Noise removal in resting-state and task fMRI: functional connectivity and activation maps

Area of Science:

• Neuroimaging
• Brain Function Analysis

Background:

• Blood-oxygen-level dependent (BOLD)-based functional magnetic resonance imaging (fMRI) is crucial for mapping brain function and connectivity.
• The BOLD signal is susceptible to non-neuronal noise from head motion, physiological factors, and scanner artifacts, necessitating effective denoising.
• Reliable functional connectivity (FC) results depend on accurately recovering the neural signal from noise.

Purpose of the Study:

• To investigate the impact of seven widely used denoising methods on both resting-state and task fMRI data.
• To compare the performance of different pre-processing pipelines in mitigating noise and improving signal quality.
• To provide an evidence-based reference for selecting appropriate denoising strategies in fMRI studies.

Main Methods:

• Evaluated seven denoising methods applied to resting-state and task fMRI data.
• Utilized task fMRI with established brain activations as a ground truth for comparison.
• Assessed cleaned data using measures of motion, data quality, resting-state networks, task activations, and functional connectivity (FC).

Main Results:

• All advanced denoising pipelines improved signal quality and reduced motion artifacts compared to minimal preprocessing.
• Significant variability was observed in brain activation and FC estimates across different methods.
• Independent component analysis (ICA)-based pipelines demonstrated generally more reliable and accurate results.

Conclusions:

• Advanced denoising methods enhance fMRI data quality and reduce motion artifacts.
• ICA-based denoising pipelines offer superior reliability and accuracy for brain activation and FC estimation.
• This study provides a valuable reference for researchers in selecting optimal fMRI denoising techniques.