ArticleFrontiers in neuroscience2020
Heart Rate Variability as an Index of Differential Brain Dynamics at Rest and After Acute Stress Induction.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Heart rate variability as a biomarker of functional outcomes in persons with acquired brain injury: Systematic review and meta-analysis.Neuroscience and biobehavioral reviews · 2021Pooled it
- Global cortical arousal effects in fMRI reveal brain markers of state and trait anxiety.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- Default mode network connectivity contributes the augment effect stress recovery by natural viewing.Neurobiology of stress · 2025Article
- Brain activation and heart rate variability as markers of autonomic function under stress.Scientific reports · 2025Article
- Relationship between central autonomic effective connectivity and heart rate variability: A Resting-state fMRI dynamic causal modeling study.NeuroImage · 2024Article
- A predictor-informed multi-subject bayesian approach for dynamic functional connectivity.PloS one · 2024Article
- Heart Rate Variability for Outcome Prediction in Intracerebral and Subarachnoid Hemorrhage: A Systematic Review.Journal of clinical medicine · 2023Review
- Dynamic fluctuations in ascending heart-to-brain communication under mental stress.American journal of physiology. Regulatory, integrative and comparative physiology · 2023Article
- The Role of Emotion Regulation for General Self-Efficacy in Adolescents Assessed Through Both Neurophysiological and Self-Reported Measures.Psychology research and behavior management · 2023Article
- Heart-brain synchronization breakdown in Parkinson's disease.NPJ Parkinson's disease · 2022Article
- The Usefulness of Assessing Heart Rate Variability in Patients with Acute Myocardial Infarction (HeaRt-V-AMI).Sensors (Basel, Switzerland) · 2022Observational
- Neurophysiological and emotional influences on team communication and metacognitive cyber situational awareness during a cyber engineering exercise.Frontiers in human neuroscience · 2022Article
- Prefrontally modulated vagal neuroimmunomodulation is associated with telomere length.Frontiers in neuroscience · 2022Article
- The influence of external stressors on physiological testing: Implication for return-to-play protocols.Current research in physiology · 2022Article
- State-related neural influences on fMRI connectivity estimation.NeuroImage · 2021Review
Corrections and comments
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Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The brain continuously receives input from the internal and external environment. Using this information, the brain exerts its influence on both itself and the body to facilitate an appropriate response. The dynamic interplay between the brain and the heart and how external conditions modulate this relationship deserves attention. In high-stress situations, synchrony between various brain regions such as the prefrontal cortex and the heart may alter. This flexibility is believed to facilitate transitions between functional states related to cognitive, emotional, and especially autonomic activity. This study examined the dynamic temporal functional association of heart rate variability (HRV) with the interaction between three main canonical brain networks in 38 healthy male subjects at rest and directly after a psychosocial stress task. A sliding window approach was used to estimate the functional connectivity (FC) among the salience network (SN), central executive network (CEN), and default mode network (DMN) in 60-s windows on time series of blood-oxygen-level dependent (BOLD) signal. FC between brain networks was calculated by Pearson correlation. A multilevel linear mixed model was conducted to examine the window-by-window association between the root mean square of successive differences between normal heartbeats (RMSSD) and FC of network-pairs across sessions. Our findings showed that the minute-by-minute correlation between the FC and RMSSD was significantly stronger between DMN and CEN than for SN and CEN in the baseline session [
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