Connectomic signatures advance the understandings of transdiagnostic heterogeneity in psychosis
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.
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BibTeX
@article{du2026connectomic,
title = {{Connectomic signatures advance the understandings of transdiagnostic heterogeneity in psychosis}},
author = {Yu Du and Jingjing Zhang and Ting Ma and Chenfei Ye},
journal = {Communications Medicine},
year = {2026},
month = oct,
doi = {10.1038/s43856-026-01967-y},
url = {https://www.nature.com/articles/s43856-026-01967-y},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Connectomic signatures advance the understandings of transdiagnostic heterogeneity in psychosis
AU - Yu Du
AU - Jingjing Zhang
AU - Ting Ma
AU - Chenfei Ye
PY - 2026
DA - 2026/10/10
JO - Communications Medicine
DO - 10.1038/s43856-026-01967-y
UR - https://www.nature.com/articles/s43856-026-01967-y
AB - 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.
SN - 2730-664X
ER -