Liu Xiaoying, Song Guihua, Zhuang Xiaoyun, Zhang Ying, Wang Xiaoyang, Qin Yin
Department of Rehabilitation Medicine, 900th Hospital of PLA Joint Logistic Support Force, Fuzhou, Fujian, China.
Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China.
Front Aging Neurosci. 2025 Aug 26;17:1640378. doi: 10.3389/fnagi.2025.1640378. eCollection 2025.
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.
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.
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 ( = 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 ( = -0.389, = 0.025).
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.
目前的研究主要集中在中风后的单一功能障碍，但中风后认知和运动功能障碍（PSCMD）的脑动力学潜在变化仍不清楚，这阻碍了对其康复效果的深入理解。因此，目的是探讨PSCMD的动态脑网络特征。
收集了75例中风后运动功能障碍（PSMD）患者、33例PSCMD患者和35例健康对照（HCs）的临床特征和静息态功能磁共振成像（rs-fMRI）数据。采用隐马尔可夫模型（HMM）对rs-fMRI数据进行分析，旨在识别脑活动的重复状态，同时进一步评估PSCMD中的时间特性和激活模式。此外，系统评估了HMM状态特征与临床量表评分之间的相关性。
最终确定了五个HMM状态。结果显示，与HCs相比，PSMD和PSCMD组在时空属性动力学方面有显著变化，包括分数占有率（FO）、寿命（LT）和转移概率（TP）。此外，PSCMD患者在状态3中的FO高于PSMD（ = 0.006）。状态3的主要特征是感觉运动和高阶认知网络的低激活，以及右前额叶-顶叶网络的高激活，这可能反映了PSCMD后脑的适应性变化。此外，HMM状态3的FO与MoCa评分呈负相关（ = -0.389， = 0.025）。
PSCMD患者可观察到异常的动态脑重组模式。未来可通过HMM衍生的脑状态优化神经调节策略。
