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<p>The diagnosis of Alzheimer’s disease (AD) and frontotemporal dementia (FTD) is currently a hot research topic, but no one has tried to solve this problem from the perspective of constructing heterogeneous whole-brain neural networks and analyzing their dynamic behavior. This paper presents a method for constructing such a network based on the small-world small-world (SW-SW) architecture and phase locking value (PLV) matrix. The proposed model, termed the clustered SW-SW neural network (CSNN), consists of 19 coupled sub-networks, each structured as an SW-SW neural network. The coupling strength between neurons within each sub-network is determined by a PLV matrix derived from functional brain network data. Using electroencephalography (EEG) data from normal controls (NC), patients with AD, and those with FTD, we constructed three distinct CSNNs and compared their dynamic behaviors. Numerical simulations revealed dynamic behavioral patterns consistent with empirical observations, demonstrating the feasibility of the proposed neural network construction method.</p>

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Heterogeneous whole-brain neural network constructed by SW-SW architecture and PLV matrix

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • Minglin Ma,
  • Zhiyi Yuan,
  • Xue Zhao

摘要

The diagnosis of Alzheimer’s disease (AD) and frontotemporal dementia (FTD) is currently a hot research topic, but no one has tried to solve this problem from the perspective of constructing heterogeneous whole-brain neural networks and analyzing their dynamic behavior. This paper presents a method for constructing such a network based on the small-world small-world (SW-SW) architecture and phase locking value (PLV) matrix. The proposed model, termed the clustered SW-SW neural network (CSNN), consists of 19 coupled sub-networks, each structured as an SW-SW neural network. The coupling strength between neurons within each sub-network is determined by a PLV matrix derived from functional brain network data. Using electroencephalography (EEG) data from normal controls (NC), patients with AD, and those with FTD, we constructed three distinct CSNNs and compared their dynamic behaviors. Numerical simulations revealed dynamic behavioral patterns consistent with empirical observations, demonstrating the feasibility of the proposed neural network construction method.