The brain continually reorganizes its functional network to adapt to post-stroke functional impairments. Previous studies using static modularity analysis have presented global-level behavior patterns of this network reorganization. However, it is far from understood how the brain reconfigures its functional network dynamically following a stroke. This study collected resting-state functional MRI data from 15 stroke patients, with mild (n = 6) and severe (n = 9) two subgroups based on their clinical symptoms. Additionally, 15 age-matched healthy subjects were considered as controls. By applying a multilayer network method, a dynamic modular structure was recognized based on a time-resolved function network. Then dynamic network measurements (recruitment, integration, and flexibility) were calculated to characterize the dynamic reconfiguration of post-stroke brain functional networks, hence, to reveal the neural functional rebuilding process. It was found from this investigation that severe patients tended to have reduced recruitment and increased between-network integration, while mild patients exhibited low network flexibility and less network integration. It is also noted that this severity-dependent alteration in network interaction was not able to be revealed by previous studies using static methods. Clinically, the obtained knowledge of the diverse patterns of dynamic adjustment in brain functional networks observed from the brain signal could help understand the underlying mechanism of the motor, speech, and cognitive functional impairments caused by stroke attacks. The proposed method not only could be used to evaluate patients' current brain status but also has the potential to provide insights into prognosis analysis and prediction.

翻译：大脑持续重组其功能网络以适应卒中后的功能障碍。以往采用静态模块化分析的研究虽揭示了这种网络重组的全局行为模式，但人们对脑卒中后大脑如何动态重构功能网络仍知之甚少。本研究采集了15例脑卒中患者的静息态功能MRI数据，根据临床症状将其分为轻度（n=6）和重度（n=9）两个亚组，同时纳入15例年龄匹配的健康受试者作为对照组。通过应用多层网络方法，基于时间分辨功能网络识别出动态模块结构，进而计算动态网络指标（招募率、整合度和灵活性）以表征卒中后脑功能网络的动态重配置，从而揭示神经功能重建过程。研究发现：重度患者倾向于表现出较低的招募率和较高的网络间整合度，而轻度患者则呈现较低的网络灵活性和较少的网络间整合。值得注意的是，这种严重程度依赖的网络交互改变无法被以往基于静态方法的研究所揭示。在临床层面，从脑信号中观察到的脑功能网络动态调整的不同模式所获得的知识，有助于理解卒中所致运动、言语和认知功能障碍的潜在机制。所提出的方法不仅可用于评估患者当前脑功能状态，更有望为预后分析和预测提供重要参考。