Mind-Body Interventions Reduce Whole-Brain and Network-Based Entropy in Older Adults
Poster No:
1397
Submission Type:
Abstract Submission
Authors:
James Teng1, Madhura Phansikar1, Anita Shankar1, Megan Fisher1, Nathan McPherson1, Rebecca Andridge2, Ruchika Prakash1
Institutions:
1The Ohio State University, Columbus, OH, 2Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH
First Author:
Co-Author(s):
Rebecca Andridge, Dr
Division of Biostatistics, College of Public Health, The Ohio State University
Columbus, OH
Division of Biostatistics, College of Public Health, The Ohio State University
Columbus, OH
Introduction:
Mindfulness-based therapies are a promising, non-pharmacological intervention candidate (Tang et al., 2015) to mitigate age-related reductions in neural efficiency (Yue et al., 2023). Prior work has shown strengthened connectivity of the posterior cingulate cortex (PCC) of the DMN (Kral et al., 2019) and the inferior frontal gyrus (IFG) of the Frontoparietal Network (FPN) following mindfulness training (Taren et al., 2017). Recent work has also shown that entropy, a measure of network specialization (Wang et al., 2014), is potentially a sensitive and flexible biomarker of pathological neural change with aging: we found associations between advancing age and higher levels of entropy at the nodal, network, and whole-brain levels (Shankar et. al, 2024). Moreover, greater whole-brain entropy with age moderated age-related declines in cognition, such that older adults with higher entropy show a steeper age-related decline in fluid cognition. In the present study, we build on this to examine whether two mind-body interventions-mindfulness meditation and lifestyle education-can reduce entropy, immediately after 8-weeks of interventions, and at 12-months follow-up.
Methods:
150 older adults (aged 65-85 years) participated in an 8-week randomized clinical trial designed to examine the effects of a mindfulness-based stress reduction (MBSR, N=52) compared to an active lifestyle education (LifeEd, N=55) group. fMRI data was collected from 107 participants at three timepoints (pre-intervention, post-intervention, and at 12-month follow-up) during rest and a gradual-onset continuous performance task (gradCPT, Rosenberg et al., 2013). Edge time series was calculated as the element-wise dot-product of z-scored parcel time series for each node-pair in the functional connectivity matrix (Faskowitz et al., 2020). These edge time series were then k-means clustered to assign edge community participation, and entropy was calculated as a portion of the node's total connections to each specific edge community. Average entropy scores were then calculated for the whole brain, the DMN and the FPN, and nodes of the PCC and the IFG. Longitudinal effects of the interventions were examined using a linear mixed model, with Group (MBSR, LifeEd), Time (pre-intervention, post-intervention, 12-month follow-up), and the Group×Time interaction entered as fixed effects, participant intercepts entered as random effects, and whole-brain entropy scores entered as the dependent variable. This was repeated for entropy scores at the network, and at the nodal level.
Results:
Our linear mixed model, examining the effects of the two interventions on whole-brain entropy during the gradCPT, did not show a main effect of time (F(2,140.3)=1.19, p=.31) or a Group×Time interaction (F(2,140.3)=0.84, p=.43; Fig 1A). In contrast, whole-brain entropy during resting-state, showed a significant main effect of Time (F(2,144.4)=5.52, p=.0049; Fig 1B). Our results showed that across participants in both groups, entropy was significantly reduced at the end of the study. Moreover, we found a main effect of Time when examining changes in entropy at the network level. Resting entropy decreased in the DMN (F(2,147.9)=4.40, p=.0139; Fig 2C), and the FPN (F(2,154.1)=5.52, p=.0035; Fig 2D), but entropy during task was not reduced (Fig 2A, B). Nodal entropy did not significantly decrease during rest or task-fMRI. Additionally, there were no differential effects of the intervention on entropy.
Conclusions:
We found that both mind-body interventions protected neural specialization in older adults, as indicated by reduced resting entropy at the whole brain and network levels. This suggests intrinsic brain reorganization can be entrained across multiple macroscale networks. Conversely, we did not find entropy changes during task, which indicate that more state-like task performance may not manifest alongside compensatory neural reorganization. Future directions include examining the mediating influence of intervention adherence.
Lifespan Development:
Aging 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Task-Independent and Resting-State Analysis
Novel Imaging Acquisition Methods:
Imaging Methods Other
Keywords:
Aging
FUNCTIONAL MRI
Modeling
Other - Network Connectivity
1|2Indicates the priority used for review
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Provide references using APA citation style.
Kral, T. R. (2019). Mindfulness-based stress reduction-related changes in posterior cingulate resting brain connectivity. [i]Social Cognitive and Affective Neuroscience, 14[/i](7), 777-787.
Rosenberg, M. (2013). Sustaining visual attention in the face of distraction: A novel gradual-onset continuous performance task. [i]Attention, Perception, & Psychophysics, 75[/i], 426-439.
Shankar, A., Tanner, J. C., Mao, T., Betzel, R. F., & Prakash, R. S. (2024). Edge-community entropy is a novel neural correlate of aging and moderator of fluid cognition. [i]Journal of Neuroscience, 44[i/](25).
Tang, Y. Y. (2015). The neuroscience of mindfulness meditation. [i]Nature reviews neuroscience, 16[/i](4), 213-225.
Taren, A. A. (2017). Mindfulness meditation training and executive control network resting state functional connectivity: A randomized controlled trial. [i]Psychosomatic Medicine, 79[/i](6), 674-683.
Wang, Z. (2014). Brain entropy mapping using fMRI. [i]PloS One, 9[/i](3), e89948.
Yue, W. L. (2023). Mindfulness-based therapy improves brain functional network reconfiguration efficiency. [i]Translational Psychiatry, 13[/i](1), 345.