Genetic, Individual, and Familial Risk Correlates of Brain Network Controllability in Major Depressive Disorder

TL;DR

This study investigates how brain network controllability relates to genetic and familial risk factors in major depressive disorder, revealing differences between patients and controls and associations with genetic scores and family history.

Contribution

It demonstrates that network controllability measures differ in MDD patients and are linked to genetic and familial risk factors, advancing understanding of neurobiological underpinnings of depression.

Findings

Controllability differs between MDD patients and healthy controls.

Controllability is associated with polygenic scores for MDD.

Controllability varies with familial risk of MDD and bipolar disorder.

Abstract

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.