Background Current research is predominantly focused on the single dysfunction after stroke, but the potential changes in brain dynamics of post-stroke cognitive and motor dysfunction (PSCMD) remain unclear, which hinders a deep understanding of its rehabilitation effects. Therefore, the objective is to explore the dynamic brain network characteristics of PSCMD. Methods The clinical features and resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 75 patients with post-stroke motor dysfunction (PSMD), 33 patients with PSCMD, and 35 healthy controls (HCs). Hidden markov model (HMM) was employed for the rs-fMRI data, aiming to identify the repetitive states of brain activity while further assessing the temporal properties and activation patterns in PSCMD. Additionally, the correlation between the HMM state characteristics and clinical scale scores was systematically evaluated. Results Five HMM states were ultimately identified. According to the results, PSMD and PSCMD groups showed significant changes in the dynamics of spatiotemporal attributes versus HCs, including fractional occupancy (FO), Lifetime (LT), and transition probability (TP). Furthermore, PSCMD patients exhibited greater FO than PSMD ( p = 0.006) in state 3. State 3 was mainly characterized by low activation of sensorimotor and higher-order cognitive networks, as well as the high activation of the right prefrontal-parietal network, which may reflect adaptive changes in the brain after PSCMD. Besides, the FO of HMM state 3 exhibited a negative connection with the MoCa score ( r = −0.389, p = 0.025). Conclusion An abnormal dynamic brain reorganization pattern could be observed in PSCMD patients. Neuromodulation strategies can be optimized by HMM-derived brain states in the future.
Liu et al. (2025) studied this question.