Dynamic Reconfiguration of Brain Functional Network in Stroke

TL;DR

This study investigates how brain functional networks dynamically reconfigure after stroke using multilayer network analysis of fMRI data, revealing severity-dependent patterns that static methods cannot detect, with implications for prognosis and rehabilitation.

Contribution

It introduces a dynamic multilayer network approach to analyze post-stroke brain reorganization, uncovering severity-dependent network patterns not seen with static analysis.

Findings

Severe stroke patients show reduced recruitment and increased inter-network integration.

Mild stroke patients exhibit low network flexibility and less integration.

Dynamic analysis reveals severity-dependent brain reconfiguration patterns.

Abstract

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.