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

Area of Science:

• Neuroimaging
• Neuroscience
• Biomedical Engineering

Background:

• Resting-state functional MRI (rs-fMRI) is crucial for studying brain function and disorders.
• Non-neural noise significantly impacts rs-fMRI functional connectivity (FC) analysis.
• Existing noise correction tools are not optimized for rat rs-fMRI data.

Purpose of the Study:

• To develop a robust data processing pipeline for denoising rat rs-fMRI data.
• To address limitations of existing human-centric noise correction methods.
• To facilitate accurate FC analysis in rodent models.

Main Methods:

• Implemented a novel denoising approach using Marchenko-Pastur Principal Component Analysis (MP-PCA).
• Utilized FMRIB's ICA-based Xnoiseifier (FIX) for automated artifact classification and removal.
• Incorporated global signal regression (GSR) and Independent Component Analysis (ICA) cleaning.

Main Results:

• MP-PCA significantly enhanced the temporal signal-to-noise ratio in rat rs-fMRI.
• The FIX classifier demonstrated high accuracy in identifying and removing artifacts.
• ICA cleaning and GSR were essential for artifact correction and reducing within-group variability.

Conclusions:

• The developed pipeline effectively removes random and structured noise from rat rs-fMRI.
• Reduced within-group variability aids in detecting group differences in FC.
• This approach is valuable for investigating brain disorders in rat models, such as Alzheimer's disease.