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Background <p>Major depressive disorder (MDD) is associated with widespread disruptions in brain network dynamics. Although noninvasive brain stimulation (NIBS) has shown promise as an alternative treatment, its efficacy remains limited due to a lack of individualized targeting strategies that account for functional and topological heterogeneity in brain networks.</p> Methods <p>This study developed a novel EEG-based framework to personalize NIBS strategies in MDD. Resting-state EEG data from 30 healthy controls and 34 MDD patients were analyzed. Functional connectivity was estimated across five frequency bands using phase locking value (PLV), amplitude envelope correlation (AEC), and weighted phase lag index (wPLI). Spectral graph embedding and structural controllability theory were applied to identify candidate stimulation targets. A multi-objective optimization algorithm (NSGA-II) was used to select optimal node–frequency–amplitude combinations minimizing control energy while maximizing network efficiency gain and structural restoration. Kuramoto-based neural simulations were conducted to evaluate stimulation efficacy in silico, quantifying changes in global synchrony, modularity, and local efficiency.</p> Results <p>MDD patients exhibited hyperconnectivity in PLV and AEC and reduced wPLI compared to controls. Control nodes in MDD were more centrally distributed, particularly around Cz in alpha and beta bands. NSGA-II optimization yielded subject-specific stimulation strategies with favorable trade-offs. Simulated stimulation significantly enhanced global synchrony (median <i>R</i> = 0.68, SD = 0.30), reduced network modularity (median ΔQ = − 0.0017, SD = 2.93), and improved local efficiency (median ΔEff = 0.0158, SD = 0.0038). Individualized stimulation plans consistently outperformed random controls in restoring network-level metrics.</p> Conclusions <p>The proposed framework enables data-driven, mathematically interpretable, and simulation-validated planning of personalized brain stimulation strategies for MDD. These findings highlight the potential of EEG-based network analysis and multi-objective optimization in guiding precision neuromodulation interventions.</p>

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Personalized EEG-guided brain stimulation targeting in major depression via network controllability and multi-objective optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月23日

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  • Aihua Wang,
  • Jingnan Sun

摘要

Background

Major depressive disorder (MDD) is associated with widespread disruptions in brain network dynamics. Although noninvasive brain stimulation (NIBS) has shown promise as an alternative treatment, its efficacy remains limited due to a lack of individualized targeting strategies that account for functional and topological heterogeneity in brain networks.

Methods

This study developed a novel EEG-based framework to personalize NIBS strategies in MDD. Resting-state EEG data from 30 healthy controls and 34 MDD patients were analyzed. Functional connectivity was estimated across five frequency bands using phase locking value (PLV), amplitude envelope correlation (AEC), and weighted phase lag index (wPLI). Spectral graph embedding and structural controllability theory were applied to identify candidate stimulation targets. A multi-objective optimization algorithm (NSGA-II) was used to select optimal node–frequency–amplitude combinations minimizing control energy while maximizing network efficiency gain and structural restoration. Kuramoto-based neural simulations were conducted to evaluate stimulation efficacy in silico, quantifying changes in global synchrony, modularity, and local efficiency.

Results

MDD patients exhibited hyperconnectivity in PLV and AEC and reduced wPLI compared to controls. Control nodes in MDD were more centrally distributed, particularly around Cz in alpha and beta bands. NSGA-II optimization yielded subject-specific stimulation strategies with favorable trade-offs. Simulated stimulation significantly enhanced global synchrony (median R = 0.68, SD = 0.30), reduced network modularity (median ΔQ = − 0.0017, SD = 2.93), and improved local efficiency (median ΔEff = 0.0158, SD = 0.0038). Individualized stimulation plans consistently outperformed random controls in restoring network-level metrics.

Conclusions

The proposed framework enables data-driven, mathematically interpretable, and simulation-validated planning of personalized brain stimulation strategies for MDD. These findings highlight the potential of EEG-based network analysis and multi-objective optimization in guiding precision neuromodulation interventions.