  Resting state functional MRI (rs-fMRI) is a widespread and powerful tool for
investigating functional connectivity and brain disorders. However, functional
connectivity analysis can be seriously affected by random and structured noise
from non-neural sources such as physiology. Thus, it is essential to first
reduce thermal noise and then correctly identify and remove non-neural
artefacts from rs-fMRI signals through optimized data processing methods.
However, existing tools that correct for these effects have been developed for
human brain and are not readily transposable to rat data. Therefore, the aim of
the present study was to establish a data processing pipeline that can robustly
remove random and structured noise from rat rs-fMRI data. It includes a novel
denoising approach based on the Marchenko-Pastur Principle Component Analysis
(MP-PCA) method, FMRIB's ICA-based Xnoiseifier (FIX) for automatic artefact
classification and cleaning, and global signal regression. Our results show
that: I) MP-PCA denoising substantially improves the temporal signal-to-noise
ratio; II) the pre-trained FIX classifier achieves a high accuracy in artefact
classification; III) both artefact cleaning and global signal regression are
essential steps in minimizing the within-group variability in control animals
and identifying functional connectivity changes in a rat model of sporadic
Alzheimer's disease, as compared to controls.
