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Neuroimaging biomarkers define neurophysiological subtypes with distinct trajectories in schizophrenia

Nature Mental Health volume 1, pages 186–199 (2023)Cite this article

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Abstract

Technical developments and improved access to neuroimaging techniques have brought us closer to understanding the neuropathological origins of schizophrenia. Using data-driven disease-progression modelling on cross-sectional magnetic resonance imaging (MRI) from 1,124 patients with schizophrenia, we characterize two distinct but stable ‘trajectories’ of brain atrophy, separately beginning in the Broca’s area (subtype1) and the hippocampus (subtype2). The two trajectories are replicated in cross-validation samples. Individuals within each subtype are further classified into two stages (‘pre-atrophy’ and ‘post-atrophy’). These subtypes show different atrophy patterns and symptom profiles. Longitudinal data from 523 patients with schizophrenia treated by antipsychotics only or adjunct transcranial magnetic stimulation (TMS) reveal that antipsychotics-only effects relate to phenotypic subtype (more effective in the subtype1) while adjunct transcranial-magnetic-stimulation effects relate to the stage (superior outcomes in the pre-atrophy stage). These findings suggest distinct pathophysiological processes underlying schizophrenia that potentially yield to stratification and prognostication—a key requirement for personalizing treatments in enduring illnesses.

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Fig. 1: Pathophysiological progression of brain atrophy in schizophrenia.
Fig. 2: Atrophy patterns in four subtypes of schizophrenia.
Fig. 3: Subtypes characterized by clinical variables.
Fig. 4: Treatment outcome and subtypes of schizophrenia in patients with follow-up data.

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Data availability

Data of COBRE, NMorphCH, FBIRN and NUSDAST were obtained from the SchizConnect, a publicly available website (http://www.schizconnect.org/documentation#by_project). The NMorphCH dataset and NUSDAST dataset were download through a query interface at the SchizConnect (http://www.schizconnect.org/queries/new). The COBRE dataset was download from the Center for Biomedical Research Excellence in Brain Function and Mental Illness (COBRE) (https://coins.trendscenter.org/). The FBIRN dataset was download from https://www.nitrc.org/projects/fbirn/. The DS000115 dataset was download from OpenfMRI database (https://www.openfmri.org/). Data from the other datasets (cross-sectional datasets #1, #2, #3, #4, longitudinal AMP and TMS data) are not publicly available for download, but access requests can be made to the respective study investigators: cross-sectional data (datasets #1, #2, #3, #4)—corresponding author J. Feng; APM data—J. Wang (jijunwang27@163.com), X. Yu (yuxin@bjmu.edu.cn), W. Yue (dryue@bjmu.edu.cn) and C. Luo (chengluo@uestc.edu.cn); TMS data—J. Wang (jijunwang27@163.com), G. Ji (jigongjun@163.com), L. Cui (cui_fmmu@163.com) and C. Luo (chengluo@uestc.edu.cn). Requests for raw and analysed data can be made to the corresponding author J. Feng and will be promptly reviewed by the Fudan University Ethics Committee to verify whether the request is subject to any intellectual property or confidentiality obligations.

Code availability

Python of the SuStaIn algorithm is available on the UCL-POND GitHub (https://github.com/ucl-pond). The T1-weighted images were processed using the Computational Anatomy Toolbox (http://www.neuro.uni-jena.de/cat/) within SPM12 (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/). The visualization of ROI-wise z score images was conducted using BrainNetViewer (https://www.nitrc.org/projects/bnv/). Statistical analyses, including correlation analysis, t test and ANOVA, were conducted using MATLAB (version: R2018b) and SPSS Statistics (version: 26.0). Other custom codes developed in the current study are available at GitHub (https://github.com/YuchaoJiang91/Disease-Progress-Model).

