Edge-centric network reveals altered functional integration and dispersion in major depressive disorder

Area of Science:

• Neuroscience
• Computational Psychiatry
• Network Science

Background:

• Brain networks are typically studied using node-centric approaches.
• Understanding functional connectivity requires examining higher-order network properties.

Purpose of the Study:

• To investigate major depressive disorder (MDD) using an edge-centric network analysis.
• To identify novel biomarkers for MDD by assessing network perturbations.

Main Methods:

• Mapped edge-centric networks from resting-state fMRI data in 400 MDD patients and 441 controls.
• Computed edge-community normalized entropy and edge functional connectivity (eFC).
• Analyzed differences between MDD patients and healthy controls, including first-episode drug-naive and recurrent patient subgroups.

Main Results:

• MDD patients exhibited increased entropy in subcortical and frontoparietal networks and decreased eFC in visual and sensory-motor networks compared to controls.
• These alterations were less pronounced in first-episode drug-naive patients but evident in recurrent patients.
• Subcortical network entropy correlated positively with depression severity; visual network eFC correlated negatively with depression and anxiety scores.

Conclusions:

• Edge-centric network analysis reveals spatiotemporal flexibility and diversity alterations in MDD.
• Findings suggest potential novel biomarkers for MDD based on high-order network properties.
• Abnormalities in network entropy and eFC are associated with depression severity and chronicity.