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Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI
Rasmus M Birn1, Jason B Diamond, Monica A Smith
1Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, 10 Center Dr., Bldg. 10, Rm. 1D80 Bethesda, MD 20892-1148, USA. rbirn@nih.gov
Neuroimage
|April 25, 2006
Summary
Subtle breathing variations significantly impact fMRI signals, affecting both task activation and resting-state analyses. Controlling for respiration improves the accuracy of brain activity detection in functional MRI studies.
Area of Science:
- Neuroimaging
- Physiological monitoring
- Brain activity analysis
Background:
- Low-frequency (<0.1 Hz) spontaneous breathing variations correlate with fMRI signal changes in gray matter and near blood vessels.
- These respiration-induced signal changes are distinct from those caused by breathing motion (~0.3 Hz).
Purpose of the Study:
- To investigate the impact of low-frequency respiration variations on task-based fMRI activation.
- To assess the influence of these variations on resting-state functional connectivity analysis.
Main Methods:
- Analyzing BOLD signal changes correlated with respiratory volume variations (~0.03 Hz).
- Comparing fMRI results with and without monitoring/removing respiration variations.
- Evaluating the effect of regressing out global signal changes or constant breathing cues.
Main Results:
- Respiration-induced BOLD signal changes localize to blood vessels and high blood volume regions, similar to breath-hold challenges.
- These signal changes overlap with the 'default mode' network, suggesting a potential classification of resting networks based on respiration.
- Controlling for respiration variations significantly improved task-related activation/deactivation identification.
Conclusions:
- Low-frequency breathing variations are a significant confound in fMRI studies.
- Managing respiration effects enhances the reliability of task-based fMRI and resting-state analyses.
- Respiration-induced signal changes offer insights into brain network organization at rest.
