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Mapping the mouse brain with rs-fMRI: An optimized pipeline for functional network identification.
Valerio Zerbi1, Joanes Grandjean2, Markus Rudin3
1Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland.
Neuroimage
|August 23, 2015
Summary
This study validates the FIX artifact removal method for mouse resting-state fMRI (rs-fMRI), improving data quality and enabling robust identification of 23 brain networks for neurological disorder research.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Medical Imaging
Background:
- Resting-state fMRI (rs-fMRI) is crucial for translational research in mouse models.
- Characterizing robust resting-state networks in mice is challenging due to signal noise.
Purpose of the Study:
- To evaluate the performance of the FIX (FMRIB's Improved Head Motion Tracking) artifact removal method for mouse rs-fMRI data.
- To assess the utility of FIX in improving the reliability of brain connectivity measurements in mice.
Main Methods:
- Applied the FIX artifact removal pipeline to mouse rs-fMRI datasets.
- Assessed FIX accuracy in distinguishing true neural signals from noise.
- Analyzed rs-fMRI connectivity data pre-processed with FIX.
Main Results:
- FIX accurately identified true signals (100%) and noise (>98%) in mouse rs-fMRI data.
- FIX significantly reduced within- and between-subject variability in connectivity measurements.
- Identified and mapped 23 distinct resting-state circuits in mice, including default mode network-like topography.
Conclusions:
- FIX is an effective tool for cleaning mouse rs-fMRI data, enhancing reliability.
- The identified mouse resting-state networks provide a valuable reference for neurological disorder research.
- Publicly available data facilitates future studies using mouse models.
