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Multi-scale Residual Graph Attention Network for Major Depressive Disorder Recognition

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Database Systems for Advanced Applications (DASFAA 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14856))

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Abstract

Major depressive disorder (MDD) has emerged as one of the most prevalent mental disorders. As a typical time series data, electroencephalogram (EEG) is an objective physiological signal. The conventional method for recognizing MDD based on EEG suffers from long-term forgetting. As the length of the time series obtained from a single scale model increases, the time-related hidden states increase, leading to a greater likelihood of flooding previously valid information. Additionally, some existing methods rely on thresholds to weight the brain network connectivity importance, which cannot capture changes in global dynamic interactions and introduces additional biases. To address the above issues, we propose a novel MDD recognition method based on the multi-scale residual graph attention network (MReGAN). On the one hand, this method introduces a multi-scale residual module, which utilizes multi-scale feature representation to obtain complex multi-level changes. It is combined with a dilated causal convolution network to preserve the interaction information of different time periods and solve the problem of long-term forgetting. On the other hand, this method utilizes the multi-scale graph attention mechanism to directly capture the differences between MDD and normal control (NC) in the core topology and significant patterns of the brain functional connectivity network (BFCN), capturing global dynamic interaction patterns. Experimental results on benchmark datasets validate the exceptional performance and computational efficiency of MReGAN. Furthermore, comprehensive analysis shows that the connections between Fp 1 and Fp 2 channels in the Delta frequency band may serve as potential biomarkers.

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References

  1. Bian, Z., Li, Q., Wang, L., Lu, C., Yin, S., Li, X.: Relative power and coherence of EEG series are related to amnestic mild cognitive impairment in diabetes. Front. Aging Neurosci. 6, 11 (2014)

    Article  Google Scholar 

  2. Cai, H., et al.: A multi-modal open dataset for mental-disorder analysis. Sci. Data 9(1), 1–10 (2022)

    Article  MathSciNet  Google Scholar 

  3. Cao, Y., Cui, L., Zhang, L., Yu, F., Li, Z., Xu, Y.: MMTN: multi-modal memory transformer network for image-report consistent medical report generation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 277–285 (2023)

    Google Scholar 

  4. Cavanagh, J.F.: EEG: depression rest. OpenNeuro (2021)

    Google Scholar 

  5. Choi, H., Mun, S., Joo, E.J., Lee, K.Y., Kang, H.G., Lee, J.: Serum proteomic analysis of major depressive disorder patients and their remission status: novel biomarker set of zinc-alpha-2-glycoprotein and keratin type ii cytoskeletal 1. Int. J. Biol. Macromol. 183, 2001–2008 (2021)

    Article  Google Scholar 

  6. Freeman, M.: The world mental health report: transforming mental health for all. World Psychiatry 21(3), 391–392 (2022)

    Article  Google Scholar 

  7. Goswami, A., Poddar, S., Mehrotra, A., Ansari, G.: Depression detection using spatial images of multichannel EEG data. In: Unhelker, B., Pandey, H.M., Raj, G. (eds.) Applications of Artificial Intelligence and Machine Learning. LNEE, vol. 925, pp. 569–579. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-4831-2_46

  8. Guan, K., Zhang, Z., Chai, X., Tian, Z., Liu, T., Niu, H.: EEG based dynamic functional connectivity analysis in mental workload tasks with different types of information. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 632–642 (2022)

    Article  Google Scholar 

  9. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  10. Li, R., Yang, D., Fang, F., Hong, K.S., Reiss, A.L., Zhang, Y.: Concurrent fNIRS and EEG for brain function investigation: a systematic, methodology-focused review. Sensors 22(15), 5865–5886 (2022)

    Article  Google Scholar 

  11. Li, R., Zhong, T., Jiang, X., Trajcevski, G., Wu, J., Zhou, F.: Mining spatio-temporal relations via self-paced graph contrastive learning. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 936–944 (2022)

    Google Scholar 

  12. Liu, W., Jia, K., Wang, Z., Ma, Z.: A depression prediction algorithm based on spatiotemporal feature of EEG signal. Brain Sci. 12(5), 630 (2022)

