Changes in functional connectivity dynamics with aging: A dynamical phase synchronization approach
Changes in functional connectivity dynamics with aging: A dynamical phase synchronization approach
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DOI：
10.1016/j.neuroimage.2018.12.008
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发表时间：
2019-03-01
期刊：
影响因子：
5.7
通讯作者：
Takahashi, Tetsuya
中科院分区：
文献类型：
--
作者：
Nobukawa, Sou;Kikuchi, Mitsuru;Takahashi, Tetsuya
人脑网络的动态引起了广泛的关注，人们认识到功能连接不是静态的，而是随着时间的推移而改变其模式，即使在休息状态下也是如此。我们假设，对来自不同大脑区域的信号之间连续捕获的时变瞬时相位同步进行分析可能会为已经确定的网络动态添加另一个维度。为了验证这一假设，以帮助阐明衰老的生理机制，我们检查了健康年轻受试者和健康老年受试者整个大脑静息状态脑电图活动的瞬时相位同步事件的时间序列。然后，我们使用多尺度熵来表征相位同步的时间动态，它量化了多个时间尺度上大脑信号动态的复杂性。替代分析的结果证实，相位同步的时间动态源于神经网络系统中的确定性过程。组比较显示，阿尔法波段老年受试者相位同步时间动态的区域特异性复杂性主要在额叶脑区域，而相位滞后指数等比较相位同步方法无法识别这一点。老年受试者功能连接时间动态的复杂性增强可能反映了衰老过程中的一般网络改变理论。这是第一份报告，描述了捕获瞬时相位同步动态并表征其时间组织的重要性。将这种方法应用于神经生理学数据可以提供对健康和病理条件下动态神经网络过程的新理解。
The dynamics of the human brain network has attracted broad attention, in recognition of the concept that functional connectivity is not static, but changes its pattern over time, even in the resting state. We hypothesized that analysis of continuously captured time-varying instantaneous phase synchronization between signals from different brain regions might add another dimension to already identified network dynamics. To validate this hypothesis as an aid to elucidating the physiological mechanisms of aging, we examined time-series of instantaneous phase synchronization events in resting-state EEG activity across the brain, in healthy younger and healthy older subjects. We then characterized the temporal dynamics of phase synchronization using multiscale entropy, which quantifies the complexity of brain signal dynamics over multiple time scales. The results of surrogate analyses confirmed that the temporal dynamics of phase synchronization arise from deterministic processes in the neural network system. Group comparison showed region-specific enhanced complexity of temporal dynamics of phase synchronization in older subjects in alpha band predominantly in frontal brain regions, which was not identified by a comparative phase synchronization approach such as phase lag index. Enhanced complexity of temporal dynamics of functional connectivity in older subjects might reflect a general network alteration theory in aging. This is a first report describing the importance of capturing the dynamics of instantaneous phase synchronization and characterizing its temporal organization. Applying this method to neurophysiologic data may provide a novel understanding of dynamical neural network processes in both healthy and pathological conditions.