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

Area of Science:

• Neuroscience
• Psychiatry
• Medical Imaging

Background:

• Major depressive disorder (MDD) is a complex mood disorder with known abnormalities in large-scale brain networks.
• Previous research primarily focused on static functional connectivity, overlooking the dynamic nature of brain activity in MDD.

Purpose of the Study:

• To investigate the disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD using graph theory.
• To explore the relationship between altered dFNC, depressive symptom severity, and cognitive performance in a Chinese population.

Main Methods:

• Utilized resting-state functional magnetic resonance imaging (fMRI) data from 182 MDD patients and 218 healthy controls.
• Applied group information guided independent component analysis (GIG-ICA) and a sliding window method with k-means clustering to estimate dFNC.
• Calculated network properties (global efficiency, local efficiency, node strength, harmonic centrality) for each subject.

Main Results:

• Identified five dynamic functional states, with three showing significant group differences in occurrence.
• MDD patients spent more time in a weakly-connected state (State 2) associated with self-focused thinking.
• Observed altered functional connectivity within prefrontal, sensorimotor, and cerebellum networks in MDD, alongside reduced harmonic centrality in specific brain regions.

Conclusions:

• This study provides novel evidence for aberrant time-varying brain activity and network disruptions in MDD within a Chinese population.
• Disrupted dynamic functional network connectivity is associated with depressive symptom severity and cognitive impairments in MDD.
• Findings highlight the importance of considering dynamic network alterations in understanding the neurobiology of MDD.