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Resting-state fMRI confounds and cleanup.
Kevin Murphy1, Rasmus M Birn, Peter A Bandettini
1Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF10 3AT, UK. murphyk2@cardiff.ac.uk
Neuroimage
|April 11, 2013
Summary
Resting-state fMRI measures brain connectivity using BOLD signals. This study details common signal confounds and methods to remove them, ensuring accurate functional connectivity analysis.
Area of Science:
- Neuroimaging
- Biophysics
- Physiology
Background:
- Resting-state functional magnetic resonance imaging (fMRI) assesses brain functional connectivity via temporal similarity of blood oxygenation level dependent (BOLD) signals.
- Non-neural physiological processes can introduce confounds, leading to spurious functional connectivity measures.
Purpose of the Study:
- To elucidate the origins of BOLD signal confounds in resting-state fMRI.
- To review techniques for removing physiological and motion-related noise from resting-state fMRI data.
- To emphasize the importance of noise reduction for accurate interpretation of brain connectivity.
Main Methods:
- Description of BOLD signal generation based on MR physics and cerebral physiology.
- Discussion of potential confounds including motion, cardiac and respiratory cycles, CO₂ levels, blood pressure, and vasomotion.
- Review of two classes of denoising techniques: those using external physiological recordings and data-driven methods.
Main Results:
- Identified sources of signal variability unrelated to neural activity.
- Presented methods for mitigating confounds from physiological cycles and motion.
- Highlighted the necessity of noise reduction for reliable group-level analysis.
Conclusions:
- Understanding BOLD signal origins is crucial for identifying confounds in resting-state fMRI.
- Effective noise reduction strategies are essential for accurate functional connectivity analysis.
- Proper cleanup of resting-state fMRI data ensures reliable interpretation of brain network findings.
