Abnormal Dynamic Functional Network Connectivity Estimated from Default Mode Network Predicts Symptom Severity in...

Area of Science:

• Neuroscience
• Psychiatry
• Medical Imaging

Background:

• Major depressive disorder (MDD) is characterized by persistent sadness and anhedonia.
• The default mode network (DMN) exhibits altered static functional network connectivity (FNC) in MDD.
• Previous research has not fully explored the dynamic connectivity within the DMN in MDD.

Purpose of the Study:

• To investigate dynamic functional network connectivity (dFNC) within the DMN subnodes in a large cohort of MDD patients.
• To compare dFNC patterns between MDD patients and healthy controls (HCs).
• To explore the relationship between dFNC abnormalities and MDD symptom severity.

Main Methods:

• Analysis of resting-state fMRI data from 262 MDD patients and 277 HCs.
• Estimation of dFNCs for seven DMN subnodes using a sliding window approach.
• Clustering of dFNCs into five brain states and classification using logistic regression.

Main Results:

• Disrupted DMN connectivity patterns were observed in MDD patients compared to HCs.
• MDD subjects showed stronger anterior cingulate cortex (ACC) connectivity and weaker connectivity between the precuneus (PCu) and posterior cingulate cortex (PCC).
• MDD patients exhibited altered state transition probabilities, with a higher likelihood of transitioning from weaker to stronger ACC connectivity, associated with symptom severity.

Conclusions:

• This study provides novel evidence of abnormal time-varying DMN configuration in MDD.
• Altered dFNC within the DMN, particularly involving the ACC, PCu, and PCC, is linked to MDD.
• These findings suggest potential biomarkers for future MDD interventions.