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Assisting schizophrenia diagnosis using clinical electroencephalography and interpretable graph neural networks: a real-world and cross-site study

Neuropsychopharmacology volume 48, pages 1920–1930 (2023)Cite this article

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Abstract

Schizophrenia (SCZ) is a chronic and serious mental disorder with a high mortality rate. At present, there is a lack of objective, cost-effective and widely disseminated diagnosis tools to address this mental health crisis globally. Clinical electroencephalogram (EEG) is a noninvasive technique to measure brain activity with high temporal resolution, and accumulating evidence demonstrates that clinical EEG is capable of capturing abnormal SCZ neuropathology. Although EEG-based automated diagnostic tools have obtained impressive performance on individual datasets, the transportability of potential EEG biomarkers in cross-site real-world application is still an open question. To address the challenges of small sample sizes and population heterogeneity, we develop an advanced interpretable deep learning model using multimodal clinical EEG features and demographic information as inputs to graph neural networks, and further propose different transfer learning strategies to adapt to different clinical scenarios. Taking the disease discrimination of health control (HC) and SCZ with 1030 participants as a use case, our model is trained on a small clinical dataset (N = 188, Chinese) and enhanced using a large-scale public dataset (N = 508, American) of adult participants. Cross-site validation from an independent dataset of adult participants (N = 157, Chinese) produced stable performance, with AUCs of 0.793–0.852 and accuracies of 0.786–0.858 for different SCZ prevalence, respectively. In addition, cross-site validation from another dataset of adolescent boys (N = 84, Russian) yielded an AUC of 0.702 and an accuracy of 0.690. Moreover, feature visualization further revealed that the ranking of feature importance varied significantly among different datasets, and that EEG theta and alpha band power appeared to be the most significant and translational biomarkers of SCZ pathology. Overall, our promising results demonstrate the feasibility of SCZ discrimination using EEG biomarkers in multiple clinical settings.

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Fig. 1: Study design of classification-based diagnosis of schizophrenia.
Fig. 2: Graph structure of EEG channels used in the graph convolutional neural networks.
Fig. 3: Performance comparison for cross-site validation on the Hangzhou dataset.
Fig. 4: Feature comparisons between the Chengdu and Hangzhou datasets.

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Data availability

The TUH dataset can be open-accessed at https://isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml, and the Moscow dataset can be open-accessed at http://brain.bio.msu.ru/eeg_schizophrenia.htm. The Chengdu/Hangzhou clinical EEG datasets supporting the findings of this study are accessible from the corresponding author upon reasonable request, with the approval of the Institutional Review Board of the West China Hospital/the Hangzhou Seventh People’s Hospital.

Code availability

The codes of the proposed approach are accessible at https://github.com/ChenPeiyin/Diagnosis-based-EEG-A-cross-site-study. Codes of traditional machine learning algorithms used in this study are publicly available at https://github.com/scikit-learn/scikit-learn. The code used for SHAP analysis is available at https://github.com/slundberg/shap.

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Funding

This work was partly supported by STI2030-Major Projects (grant number 2022ZD0212400, 2021ZD0200404), the Fundamental Research Funds for the Central Universities (grant number 226-2022-00138), Hangzhou Biomedical and Health Industry Special Projects for Science and Technology (2021WJCY240), the National Natural Science Foundation of China (grant number 81920108018), the Key R & D Program of Zhejiang (grant number 2022C03096), Project for Hangzhou Medical Disciplines of Excellence.

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Author notes

  1. These authors contributed equally: Haiteng Jiang, Peiyin Chen.

  2. These authors jointly supervised this work: Yaoyun Zhang, Tao Li.

Authors and Affiliations

  1. Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, China

    Haiteng Jiang, Chengqian Liang, Rui Xue, Xiaojing Li, Wei Deng & Tao Li

  2. Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China

    Haiteng Jiang & Tao Li

  3. NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China

    Haiteng Jiang & Tao Li

  4. Alibaba Damo Academy, 969 West Wen Yi Road, Yu Hang District, Hangzhou, Zhejiang, China

    Peiyin Chen, Fei Huang, Songfang Huang & Yaoyun Zhang

  5. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

    Peiyin Chen & Zhongke Gao

  6. College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China

    Zhaohong Sun

  7. Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China

    Liansheng Zhao & Qiang Wang

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Contributions

The work presented here was carried out in collaboration among all authors. HJ, YZ, and TL designed the initial methods and experiments. HJ, LZ, QW, XL, WD, ZG, FH, SH, and TL discussed and refined the study design. CL and RX carried out the data collection, HJ, PC and YZ analyzed the data, HJ and YZ interpreted the results and drafted the paper. All authors have attributed to, read, and approved the manuscript.

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Correspondence to Yaoyun Zhang or Tao Li.

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Jiang, H., Chen, P., Sun, Z. et al. Assisting schizophrenia diagnosis using clinical electroencephalography and interpretable graph neural networks: a real-world and cross-site study. Neuropsychopharmacol. 48, 1920–1930 (2023). https://doi.org/10.1038/s41386-023-01658-5

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