Keywords: fMRI Analysis, fMRI (resting state), Denoising, thermal noise, data preprocessing
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.
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