Author: Tara Chand1 2 3 4 , Meng Li2 4 , Hamidreza Jamalabadi1 4 , Gerd Wagner2 , Anton Lord4 5 , Sarah Alizadeh1 4 , Lena V Danyeli2 4 6 , Luisa Herrmann1 2 4 , Martin Walter1 2 3 4 6 , Zumrut D Sen1 2 4
1 Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany.
2 Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.
3 Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
4 Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg, Germany.
5 QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
6 Leibniz Institute for Neurobiology, Magdeburg, Germany.
Conference/Journal: Front Neurosci
Date published: 2020 Jul 2
Other: Volume ID: 14 , Pages: 645 , Special Notes: doi: 10.3389/fnins.2020.00645. , Word Count: 303
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 [b = 4.36, t(5025) = 3.20, p = 0.006]. Additionally, this differential relationship between network pairs and RMSSD disappeared after the stress task; FC between DMN and CEN showed a weaker correlation with RMSSD in comparison to baseline [b = -3.35, t(5025) = -3.47, p = 0.006]. These results suggest a dynamic functional interplay between HRV and the functional association between brain networks that varies depending on the needs created by changing conditions.
KEYWORDS: dynamic functional connectivity; heart rate variability; heart-brain interaction; resting-state fMRI; stress.
PMID: 32714132 PMCID: PMC7344021 DOI: 10.3389/fnins.2020.00645