Genetic, individual, and familial risk correlates of brain network controllability in major depressive disorder
Tim Hahn, Nils R. Winter, Jan Ernsting, Marius Gruber, Marco Mauritz, L. Fisch, Ramona Leenings, Kelvin Sarink, Julian Blanke, Vincent Holstein, Daniel Emden, Marie Beisemann, Nils Opel, Dominik Grotegerd, Susanne Meinert, Walter Heindel, Stephanie H. Witt, Marcella Rietschel, Markus M. Nöthen, Andreas J. Forstner, Tilo Kircher, Igor Nenadić, Andreas Jansen, Bertram Müller‐Myhsok, Till F. M. Andlauer, Martin Walter, Martijn P. van den Heuvel, Hamidreza Jamalabadi, Udo Dannlowski, Jonathan Repple
Abstract
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.