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Gray matter volume heterogeneity by stage, site of origin and pathophysiology in schizophrenia

Nature Mental Health (2025)Cite this article

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Abstract

Schizophrenia is characterized with greater variability beyond the mean differences in brain structures. This variability is assumed to be static, reflecting the presence of heterogeneous subgroups, but this assumption and alternative explanations remain untested. Here we examine whether gray matter volume variability decreases in later stages of schizophrenia using magnetic resonance imaging of 1,792 individuals with schizophrenia and 1,523 healthy controls. Compared with healthy controls, greater variability (false-discovery-rate-corrected P < 0.05) was found in 50 regions across the entire patient group. The average variability across all regions was greater in the first-episode than chronic stage (t = 10.8, P = 1.7 × 10–7). The areas with the largest variability were located at the frontotemporal cortex and thalamus (first-episode), or the hippocampus and caudate (chronic). This study offers novel insights into the diversity of brain alterations in schizophrenia, emphasizing that brain-based heterogeneity is not a static feature; it is more pronounced at the onset of the disorder but reduced over the long term.

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Fig. 1: Group difference of mean volume and variability in regional gray matter measures between patients with schizophrenia and healthy controls.
Fig. 2: Individual-level deviation from the healthy level in regional GMV changes.
Fig. 3: Partial least squares correlation analysis between regional variability and human brain gene expression data.
Fig. 4: Distinct neuroanatomical characteristics in NDM-based spatial phenotypes of schizophrenia population.

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Data availability

COBRE, NMorphCH, FBIRN and NUSDAST data were obtained from SchizConnect at https://www.nitrc.org/projects/schizconnect/. The COBRE dataset was download from the Center for Biomedical Research Excellence in Brain Function and Mental Illness (COBRE) at http://coins.mrn.org/. The NMorphCH dataset was download from https://nunda.northwestern.edu/nunda/data/projects/NMorphCH. The FBIRN dataset was downloaded from https://www.nitrc.org/projects/fbirn/. The NUSDAST dataset was download from the Northwestern University Schizophrenia Data and Software Tool. The DS000115 dataset was download from the OpenfMRI database at https://www.openfmri.org/. ENIGMA summary statistics of thinner cortical thickness map were obtained from the ENIGMA Toolbox at https://github.com/MICA-MNI/ENIGMA (v.2.0.0, July 2022; ref. 25). Gene expression data of brain tissue samples was obtained from the Allen Human Brain Atlas dataset (AHBA) at http://human.brain-map.org. All data needed to evaluate the conclusions in the paper are present in the paper and/or Supplementary Information.

Code availability

Brain images were processed by using FreeSurfer (v.7.3) (http://surfer.nmr.mgh.harvard.edu/; ref. 52). Gene enrichment analysis was performed using Metascape (v.3.5) (https://metascape.org). The visualization of brain mapping images was conducted using the ENIGMA Toolbox (v.2.0.0) (https://enigma-toolbox.readthedocs.io/en/latest/index.html). The code for spatial permutation test is available at https://github.com/frantisekvasa/rotate_parcellation.

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Acknowledgements

This work was supported by the grant from Science and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence Project (no. 2022ZD0212800 to Y.J.). This work was supported by National Natural Science Foundation of China (no. 82202242 to Y.J., no. 82071997 to W.C.). This work was supported by the projects from China Postdoctoral Science Foundation (no. BX2021078 and 2021M700852 to Y.J.). This work was supported by the Shanghai Rising-Star Program (no. 21QA1408700 to W.C.) and the Shanghai Sailing Program (grant no. 22YF1402800 to Y.J.) from Shanghai Science and Technology Committee. This work was supported by National Key R&D Program of China (grant no. 2019YFA0709502 to J.F.), the grant from Shanghai Municipal Science and Technology Major Project (grant no. 2018SHZDZX01 to J.F.), ZJ Laboratory, and Shanghai Center for Brain Science and Brain-Inspired Technology, and the grant from the 111 Project (grant no. B18015 to J.F.). This work was supported by grants from the National Key R&D Program of China (grant no. 2022ZD0208500 to D.Y.), Natural Science Basic Research Program of Shaanxi (grant no. 2025SYS-SYSZD-061 to L.-B.C.) and the CAMS Innovation Fund for Medical Sciences (grant no. 2019-I2M-5-039 to C.L.). L.P. acknowledges research support from the Canada First Research Excellence Fund, awarded to the Healthy Brains, Healthy Lives initiative at McGill University (through New Investigator Supplement to L.P.); Monique H. Bourgeois Chair in Developmental Disorders and Graham Boeckh Foundation (Douglas Research Centre, McGill University) and a salary award from the Fonds de recherche du Quebec-Santé (FRQS nos. 366934 and 313133).

