Personalized EEG-guided brain stimulation targeting in major depression via network controllability and...

Area of Science:

• Neuroscience
• Computational Psychiatry
• Medical Engineering

Background:

• Major depressive disorder (MDD) involves disrupted brain network dynamics.
• Current noninvasive brain stimulation (NIBS) efficacy is limited by a lack of individualized targeting.
• Heterogeneity in brain networks necessitates personalized treatment approaches.

Purpose of the Study:

• To develop a novel EEG-based framework for personalizing NIBS strategies in MDD.
• To identify optimal stimulation targets by integrating functional and topological network properties.
• To validate the efficacy of personalized stimulation through in silico neural simulations.

Main Methods:

• Resting-state EEG data from MDD patients and healthy controls were analyzed.
• Functional connectivity was estimated using PLV, AEC, and wPLI across five frequency bands.
• Spectral graph embedding, structural controllability, and NSGA-II optimization were used to determine personalized stimulation parameters.
• Kuramoto neural simulations evaluated the impact of stimulation on network synchrony, modularity, and efficiency.

Main Results:

• MDD patients showed distinct connectivity patterns compared to controls.
• Individualized stimulation strategies were identified using multi-objective optimization.
• Simulated stimulation significantly improved global synchrony, reduced modularity, and enhanced local efficiency.
• Personalized stimulation plans outperformed random targeting in restoring network metrics.

Conclusions:

• The developed framework enables data-driven, interpretable, and simulation-validated planning of personalized brain stimulation for MDD.
• EEG-based network analysis combined with multi-objective optimization holds potential for precision neuromodulation.
• This approach offers a pathway to more effective NIBS interventions for major depressive disorder.