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Whole-brain Dynamical Modeling Based on Bifurcation Theory
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Qi-li GUO1, Jing WEI2, Yu-xuan LIU1, Ya-ru XU1, Zhi-peng HAO1, Yan-li YANG1, *
Science Technology and Engineering | 2025, 25(18) : 7700 - 7709
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Science Technology and Engineering | 2025, 25(18): 7700-7709
• Papers·Automation and Computational Technology •
Whole-brain Dynamical Modeling Based on Bifurcation Theory
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Qi-li GUO1, Jing WEI2, Yu-xuan LIU1, Ya-ru XU1, Zhi-peng HAO1, Yan-li YANG1, *
Affiliations
  • 1 College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030600, China
  • 2 College of Information, Shanxi University of Finance and Economics, Taiyuan 030012, China
Published: 2025-06-28 doi: 10.12404/j.issn.1671-1815.2405388
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Traditional whole-brain dynamical modeling techniques are typically constrained by static single features, neglecting dynamic fluctuations in brain networks and lacking qualitative analysis of corresponding indicators, which limits modeling accuracy and comprehensibility. In order to address this issue, a multi-objective expectation maximization algorithm based on bifurcation analysis was proposed. This approach integrates a dynamic mean-field model with brain structural-functional features extracted from multi-mode imaging data for modeling purposes. Bifurcation theory was employed to qualitatively analyze multiple constraint indicators of the model, including functional connectivity, dynamic functional connectivity, and metastability for model inversion. Initial parameter values were determined through bifurcation analysis, and parameter combinations were iteratively refined using an expectation maximization algorithm. Quantitative analysis validates the accuracy and stability of this method.

dynamic mean field  /  bifurcation analysis  /  multi-mode  /  multi-objective
Qi-li GUO, Jing WEI, Yu-xuan LIU, Ya-ru XU, Zhi-peng HAO, Yan-li YANG. Whole-brain Dynamical Modeling Based on Bifurcation Theory[J]. Science Technology and Engineering, 2025 , 25 (18) : 7700 -7709 . DOI: 10.12404/j.issn.1671-1815.2405388
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Year 2025 volume 25 Issue 18
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Article Info
doi: 10.12404/j.issn.1671-1815.2405388
  • Receive Date：2024-07-17
  • Online Date：2025-12-17
  • Published：2025-06-28
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  • Received：2024-07-17
  • Revised：2025-03-21
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    1 College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030600, China
    2 College of Information, Shanxi University of Finance and Economics, Taiyuan 030012, China
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