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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Vydané v:	BMC psychiatry Ročník 25; číslo 1; s. 723 - 11
Hlavní autori:	Wang, Aihua, Sun, Jingnan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 23.07.2025 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	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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Abstract	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.
AbstractList	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 [DELA]Q = - 0.0017, SD = 2.93), and improved local efficiency (median [DELA]Eff = 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. Keywords: Major depressive disorder, EEG, Brain network controllability, Personalized stimulation 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. 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. 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. 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. 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. Abstract 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. 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. 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. 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 [DELA]Q = - 0.0017, SD = 2.93), and improved local efficiency (median [DELA]Eff = 0.0158, SD = 0.0038). Individualized stimulation plans consistently outperformed random controls in restoring network-level metrics. 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. BackgroundMajor 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.MethodsThis 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.ResultsMDD 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.ConclusionsThe 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. 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.BACKGROUNDMajor 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.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.METHODSThis 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.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.RESULTSMDD 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.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.CONCLUSIONSThe 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.
ArticleNumber	723
Audience	Academic
Author	Sun, Jingnan Wang, Aihua
Author_xml	– sequence: 1 givenname: Aihua surname: Wang fullname: Wang, Aihua email: wangaihua@qymail.bhu.edu.cn organization: School of Mathematical Sciences, Bohai University – sequence: 2 givenname: Jingnan surname: Sun fullname: Sun, Jingnan organization: School of Biomedical Engineering, Tsinghua University
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/40702439$$D View this record in MEDLINE/PubMed
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Keywords	Personalized stimulation EEG Brain network controllability Major depressive disorder
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Snippet	Background Major depressive disorder (MDD) is associated with widespread disruptions in brain network dynamics. Although noninvasive brain stimulation (NIBS)... Major depressive disorder (MDD) is associated with widespread disruptions in brain network dynamics. Although noninvasive brain stimulation (NIBS) has shown... Background Major depressive disorder (MDD) is associated with widespread disruptions in brain network dynamics. Although noninvasive brain stimulation (NIBS)... BackgroundMajor depressive disorder (MDD) is associated with widespread disruptions in brain network dynamics. Although noninvasive brain stimulation (NIBS)... Abstract Background Major depressive disorder (MDD) is associated with widespread disruptions in brain network dynamics. Although noninvasive brain stimulation...
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SubjectTerms	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
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Title	Personalized EEG-guided brain stimulation targeting in major depression via network controllability and multi-objective optimization
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