In a recent study published in Nature, researchers used Diffusion Tensor Imaging (DTI) data (a type of MRI scan) to investigate the association of genetic and familial risk of major depressive disorder with network controllability. The results of the study show that network controllability is related to genetic, individual, and familial risk in MDD patients, which could inform how patients respond to treatment.
What is network controllability?
Complex network theory sees the brain as a dynamic network between different regions of the brain. Different types of analysis, termed connectome analysis, have allowed researchers to further our understanding of the topological organisation of the brain. However, it does not allow for active manipulation and control of the brain. Most therapeutic interventions for mental health treatment, both medication and psychotherapy, involve altering or controlling the large-scale dynamics of the brain.
Network Control Theory is a tool used in psychiatry to quantify network controllability – i.e., the influence of one brain region over others in terms of their activity, interactions with other networks, and structure. How network controllability is related to mental health has been investigated previously. For example, Jeganathan et al. showed altered controllability in young people with bipolar disorder, Braun et al. showed the same in patients with schizophrenia, and Kenett et al. showed regional associations between controllability and mild depressive symptoms. In this study, researchers set out to comprehensively characterise variation in network controllability based on demographic, disease-related, genetic, personal, and familial risk in Major Depressive Disorder (MDD).
Measuring controllability
To understand and investigate how different factors such as age, gender, genetics, family history and body mass index (BMI) might alter network controllability in individuals with MDD (compared to healthy individuals), researchers first had to quantify network controllability. They did this by first taking DTI data and using it to define anatomical brain networks by dividing the brain into 114 anatomically distinct regions. They then used a simplified model of brain dynamics to quantify network controllability over time, both in the whole brain and in the different regions (as summarised in Figure 1).
The researchers then compared network controllability between MDD patients and healthy individuals and tested whether these measures varied with age, gender, current symptom severity, or remission status. They then assessed whether network controllability in MDD patients was associated with polygenic scores for MDD, Bipolar Disorder, and psychiatric cross-disorder risk as well as with familial risk of MDD and bipolar disorder. Finally, they quantified the effects of body mass index on network controllability in MDD.
The findings
The study found that controllability measures differ between healthy controls and MDD patients, and that modal and average controllability in MDD patients, could be predicted based on polygenic scores for MDD and psychiatric cross-disorder risk, as well as associations with familial risk of MDD and bipolar disorder. The study also found that controllability varies with body mass index.
Altogether, this suggests that individual differences in demographic, disease-related, genetic, individual, and familial risk factors are associated with network controllability. The study also found that gender and age affected controllability measures – this is notable as women are disproportionally affected by MDD.
They also found that the associations identified mainly found in whole-brain (rather than region-specific) network controllability. This suggests subtle changes in how not only single regions, but a larger set of regions in the brain can drive state transitions – which is novel as previous studies have restricted their investigations to a set of 30 regions, so may have missed whole-brain effects. In summary, these findings suggest that Network Control Theory could more effectively guide therapeutic interventions than current approaches.
