{
  "abstract": "Structural magnetic resonance imaging supports the characterization of Alzheimer’s disease (AD)-related cognitive impairment. But existing cross-cohort AD classification methods remain limited in addressing acquisition and population shifts when source data cannot be revisited and target-side adaptation is excluded. In particular, current cross-cohort AD classification may underutilize diverse disease-related information learned from source data, while explicit characterization of regional, scale-dependent gray-matter morphology remains limited. To address these gaps, we propose FSE-CNN for source-free cross-cohort classification of AD-related cognitive status. First, the neural pathway derives decision evidence from model states retained during source training, with the inference configuration determined exclusively from source data. Second, the spatial-scale geometric evidence encoding module characterizes gray-matter morphology through subject-relative spatial organization and finite physical scales, providing complementary geometric evidence for cross-cohort classification. Finally, the resulting neural and geometric evidence are combined through protocol-specific fusion. We evaluated FSE-CNN on two cross-cohort protocols. In OASIS-2 → OASIS-1, FSE-CNN achieved 83.48% accuracy, 85.14% balanced accuracy, and 88.60% AUC, numerically exceeding Harmonizing Flows by 1.70, 1.26, and 0.39 percentage points, respectively. In ADNI → OASIS-2, it achieved 85.79% accuracy, 85.55% balanced accuracy, and 85.03% AUC, with corresponding gains of 0.71, 0.67, and 0.21 percentage points. Overall, these results demonstrate the effectiveness of FSE-CNN for cross-cohort AD-related cognitive-status classification.",
  "abstract_shown": true,
  "affiliations": [
    "Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China",
    "College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China",
    "College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Tuoru Li"
    },
    {
      "affs": [
        2
      ],
      "name": "Shurong Dong",
      "orcid": "0000-0002-8715-7072"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Weidong Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3yxkqsrqmep83.bib",
  "cited_by": 0,
  "doi": "10.3390/fractalfract10110737",
  "fulltext_url": null,
  "id": "3yxkqsrqmep83",
  "issns": [
    "2504-3110"
  ],
  "landing_url": "https://www.mdpi.com/2504-3110/10/11/737",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "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": "W7223815005",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/3yxkqsrqmep83.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:32Z",
      "native_id": "10.3390/fractalfract10110737",
      "source": "crossref",
      "url": "https://www.mdpi.com/2504-3110/10/11/737"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2738|Dementia and Cognitive Impairment Research",
      "subfield": "psychiatry"
    }
  ],
  "title": "FSE-CNN: Fusion of Training-Trajectory Neural Evidence and Spatial-Scale Morphology for Cross-Cohort Brain MRI Classification",
  "title_text": "FSE-CNN: Fusion of Training-Trajectory Neural Evidence and Spatial-Scale Morphology for Cross-Cohort Brain MRI Classification",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:32Z",
  "url": "https://research.draconitic.com/paper/3yxkqsrqmep83",
  "venue": "Fractal and Fractional",
  "versions": []
}