Functional brain abnormalities in major depressive disorder using a multiscale community detection approach

Area of Science:

• Neuroscience
• Computational Psychiatry
• Medical Imaging Analysis

Background:

• Major Depressive Disorder (MDD) is linked to abnormal brain activity, but the specific network connections are not fully understood.
• Existing methods lack the granularity to explore intricate brain region interconnections in MDD.

Purpose of the Study:

• To develop and validate a novel multiscale community detection method for identifying brain functional network differences between MDD patients and healthy controls (NC).
• To pinpoint specific brain regions and subnetworks exhibiting abnormalities in MDD.

Main Methods:

• Utilized the Brainnetome Atlas to parcellate the brain into 246 regions and extract regional time series.
• Constructed brain functional networks using Pearson correlation and applied multiscale community detection.
• Employed modularized Qcut, Normalized Mutual Information (NMI), and Variation of Information (VI) to determine optimal community structures.
• Used the Jaccard index to quantify differences in brain region abnormalities between MDD and NC groups.

Main Results:

• Identified significant abnormalities in multiple brain regions, including the frontal lobe, temporal lobe, parietal lobe, precuneus, insula, cingulate gyrus, hippocampus, and basal ganglia.
• Discovered a novel subnetwork associated with cognitive function, comprising the insular gyrus and inferior frontal gyrus.
• Demonstrated the method's efficacy in differentiating brain network patterns between MDD patients and healthy controls.

Conclusions:

• The proposed multiscale community detection method effectively detects functional brain abnormalities in MDD.
• Findings provide valuable insights for the diagnosis and treatment of major depressive disorder.
• The identified subnetwork offers a potential target for understanding and addressing cognitive deficits in MDD.