Tai Chi Practice Buffers Aging Effects in Functional Brain Connectivity


Author: Jonathan Cerna1, Prakhar Gupta2, Maxine He1, Liran Ziegelman1, Yang Hu3, Manuel E Hernandez1,4,5,6,7

Affiliation: <sup>1</sup> Neuroscience Program, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.

<sup>2</sup> Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.

<sup>3</sup> Department of Kinesiology, San Jose State University, San Jose, CA 95192, USA.

<sup>4</sup> Department of Biomedical and Translational Sciences, Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.

<sup>5</sup> Department of Kinesiology and Community Health, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.

<sup>6</sup> Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.

<sup>7</sup> Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.
Conference/Journal: Brain Sci
Date published: 2024 Sep 6
Other:
 Volume ID: 14   ,  Issue ID: 9   ,  Pages: 901   ,  Special Notes: doi: 10.3390/brainsci14090901.   ,  Word Count: 270  
    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, n = 15), healthy older adult controls (OACs, n = 15), or Tai Chi older adult practitioners (TCOAs, n = 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.



Keywords: Tai Chi; electroencephalography; healthy aging; mind–body practice; recurrent neural network dynamics; resting state; source localization.



PMID: 39335397 PMCID: PMC11430092 DOI: 10.3390/brainsci14090901