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Resolving heterogeneity in schizophrenia through a novel systems approach to brain structure: individualized structural covariance network analysis

Molecular Psychiatry volume 26, pages 7719–7731 (2021)Cite this article

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Abstract

Reliable mapping of system-level individual differences is a critical first step toward precision medicine for complex disorders such as schizophrenia. Disrupted structural covariance indicates a system-level brain maturational disruption in schizophrenia. However, most studies examine structural covariance at the group level. This prevents subject-level inferences. Here, we introduce a Network Template Perturbation approach to construct individual differential structural covariance network (IDSCN) using regional gray-matter volume. IDSCN quantifies how structural covariance between two nodes in a patient deviates from the normative covariance in healthy subjects. We analyzed T1 images from 1287 subjects, including 107 first-episode (drug-naive) patients and 71 controls in the discovery datasets and established robustness in 213 first-episode (drug-naive), 294 chronic, 99 clinical high-risk patients, and 494 controls from the replication datasets. Patients with schizophrenia were highly variable in their altered structural covariance edges; the number of altered edges was related to severity of hallucinations. Despite this variability, a subset of covariance edges, including the left hippocampus–bilateral putamen/globus pallidus edges, clustered patients into two distinct subgroups with opposing changes in covariance compared to controls, and significant differences in their anxiety and depression scores. These subgroup differences were stable across all seven datasets with meaningful genetic associations and functional annotation for the affected edges. We conclude that the underlying physiology of affective symptoms in schizophrenia involves the hippocampus and putamen/pallidum, predates disease onset, and is sufficiently consistent to resolve morphological heterogeneity throughout the illness course. The two schizophrenia subgroups identified thus have implications for the nosology and clinical treatment.

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Fig. 1: The workflow for constructing individual differential structural covariance network (IDSCN).
Fig. 2: Division of patients into two different subgroups in the primary FES dataset.
Fig. 3: Significant difference in depression and anxiety score between the two patient subgroups in the primary FES dataset.
Fig. 4: Validation of the patient subgroup results in two independent FES datasets.
Fig. 5: Patient subgroup results in three chronic schizophrenia datasets.
Fig. 6: Patient subgroup results for the clinical high-risk dataset.

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Acknowledgements

The neuroimaging and genetic data used here are from ZIB Consortium. JZ was supported by Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01) and ZJLab, 2016YFC0906402 and National Natural Science Foundation of China (NSFC 61973086). JF was supported by the 111 Project (No. B18015), the key project of Shanghai Science and Technology (No. 16JC1420402), National Key R&D Program of China (No. 2018YFC1312900), and National Natural Science Foundation of China (NSFC 91630314). LP acknowledges salary support from the Tanna Schulich Chair of Neuroscience and Mental Health. JW was supported by National Natural Science Foundation of China (81971251), Science and Technology Commission of Shanghai Municipality (No. 19411969100, 19410710800), and Clinical Research Center at Shanghai Mental Health Center (CRC2018ZD01, CRC2018ZD04, CRC2018YB01, and CRC2019ZD02).

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  1. These authors contributed equally: Zhaowen Liu, Lena Palaniyappan

Authors and Affiliations

  1. Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Zhaowen Liu

  2. Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Zhaowen Liu

  3. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Zhaowen Liu

  4. Department of Psychiatry and Robarts Research Institute, University of Western Ontario, London, ON, Canada

    Lena Palaniyappan

  5. Lawson Health Research Institute, London, ON, Canada

    Lena Palaniyappan

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

    Xinran Wu, Jiangnan Du, Qi Zhao, Chao Xie, Barbara J. Sahakian, Trevor W. Robbins, Jie Zhang & Jianfeng Feng

  7. Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, P. R. China

    Xinran Wu, Jiangnan Du, Qi Zhao, Chao Xie, Ching-Po Lin, Jie Zhang & Jianfeng Feng

  8. School of Computer Science and Technology, East China Normal University, Shanghai, P. R. China

    Kai Zhang

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

    Yingying Tang, Wenjun Su, Chunbo Li & Jijun Wang

  10. Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P. R. China

    Yarui Wei, Kangkang Xue, Shaoqiang Han & Jingliang Cheng

  11. Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, P. R. China

    Yarui Wei, Kangkang Xue, Shaoqiang Han & Jingliang Cheng

  12. Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, P. R. China

    Yarui Wei, Kangkang Xue, Shaoqiang Han & Jingliang Cheng

  13. Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, P. R. China

    Yarui Wei, Kangkang Xue, Shaoqiang Han & Jingliang Cheng

  14. Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, P. R. China

    Yarui Wei, Kangkang Xue, Shaoqiang Han & Jingliang Cheng

  15. Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, P. R. China

    Yarui Wei, Kangkang Xue, Shaoqiang Han & Jingliang Cheng

  16. Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, P. R. China

    Yarui Wei, Kangkang Xue, Shaoqiang Han & Jingliang Cheng

  17. Institute of Brain Sciences, National Yang-Ming Chiao-Tung University, Taipei, Taiwan

    Shih-Jen Tsai

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

    Shih-Jen Tsai

  19. Brain Research Center, National Yang-Ming Chiao-Tung University, Taipei, Taiwan

    Ching-Po Lin

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

    Ching-Po Lin

  21. CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, P. R. China

    Jijun Wang

  22. Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, P. R. China

    Jijun Wang

  23. Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK

    Barbara J. Sahakian

  24. Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK

    Trevor W. Robbins

  25. Shanghai Center for Mathematical Sciences, Shanghai, P. R. China

    Jianfeng Feng

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

    Jianfeng Feng

  27. Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, P. R. China

    Jianfeng Feng

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

    Jianfeng Feng

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  1. Zhaowen Liu
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Correspondence to Jingliang Cheng, Jie Zhang or Jianfeng Feng.

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LP 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. All other authors report no biomedical financial interests or potential conflicts of interest. None of the above-listed companies or funding agencies have had any influence on the content of this article.

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Liu, Z., Palaniyappan, L., Wu, X. et al. Resolving heterogeneity in schizophrenia through a novel systems approach to brain structure: individualized structural covariance network analysis. Mol Psychiatry 26, 7719–7731 (2021). https://doi.org/10.1038/s41380-021-01229-4

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