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Cortical and subcortical brain networks predict prevailing heart rate
Amy Isabella Sentis1,2, Javier Rasero2,3,4, Peter J Gianaros2,5
1Medical Scientist Training Program, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Psychophysiology
|July 2, 2024
Summary
Higher brain regions influence resting heart rate, impacting cardiovascular disease risk. This study used fMRI and machine learning to identify brain networks predicting heart rate variations in healthy adults.
Area of Science:
- Neuroscience
- Cardiovascular Physiology
- Computational Biology
Background:
- Resting heart rate is a risk factor for cardiovascular disease (CVD).
- Autonomic control of heart rate is primarily linked to the brainstem.
- The role of higher cortical and subcortical brain regions in heart rate regulation is less understood.
Purpose of the Study:
- To identify brain networks that predict variations in resting heart rate in healthy adults.
- To explore the neural underpinnings of heart rate regulation beyond the brainstem.
- To investigate the potential of brain activity as a biomarker for cardiovascular risk.
Main Methods:
- Utilized functional magnetic resonance imaging (fMRI) to measure whole-brain hemodynamic signals.
- Employed machine learning algorithms to predict instantaneous heart period (inter-beat interval) from fMRI data.
- Analyzed both task-based and resting-state fMRI data from two independent datasets with repeated measures.
Main Results:
- Machine learning models successfully predicted instantaneous heart period from whole-brain fMRI data within and across individuals.
- Prediction accuracy was highest for within-participant models.
- Identified a network of cortical and subcortical brain regions, associated with visceral functions, as reliable predictors of heart period variation.
Conclusions:
- Higher-level brain networks contribute to the regulation of heart rate.
- These findings support the concept of brain-heart interactions in cardiovascular health.
- This research offers a potential avenue for developing brain-based biomarkers for CVD risk.
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