建模脑区间连接路径的动态变化，提升神经疾病诊断潜力
NeuroPathNet: Dynamic Path Trajectory Learning for Brain Functional Connectivity Analysis
- 基于静态脑区划分，用时序神经网络捕捉连接强度变化轨迹
- 在3个fMRI数据集上超越主流方法，多指标表现更优
- 适合脑网络动态分析与神经疾病临床研究者参考
理解脑功能网络随时间的演化对认知机制解析和神经疾病诊断具有重要意义。现有方法难以捕捉特定功能社区间连接的时序特征。为此，本文提出一种路径级轨迹建模框架NeuroPathNet，用于表征脑功能分区间连接路径的动态行为。基于医学支持的静态分区方案（如Yeo和Smith ICA），提取每对功能分区间连接强度的时间序列，并用时序神经网络进行建模。在三个公开的功能磁共振成像(fMRI)数据集上验证模型性能，结果表明其在多项指标上优于现有主流方法。该研究可推动脑网络动态图学习方法的发展，并为神经疾病诊断提供潜在临床应用价值。
原文摘要 · Abstract (English)
Understanding the evolution of brain functional networks over time is of great significance for the analysis of cognitive mechanisms and the diagnosis of neurological diseases. Existing methods often have difficulty in capturing the temporal evolution characteristics of connections between specific functional communities. To this end, this paper proposes a new path-level trajectory modeling framework (NeuroPathNet) to characterize the dynamic behavior of connection pathways between brain functional partitions. Based on medically supported static partitioning schemes (such as Yeo and Smith ICA), we extract the time series of connection strengths between each pair of functional partitions and model them using a temporal neural network. We validate the model performance on three public functional Magnetic Resonance Imaging (fMRI) datasets, and the results show that it outperforms existing mainstream methods in multiple indicators. This study can promote the development of dynamic graph learning methods for brain network analysis, and provide possible clinical applications for the diagnosis of neurological diseases.
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