An Autonomic Network: Synchrony Between Slow Rhythms of Pulse and Brain Resting State Is Associated with Personality and Emotions

Abstract

The sympathetic system’s role in modulating vasculature and its influence on emotions and personality led us to test the hypothesis that interactions between brain resting-state networks (RSNs) and pulse amplitude (indexing sympathetic activity) would be associated with emotions and personality. In 203 participants, we characterized RSN spatiotemporal characteristics, and phase–amplitude associations of RSN fluctuations with pulse and respiratory recordings. We found that RSNs are spatially reproducible within participants and were temporally associated with low frequencies (LFs < 0.1 Hz) in physiological signals. LF fluctuations in pulse amplitude were not related to cardiac electrical activity and preceded LF fluctuations in RSNs, while LF respiratory amplitude fluctuations followed LF fluctuations in RSNs. LF phase dispersion (PD) (lack of synchrony) between RSNs and pulse (PDpulse) (not respiratory) correlated with the common variability in measures of personality and emotions, with more synchrony being associated with more positive temperamental characteristics. Voxel-level PDpulse mapping revealed an “autonomic brain network,” including sensory cortices and dorsal attention stream, with significant interactions with peripheral signals. Here, we uncover associations between pulse signal amplitude (presumably of sympathetic origin) and brain resting state, suggesting that interactions between central and autonomic nervous systems are important for characterizing personality and emotions.

Modeling the hemodynamic response function using simultaneous EEG-fMRI data and convolutional sparse coding analysis with rank-1 constraints

“…Large amplitude peaks or transients in the BOLD, however, in addition to neural events may also reflect motion (Power et al, 2012) and physiological noise, such as spontaneous fluctuations in arterial CO2 (Golestani et al, 2016;Prokopiou et al, , 2016, cardiac pulsatility (Glover et al, 2000), respiration and heart rate variability (Birn et al, 2008;Chang et al, , 2009Kassinopoulos and Mitsis, 2019). These non-neuronal sources of BOLD signal variability have been shown to elicit networks of coherent BOLD activity, which resemble previously reported resting-state networks derived from fMRI data (Chen et al, 2019;Nalci et al, 2019;Nikolaou et al, 2016;Shokri-Kojori et al, 2018). In addition, covariation of the BOLD signal in different brain regions that is sufficient to give rise to spatial patterns of resting-state activity can be also observed at the timings of lower amplitude peaks, or even at regularly or randomly selected timepoints along the time course of the signal.…”

Correspondence between cerebral glucose metabolism and BOLD reveals relative power and cost in human brain

“…Nevertheless, the relevance of these morphological findings remains to be determined. Our work is limited to assessing activity demand and metabolic supply from a static perspective, but future work should involve studying dynamic changes in rPWR and rCST due to physiological 51 or circadian 52 interactions with brain function, using concurrent imaging of neuronal activity (e.g., measures of glutamatergic function or lFCD) and cerebral metabolic supply (e.g., CMRglc or CBF 53,54 ).…”

Heartbeat Induces a Cortical Theta-Synchronized Network in the Resting State

“…Module 1 contained left inferior temporal regions including the parahippocampal gyrus and posteromedial regions. Interestingly, an autonomic network identified in a previous fMRI study was composed of regions showing stronger interactions with low-frequency peripheral pulse amplitude fluctuations (occurring at ∼0.01–0.09 Hz) than with other brain regions (Shokri-Kojori et al, 2018), and the network had some topological overlap with module 1 such that it also contained a large portion of posteromedial regions and the parahippocampal gyrus. Notably, this previous study also used an HCP dataset (fMRI and behavior data, 18 participants in the previous study were also included in our study) as in our study.…”

“…In the recent reviews of Azzalini et al (2019), authors mentioned that because HRV was reported to be largely driven by the brain, the HRV-associated resting-state brain networks are likely to be associated with descending influences from the brain to heart (Azzalini et al, 2019). Another recent fMRI study showed regions associated with a low-frequency peripheral pulse fluctuation called an autonomic network (Shokri-Kojori et al, 2018). Based on these studies showing cortical regions or networks related to cardiac activity-related measures, we showed ascending cardiac afferent signal-induced phase synchronization between cortical regions in this study.…”

“…Most of the hubs of the HIN with high strength and betweenness centrality were concentrated around the left inferior temporal gyrus and the parahippocampal gyrus. In particular, the parahippocampal gyrus has been reported to be related to the cardiac cycle duration (Kim et al, 2019) and to be part of an autonomic network (Shokri-Kojori et al, 2018). Additionally, the bilateral orbitofrontal region also had high strength and betweenness centrality, which is a visceromotor region that sends motor signals to the viscera (Kleckner et al, 2017).…”