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Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal

Karin Shmueli1, Peter van Gelderen, Jacco A de Zwart

  • 1Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, 10 Center Drive, Bldg. 10, Rm. B1D-728, Bethesda, MD 20892-1065, USA. shmuelik@mail.nih.gov

Neuroimage
|September 18, 2007
PubMed

Insights

Heart rate fluctuations correlate with brain activity signals in functional MRI (fMRI). Accounting for cardiac rate variations improves fMRI data analysis and statistical power for future studies.

Area of Science:

  • Neuroimaging
  • Physiological Monitoring

Background:

  • Low-frequency heart rate fluctuations (<0.1 Hz) are present during functional magnetic resonance imaging (fMRI) scans.
  • These cardiac fluctuations may contribute to low-frequency blood-oxygenation-level-dependent (BOLD) signal changes observed in resting-state fMRI and task-based paradigms.

Purpose of the Study:

  • To investigate the temporal relationship between cardiac rate and BOLD signal fluctuations in resting-state fMRI.
  • To determine if incorporating cardiac rate timecourses as regressors can improve the explanation of physiological noise in fMRI data.

Main Methods:

  • Resting-state BOLD fMRI and physiological data were acquired at 3 Tesla.
  • Cross-correlation and regression analyses were performed, including time-shifted cardiac rate timecourses alongside established physiological regressors (RETROICOR, respiration volume per unit time).

Main Results:

  • Significant temporal correlations were found between cardiac rate and BOLD signal timecourses, notably negative correlations in gray matter at 6-12s lags and positive correlations at 30-42s lags.
  • Time-shifted cardiac rate regressors explained an additional ~1% of whole-brain BOLD signal variance, comparable to respiration regressors.
  • This improvement in variance explanation was observed even when cardiac regressors were combined with other physiological regressors.

Conclusions:

  • Time-shifted cardiac rate regressors are effective in accounting for physiological noise variance in fMRI.
  • Including these cardiac-related regressors can enhance the statistical power of both resting-state and task-based fMRI studies.
  • This approach offers a valuable method for refining fMRI data analysis and improving the detection of neural activity.

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