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

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...

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Vydáno v:BMC psychiatry Ročník 25; číslo 1; s. 723 - 11
Hlavní autoři: Wang, Aihua, Sun, Jingnan
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 23.07.2025
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
Adult
Algorithms
Brain - physiopathology
Brain network controllability
Brain research
Case-Control Studies
Depressive Disorder, Major - physiopathology
Depressive Disorder, Major - therapy
EEG
Electroencephalography
Electroencephalography - methods
Embedding
Female
Frequency dependence
Humans
Major depressive disorder
Male
Mathematical optimization
Medicine
Medicine & Public Health
Mental depression
Mental disorders
Middle Aged
Nerve Net - physiopathology
Neural networks
Neuromodulation
Neurophysiology
Noninvasive brain stimulation for mental disorders
Optimization
Personalized stimulation
Precision Medicine - methods
Psychiatry
Psychotherapy
Research methodology
Time series
Transcranial magnetic stimulation
Personalized stimulation
EEG
Brain network controllability
Major depressive disorder
ISSN:1471-244X, 1471-244X
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Shrnutí: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.
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ISSN:1471-244X
1471-244X
DOI:10.1186/s12888-025-07171-x