Individual large-scale functional network mapping for major depressive disorder with electroconvulsive therapy.

Area of Science:

• Neuroscience
• Psychiatry
• Medical Imaging

Background:

• Major depressive disorder (MDD) is a prevalent mental health condition with unclear treatment mechanisms.
• Electroconvulsive therapy (ECT) is an effective but not fully understood treatment for MDD.
• Personalized functional connectivity mapping offers insights into brain disorders and treatment responses.

Purpose of the Study:

• To investigate the neurophysiological effects of ECT on functional network connectivity (FNC) in MDD patients.
• To explore the relationship between static and dynamic FNC and clinical symptoms in MDD.
• To determine if individual FNC patterns can predict ECT treatment outcomes.

Main Methods:

• Resting-state functional magnetic resonance imaging (rs-fMRI) was used in 46 MDD patients (pre- and post-ECT) and 46 healthy controls (HC).
• Spatially regularized non-negative matrix factorization (NMF) identified individual functional networks (FNs).
• Static and dynamic FNCs were analyzed and used as predictive features for treatment outcomes.

Main Results:

• ECT modulated both static and dynamic large-scale FNCs in MDD patients at an individual level.
• Dynamic FNCs correlated significantly with depression and anxiety symptom severity.
• Individual dynamic FNCs demonstrated superior predictive power for ECT treatment outcomes compared to static FNCs.

Conclusions:

• ECT influences brain functional connectivity in MDD patients.
• Dynamic functional connectivity analysis provides valuable biomarkers for predicting ECT efficacy.
• Findings support using personalized FNC for improved diagnostic accuracy and tailored treatment selection in MDD.