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.

Figure 1.

Spatiotemporal characteristics of RSNs. For each of MVN, SMN, DMN, and ECN, the spatial distribution of regions with highest association with each network (indexed by spatial ICA component z-scores) is shown under the first column for the average of 203 subjects (with no underlay image). For each RSN, the associations were similar for raw (top row) and clean (bottom row) datasets. The within-subject reproducibility of these spatial maps was assessed across the 4 fMRI runs using ICC(3, 1) for raw and clean datasets (with ICBM152 underlay image). The reproducibility maps are shown after thresholding (p < 0.05, Bonferroni). The spectral average of RSN time courses (594 bins) for each run is shown under the third column followed by the within-subject reproducibility of the amplitude of frequency bins of RSN fluctuations for raw and clean datasets (ICC(3, 1), p < 0.05, Bonferroni). Brain images are shown in radiological view.

Figure 2.

Amplitude and phase associations between RSNs and pulse amplitude fluctuations. First column shows the amplitude of coherence between RSNs and pulse signals for different frequency bins (n = 256), averaged across subjects for each fMRI run for raw (top row) and clean (bottom row) datasets. The within-subject reproducibility of these amplitude associations for each frequency bin was characterized using ICC(3, 1). The third column shows the coherence phase for each frequency bin averaged across subjects for each run, followed by the fourth column showing within-subject reproducibility of these phase measures for raw and clean datasets.

Figure 3.

Amplitude and phase associations between RSNs and respiratory amplitude fluctuations. First column shows the amplitude of coherence between RSNs and respiratory signals for different frequency bins (n = 256), averaged across subjects for each fMRI run for raw (top row) and clean (bottom row) datasets. The within-subject reproducibility of these amplitude associations for each frequency bin was characterized using ICC(3, 1). The third column shows the coherence phase for each frequency bin averaged across subjects for each run, followed by the fourth column showing within-subject reproducibility of these phase measures for raw and clean datasets.

Figure 4.

Average phase lag across 4 fMRI runs for pulse and respiratory amplitude fluctuations relative to RSNs at LFs (0.01−0.09 Hz). (a) Distribution of subjects’ average phase (across 4 runs) relative to pulse signal shown for MVN, SMN, ADN, DMN, ECN, RFPN, and LFPN. The dark thick lines show the group average of phase estimates and the thin lines show significant pairwise differences between RSN phase estimates. The positive phase for all networks indicated that the pulse amplitude fluctuations preceded those in the brain. The networks varied in their phase lag relative to pulse amplitude fluctuations (F(4.04, 816.47) = 16.79, p < 0.0001; *p = 0.056, **p < 0.0001, 2-tailed). (b) Lateral (top) and medial (bottom) views of phase lag of pulse amplitude fluctuations relative to resting brain fluctuations calculated for each voxel individually and averaged over 4 runs. (c) Phase lag of respiratory amplitude fluctuations relative to RSNs. The negative lag indicates that the respiratory amplitude fluctuations follow those in the brain. The networks varied in their phase lag relative to respiratory amplitude fluctuations (F(5.19, 1049.11) = 42.46, p < 0.0001; **p < 0.0001, 2-tailed). (d) Lateral (top) and medial (bottom) views of phase lag of respiratory amplitude fluctuations relative to resting brain fluctuations calculated for each voxel individually and averaged over 4 runs.

Figure 5.

Association between the PD relative to pulse amplitude fluctuations (PD_pulse; 0.01−0.09 Hz) and personality and emotion PCs. (a) The scatter plot of average PD_pulse across 4 runs across 7 RSNs for each subject and the corresponding personality PC score. Subjects are color coded from blue to red for low to high PD_pulse. (b) Subjects’ PD_pulse for each run plotted against personality PC score. Subjects are color coded based on their relative PD_pulse rank in part (a). (c) Average PD_pulse across 4 runs for subjects and their corresponding emotion PC score. (d) Subjects’ PD_pulse for each run plotted against emotion PC score.

Figure 6.

Autonomic network: phase dispersion (PD_pulse) map for pulse signal and associations with personality and emotions. (a) Lateral (top) and medial (bottom) views of voxel-level average of PD_pulse (0.01−0.09 Hz) across the 4 fMRI runs and the 203 participants in each voxel (no underlay image used). PD_pulse was calculated for each brain voxel, independently, highlighting the brain regions within the autonomic network with higher interactions peripheral signals. See Supplementary Table S16 for the list of regions with significantly higher PD_pulse than the rest of the brain. (b) Lateral (top) and medial (bottom) views of regions showing significant correlation (p_FWE < 0.05) between PD_pulse and first PC of personality category (see Supplementary Tables S2 and S17). (c) Lateral (top) and medial (bottom) views of regions showing significant correlation (p_FWE < 0.05) between PD_pulse and first PC of emotion category (see Supplementary Tables S3 and S18).