Genetic, individual, and familial risk correlates of brain network controllability in major depressive disorder

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.

Network controllability measures of subnetworks: implications for neurosciences

“…In neuroscience, network control theory is increasingly used [10,18,19,21,39], for good reasons, to study neural, biological, and psychological constructs. It relates fundamental theory-driven results from controls literature to the study of networks that are the natural points of interest in neurosciences [14].…”

“…For this and similar questions, assuming linear temporal dynamics, network control theory provides a set of measures that mathematically quantify the relevance of the nodes [17]. Specifically, average-, and modal-controllability are among the most frequently-used measures in neurosciences to study neural [9,10,[18][19][20] as well as psychological symptom dynamics [21,22] and are suggested to measure the average ability of nodes to affect network dynamics (see Karrer et al for a detailed mathematical introduction [23]).…”

Thalamic network controllability changes and cognitive impairment in multiple sclerosis

“…11,47 These findings strongly implicate thalamic changes as promising biomarkers for cognitive dysfunction in people with MS. Despite the lack of previous studies exploring network controllability changes in cognitive impairment in MS, network controllability measures have already been recognized as a powerful tool to identify disease-related changes in various conditions [24][25][26][27] and explain cognitive performance in both health and disease. 22,[49][50][51] Our present study originally explored network controllability alterations in MS, and revealed the specific effect that the subcortical network and particularly the thalamus has on driving network changes across the whole brain in MS and CIMS.…”

“…This approach allows us to move beyond determining whether there are changes in the functional connectivity of the brain, towards identifying which brain regions or networks are driving those changes and influencing the transition of brain states. Network controllability analysis has been validated as a powerful tool in exploring biomarkers in multiple neurological and neuropsychological diseases, including explaining the emergence of visual hallucinations in Parkinson’s Disease, 24 reflecting the genetic, individual and familial risk in major depressive disorder, 25 explaining psychopathological symptoms in schizophrenia, 26 and predicting positive syndrome in psychosis spectrum. 27 In MS, a relevant measure, the energy required by brain state transitions, has proven to be useful for distinguishing patients in different disability status.…”

Long-term abstinence reduces optimal energy required for transitions from the exogenous networks to the frontoparietal network in methamphetamine addicts

“…Subsequently, we need to study and quantify the causal relationship between structure and brain functional states. Network control theory (NCT) (Gu et al 2015; Gu et al 2017) is a powerful computational framework to characterize how structural connectivity constrains the brain state transitions and has exhibited its utility in psychiatric diseases (Parkes et al 2021; Hahn et al 2023) and cognitive function (Cui et al 2020; Stiso et al 2019). Here, we applied NCT constrained by the Brainnetome atlas (Fan et al 2016) to explore the impact of long-term abstinence on brain state transitions constrained by empirical structural connectome and explored the contribution of 19 receptors and transporters (Hansen et al 2022) to the spatial distribution of influence on brain regions resulting from long-term abstinence.…”