{
  "abstract": "Background: Alzheimer's disease (AD) is an irreversible neurodegenerative disorder where early diagnosis at the mild cognitive impairment (MCI) stage is critical for timely intervention. While structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) provide complementary pathological information, clinical translation is limited by high feature heterogeneity and the heavy computational burden of 3D deep learning networks. This study aims to propose a lightweight, hybrid multimodal fusion model to overcome these limitations and improve diagnostic efficiency. Methods: A lightweight MobileViT-XS network was utilized to extract global and local pathological features from gray-matter sMRI images to reduce computational costs. Concurrently, graph theory was applied to rs-fMRI data to construct brain functional networks and calculate global topological properties. Following pretraining, feature-level fusion was performed to integrate sMRI features, rs-fMRI properties, demographic information, and two clinical neuropsychological examinations. Results: The validation results show that the proposed hybrid model achieved high diagnostic performance in the three-class classification task, yielding an accuracy of 0.928 ± 0.010, sensitivity of 0.901 ± 0.014, precision of 0.933 ± 0.014, and an F1 score of 0.911 ± 0.012. On the held-out testing subset of the independently collected dataset after dataset-specific fine-tuning, the proposed model achieved an accuracy of 0.857, with a sensitivity of 0.867, a precision of 0.867, and an F1 score of 0.859. Conclusions: The proposed framework successfully minimizes computational overhead while maintaining high classification accuracy. These findings suggest that the hybrid multimodal fusion model provides a promising computer-aided approach for three-class classification of AD, MCI, and NC.",
  "abstract_shown": true,
  "affiliations": [
    "Jilin Provincial Key Laboratory for Numerical Simulation, Jilin Normal University, Siping 136000, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Junhu Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Bing Zhu"
    },
    {
      "affs": [
        0
      ],
      "name": "Miao Yu"
    },
    {
      "affs": [
        0
      ],
      "name": "Shichao Du"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/023ztrxa4yhtw.bib",
  "cited_by": 0,
  "doi": "10.3390/neurolint18100191",
  "fulltext_url": "https://europepmc.org/articles/PMC13708670",
  "id": "023ztrxa4yhtw",
  "issns": [
    "2035-8385",
    "2035-8377"
  ],
  "landing_url": "https://europepmc.org/article/MED/42931145",
  "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",
      "2738|Dementia and Cognitive Impairment Research"
    ]
  ],
  "openalex": "W7221690139",
  "pdf_url": "https://europepmc.org/articles/PMC13708670?pdf=render",
  "pmcid": "PMC13708670",
  "pmid": "42931145",
  "published": "2026-10-13",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/023ztrxa4yhtw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:01Z",
      "native_id": "MED:42931145",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42931145"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2738|Dementia and Cognitive Impairment Research",
      "subfield": "psychiatry"
    }
  ],
  "title": "Early Diagnostic Classification of Alzheimer's Disease Using Structural and Resting-State Functional MRI.",
  "title_text": "Early Diagnostic Classification of Alzheimer's Disease Using Structural and Resting-State Functional MRI.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:01Z",
  "url": "https://research.draconitic.com/paper/023ztrxa4yhtw",
  "venue": "Neurology international",
  "versions": []
}