Abstract
Major neuropsychiatric disorders such as major depressive disorder (MDD) and schizophrenia (SCZ), as well as the neurodevelopmental disorder autism spectrum disorder (ASD), are traditionally treated as distinct clinical entities. However, genome-wide association studies indicate shared genetic risks, motivating a transdiagnostic view. Resting-state functional connectivity (rsFC) is a promising biomarker for these disorders, but its high dimensionality complicates inference of inter-disorder relationships in the native feature space. Here, we develop an rsFC-based embedding-relation workflow that quantifies disorder relationships in a connectivity-informed, low-dimensional embedding space. Central to the workflow is a mutual information-based embedding framework that evaluates candidate embedding approaches and selects an optimal strategy. Using synthetic connectivity data, the framework indicates that rsFC embeddings are best represented in a spherical space under a moderate level of supervision. Building on this insight, we applied the workflow to curated, multi-disorder rsFC datasets to derive shared embedding spaces encompassing the connectivity features of ASD, MDD, and SCZ. In these spaces, we consistently observed a robust three-way relationship: a pronounced neurobiological dissimilarity between ASD and MDD, contrasted with greater similarity between SCZ and both disorders. These findings support a dimensional, transdiagnostic perspective on neuropsychiatric disorders and offer new insights into their shared and distinct neural underpinnings.
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Data availability
The employed UTO and HuShoWa datasets are derived from the SRPBS database under the Brain/MINDS Beyond human brain MRI project [14], which is consisted of resting-state fMRI scans from 8 different sites. The data is openly accessible via https://bicr-resource.atr.jp/srpbsfc/.
Code availability
The Python code for executing our embedding-relation workflow on designated rsFC datasets can be found at https://github.com/LeonBai/rsFC_embedding.
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Acknowledgements
We thank Dr. Ayumu Yamashita (The University of Tokyo) and Prof. Kenji Doya (OIST) for comments on an early version of the manuscript. This work was supported by the Japan Agency for Medical Research and Development (AMED) under Grant Numbers JP24dm0307008, JP25wm0625204s0102, and JP25wm0625122s0502. The funders had no role in the study design, data collection, analysis, or results interpretation.
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W.J.B. conceives the general idea and performs the overall analysis, while O.Y., Y.S., and J.Y. provide fruitful feedback and suggestions throughout the analysis and revision procedure. W.J.B. wrote the draft of the manuscript. All authors discussed the results and commented on the manuscript.
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Bai, W., Yamashita, O., Sakai, Y. et al. Inferring relationships among major psychiatric disorders in a resting-state functional connectivity-informed embedding space. npj Syst Biol Appl (2026). https://doi.org/10.1038/s41540-026-00699-y
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DOI: https://doi.org/10.1038/s41540-026-00699-y
