功能连接
功能磁共振成像
神经科学
工作记忆
心理学
清醒
功能损害
认知
大脑定位
睡眠剥夺
空间记忆
功能集成
职能组织
透视图(图形)
标准差
功能成像
平衡(能力)
听力学
神经功能成像
空间认知
默认模式网络
认知心理学
功能数据分析
医学
眼球运动
发展心理学
识别记忆
认知障碍
作者
Lei Xu,Jiahao Yan,Haoyuan Zhang,Jinhan Zhang,Jinhan Zhang,Xi Zhang,Jing Zhang,Jing Zhang
标识
DOI:10.1016/j.brainresbull.2026.111825
摘要
BACKGROUND: Although resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional gradients have been widely used to describe cortical hierarchical organization, their application has rarely targeted acute sleep deprivation (ASD)-related cognitive vulnerability. In particular, whether ASD induces systematic reorganization of gradient architecture and whether this reorganization contributes to spatial working memory (SWM) impairment have not yet been systematically examined. METHODS: Fifty healthy young adult males were recruited. A 1-back task was administered to assess SWM performance before and after ASD. T1-weighted and rs-fMRI data were acquired. Functional gradient-based metrics, including standard deviation of gradient values, range of gradient values, network gradient values, and inter-network relative distances, were computed to characterize cortical hierarchical organization and were subsequently correlated with SWM behavioral performance. RESULTS: Compared with the rested wakefulness condition, ASD significantly impaired SWM performance. Functional gradient analysis revealed significant alterations in both global (standard deviation and range) and local (gradient values of specific subnetworks) features of the top three principal gradients. Notably, the standard deviation of Gradient 2 was significantly negatively correlated with omission rate. In addition, relative distances between multiple networks within Gradient 2 and 3 were also closely associated with SWM performance. CONCLUSION: From the perspective of functional gradients, the present study highlights the global and local gradient reorganization following ASD, as well as the importance of maintaining a balance between functional segregation and integration across subnetworks in sustaining SWM performance.
科研通智能强力驱动
Strongly Powered by AbleSci AI