{
  "abstract": "Conformal prediction is increasingly proposed for clinical decision support because it provides distribution-free coverage guarantees that hold for any model and any data distribution. The guarantee routinely reported, however, is marginal, and we show that in multi-class diagnostic classification it can be satisfied exactly while individual diagnoses are covered very unevenly. On a four-way mood and psychosis classification task over 1,520 subjects from three studies and 14 acquisition sites, marginal split-conformal calibration attained 0.9000 empirical coverage against a nominal 0.90, while healthy controls were covered at 0.941 and schizoaffective disorder at 0.818. The surplus and the deficit cancel, so the aggregate figure is uninformative about either. Class-conditional (Mondrian) calibration reduced this 12.3-point disparity to 0.4 points at a cost of 0.07 labels in mean set size, under 3%. Holding coverage fixed across diagnoses has a further consequence: the residual variation in set size can no longer be attributed to class prior or per-class accuracy, and becomes interpretable as a property of the subject. We show that the resulting prediction sets separate subjects into confident, boundary, ambiguous and unresolved strata, and that the proportion of subjects independently flagged as label-ambiguous by a structural-MRI model trained separately on the same cohort rises monotonically across these strata (34.1%, 57.3%, 68.1%, 81.8%; p = 8.8e-18). No schizoaffective subject and 0.9% of bipolar subjects reach the confident stratum, against 18.0% of controls and 13.4% of schizophrenia subjects. Set size at matched coverage also provides a comparison between representations that accuracy cannot. Applied to structural MRI, functional MRI and their fusion, it reveals a sign reversal that accuracy conceals: fusion reduces set size for bipolar (-0.25) and schizoaffective (-0.23) subjects and increases it for controls (+0.16), and the aggregate fusion benefit itself changes sign below alpha = 0.10, while top-1 accuracy rises monotonically from 0.495 to 0.578 to 0.611 across the three models. Reporting marginal coverage alone is insufficient whenever diagnostic classes are unbalanced.",
  "abstract_shown": true,
  "affiliations": [
    "Gerogia State University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "H. Rokham"
    },
    {
      "name": "H. Falakshahi"
    },
    {
      "name": "V. Calhoun"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1qtyf58jspjh0.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.23.753958",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753958v1.full",
  "id": "1qtyf58jspjh0",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753958v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ],
    [
      "openalex",
      "2805|Functional Brain Connectivity Studies"
    ]
  ],
  "openalex": "W7214743775",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753958v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1qtyf58jspjh0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:15Z",
      "native_id": "10.64898/2026.09.23.753958",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753958v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Beyond Marginal Coverage: Class-Conditional Conformal Prediction in Multi-Class Psychiatric Neuroimaging",
  "title_text": "Beyond Marginal Coverage: Class-Conditional Conformal Prediction in Multi-Class Psychiatric Neuroimaging",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:15Z",
  "url": "https://research.draconitic.com/paper/1qtyf58jspjh0",
  "venue": "bioRxiv",
  "versions": []
}