Cerna Jonathan, Gupta Prakhar, He Maxine, Ziegelman Liran, Hu Yang, Hernandez Manuel E
Neuroscience Program, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.
Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.
Brain Sci. 2024 Sep 6;14(9):901. doi: 10.3390/brainsci14090901.
Tai Chi (TC) practice has been shown to improve both cognitive and physical function in older adults. However, the neural mechanisms underlying the benefits of TC remain unclear. Our primary aims are to explore whether distinct age-related and TC-practice-related relationships can be identified with respect to either temporal or spatial (within/between-network connectivity) differences. This cross-sectional study examined recurrent neural network dynamics, employing an adaptive, data-driven thresholding approach to source-localized resting-state EEG data in order to identify meaningful connections across time-varying graphs, using both temporal and spatial features derived from a hidden Markov model (HMM). Mann-Whitney U tests assessed between-group differences in temporal and spatial features by age and TC practice using either healthy younger adult controls (YACs, = 15), healthy older adult controls (OACs, = 15), or Tai Chi older adult practitioners (TCOAs, = 15). Our results showed that aging is associated with decreased within-network and between-network functional connectivity (FC) across most brain networks. Conversely, TC practice appears to mitigate these age-related declines, showing increased FC within and between networks in older adults who practice TC compared to non-practicing older adults. These findings suggest that TC practice may abate age-related declines in neural network efficiency and stability, highlighting its potential as a non-pharmacological intervention for promoting healthy brain aging. This study furthers the triple-network model, showing that a balancing and reorientation of attention might be engaged not only through higher-order and top-down mechanisms (i.e., FPN/DAN) but also via the coupling of bottom-up, sensory-motor (i.e., SMN/VIN) networks.
太极拳练习已被证明可改善老年人的认知和身体功能。然而,太极拳益处背后的神经机制仍不清楚。我们的主要目的是探讨是否能在时间或空间(网络内/网络间连接性)差异方面识别出与年龄相关和太极拳练习相关的不同关系。这项横断面研究考察了递归神经网络动力学,采用自适应、数据驱动的阈值方法对源定位的静息态脑电图数据进行分析,以便利用从隐马尔可夫模型(HMM)得出的时间和空间特征来识别随时间变化的图中的有意义连接。曼-惠特尼U检验使用健康年轻成人对照组(YACs,n = 15)、健康老年成人对照组(OACs,n = 15)或太极拳老年练习者(TCOAs,n = 15)评估了年龄和太极拳练习在时间和空间特征上的组间差异。我们的结果表明,衰老与大多数脑网络内的网络内和网络间功能连接性(FC)下降有关。相反,太极拳练习似乎减轻了这些与年龄相关的下降,与不练习的老年人相比,练习太极拳的老年人在网络内和网络间的FC增加。这些发现表明,太极拳练习可能减轻与年龄相关的神经网络效率和稳定性下降,凸显了其作为促进健康脑衰老的非药物干预措施的潜力。这项研究进一步完善了三网络模型,表明注意力的平衡和重新定向可能不仅通过高阶和自上而下的机制(即额顶叶网络/背侧注意网络)实现,还通过自下而上的感觉运动网络(即体感运动网络/视觉网络)的耦合实现。