PIRACY: An Optimized Pipeline for Functional Connectivity Analysis in the Rat Brain

Abstract

Resting state functional MRI (rs-fMRI) is a widespread and powerful tool for investigating functional connectivity (FC) and brain disorders. However, FC 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 artifacts 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 Principal Component Analysis (MP-PCA) method, FMRIB’s ICA-based Xnoiseifier (FIX) for automatic artifact classification and cleaning, and global signal regression (GSR). 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 artifact classification, and (III) both independent component analysis (ICA) cleaning and GSR are essential steps in correcting for possible artifacts and minimizing the within-group variability in control animals while maintaining typical connectivity patterns. Reduced within-group variability also facilitates the exploration of potential between-group FC changes, as illustrated here in a rat model of sporadic Alzheimer’s disease.

Beyond BOLD: Evidence for diffusion fMRI contrast in the human brain distinct from neurovascular response

“…First, that GSR does not result in a plain mathematical demeaning of FC matrices but contributes to highlighting genuine (anti-)correlations across the brain, should they exist. This finding is in agreement with recent studiesboth in humans and rodentsshowing that ICA cleaning + GSR also improves the differentiation between populations based on resting-state patterns compared to ICA cleaning alone (29,38). While GSR is known to mathematically favor anti-correlations (39), there is also evidence of genuine anti-correlations in BOLD resting-state functional connectivity, which are reportedly related to certain brain networks being specifically inactivated while other networks are active, for accrued efficiency ( 40), e.g.…”

“…Pair-wise Pearson correlation coefficients were calculated between the mean time-courses across each ROI, following global signal regression (GSR), manual independent component analysis (ICA) cleaning (29, 68, 69) with high-pass temporal filtering (f > 0.01 Hz) and 40 independent components, or both. For group analyses, resting-state functional connectivity matrices were averaged across subjects.…”

“…Pair-wise Pearson correlation coefficients were calculated between the mean time-courses across each ROI. The correlation coefficients were calculated following global signal regression (GSR), manual independent component analysis (ICA) cleaning (Beckmann and Smith, 2004;Griffanti et al, 2017;Diao et al, 2021) or both. Group analyses were done averaging the resting-state functional connectivity matrices across the subjects.…”

“…The notoriously low sensitivity of dfMRI is balanced by recent improvements in spatio-temporal resolution of the acquisition (e.g. stronger gradients on clinical systems and multi-slice acceleration) and in image pre-processing, in particular Marchenko-Pastur PCA (MP-PCA) denoising (Veraart et al, 2016;Ades-Aron et al, 2021;Diao et al, 2021) to boost the temporal signal-to-noise ratio (tSNR).…”

“…If at least 20 voxels in the ROI had a zstatistic > 3.1, the estimated response function was taken to the group analysis. For the calculation of group averages, subject-level estimates were first normalized using the 8 seconds before the stimulus onset as baseline.RS fMRI analysis.Pair-wise Pearson correlation coefficients were calculated between the mean time-courses across each ROI, following global signal regression (GSR), manual independent component analysis (ICA) cleaning(29,68,69) with high-pass temporal filtering (f > 0.01 Hz) and 40 independent components, or both. For group analyses, resting-state functional connectivity matrices were averaged across subjects.…”