  Background: A therapeutic intervention in psychiatry can be viewed as an
attempt to influence the brain's large-scale, dynamic network state transitions
underlying cognition and behavior. 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.
  Methods: 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).
  Results: 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.
  Conclusions: 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.
