{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "School 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",
    "School 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",
    "Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu 610041, China",
    "School 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",
    "School 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: skbs111@163.com",
    "School 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: fali.li@uestc.edu.cn",
    "School 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: xupeng@uestc.edu.cn"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 12,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yueheng Peng",
      "orcid": "0000-0001-5389-8609"
    },
    {
      "affs": [
        1
      ],
      "name": "Yue Yu (260119)"
    },
    {
      "affs": [
        1
      ],
      "name": "Nan Zhou"
    },
    {
      "affs": [
        1
      ],
      "name": "Xianyong Fan"
    },
    {
      "affs": [
        2
      ],
      "name": "Lu Jiang"
    },
    {
      "affs": [
        2
      ],
      "name": "Guolin He"
    },
    {
      "affs": [
        3
      ],
      "name": "Cunbo Li",
      "orcid": "0000-0002-1954-6113"
    },
    {
      "affs": [
        3
      ],
      "name": "Chanlin Yi",
      "orcid": "0000-0001-7989-2440"
    },
    {
      "affs": [
        3
      ],
      "name": "Dezhong Yao",
      "orcid": "0000-0002-8042-879X"
    },
    {
      "affs": [
        4
      ],
      "name": "Kaibo Shi",
      "orcid": "0000-0002-9863-9229"
    },
    {
      "affs": [
        5
      ],
      "name": "Fali Li",
      "orcid": "0000-0002-2450-4591"
    },
    {
      "affs": [
        6
      ],
      "name": "Peng Xu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/de4sswjagcktk.bib",
  "cited_by": 12,
  "doi": "10.1016/j.brainresbull.2026.111999",
  "fulltext_url": null,
  "id": "de4sswjagcktk",
  "issns": [
    "0361-9230",
    "1873-2747"
  ],
  "landing_url": "https://doi.org/10.1016/j.brainresbull.2026.111999",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2736|Treatment of Major Depression"
    ]
  ],
  "openalex": "W7164364738",
  "pdf_url": null,
  "pmcid": null,
  "pmid": "42276172",
  "published": "2026-06-11",
  "publisher": "Elsevier BV",
  "ris": "https://research.draconitic.com/paper/de4sswjagcktk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:54Z",
      "native_id": "W7164364738",
      "source": "openalex",
      "url": "https://doi.org/10.1016/j.brainresbull.2026.111999"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2736|Treatment of Major Depression",
      "subfield": "pharmacology-toxicology"
    }
  ],
  "title": "Multi-state neural dynamics encode antidepressant response: Fusion of resting and task networks",
  "title_text": "Multi-state neural dynamics encode antidepressant response: Fusion of resting and task networks",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T13:21:08Z",
  "url": "https://research.draconitic.com/paper/de4sswjagcktk",
  "venue": "Brain Research Bulletin",
  "versions": []
}