Dynamic Reconfiguration of Brain Functional Network in Stroke

• Published in: IEEE journal of biomedical and health informatics Vol. 28; no. 6; pp. 3649 - 3659
• Main Authors: Wu, Kaichao, Jelfs, Beth, Neville, Katrina, Mahmoud, Seedahmed S., He, Wenzhen, Fang, Qiang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Aged; Bioinformatics; Brain; Brain - diagnostic imaging; Brain - physiopathology; Brain Mapping - methods; Cognitive ability; Dynamics; Female; Flexibility; fMRI; Functional magnetic resonance imaging; functional network; Humans; Image Processing, Computer-Assisted - methods; Integration; Lesions; Magnetic Resonance Imaging - methods; Male; Middle Aged; Modular structures; Modularity; Multilayers; Nerve Net - diagnostic imaging; Nerve Net - physiopathology; Neuroplasticity; Reconfiguration; Statistical analysis; Stroke; Stroke (medical condition); Stroke - diagnostic imaging; Stroke - physiopathology; Subgroups
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2024.3371097

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 temporal network method, a dynamic modular structure was recognized based on a time-resolved function network. The dynamic network measurements (recruitment, integration, and flexibility) were calculated to characterize the dynamic reconfiguration of post-stroke brain functional networks, hence, revealing 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's also noted that previous studies using static methods could not reveal this severity-dependent alteration in network interaction. Clinically, the obtained knowledge of the diverse patterns of dynamic adjustment in brain functional networks observed from the brain neuronal images could help understand the underlying mechanism of the motor, speech, and cognitive functional impairments caused by stroke attacks. The present 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.