Author information

Authors and Affiliations

  1. School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, P.R. China

    Yuchao Jiang

  2. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, P.R. China

    Yuchao Jiang, Xiao Chang, Jie Zhang, Wei Cheng & Jianfeng Feng

  3. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, P.R. China

    Yuchao Jiang, Xiao Chang, Jie Zhang, Wei Cheng & Jianfeng Feng

  4. Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Quebec, Canada

    Lena Palaniyappan

  5. Robarts Research Institute, Western University, London, Ontario, Canada

    Lena Palaniyappan

  6. Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, P.R. China

    Enpeng Zhou & Xin Yu

  7. Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan

    Shih-Jen Tsai

  8. Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan

    Ching-Po Lin

  9. Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China

    Jingliang Cheng

  10. Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China

    Yingying Tang & Jijun Wang

  11. The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China

    Cheng Luo & Dezhong Yao

  12. High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P.R. China

    Cheng Luo & Dezhong Yao

  13. Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P.R. China

    Cheng Luo & Dezhong Yao

  14. Schizophrenia Imaging Lab, Xijing 986 Hospital, Fourth Military Medical University, Xi’an, P.R. China

    Long-Biao Cui

  15. Shanxi Provincial Key Laboratory of Clinic Genetics, Fourth Military Medical University, Xi’an, P.R. China

    Long-Biao Cui

  16. Department of Radiology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, P.R. China

    Long-Biao Cui

  17. Department of Neurology, Huashan Hospital, Fudan University, Shanghai, P.R. China

    Wei Cheng

  18. Fudan ISTBI—ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, P.R. China

    Wei Cheng

  19. MOE Frontiers Center for Brain Science, Fudan University, Shanghai, P.R. China

    Jianfeng Feng

  20. Zhangjiang Fudan International Innovation Center, Shanghai, P.R. China

    Jianfeng Feng

  21. School of Data Science, Fudan University, Shanghai, P.R. China

    Jianfeng Feng

  22. Department of Computer Science, University of Warwick, Coventry, UK

    Jianfeng Feng

Authors
  1. Yuchao Jiang
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  2. Lena Palaniyappan
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Contributions

Y.J. and J.F. conceptualized and designed the project. J.Z., E.Z., X.Y., S.-J.T., C.-P.L., J.C., Y.T., J.W., C.L., D.Y. and L.-B.C. acquired the data. Y.J. and X.C. analyzed and interpreted the data. Y.J. & L.P. drafted the original paper. L.P., X.C. and W.C. critically reviewed the paper for important intellectual content. Y.J. and X.C. performed the statistical analysis. J.F. supervised the project.

Corresponding author

Correspondence to Jianfeng Feng.

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Competing interests

L.P. reports personal fees for serving as Chief Editor from the Canadian Medical Association Journals; a speaker and consultancy fee from Janssen Canada and Otsuka Canada, SPMM Course Limited, UK, Canadian Psychiatric Association; book royalties from Oxford University Press; and investigator-initiated educational grants from Janssen Canada, Sunovion and Otsuka Canada outside of the submitted work. The other authors declare no competing interests.

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Extended Data

Extended Data Fig. 1 High consistency between the case–control difference (that is, effect size).

(a) discovery sample (n = 1,792). (b) validation sample (n = 4,474). (c) It shows a highly spatial correlation (r = 0.678, p = 2.7×10-12) between discovery data and validation data. Spearman correlation test is used for data analysis in figure c.

Extended Data Fig. 2 Partial least squares (PLS) correlation analysis between individual deviation and human brain gene expression data.

(a) Spatial map of the first component (PLS1) in the left hemisphere. (b) The spatial correlation between the PLS1 score and individual deviation in schizophrenia, measured by PD- score. (c) The ranked gene weights of PLS1 (FDR p < 0.05). (d) Top 20 gene ontology (GO) biological processes by enrichment analysis. Circle color: different biological processes. Circle size: number of genes. (e) Gene enrichment analysis in Cell Type Signatures. (f) Gene enrichment analysis in human disease-associated database. Partial least squares correlation analysis is used for data analysis in figure b. Gene enrichment analysis is used for data analysis in figures e and f. Benjamini–Hochberg procedure is used for multiple comparisons adjustments.

Extended Data Fig. 3 Performance of network diffusion model (NDM) on estimating cross-sectional GMV differences in group-level patients with schizophrenia.

(a) Model performance is indexed by the Spearman correlation coefficient (r value) between observed values and estimated values across brain regions. (b) Model performance with each bilateral region as the source seed of NDM. The r values are mapped to a brain template to show model performance of each region being the NDM seed. Red stars represent the optimal seed region (bilateral) with best performance. NDMMC, morphological covariance connectome type-based NDM; NDMSC, structural connectome type-based NDM; NDMFC, functional connectome type-based NDM.

Supplementary information

Supplementary Information

Supplementary Methods 1–5, Results 1–7, Discussion and Tables 1–11.

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Jiang, Y., Palaniyappan, L., Chang, X. et al. Gray matter volume heterogeneity by stage, site of origin and pathophysiology in schizophrenia. Nat. Mental Health (2025). https://doi.org/10.1038/s44220-025-00449-9

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