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

processes underlying schizophrenia that potentially yield to stratification and prognostication—a key requirement for personalizing treatments in enduring illnesses. Access through your

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SIMILAR CONTENT BEING VIEWED BY OTHERS PROGRESSIVE BRAIN ABNORMALITIES IN SCHIZOPHRENIA ACROSS DIFFERENT ILLNESS PERIODS: A STRUCTURAL AND FUNCTIONAL MRI STUDY Article Open access 05 January

2023 HETEROGENEOUS PATTERNS OF BRAIN ATROPHY IN SCHIZOPHRENIA LOCALIZE TO A COMMON BRAIN NETWORK Article 12 December 2024 MIND THE GAP: FROM NEURONS TO NETWORKS TO OUTCOMES IN MULTIPLE

SCLEROSIS Article 12 January 2021 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 ([email protected]), X. Yu

([email protected]), W. Yue ([email protected]) and C. Luo ([email protected]); TMS data—J. Wang ([email protected]), G. Ji ([email protected]), L. Cui ([email protected]) and C. Luo

([email protected]). 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

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Article  PubMed  PubMed Central  Google Scholar  Download references 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 * These authors contributed equally: Yuchao Jiang, Jijun Wang, Enpeng

Zhou. AUTHORS AND AFFILIATIONS * 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 * 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 * 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 * 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 * Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Quebec, Quebec, Canada Lena Palaniyappan *

Robarts Research Institute, University of Western Ontario, London, Ontario, Canada Lena Palaniyappan * Lawson Health Research Institute, London, Ontario, Canada Lena Palaniyappan * 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 * 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 * Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical

Sciences, Chengdu, PR China Cheng Luo & Dezhong Yao * Department of Medical Psychology, Anhui Medical University, Hefei, PR China Gongjun Ji * 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 * 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 * Shanghai Changning Mental Health Center, Shanghai, PR China Chu-Chung Huang * Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan Shih-Jen Tsai * Chinese

Institute for Brain Research, Beijing, PR China Weihua Yue * PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, PR China Weihua Yue * Department of Clinical

Psychology, Fourth Military Medical University, Xi’an, PR China Long-Biao Cui * 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 * Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China Kai Wang

* Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China Kai Wang * Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei,

China Kai Wang * Anhui Institute of Translational Medicine, Hefei, China Kai Wang * Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, PR China Jingliang

Cheng * Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan Ching-Po Lin * Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer

Center, Shanghai, China Wei Cheng * Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China Wei Cheng * Fudan ISTBI—ZJNU Algorithm Centre for Brain-Inspired

Intelligence, Zhejiang Normal University, Jinhua, China Wei Cheng & Jianfeng Feng * MOE Frontiers Center for Brain Science, Fudan University, Shanghai, PR China Jianfeng Feng *

Zhangjiang Fudan International Innovation Center, Shanghai, PR China Jianfeng Feng * School of Data Science, Fudan University, Shanghai, China Jianfeng Feng * Department of Computer Science,

University of Warwick, Coventry, UK Jianfeng Feng Authors * Yuchao Jiang View author publications You can also search for this author inPubMed Google Scholar * Jijun Wang View author

publications You can also search for this author inPubMed Google Scholar * Enpeng Zhou View author publications You can also search for this author inPubMed Google Scholar * Lena

Palaniyappan View author publications You can also search for this author inPubMed Google Scholar * Cheng Luo View author publications You can also search for this author inPubMed Google

Scholar * Gongjun Ji View author publications You can also search for this author inPubMed Google Scholar * Jie Yang View author publications You can also search for this author inPubMed 

Google Scholar * Yingchan Wang View author publications You can also search for this author inPubMed Google Scholar * Yuyanan Zhang View author publications You can also search for this

author inPubMed Google Scholar * Chu-Chung Huang View author publications You can also search for this author inPubMed Google Scholar * Shih-Jen Tsai View author publications You can also

search for this author inPubMed Google Scholar * Xiao Chang View author publications You can also search for this author inPubMed Google Scholar * Chao Xie View author publications You can

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publications You can also search for this author inPubMed Google Scholar 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.

ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. INTEGRATED SUPPLEMENTARY

INFORMATION 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. SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION Supplementary methods 1–10, tables 1–14 and figs. 1–10. REPORTING

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permissions ABOUT THIS ARTICLE CITE THIS ARTICLE 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 Download citation * Received: 28 May 2022 * Accepted: 23 January 2023 * Published: 22 March

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