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A Data-Driven Framework for Whole-Brain Network Modeling with Simultaneous EEG-SEEG Data

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Intelligent Information Processing XII (IIP 2024)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 703))

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Abstract

Whole-brain network modeling (WBM) offers a pivotal tool to explore the large-scale spatiotemporal dynamics of the brain at rest, during cognitive tasks, and under external stimulation. However, it is unclear how to fuse multi-modal neural dynamics in a united WBM framework and predict the whole-brain spatiotemporal neural responses to electrical stimulation. In this study, we present a computational framework with whole-brain network modeling, parameter optimization, and model validation using simultaneous EEG-SEEG data during intracranial brain stimulation. To test the efficacy of WBM in revealing brain-wide neural dynamics, our experiments utilize synthetic electrophysiological data, real EEG data, and real EEG-SEEG signals. Experimental results demonstrate that our WBM framework accurately captures the spatiotemporal brain activities by jointly leveraging the higher spatial resolution from SEEG and the whole-brain coverage from EEG. Notably, our model shows a higher correlation between the functional connectivity (FC) matrix of EEG and that of the inferred whole-brain neural dynamics from WBM (r=0.86), compared to the FC from EEG source localization (r=0.48). Together, we demonstrate the capability and flexibility of WBM framework to uncover the whole-brain spatiotemporal neural activity and its potential to provide new insights into the input-response mechanism of the brain.

This work was funded in part by the National Key R &D Program of China (2021YFF1200804), UQ-Research Training Program (UQ-RTP) Scholarship, National Natural Science Foundation of China (62001205), Shenzhen Science and Technology Innovation Committee (2022410129, KCXFZ2020122117340001).

K. Lou and J. Li—Co-first authors.

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Acknowledgements

We thank the researchers and participants who provided the open-source datasets used in this study. We are grateful for the insightful discussions with Dr. Chen Yao at Shenzhen Second Hospital, Dr. Liang Chen and Dr. Shuhao Mei at Huashan Hospital.

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Authors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Kexin Lou, Jingzhe Li & Quanying Liu

  2. The University of Queensland, Brisbane, Australia

    Kexin Lou & Markus Barth

Authors
  1. Kexin Lou
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  2. Jingzhe Li
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  3. Markus Barth
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  4. Quanying Liu
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Corresponding author

Correspondence to Quanying Liu .

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Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Zhongzhi Shi

  2. University of Oslo, Oslo, Norway

    Jim Torresen

  3. De Montfort University, Leicester, UK

    Shengxiang Yang

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Lou, K., Li, J., Barth, M., Liu, Q. (2024). A Data-Driven Framework for Whole-Brain Network Modeling with Simultaneous EEG-SEEG Data. In: Shi, Z., Torresen, J., Yang, S. (eds) Intelligent Information Processing XII. IIP 2024. IFIP Advances in Information and Communication Technology, vol 703. Springer, Cham. https://doi.org/10.1007/978-3-031-57808-3_24

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  • DOI: https://doi.org/10.1007/978-3-031-57808-3_24

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