目的:尽管在社交焦虑症(SAD)患者中观察到功能性脑网络的静态异常,大脑连接体动力学在宏观尺度的网络水平仍然模糊。因此,我们使用多变量数据驱动方法来搜索SAD中的动态功能网络连接(dFNC)改变。
方法:我们进行了空间独立成分分析,并使用了带有k均值聚类算法的滑动窗口方法,表征大脑静息状态网络的复发状态;然后在SAD患者和健康对照(HC)之间比较不同状态下的状态转换指标和FNC强度,并探讨其与SAD临床特征的关系。
结果:确定了四种不同的复发状态。与HC相比,SAD患者在最高频率状态3中表现出更高的分数窗口和平均停留时间,代表“广泛较弱”的FNC,但在第2和第4州较低,代表“局部更强”和“广泛更强”的FNC,分别。在状态1中,代表“广泛适度”FNC,SAD患者的FNC下降主要在默认模式网络与注意和感知网络之间。一些异常的dFNC特征与疾病持续时间相关。
结论:大规模静息态网络中这些异常的脑功能同步动力学模式可能为SAD的神经功能基础提供新的见解。
OBJECTIVE: Although static abnormalities of functional brain networks have been observed in patients with social anxiety disorder (SAD), the brain
connectome dynamics at the macroscale network level remain obscure. We therefore used a multivariate data-driven method to search for dynamic functional network connectivity (dFNC) alterations in SAD.
METHODS: We conducted spatial independent component analysis, and used a sliding-window approach with a k-means clustering algorithm, to characterize the recurring states of brain resting-state networks; then state transition metrics and FNC strength in the different states were compared between SAD patients and healthy controls (HC), and the relationship to SAD clinical characteristics was explored.
RESULTS: Four distinct recurring states were identified. Compared with HC, SAD patients demonstrated higher fractional windows and mean dwelling time in the highest-frequency State 3, representing \"widely weaker\" FNC, but lower in States 2 and 4, representing \"locally stronger\" and \"widely stronger\" FNC, respectively. In State 1, representing \"widely moderate\" FNC, SAD patients showed decreased FNC mainly between the default mode network and the attention and perceptual networks. Some aberrant dFNC signatures correlated with illness duration.
CONCLUSIONS: These aberrant patterns of brain functional synchronization dynamics among large-scale resting-state networks may provide new insights into the neuro-functional underpinnings of SAD.