 1890/7-1, JA 1890/7-2), Tim Hahn (HA 7070/2-2), Bertram Müller-Myhsok (MU1315/8-2), Astrid Dempfle (DE 1614/3-1, DE 1614/3-2). CP1, biobank: Petra Pfefferle (PF 784/1-1, PF 784/1-2), Harald Renz (RE 737/20-1, 737/20-2). CP2, administration. TK (KI 588/15-1, KI 588/17-1), UD (DA 1151/6-1), Carsten Konrad (KO 4291/4-1). Open Access funding enabled and organized by Projekt DEAL. Publisher Copyright: © 2023, The Author(s).
PY - 2023/3
Y1 - 2023/3
N2 - Many therapeutic interventions in psychiatry can be viewed as attempts to influence the brain’s large-scale, dynamic network state transitions. Building on connectome-based graph analysis and control theory, Network Control Theory is emerging as a powerful tool to quantify network controllability—i.e., the influence of one brain region over others regarding dynamic network state transitions. If and how network controllability is related to mental health remains elusive. Here, from Diffusion Tensor Imaging data, we inferred structural connectivity and inferred calculated network controllability parameters to investigate their association with genetic and familial risk in patients diagnosed with major depressive disorder (MDD, n = 692) and healthy controls (n = 820). First, we establish that controllability measures differ between healthy controls and MDD patients while not varying with current symptom severity or remission status. Second, we show that controllability in MDD patients is associated with polygenic scores for MDD and psychiatric cross-disorder risk. Finally, we provide evidence that controllability varies with familial risk of MDD and bipolar disorder as well as with body mass index. In summary, we show that network controllability is related to genetic, individual, and familial risk in MDD patients. We discuss how these insights into individual variation of network controllability may inform mechanistic models of treatment response prediction and personalized intervention-design in mental health.
AB - Many therapeutic interventions in psychiatry can be viewed as attempts to influence the brain’s large-scale, dynamic network state transitions. Building on connectome-based graph analysis and control theory, Network Control Theory is emerging as a powerful tool to quantify network controllability—i.e., the influence of one brain region over others regarding dynamic network state transitions. If and how network controllability is related to mental health remains elusive. Here, from Diffusion Tensor Imaging data, we inferred structural connectivity and inferred calculated network controllability parameters to investigate their association with genetic and familial risk in patients diagnosed with major depressive disorder (MDD, n = 692) and healthy controls (n = 820). First, we establish that controllability measures differ between healthy controls and MDD patients while not varying with current symptom severity or remission status. Second, we show that controllability in MDD patients is associated with polygenic scores for MDD and psychiatric cross-disorder risk. Finally, we provide evidence that controllability varies with familial risk of MDD and bipolar disorder as well as with body mass index. In summary, we show that network controllability is related to genetic, individual, and familial risk in MDD patients. We discuss how these insights into individual variation of network controllability may inform mechanistic models of treatment response prediction and personalized intervention-design in mental health.
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U2 - https://doi.org/10.1038/s41380-022-01936-6
DO - https://doi.org/10.1038/s41380-022-01936-6
M3 - Article
C2 - 36639510
SN - 1359-4184
VL - 28
SP - 1057
EP - 1063
JO - Molecular psychiatry
JF - Molecular psychiatry
IS - 3
ER -