A Computational Network Control Theory Analysis of Depression Symptoms

Abstract

Rumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory (NCT) approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how NCT relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and executive control networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.

Assessment of Disrupted Brain Structural Connectome in Depressive Patients With Suicidal Ideation Using Generalized Q-Sampling MRI

“…Neuropsychiatric disorders are considered to be dysconnectivity syndromes, and graph theory has previously been used to quantify the variations in functional and structural network properties, which can be used to explore various disorders (Bullmore and Sporns, 2009;Nakamura et al, 2009;Kim et al, 2019). A previous DTI study found that depression is correlated with complex brain networks by network controllability analysis, yet the chosen partitions of brain modularity might bias its result (Kenett et al, 2018). Hence, we adopted graph theoretical analysis to investigate network alternations.…”

The missing role of gray matter in studying brain controllability

“…This approach has the potential to inform theories of dynamic cognitive processes, clinical neuroscience, neurodegeneration, and brain reserve. Specifically, there is evidence that these global brain state transitions are impaired in clinical populations (Braun et al, 2019 ; Jeganathan et al, 2018 ; Kenett, Beaty, & Medaglia, 2018 ) and that such impairments can be traced back to specific driver nodes (Jeganathan et al, 2018 ; Kenett, Beaty, et al, 2018 ; Muldoon et al, 2016 ; Zoeller et al, 2019 ). However, thus far, these control properties have been exclusively derived from WM fiber tracts without the consideration of gray matter (GM) properties.…”

Language Tasks and the Network Control Role of the Left Inferior Frontal Gyrus

“…In a broad sense, cognitive control in the language domain is a special case of a network control problem for the brain (Medaglia, 2019): how does the brain achieve the neural states necessary to produce context-appropriate responses? Since the first theoretical network controllability analyses in large scale diffusion MRI networks (Gu et al, 2015), NCT has been used to characterize the energy required to integrate or segregate network activity (Betzel et al, 2016;Gu et al, 2017;Tang et al, 2017;Wu-Yan et al, 2018), identify correlates of cognitive function in and out of the executive domain (Kenett et al, 2018a(Kenett et al, , 2018bCornblath et al, 2019;Lee et al, 2019), and predict or correlate the effects of brain stimulation on the brain and behavior (Khambhati et al, 2019;Medaglia et al, 2018a;Beynel et al, 2019;Stiso et al, 2019).…”