Dataset Information

Association between heart rate variability and fluctuations in resting-state functional connectivity.

ABSTRACT: Functional connectivity has been observed to fluctuate across the course of a resting state scan, though the origins and functional relevance of this phenomenon remain to be shown. The present study explores the link between endogenous dynamics of functional connectivity and autonomic state in an eyes-closed resting condition. Using a sliding window analysis on resting state fMRI data from 35 young, healthy male subjects, we examined how heart rate variability (HRV) covaries with temporal changes in whole-brain functional connectivity with seed regions previously described to mediate effects of vigilance and arousal (amygdala and dorsal anterior cingulate cortex; dACC). We identified a set of regions, including brainstem, thalamus, putamen, and dorsolateral prefrontal cortex, that became more strongly coupled with the dACC and amygdala seeds during states of elevated HRV. Effects differed between high and low frequency components of HRV, suggesting specific contributions of parasympathetic and sympathetic tone on individual connections. Furthermore, dynamics of functional connectivity could be separated from those primarily related to BOLD signal fluctuations. The present results contribute novel information about the neural basis of transient changes of autonomic nervous system states, and suggest physiological and psychological components of the recently observed non-stationarity in resting state functional connectivity.

SUBMITTER: Chang C

PROVIDER: S-EPMC3746190 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Association between heart rate variability and fluctuations in resting-state functional connectivity.

Chang Catie C Metzger Coraline D CD Glover Gary H GH Duyn Jeff H JH Heinze Hans-Jochen HJ Walter Martin M

NeuroImage 20121212

Functional connectivity has been observed to fluctuate across the course of a resting state scan, though the origins and functional relevance of this phenomenon remain to be shown. The present study explores the link between endogenous dynamics of functional connectivity and autonomic state in an eyes-closed resting condition. Using a sliding window analysis on resting state fMRI data from 35 young, healthy male subjects, we examined how heart rate variability (HRV) covaries with temporal change ...[more]

PMID: 23246859

Similar Datasets

Project description:Global signal regression (GSR) is one of the most debated preprocessing strategies for resting-state functional MRI. GSR effectively removes global artifacts driven by motion and respiration, but also discards globally distributed neural information and introduces negative correlations between certain brain regions. The vast majority of previous studies have focused on the effectiveness of GSR in removing imaging artifacts, as well as its potential biases. Given the growing interest in functional connectivity fingerprinting, here we considered the utilitarian question of whether GSR strengthens or weakens associations between resting-state functional connectivity (RSFC) and multiple behavioral measures across cognition, personality and emotion. By applying the variance component model to the Brain Genomics Superstruct Project (GSP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 47% across 23 behavioral measures after GSR. In the Human Connectome Project (HCP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 40% across 58 behavioral measures, when GSR was applied after ICA-FIX de-noising. To ensure generalizability, we repeated our analyses using kernel regression. GSR improved behavioral prediction accuracies by an average of 64% and 12% in the GSP and HCP datasets respectively. Importantly, the results were consistent across methods. A behavioral measure with greater RSFC-explained variance (using the variance component model) also exhibited greater prediction accuracy (using kernel regression). A behavioral measure with greater improvement in behavioral variance explained after GSR (using the variance component model) also enjoyed greater improvement in prediction accuracy after GSR (using kernel regression). Furthermore, GSR appeared to benefit task performance measures more than self-reported measures. Since GSR was more effective at removing motion-related and respiratory-related artifacts, GSR-related increases in variance explained and prediction accuracies were unlikely the result of motion-related or respiratory-related artifacts. However, it is worth emphasizing that the current study focused on whole-brain RSFC, so it remains unclear whether GSR improves RSFC-behavioral associations for specific connections or networks. Overall, our results suggest that at least in the case for young healthy adults, GSR strengthens the associations between RSFC and most (although not all) behavioral measures. Code for the variance component model and ridge regression can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/preprocessing/Li2019_GSR.

Project description:A growing number of brain imaging studies show functional connectivity (FC) between regions during emotional and cognitive tasks in humans. However, emotions are accompanied by changes in physiological parameters such as heart rate and respiration. These changes may affect blood oxygen level-dependent signals, as well as connectivity between brain areas. This study aimed to clarify the effects of physiological noise on the connectivity between areas related to the default mode network using resting-state functional magnetic resonance imaging (rs-fMRI). Healthy adult volunteers (age range: 19-51 years, mean age: 26.9 ± 9.1 years, 8 males and 8 females) underwent rs-fMRI for 10 min using a clinical 3T scanner (MAGNETOM Trio A Tim System, Siemens) with simultaneously recorded respiration and cardiac output. Physiological noise signals were subsequently removed from the acquired fMRI data using the DRIFTER toolbox. Image processing and analysis of the FC between areas related to the default mode network were performed using DPARSF. Network-Based Statistic (NBS) analysis of the functional connectome of the DMN and DMN-related area was used to perform three groups of comparison: without physiological noise correction, with cardiac noise correction, and with cardiac and respiratory noise correction. NBS analysis identified 36 networks with significant differences in three conditions in FC matrices. Post hoc comparison showed no differences between the three conditions, indicating that all three had the same networks. Among the 36 networks, strength of FC of 8 networks was modified under physiological noise correction. Connectivity between left and right anterior medial frontal regions increased strength of connectivity. These areas are located on the medial cerebral hemisphere, close to the sagittal sinus and arteries in the cerebral hemispheres, suggesting that medial frontal areas may be sensitive to cardiac rhythm close to arteries. The other networks observed temporal regions and showed a decrease in their connectivity strength by removing physiological noise, indicating that physiological noise, especially respiration, may be sensitive to BOLD signal in the temporal regions during resting state. Temporal lobe was highly correlated with anxiety-related respiration changes (Masaoka and Homma, 2000), speech processing, and respiratory sensation. These factors may affect the rs-fMRI signaling sensitivity.

Dataset Information

Association between heart rate variability and fluctuations in resting-state functional connectivity.

Publications

Association between heart rate variability and fluctuations in resting-state functional connectivity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets