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.
Highlights
Resting state functional MRI based on spontaneous low-frequency fluctuations in the blood oxygen level dependent (BOLD) signal in the resting brain is a widely used non-invasive tool for studying intrinsic functional organization in health and disease (Fox and Raichle, 2007; Fornito and Bullmore, 2010)
To assess the robustness of the PIRACY pipeline, an independent cohort with 56 rs-fMRI datasets was acquired from seven different rats at four timepoints ranging from 2 to 21 weeks (Figure 1)
The number of components classified as artifacts decreased, whereas the z-statistic for signal components increased when Marchenko-Pastur Principal Component Analysis (MP-PCA) denoising was applied prior to independent component analysis (ICA) decomposition (Figure 5)
Summary
Resting state functional MRI (rs-fMRI) based on spontaneous low-frequency fluctuations in the blood oxygen level dependent (BOLD) signal in the resting brain is a widely used non-invasive tool for studying intrinsic functional organization in health and disease (Fox and Raichle, 2007; Fornito and Bullmore, 2010). The BOLD signal is contaminated by multiple physiological and non-physiological sources of noise, such as respiratory and cardiac cycles, thermal noise, changes in blood pressure, and head motion (Kruger and Glover, 2001; Birn, 2012; Van Dijk et al, 2012; Murphy et al, 2013). These nonneuronal sources can severely affect rs-fMRI time series and thereby confound the connectivity analysis (Cole et al, 2010; Power et al, 2014). Dedicated pipelines for rodent rs-fMRI processing are just starting to emerge (Zerbi et al, 2015; Bajic et al, 2017)
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