{
  "abstract": "Major Depressive Disorder (MDD) remains one of the most prevalent psychiatric conditions worldwide, with a marked increase in incidence and severity following the COVID-19 pandemic. Despite its rising burden, clinical diagnosis continues to rely on subjective assessments, lacking objective and reproducible neurobiological markers. Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a promising non-invasive imaging technique capable of capturing the spatiotemporal dynamics of cerebral hemodynamics, particularly within the prefrontal cortex-a key region involved in emotional regulation and cognitive control. In this study, we propose a spatiotemporal feature-based framework for MDD classification and emotion-related brain state recognition. Thirty MDD patients and thirty demographically matched healthy controls underwent fNIRS recording while exposed to emotional auditory stimuli. By analyzing group-level differences in oxyhemoglobin concentrations and β_values derived from a general linear model (GLM), we identified significant prefrontal abnormalities in the MDD group, particularly under negative emotional conditions. These alterations were most prominent in the dorsolateral prefrontal cortex (DLPFC), a region consistently implicated in emotion dysregulation. The classification model, based on fNIRS-derived activation features, achieved 90% accuracy for MDD detection and 85% for emotion recognition. These results demonstrate that integrating spatial and temporal features improves sensitivity to depression-related cortical dysfunction, offering a potential objective biomarker for diagnosis and novel insight into emotion-specific neurocircuitry in MDD. This work supports the clinical feasibility of fNIRS-based biomarkers and advances understanding of affective brain processing in depression.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer & Artifical Intelligence (College Of Software), Lanzhou University of Technology, Lanzhou, China.",
    "Guangyuan Mental Health Center, GuangYuan, China.",
    "School of Information & Engineering, Lanzhou University, Lanzhou, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jinlong Chao"
    },
    {
      "affs": [
        1
      ],
      "name": "Rui Yang"
    },
    {
      "affs": [
        2
      ],
      "name": "Hong Peng"
    },
    {
      "affs": [
        0
      ],
      "name": "Jian Shi"
    },
    {
      "affs": [
        0
      ],
      "name": "Qiuxia Shi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ce27qhkpdzker.bib",
  "cited_by": 0,
  "doi": "10.3389/fpsyt.2026.1907020",
  "fulltext_url": "https://europepmc.org/articles/PMC13680482",
  "id": "ce27qhkpdzker",
  "issns": [
    "1664-0640"
  ],
  "landing_url": "https://europepmc.org/article/MED/42903202",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2741|Optical Imaging and Spectroscopy Techniques"
    ]
  ],
  "openalex": "W7220713513",
  "pdf_url": "https://europepmc.org/articles/PMC13680482?pdf=render",
  "pmcid": "PMC13680482",
  "pmid": "42903202",
  "published": "2026-10-06",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/ce27qhkpdzker.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:57Z",
      "native_id": "MED:42903202",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42903202"
    },
    {
      "first_seen": "2026-10-30T12:20:18Z",
      "native_id": "10.3389/fpsyt.2026.1907020",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpsyt.2026.1907020/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Optical Imaging and Spectroscopy Techniques",
      "subfield": "imaging"
    }
  ],
  "title": "Spatiotemporal feature-based investigation of major depressive disorder and emotion recognition using functional near infrared spectroscopy.",
  "title_text": "Spatiotemporal feature-based investigation of major depressive disorder and emotion recognition using functional near infrared spectroscopy.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:18Z",
  "url": "https://research.draconitic.com/paper/ce27qhkpdzker",
  "venue": "Frontiers in psychiatry",
  "versions": []
}