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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 (grant no. 2021ZD0200204 to J.Z.; no. 2022ZD0212800 to Y.T.). This work was supported by National Natural Science Foundation of China (no. 82202242 to Y.J.; no. 82071997 to W.C.; no. 81825009 to W.Y.; no. 82271949 to L.-B.C.; no. 82151314 to J.W.). This work was supported by grants from the National Key R&D Program of China (no. 2022ZD0208500 to D.Y.) and the CAMS Innovation Fund for Medical Sciences (no. 2019-I2M-5-039 to C.L.). This work was supported by the Shanghai Rising-Star Program (no. 21QA1408700 to W.C.) and the Shanghai Sailing Program (22YF1402800 to Y.J.) from Shanghai Science and Technology Committee. This work was supported by the projects from China Postdoctoral Science Foundation (no. BX2021078 and 2021M700852 to Y.J.). This work was supported by National Key R&D Program of China (no. 2019YFA0709502 to J.F.), the grant from Shanghai Municipal Science and Technology Major Project (no. 2018SHZDZX01 to J.F.), ZJ Lab, Shanghai Center for Brain Science and Brain-Inspired Technology, and the grant from the 111 Project (no. B18015 to J.F.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. L.P. acknowledges support from the Monique H. Bourgeois Chair (McGill University) and Tanna Schulich Chair of Neuroscience and Mental Health (Schulich School of Medicine & Dentistry, Western University) and a salary award from the Fonds de recherche du Quebec-Sante ́ (FRQS). We also thank the investigators who provided public access to MRI data from patients diagnosed with schizophrenia through the COBRE database funded by a Center of Biomedical Research Excellence grant 5P20RR021938/P20GM103472 from the NIH to V. Calhoun, the fBIRN data supported by grants to the Function BIRN (U24-RR021992) Testbed funded by the National Center for Research Resources at the National Institutes of Health, USA, the NMorphCH dataset funded by NIMH grant R01MH056584 and the SchizConnect funded by NIMH cooperative agreement 1U01 MH097435. This work is supported by the Zhangjiang International Brain Biobank (ZIB) Consortium.

Author information

Author notes

  1. These authors contributed equally: Yuchao Jiang, Jijun Wang, Enpeng Zhou.

Authors and Affiliations

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

    Yuchao Jiang, Xiao Chang, Chao Xie, Wei Zhang, Jinchao Lv, Di Chen, Chun Shen, Xinran Wu, Bei Zhang, Nanyu Kuang, Yun-Jun Sun, Jujiao Kang, Jie Zhang, Wei Cheng & Jianfeng Feng

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

    Yuchao Jiang, Xiao Chang, Chao Xie, Wei Zhang, Jinchao Lv, Di Chen, Chun Shen, Xinran Wu, Bei Zhang, Nanyu Kuang, Yun-Jun Sun, Jujiao Kang, Jie Zhang, Wei Cheng & Jianfeng Feng

  3. Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China

    Jijun Wang, Yingchan Wang, Yingying Tang, Tianhong Zhang & Chunbo Li

  4. 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, PR China

    Enpeng Zhou, Yuyanan Zhang, Xin Yu, Tianmei Si & Weihua Yue

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

    Lena Palaniyappan

  6. Robarts Research Institute, University of Western Ontario, London, Ontario, Canada

    Lena Palaniyappan

  7. Lawson Health Research Institute, London, Ontario, Canada

    Lena Palaniyappan

  8. 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, PR China

    Cheng Luo, Huan Huang, Hui He, Mingjun Duan & Dezhong Yao

  9. 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, PR China

    Cheng Luo & Dezhong Yao

  10. Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, PR China

    Cheng Luo & Dezhong Yao

  11. Department of Medical Psychology, Anhui Medical University, Hefei, PR China

    Gongjun Ji

  12. National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, PR China

    Jie Yang & Zhening Liu

  13. Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, PR China

    Chu-Chung Huang

  14. Shanghai Changning Mental Health Center, Shanghai, PR China

    Chu-Chung Huang

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

    Shih-Jen Tsai

  16. Chinese Institute for Brain Research, Beijing, PR China

    Weihua Yue

  17. PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, PR China

    Weihua Yue

  18. Department of Clinical Psychology, Fourth Military Medical University, Xi’an, PR China

    Long-Biao Cui

  19. Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China

    Kai Wang

  20. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China

    Kai Wang

  21. Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China

    Kai Wang

  22. Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China

    Kai Wang

  23. Anhui Institute of Translational Medicine, Hefei, China

    Kai Wang

  24. Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, PR China

    Jingliang Cheng

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

    Ching-Po Lin

  26. Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Shanghai, China

    Wei Cheng

  27. Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China

    Wei Cheng

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

    Wei Cheng & Jianfeng Feng

  29. MOE Frontiers Center for Brain Science, Fudan University, Shanghai, PR China

    Jianfeng Feng

  30. Zhangjiang Fudan International Innovation Center, Shanghai, PR China

    Jianfeng Feng

  31. School of Data Science, Fudan University, Shanghai, China

    Jianfeng Feng

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

    Jianfeng Feng

Authors
  1. Yuchao Jiang
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  2. Jijun Wang
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  3. Enpeng Zhou
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  4. Lena Palaniyappan
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  5. Cheng Luo
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  6. Gongjun Ji
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  7. Jie Yang
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  8. Yingchan Wang
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  9. Yuyanan Zhang
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  10. Chu-Chung Huang
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  11. Shih-Jen Tsai
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  12. Xiao Chang
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  13. Chao Xie
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  37. Ching-Po Lin
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  38. Dezhong Yao
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  39. Wei Cheng
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  40. Jianfeng Feng
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Consortia

