PDF(Pubmed)
Abstract：
The aging brain represents the primary risk factor for many neurodegenerative disorders. Whole-brain oscillations may contribute novel early biomarkers of aging. Here, we investigated the dynamic oscillatory neural activities across lifespan (from 18 to 88 years) using resting Magnetoencephalography (MEG) in a large cohort of 624 individuals. Our aim was to examine the patterns of oscillation microstates during the aging process. By using a machine-learning algorithm, we identify four typical clusters of microstate patterns across different age groups and different frequency bands: left-to-right topographic MS1, right-to-left topographic MS2, anterior-posterior MS3 and fronto-central MS4. We observed a decreased alpha duration and an increased alpha occurrence for sensory-related microstate patterns (MS1 & MS2). Accordingly, theta and beta changes from MS1 & MS2 may be related to motor decline that increased with age. Furthermore, voluntary \'top-down\' saliency/attention networks may be reflected by the increased MS3 & MS4 alpha occurrence and complementary beta activities. The findings of this study advance our knowledge of how the aging brain shows dysfunctions in neural state transitions. By leveraging the identified microstate patterns, this study provides new insights into predicting healthy aging and the potential neuropsychiatric cognitive decline.
摘要：
大脑老化是许多神经退行性疾病的主要危险因素。全脑振荡可能有助于衰老的新型早期生物标志物。这里,我们使用静息脑磁图(MEG)在624名个体的大型队列中研究了跨寿命期(18~88岁)的动态振荡神经活动.我们的目的是检查老化过程中振荡微状态的模式。通过使用机器学习算法，我们确定了不同年龄段和不同频段的四种典型的微状态模式簇：从左到右地形MS1，从右到左地形MS2，前后MS3和额中央MS4。我们观察到感官相关微态模式(MS1和MS2)的α持续时间减少和α发生增加。因此，MS1和MS2的θ和β变化可能与随年龄增长而增加的运动衰退有关。此外，自愿性的“自上而下的”显著性/注意力网络可能通过增加的MS3和MS4alpha发生率和互补的beta活动来反映。这项研究的发现使我们了解了衰老的大脑如何在神经状态转换中表现出功能障碍。通过利用已识别的微状态模式，这项研究为预测健康衰老和潜在的神经精神认知衰退提供了新的见解。
