Skip to content
DraconiticResearch
Search Sources API
186,123 studies31 sourcesUpdated 5 min ago
Peer-reviewedCC BY-NC-NDBrain Research Bulletin

Multi-state neural dynamics encode antidepressant response: Fusion of resting and task networks

  • Yueheng Peng1
  • Yue Yu (260119)2
  • Nan Zhou2
  • Xianyong Fan2
  • Lu Jiang3
  • Guolin He3
  • Cunbo Li4
  • Chanlin Yi4
  • Dezhong Yao4
  • Kaibo Shi5
  • Fali Li6
  • Peng Xu7
7 affiliations
  1. 1School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; International Joint Research Center for Perception and Control of Intelligent Rehabilitation Systems of Sichuan Province, Chengdu 610106, China
  2. 2School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China; International Joint Research Center for Perception and Control of Intelligent Rehabilitation Systems of Sichuan Province, Chengdu 610106, China
  3. 3Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu 610041, China
  4. 4School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
  5. 5School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China; International Joint Research Center for Perception and Control of Intelligent Rehabilitation Systems of Sichuan Province, Chengdu 610106, China. Electronic address: [email protected]
  6. 6School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China. Electronic address: [email protected]
  7. 7School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China. Electronic address: [email protected]
Open at sourceDOICite

Abstract

Although antidepressants demonstrate broad efficacy for major depressive disorder (MDD), treatment responses vary considerably across individuals. Consequently, pretreatment identification of eventual Responders (Res) versus Non-responders (Non), alongside patient-specific prediction of symptomatic improvement under a given regimen, remains clinically imperative. Capitalizing on the complementary information provided by resting and task EEGs regarding MDD-related brain abnormalities, we pooled baseline data from four distinct centers to construct brain networks differentiating Res from Non, with subsequent extraction of spatial topological features. By fusing the spatial topological features (resting and task), we classified Res versus Non and forecasted long-term medication response. Baseline features distinguished Res from Non with 96.30% accuracy and enabled reliable patient-specific prediction of eight-week outcomes. Collectively, these results indicated that resting and task EEGs provided synergistic information, enhancing both diagnostic classification and prognostic prediction. This multidimensional approach provided a promising translational framework that, with further validation, may guide personalized antidepressant selection and potentially accelerate treatment response and reduce healthcare costs.

Related

  1. The mPFC-reuniens-hippocampus pathway links brain circuitry and neural plasticity in antidepressant response
    Maxime Veleanu, Louise Schuberth, Antje Kilias +23
    Nature CommunicationsPeer-reviewedCC BY 4.0Full textNeurosciencePharmacology & toxicologyHAL
  2. Investigating the deterministic and stochastic dynamics of Monkeypox: a hybrid numerical approach using spectral method and feedforward neural networks
    Feroz Khan, Latif Ahmed, Abdul Baseer Saqib +3
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textEpidemiology & public healthCrossref
  3. Chronic intracranial neural dynamics forecast treatment response and therapeutic engagement during deep brain stimulation for OCD
    J. Zhou, R. R. Hanish, T. Merk +20
    medRxivPreprintCC BY-NCFull textPsychiatry & mental healthmedRxiv
  4. Substance Use is Not Associated with Antidepressant Response to Transcranial Magnetic Stimulation
    J. Chesley, K. Biernacki, J. Vanleuven +10
    medRxivPreprintCC BY-NC-NDFull textPsychiatry & mental healthmedRxiv
  5. Resting-state alpha scale-free dynamics predict individual differences in the balance between reward-learned and preference-based values during free choice
    Jianhong Zhu, Junya Hashimoto, Tatsuru Honda +4
    PsyArXivPreprintCC BY 4.0Full textNeurosciencePhysiologyOSF

Cite

BibTeXRIS
BibTeX
@article{peng2026multistate,
  title = {{Multi-state neural dynamics encode antidepressant response: Fusion of resting and task networks}},
  author = {Yueheng Peng and Yue Yu (260119) and Nan Zhou and Xianyong Fan and Lu Jiang and Guolin He and Cunbo Li and Chanlin Yi and Dezhong Yao and Kaibo Shi and Fali Li and Peng Xu},
  journal = {Brain Research Bulletin},
  year = {2026},
  month = jun,
  doi = {10.1016/j.brainresbull.2026.111999},
  url = {https://doi.org/10.1016/j.brainresbull.2026.111999},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Multi-state neural dynamics encode antidepressant response: Fusion of resting and task networks
AU  - Yueheng Peng
AU  - Yue Yu (260119)
AU  - Nan Zhou
AU  - Xianyong Fan
AU  - Lu Jiang
AU  - Guolin He
AU  - Cunbo Li
AU  - Chanlin Yi
AU  - Dezhong Yao
AU  - Kaibo Shi
AU  - Fali Li
AU  - Peng Xu
PY  - 2026
DA  - 2026/06/11
JO  - Brain Research Bulletin
DO  - 10.1016/j.brainresbull.2026.111999
UR  - https://doi.org/10.1016/j.brainresbull.2026.111999
AB  - Although antidepressants demonstrate broad efficacy for major depressive disorder (MDD), treatment responses vary considerably across individuals. Consequently, pretreatment identification of eventual Responders (Res) versus Non-responders (Non), alongside patient-specific prediction of symptomatic improvement under a given regimen, remains clinically imperative. Capitalizing on the complementary information provided by resting and task EEGs regarding MDD-related brain abnormalities, we pooled baseline data from four distinct centers to construct brain networks differentiating Res from Non, with subsequent extraction of spatial topological features. By fusing the spatial topological features (resting and task), we classified Res versus Non and forecasted long-term medication response. Baseline features distinguished Res from Non with 96.30% accuracy and enabled reliable patient-specific prediction of eight-week outcomes. Collectively, these results indicated that resting and task EEGs provided synergistic information, enhancing both diagnostic classification and prognostic prediction. This multidimensional approach provided a promising translational framework that, with further validation, may guide personalized antidepressant selection and potentially accelerate treatment response and reduce healthcare costs.
SN  - 0361-9230
SN  - 1873-2747
ER  -