the ZIB Consortium

  • Yuchao Jiang
  • , Xiao Chang
  • , Chao Xie
  • , Wei Zhang
  • , Jinchao Lv
  • , Di Chen
  • , Chun Shen
  • , Xinran Wu
  • , Bei Zhang
  • , Nanyu Kuang
  • , Yun-Jun Sun
  • , Jujiao Kang
  • , Jie Zhang
  • , Wei Cheng
  •  & Jianfeng Feng

Contributions

J.F. led the project. Y.J., W.C. and J.F. were responsible for the study concept and the design of the study. J.W. and E.Z. provided crucial advice for the study. Y.J., E.Z., C.X., W.Z., J.L., D.C., C.S., X.W., B.Z., N.K., Y.-J.S. and J.K. analysed the data and created the figures. Y.J. wrote the manuscript. J.W., E.Z., L.P. and W.C. made substantial contributions to the manuscript and provided critical comments. J.W., E.Z., C. Luo, G.J., J.Y., Y.W., Y.Z., C.-C.H., S.-J.T., X.C., J.Z., H. Huang, H.He, M.D., Y.T., T.Z., C. Li, X.Y., T.S., W.Y., Z.L., L.-B.C., K.W., J.C., C.-P.L. and D.Y. contributed to the data acquisition.

Corresponding authors

Correspondence to Wei Cheng or Jianfeng Feng.

Ethics declarations

Competing interests

L.P. reports personal fees from Janssen Canada, Otsuka Canada, SPMM Course Limited, UK, Canadian Psychiatric Association; book royalties from Oxford University Press; investigator-initiated educational grants from Janssen Canada, Sunovion and Otsuka Canada outside the submitted work. These interests played no role in the research reported here. Other authors declare no competing interests.

Peer review

Peer review information

Nature Mental Health thanks Johanna Seitz-Holland, Vince Calhoun, Jing Sui and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Extended Data Fig. 1 A flowchart of systematic characterization of heterogeneity in brain atrophy patterning.

(a) A total of cross-sectional MRI from 2170 individuals (1124 patients with schizophrenia) was used to characterize heterogeneity in brain atrophy patterning of schizophrenia. (b) Brain images were processed using voxel-based morphometry. GMV was extracted from ROIs based on the Automated Anatomical Labeling (AAL) atlas and adjusted by regressing out the effects of sex, age, the square of age, TIV and site effects. (c) Adjusted GMV values were normalized relative to control population using z scores. Higher z scores represent larger deviations from the normal (that is, more severe atrophy in patients with schizophrenia). (d) Brain pathophysiological model (that is, SuStaIn [31]) requires both spatial (brain regions) and temporal (z scores representing advancing atrophy severity) features as input (that is, an M × N z score matrix). Here, N represents the number of individuals with schizophrenia (N = 1124 in this study). M represents the number of ROIs (M = 17). (e) SuStaIn was used to identify diverse but distinct patterns of progression using cross-sectional neuroimaging data and to cluster individuals while accounting for disease progression. (f) Individuals with schizophrenia were classified according to the sequence of atrophy in different brain regions. For each subtype, brain-based staging was assessed from progressive spatial patterns with distinct origins. (g) Using a longitudinal sample, we examined whether subtype classification based on baseline brain features predict differential treatment response to antipsychotic medications and TMS.

Extended Data Fig. 2 Association between regional atrophy and clinical symptoms.

Spearman correlation analysis between PANSS (positive, negative and general psychopathology subscales) and GMV z scores were performed after adjusting for sex, age, the square of age, TIV and sites. Colored bar represents the r value after controlling the FWE corrected P < 0.05. L, left hemisphere; R, right hemisphere.

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Supplementary methods 1–10, tables 1–14 and figs. 1–10.

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Jiang, Y., Wang, J., Zhou, E. et al. Neuroimaging biomarkers define neurophysiological subtypes with distinct trajectories in schizophrenia. Nat. Mental Health 1, 186–199 (2023). https://doi.org/10.1038/s44220-023-00024-0

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