Wu Ke, Jiang Yaya, Luo Junhao, Chen Yijun, Peng Shaoling, Kong Xiangyu, Gong Gaolang
State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
Artificial Intelligence and Language Cognition Laboratory, Beijing International Studies University, Beijing 100024, China.
Brain Commun. 2025 Jul 22;7(4):fcaf276. doi: 10.1093/braincomms/fcaf276. eCollection 2025.
Stroke often leads to multiple behavioural impairments. Understanding the neural basis of these deficits is essential for elucidating the mechanisms of functional impairments and optimising therapeutic strategies for stroke patients. Although many studies have revealed that specific behavioural deficits are related to disruptions in distributed functional connectivity across brain networks, these studies typically focus on single behavioural traits, overlooking the multivariate characteristics of deficits after stroke. Recent studies have demonstrated that deficits within and across domains are highly correlated, suggesting a complex many-to-many mapping between brain and behaviour following stroke. Thus, the present study aims to identify meaningful multivariate patterns of functional connectivity-behaviour covariation following stroke. Specifically, we employed a multivariate data-driven approach, partial least squares correlation, to examine the relationships between whole-brain functional connectivity and an extensive array of neurological scores (including motor, attention, verbal memory, spatial memory and language domains) in a large cohort of stroke patients at 2 weeks ( = 81), 3 months ( = 78) and 12 months ( = 74) post-injury. This multivariate analysis revealed a significant latent component (LC) from 2-week post-stroke data, capturing a unique pattern of cognitive deficits across multiple domains. This pattern was strongly associated with widespread network dysfunction, characterized by decreased interhemispheric connectivity and increased intrahemispheric connectivity. Notably, the identified LC was replicated and generalized to stroke data at the 3-month and 12-month time points. Furthermore, we examined whether structural lesion features, including structural disconnection of white matter pathways and grey matter damage, could explain variance in the identified LC. Structural disconnection outperformed grey matter damage, highlighting its critical role in the functional connectivity-behaviour relationship following stroke. Mediation analysis confirmed that structural disconnection serves as the neuroanatomical basis for the association between functional connectivity and deficits. Overall, this study suggests that stroke-induced white matter disconnections are associated with widespread and consistent disruptions in brain network connectivity, which are reflected in a highly correlated behavioural profile. These results provide an integrative insight into the complex relationships among lesions, functional networks, and behavioural outcomes following stroke.
中风常常导致多种行为障碍。了解这些缺陷的神经基础对于阐明功能障碍的机制以及优化中风患者的治疗策略至关重要。尽管许多研究表明特定的行为缺陷与大脑网络中分布式功能连接的中断有关，但这些研究通常只关注单一行为特征，而忽略了中风后缺陷的多变量特征。最近的研究表明，不同领域内和跨领域的缺陷高度相关，这表明中风后大脑与行为之间存在复杂的多对多映射关系。因此，本研究旨在确定中风后功能连接与行为协变的有意义的多变量模式。具体而言，我们采用了一种多变量数据驱动的方法——偏最小二乘相关分析，来研究一大群中风患者在受伤后2周（n = 81）、3个月（n = 78）和12个月（n = 74）时全脑功能连接与一系列广泛的神经学评分（包括运动、注意力、言语记忆、空间记忆和语言领域）之间的关系。这种多变量分析从中风后2周的数据中揭示了一个显著的潜在成分（LC），它捕捉到了多个领域中独特的认知缺陷模式。这种模式与广泛的网络功能障碍密切相关，其特征是半球间连接减少和半球内连接增加。值得注意的是，所确定的LC在3个月和12个月的时间点被复制并推广到中风数据中。此外，我们研究了包括白质通路的结构断开和灰质损伤在内的结构病变特征是否能够解释所确定的LC中的变异。结构断开比灰质损伤表现更好突出了其在中风后功能连接与行为关系中的关键作用。中介分析证实结构断开是功能连接与缺陷之间关联的神经解剖学基础。总体而言，本研究表明中风引起的白质断开与大脑网络连接中广泛且一致的中断有关，这反映在高度相关的行为特征中。这些结果为中风后病变、功能网络和行为结果之间的复杂关系提供了综合见解。
