{
  "abstract": "Current consensus-driven diagnostic systems poorly capture the marked heterogeneity and high comorbidity of psychiatric disorders, necessitating brain-based transdiagnostic frameworks to advance mechanistic understanding and clinical translation. Connectome-wide association studies have consistently identified widespread large-scale dysconnectivity across diagnostic boundaries, yet normative modeling and meta-analytic evidence highlights substantial inter-individual variability in the spatial topography and magnitude of connectomic deviations. In this Perspective, we bridge this divide between cross-disorder commonalities and individualized pathology by integrating connectomic overlap and computational biotyping into a top-down, four-tiered hierarchical architecture. This framework progresses from macro-scale transdiagnostic topological scaffolds, through meso-scale phenotypic triangulation, to data-driven biotypes and micro-scale clinical translations, delineating bidirectional interplay between shared network constraints and heterogeneous pathological diversification. Building on this architecture, we outline strategies leveraging multi-cohort data harmonization and healthy-anchored normative biotyping to refine robust cross-disorder circuits and reproducible neurobiotypes. Lastly, we propose a translational roadmap to transition connectomic stratification toward prospective, treatment-relevant clinical validation in precision psychiatry.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Yu Du"
    },
    {
      "name": "Jingjing Zhang"
    },
    {
      "name": "Ting Ma"
    },
    {
      "name": "Chenfei Ye",
      "orcid": "0000-0001-5265-3546"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d9n8sep2gncb0.bib",
  "cited_by": 0,
  "doi": "10.1038/s43856-026-01967-y",
  "fulltext_url": null,
  "id": "d9n8sep2gncb0",
  "issns": [
    "2730-664X"
  ],
  "landing_url": "https://www.nature.com/articles/s43856-026-01967-y",
  "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",
      "2805|Functional Brain Connectivity Studies"
    ]
  ],
  "openalex": "W7221324315",
  "pdf_url": "https://www.nature.com/articles/s43856-026-01967-y.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-10",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/d9n8sep2gncb0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:53Z",
      "native_id": "10.1038/s43856-026-01967-y",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s43856-026-01967-y"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2805|Functional Brain Connectivity Studies",
      "subfield": "neuroscience"
    }
  ],
  "title": "Connectomic signatures advance the understandings of transdiagnostic heterogeneity in psychosis",
  "title_text": "Connectomic signatures advance the understandings of transdiagnostic heterogeneity in psychosis",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:53Z",
  "url": "https://research.draconitic.com/paper/d9n8sep2gncb0",
  "venue": "Communications Medicine",
  "versions": []
}