Authors
Lan Yang, Xiaoyu Cui, Ting Li, Xi Zhang, Ruiyun Chang, Jiayu Lu, Dandan Li, Bin Wang
Published in
IEEE transactions on neural networks and learning systems. Volume PP. Sep 07, 2026. Epub Sep 07, 2026.
Abstract
Constructing dynamic virtual brain models is essential for understanding brain functions and pathological mechanisms, crucial in computational neuroscience. Current modeling methods can be grouped into two paradigms: deep learning models for accurate simulation, and neural dynamics models emphasizing physiological interpretability. However, these methods entail a fundamental tradeoff between accuracy and interpretability. To address this challenge, we introduce the neurodynamics-informed brain simulator (NDIB-Sim), a multimodal bidirectional physics-informed neural network (PINN) model. NDIB-Sim is a unified framework integrating a data-driven module constrained by multimodal data and a multiscale neural dynamics mechanism module, jointly optimized under a composite loss function. It contains two data loss and two physical constraint terms. This design ensures that the generated brain signals adhere to fundamental neurophysiological principles while achieving high fidelity to empirical data. We also designed a dynamic weighting strategy to adaptively balance these objectives during optimization. This framework simultaneously addresses the forward problem of predicting long-term brain activity and the inverse problem of estimating individual-specific neurophysiological parameters. Extensive experiments demonstrate that NDIB-Sim can achieve high-fidelity long-term brain activity prediction from short-term observations, with an average functional connectivity similarity above 0.97. The inferred effective connectivity (EC) shows excellent reliability and strong alignment with underlying structural and functional architecture. When applied to Alzheimer's disease (AD) classification, these subject-specific parameters achieve high accuracy in distinguishing cognitively normal (CN) individuals from AD patients. This work presents a powerful computational framework that effectively reconciles mechanistic interpretability with data-driven performance, offering a novel approach for exploring brain dynamics and identifying potential disease biomarkers.
PMID:
42704803
Bibliographic data and abstract were imported from PubMed on 08 Sep 2026.
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