Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder

Abstract

Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. In contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. One hundred and eighty-two MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) to resting-state functional magnetic resonance imaging (fMRI) data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Network properties including global efficiency, local efficiency, node strength and harmonic centrality, were calculated for each subject. Five dynamic functional states were identified, three of which demonstrated significant group differences in their percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected State 2, which includes regions previously associated with self-focused thinking, a representative feature of depression. In addition, the FNCs in MDD were connected differently in different states, especially among prefrontal, sensorimotor, and cerebellum networks. MDD patients exhibited significantly reduced harmonic centrality primarily involving parietal lobule, lingual gyrus and thalamus. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, and also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in MDD in a Chinese population using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder.

Large‐scale dynamic causal modeling of major depressive disorder based on resting‐state functional magnetic resonance imaging

“…They observed that MDD patients spent much more time in a weekly-connected State 2, which involved DMN regions and exhibited significantly decreased harmonic centrality involving the parietal lobule, lingual gyrus, and thalamus. The reduced dFC in DMN reported by the two studies (Kaiser et al, 2016;Zhi et al, 2018) is in agreement with our findings that MDD is characterized by lower EC in DMN. In another graph theory study, Zheng et al (2015) reported that the connectivity degree was increased for the right anterior insula and decreased between the DMN and EXE in MDD, which supported the triple network model (Menon, 2011).…”

“…Zhi et al (2018) applied graph theory to examine the impaired topological organization of dFC in MDD and identified five dynamic functional states, three of which showed significant group differences. Zhi et al (2018) applied graph theory to examine the impaired topological organization of dFC in MDD and identified five dynamic functional states, three of which showed significant group differences.…”

“…FC between mPFC and insula, the latter of which was related to higher levels of recent rumination. Zhi et al (2018) applied graph theory to examine the impaired topological organization of dFC in MDD and identified five dynamic functional states, three of which showed significant group differences. They observed that MDD patients spent much more time in a weekly-connected State 2, which involved DMN regions and exhibited significantly decreased harmonic centrality involving the parietal lobule, lingual gyrus, and thalamus.…”

Tools of the trade: Estimating time-varying connectivity patterns from fMRI data

“…In another clinical condition, a study found that individuals with major depressive disorder (MDD) more time in a state with weak functional connectivity strength across the brain (weakly-connected dFC state) associated with self-focused thinking (Zhi et al, 2018). Atypical dFC patterns were significantly correlated with both depressive symptoms and cognitive performance (Zhi et al, 2018).…”

Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach

“…In particular, Kaiser et al (2016) found that meditation in depressed patients is associated with abnormal communication patterns of brain fluctuations (Kaiser et al, 2016). Zhi et al (2018) used the sliding-window algorithm to identify three types of node damage which were related to the severity of depressive symptoms and cognitive ability. Wang et al (2019) found decreased DFC variability between the anterior DMN and the right CEN compared with controls (Wang et al, 2019).…”