Optimizing resting state fMRI data quality using Component Analysis based on Standard-deviation Attenuation (CASA) denoising

Motivation: Noise sources, including thermal noise, affect signal-to-noise ratio (SNR) in resting-state fMRI, limiting utility and impact of this type of data. Goal(s): This study aims at enhancing data quality by integrating Component Analysis based on Standard-deviation Attenuation (CASA) technique with standard denoising methods. Approach: Employing rs-fMRI data from 19 controls, we compared the regression of motion, white matter and CSF signals (MWC approach) and the integrated CASA denoising + MWC approach, based on tSNR and RSN comparison. Results: The study showed significant enhancement in tSNR employing CASA + MWC approach and led to RSNs free from artifact-related patterns seen with the MWC method. Impact: Resting-state fMRI data with our Component Analysis based on Standard-deviation Attenuation (CASA) denoising have greater signal quality and reduced contribution of unstructured thermal noise, which is greatly beneficial for reliably evaluating functional connectivity in resting state fMRI studies.