    Article  Google Scholar 

  13. Mao, K., et al.: Prediction of depression severity based on the prosodic and semantic features with bidirectional LSTM and time distributed CNN. IEEE Trans. Affect. Comput. 14(3), 2251–2265 (2023)

    Article  Google Scholar 

  14. McGuinness, A., et al.: A systematic review of gut microbiota composition in observational studies of major depressive disorder, bipolar disorder and schizophrenia. Mol. Psychiatry 27(4), 1920–1935 (2022)

    Article  MathSciNet  Google Scholar 

  15. Mumtaz, W.: MDD Patients and Healthy Controls EEG Data (New). figshare (2016)

    Google Scholar 

  16. Saeedi, A., Saeedi, M., Maghsoudi, A., Shalbaf, A.: Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach. Cogn. Neurodyn. 15, 239–252 (2021)

    Article  Google Scholar 

  17. Sakkalis, V.: Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG. Comput. Biol. Med. 41(12), 1110–1117 (2011)

    Article  Google Scholar 

  18. Stam, C.J., Nolte, G., Daffertshofer, A.: Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum. Brain Mapp. 28(11), 1178–1193 (2007)

    Article  Google Scholar 

  19. Sun, S., Chen, H., Shao, X., Liu, L., Li, X., Hu, B.: EEG based depression recognition by combining functional brain network and traditional biomarkers. In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2074–2081. IEEE (2020)

    Google Scholar 

  20. Sun, X., Xu, Y., Zhao, Y., Zheng, X., Zheng, Y., Cui, L.: Multi-granularity graph convolution network for major depressive disorder recognition. IEEE Trans. Neural Syst. Rehabil. Eng. 32, 559–569 (2023)

    Article  Google Scholar 

  21. Sun, X., Zheng, X., Xu, Y., Cui, L., Hu, B.: Major depressive disorder recognition and cognitive analysis based on multi-layer brain functional connectivity networks. arXiv preprint arXiv:2111.01351 (2021)

  22. Vahid, A., Mückschel, M., Stober, S., Stock, A.K., Beste, C.: Conditional generative adversarial networks applied to EEG data can inform about the inter-relation of antagonistic behaviors on a neural level. Commun. Biol. 5(1), 148–158 (2022)

    Article  Google Scholar 

  23. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  24. Xia, M., Wang, J., He, Y.: BrainNet viewer: a network visualization tool for human brain connectomics. PLoS ONE 8(7), e68910 (2013)

    Article  Google Scholar 

  25. Yasin, S., Hussain, S.A., Aslan, S., Raza, I., Muzammel, M., Othmani, A.: EEG based major depressive disorder and bipolar disorder detection using neural networks: a review. Comput. Meth. Program. Biomed. 202, 106007 (2021)

    Article  Google Scholar 

  26. Zhang, B., Yan, G., Yang, Z., Su, Y., Wang, J., Lei, T.: Brain functional networks based on resting-state EEG data for major depressive disorder analysis and classification. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 215–229 (2021)

    Article  Google Scholar 

  27. Zhu, J., et al.: EEG based depression recognition using improved graph convolutional neural network. Comput. Biol. Med. 148, 105815 (2022)

    Article  Google Scholar 

Download references

Acknowledgments

This research is partially supported by the National Key R&D Program of China 2021YFF0900800, Natural Science Foundation of China (No. 92367202, No. 62202279, No. 72293581), the Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project) (No. 2021CXGC010108), Excellent Youth Science Fund Project (Overseas) of Shandong (No. 2023HWYQ-039), Natural Science Foundation of Shandong Province (No. ZR2022QF018, No. ZR2023LZH006, No. ZR2022QF114), the Fundamental Research Funds of Shandong University.

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Correspondence to Yonghui Xu or Lizhen Cui .

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Sun, X., Xu, Y., Liu, N., Zheng, Y., Cui, L. (2024). Multi-scale Residual Graph Attention Network for Major Depressive Disorder Recognition. In: Onizuka, M., et al. Database Systems for Advanced Applications. DASFAA 2024. Lecture Notes in Computer Science, vol 14856. Springer, Singapore. https://doi.org/10.1007/978-981-97-5575-2_1

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  • DOI: https://doi.org/10.1007/978-981-97-5575-2_1

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