The Physiological Component of the BOLD Signal: Impact of Age and Heart Rate Variability Biofeedback
Poster No:
1672
Submission Type:
Abstract Submission
Authors:
Richard Song1, Jungwon Min2, Shiyu Wang1, Kimberly Rogge-Obando1, Hyun Yoo2, Kaoru Nashiro2, Mara Mather2, Catherine Chang1
Institutions:
1Vanderbilt University, Nashville, TN, 2University of Southern California, Los Angeles, CA
First Author:
Co-Author(s):
Introduction:
The autonomic nervous system (ANS), which regulates physiological functions, declines with age, particularly in heart rate variability (HRV). While functional magnetic resonance imaging (fMRI) often treats physiological signals as noise, recent work suggests incorporating cardiac and respiratory signals into blood oxygenation level dependent (BOLD) analysis can provide insights into ANS health. However, how aging affects the spatial and temporal dynamics of physiological signal propagation into the BOLD signal remains unclear. This study examines age-related differences in the relationship between physiological signals and BOLD activity and explores how HRV biofeedback training modulates these dynamics.
Methods:
We analyzed two datasets: 399 participants from the NKI Rockland sample (Nooner et al., 2012) and 110 participants from the HRV-ER study (Yoo et al., 2023), which we divided into younger (18-36 years) and older (50-85 years) adults. The HRV-ER participants additionally completed a 5-week HRV biofeedback intervention, where they were randomly assigned to either increase (Osc+) or decrease (Osc-) their heart rate oscillations through daily breathing exercises. Resting-state fMRI data were collected along with concurrent physiological measurements. The NKI dataset was preprocessed using ICA-FIX denoising and included respiratory volume (RV) and heart rate (HR) signals (which - for certain analyses - were convolved with Respiratory and Cardiac Response Functions, respectively), while the HRV-ER dataset used multi-echo ICA denoising and included HR and end-tidal CO2 signals (which were convolved with a Cardiac Response Function and a standard hemodynamic response function model, respectively). For both datasets, convolved physiological regressors were fitted using a linear model to each voxel's BOLD time series across the whole brain, quantifying the spatial distribution of physiological signal integration into BOLD activity. The cross-correlation between voxelwise BOLD signals and the un-convolved physiological signals was also calculated.
Results:
Both datasets showed significantly higher HRV in younger adults. Whole-brain analyses revealed that younger adults had greater variance in BOLD signal explained by physiological regressors, particularly in the orbitofrontal cortex, lateral ventricles, basal ganglia, anterior cingulate, insula, and white matter (Figure 1a). Time-lagged cross-correlation analyses revealed age-related differences: younger adults exhibited stronger positive HR-BOLD and RV-BOLD correlations at early lags (2-6s) and stronger negative correlations at later lags (8-13s) (Figure 2a-c). CO2-BOLD correlations were also stronger at early lags (0-5s) in younger adults, reflecting a faster BOLD response to CO2 (Figure 2d). Following HRV biofeedback, younger adults in the Osc- group showed larger changes in physiological-BOLD coupling than older adults, particularly in the left orbitofrontal cortex, insula, and temporal regions (Figure 1b).
Conclusions:
Age-related differences were identified in both the magnitude and temporal dynamics of physiological-BOLD coupling. The reduction in BOLD variance observed in older adults – together with delayed coupling between BOLD and respiratory measures at positive lags – may reflect multiple underlying mechanisms, including reduced vascular compliance and changes in brain vascular structure. Altered neurovascular coupling and diminished autonomic regulation may also contribute, as areas comprising central autonomic networks showed large age-related changes. Notably, HRV biofeedback training designed to reduce heart rate oscillations increased physiological-BOLD coupling specifically in younger adults, suggesting age-dependent plasticity in autonomic regulation. These results highlight the importance of considering age-related changes in autonomic function when interpreting BOLD signals.
Lifespan Development:
Aging 2
Modeling and Analysis Methods:
Exploratory Modeling and Artifact Removal
Task-Independent and Resting-State Analysis 1
Novel Imaging Acquisition Methods:
BOLD fMRI
Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics
Keywords:
Aging
Autonomics
Cerebral Blood Flow
Cerebrovascular Disease
Data analysis
FUNCTIONAL MRI
Modeling
Statistical Methods
1|2Indicates the priority used for review
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Provide references using APA citation style.
Yoo, H. J., Nashiro, K., Min, J., Cho, C., Mercer, N., Bachman, S. L., Nasseri, P., Dutt, S., Porat, S., Choi, P., Zhang, Y., Grigoryan, V., Feng, T., Thayer, J. F., Lehrer, P., Chang, C., Stanley, J. A., Head, E., Rouanet, J., … Mather, M. (2023). Multimodal neuroimaging data from a 5-week heart rate variability biofeedback randomized clinical trial. Scientific Data, 10(1), 503. https://doi.org/10.1038/s41597-023-02396-5