Dong Kangli, Zhong Yuming, Zhang Lu, Liang Wei, Zhao Yue, Liu Jun, Chen Siya, Mahmoud Seedahmed S, Sun Yu
IEEE Trans Neural Syst Rehabil Eng. 2025;33:2870-2881. doi: 10.1109/TNSRE.2025.3590826.
Post-stroke aphasia (PSA), induced by acute brain injury, is an acquired language disorder resulting from stroke, primarily characterized by impairments across multiple linguistic functions including spontaneous speech, auditory comprehension, repetition, naming, reading, and writing. Previous studies have demonstrated that the topological features of healthy brains align with complex networks, whereas key topological features in PSA patients (e.g., interhemispheric connectivity, functional connectivity (FC) of language networks) undergo significant alterations due to acute brain injury. However, traditional graph-theoretical approaches fail to elucidate the dynamic evolutionary patterns underlying functional reorganization in brain networks. Moreover, existing research lacks systematic exploration of trajectory characteristics and economic cost regulation mechanisms in network generation among PSA patients. To address these gaps, this study introduces a framework based on generative network modeling, integrating non-geometric rules (power-law functions of topological relationships) and geometric rules (connection distance calculations) to simulate the formation process of functional brain networks. By parametrically modulating the balance between nodal connection propensity and distance cost, and comparing the optimal matching between simulated and observed networks, we explored the evolutionary mechanism of brain networks in PSA patients. Key findings include: For the FC matrix with 10% sparsity, 1) The homogeneous model combined with geometric distance-based economic costs generates optimal simulated networks; 2) PSA patients exhibit significantly higher absolute values of parameter $\eta $ compared to general stroke patients ( ${p}\lt {0}.{05}$ ), indicating increased economic costs for connections with distal nodes; 3) PSA patients show the highest $\gamma $ values, with significant reduction in inter-nodal connection propensity versus healthy controls ( ${p}\lt {0}.{05}$ ), suggesting impaired network integration efficiency; 4) Trajectory analysis reveals decreased parametric values in thalamus-related regions but elevated values in occipital and cerebellar regions among PSA patients, with distance costs showing negative correlation with stroke patients ( ${R}^{{2}}={0}.{86}$ ), uncovering region-specific trajectories of functional reorganization around lesions. By constructing a computational model incorporating economic clustering rules, this study clarifies differential network evolution patterns between PSA and general stroke patients, provides theoretical foundations for targeted neuromodulation and intervention strategy optimization, and addresses the limitations of traditional graph theory in dynamic mechanism analysis.
中风后失语症（PSA）由急性脑损伤引起，是一种因中风导致的后天性语言障碍，主要特征是多种语言功能受损，包括自发言语、听觉理解、复述、命名、阅读和书写。先前的研究表明，健康大脑的拓扑特征与复杂网络一致，而PSA患者的关键拓扑特征（如半球间连接性、语言网络的功能连接（FC））由于急性脑损伤而发生显著改变。然而，传统的图论方法无法阐明脑网络功能重组背后的动态演化模式。此外，现有研究缺乏对PSA患者网络生成中的轨迹特征和经济成本调节机制的系统探索。为了填补这些空白，本研究引入了一个基于生成网络建模的框架，整合非几何规则（拓扑关系的幂律函数）和几何规则（连接距离计算）来模拟功能性脑网络的形成过程。通过参数化调节节点连接倾向和距离成本之间的平衡，并比较模拟网络和观察网络之间的最佳匹配，我们探索了PSA患者脑网络的演化机制。主要发现包括：对于稀疏度为10%的FC矩阵，1）结合基于几何距离的经济成本的均匀模型生成了最优模拟网络；2）与一般中风患者相比，PSA患者的参数$\eta$绝对值显著更高（$p\lt0.05$），表明与远端节点连接的经济成本增加；3）PSA患者的$\gamma$值最高，与健康对照组相比，节点间连接倾向显著降低（$p\lt0.05$），表明网络整合效率受损；4）轨迹分析显示，PSA患者丘脑相关区域的参数值降低，但枕叶和小脑区域的参数值升高，距离成本与中风患者呈负相关（$R^2 = 0.86$），揭示了病变周围功能重组的区域特异性轨迹。通过构建一个包含经济聚类规则的计算模型，本研究阐明了PSA患者与一般中风患者之间不同的网络演化模式，为靶向神经调节和干预策略优化提供了理论基础，并解决了传统图论在动态机制分析中的局限性。
