Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, 100700 Beijing, China.
Department of Imaging, Dongzhimen Hospital, Beijing University of Chinese Medicine, 100700 Beijing, China.
J Integr Neurosci. 2024 Sep 29;23(10):182. doi: 10.31083/j.jin2310182.
Advancements in neuroimaging technologies have significantly deepened our understanding of the neural physiopathology associated with stroke. Nevertheless, the majority of studies ignored the characteristics of dynamic changes in brain networks. The relationship between dynamic changes in brain networks and the severity of motor dysfunction after stroke needs further investigation. From the perspective of multilayer network module reconstruction, we aimed to explore the dynamic reorganization of the brain and its relationship with motor function in subcortical stroke patients.
We recruited 35 healthy individuals and 50 stroke patients with unilateral limb motor dysfunction (further divided into mild-moderate group and severe group). Using dynamic multilayer network modularity analysis, we investigated changes in the dynamic modular reconfiguration of brain networks. Additionally, we assessed longitudinal clinical scale changes in stroke patients. Correlation and regression analyses were employed to explore the relationship between characteristic dynamic indicators and impairment and recovery of motor function, respectively.
We observed increased temporal flexibility in the Default Mode Network (DMN) and decreased recruitment of module reconfiguration in the Attention Network (AN), Sensorimotor Network (SMN), and DMN after stroke. We also observed reduced module loyalty following stroke. Additionally, correlation analysis showed that hyper-flexibility of the DMN was associated with better lower limb motor function performance in stroke patients with mild-to-moderate impairment. Regression analysis indicated that increased flexibility within the DMN and decreased recruitment coefficient within the AN may predict good lower limb function prognosis in patients with mild to moderate motor impairment.
Our study revealed more frequent modular reconfiguration and hyperactive interaction of brain networks after stroke. Notably, dynamic modular remodeling was closely related to the impairment and recovery of motor function. Understanding the temporal module reconfiguration patterns in multilayer networks after stroke can provide valuable information for more targeted treatments.
神经影像学技术的进步极大地深化了我们对与中风相关的神经生理病理学的理解。然而，大多数研究忽略了脑网络动态变化的特征。脑网络动态变化与中风后运动功能障碍严重程度之间的关系需要进一步研究。从多层网络模块重建的角度出发，我们旨在探索皮质下中风患者脑的动态重组织及其与运动功能的关系。
我们招募了 35 名健康个体和 50 名单侧肢体运动功能障碍的中风患者（进一步分为轻度-中度组和重度组）。我们使用动态多层网络模块性分析来研究脑网络动态模块重新配置的变化。此外，我们评估了中风患者的纵向临床量表变化。我们分别采用相关性和回归分析来探讨特征动态指标与运动功能障碍和恢复的关系。
我们观察到中风后默认模式网络（DMN）的时间灵活性增加，注意力网络（AN）、感觉运动网络（SMN）和 DMN 的模块重新配置的募集减少。我们还观察到中风后模块忠诚度降低。此外，相关性分析表明，DMN 的过度灵活性与轻度至中度运动障碍中风患者下肢运动功能表现较好相关。回归分析表明，DMN 内的灵活性增加和 AN 内的募集系数减少可能预示着轻度至中度运动障碍患者下肢功能预后良好。
我们的研究揭示了中风后脑网络更频繁的模块重新配置和过度活跃的交互作用。值得注意的是，动态模块重塑与运动功能的障碍和恢复密切相关。了解中风后多层网络中的时间模块重新配置模式可为更有针对性的治疗提供有价值的信息。
