Resting-State Functional Connectivity in Awake Mice Using the Silent and Motion-Resistant SORDINO
Poster No:
1993
Submission Type:
Abstract Submission
Authors:
Sung-Ho Lee1,2,3, Sheng Song1,2,3, Li-Ming Hsu1,2,3,4, Tzu-Hao Chao1,2,3, Martin MacKinnon1,2,3,5, Tzu-Wen Wang1,2, Randy Nonneman1,2, Yen-Yu Shih1,2,3,5,6
Institutions:
1Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, 2Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 3Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 4Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 5Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, 6Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC
First Author:
Sung-Ho Lee
Center for Animal MRI, University of North Carolina at Chapel Hill|Biomedical Research Imaging Center, University of North Carolina at Chapel Hill|Department of Neurology, University of North Carolina at Chapel Hill
Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC
Center for Animal MRI, University of North Carolina at Chapel Hill|Biomedical Research Imaging Center, University of North Carolina at Chapel Hill|Department of Neurology, University of North Carolina at Chapel Hill
Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC
Co-Author(s):
Sheng Song
Center for Animal MRI, University of North Carolina at Chapel Hill|Biomedical Research Imaging Center, University of North Carolina at Chapel Hill|Department of Neurology, University of North Carolina at Chapel Hill
Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC
Center for Animal MRI, University of North Carolina at Chapel Hill|Biomedical Research Imaging Center, University of North Carolina at Chapel Hill|Department of Neurology, University of North Carolina at Chapel Hill
Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC
Li-Ming Hsu
Center for Animal MRI, University of North Carolina at Chapel Hill|Biomedical Research Imaging Center, University of North Carolina at Chapel Hill|Department of Neurology, University of North Carolina at Chapel Hill|Department of Radiology, University of North Carolina at Chapel Hill
Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC
Center for Animal MRI, University of North Carolina at Chapel Hill|Biomedical Research Imaging Center, University of North Carolina at Chapel Hill|Department of Neurology, University of North Carolina at Chapel Hill|Department of Radiology, University of North Carolina at Chapel Hill
Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC
Tzu-Hao Chao
Center for Animal MRI, University of North Carolina at Chapel Hill|Biomedical Research Imaging Center, University of North Carolina at Chapel Hill|Department of Neurology, University of North Carolina at Chapel Hill
Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC
Center for Animal MRI, University of North Carolina at Chapel Hill|Biomedical Research Imaging Center, University of North Carolina at Chapel Hill|Department of Neurology, University of North Carolina at Chapel Hill
Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC
Martin MacKinnon
Center for Animal MRI, University of North Carolina at Chapel Hill|Biomedical Research Imaging Center, University of North Carolina at Chapel Hill|Department of Neurology, University of North Carolina at Chapel Hill|Department of Biomedical Engineering, University of North Carolina at Chapel Hill
Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC
Center for Animal MRI, University of North Carolina at Chapel Hill|Biomedical Research Imaging Center, University of North Carolina at Chapel Hill|Department of Neurology, University of North Carolina at Chapel Hill|Department of Biomedical Engineering, University of North Carolina at Chapel Hill
Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC
Tzu-Wen Wang
Center for Animal MRI, University of North Carolina at Chapel Hill|Biomedical Research Imaging Center, University of North Carolina at Chapel Hill
Chapel Hill, NC|Chapel Hill, NC
Center for Animal MRI, University of North Carolina at Chapel Hill|Biomedical Research Imaging Center, University of North Carolina at Chapel Hill
Chapel Hill, NC|Chapel Hill, NC
Randy Nonneman
Center for Animal MRI, University of North Carolina at Chapel Hill|Biomedical Research Imaging Center, University of North Carolina at Chapel Hill
Chapel Hill, NC|Chapel Hill, NC
Center for Animal MRI, University of North Carolina at Chapel Hill|Biomedical Research Imaging Center, University of North Carolina at Chapel Hill
Chapel Hill, NC|Chapel Hill, NC
Yen-Yu Shih
Center for Animal MRI, University of North Carolina at Chapel Hill|Biomedical Research Imaging Center, University of North Carolina at Chapel Hill|Department of Neurology, University of North Carolina at Chapel Hill|Department of Biomedical Engineering, University of North Carolina at Chapel Hill|Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill
Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC
Center for Animal MRI, University of North Carolina at Chapel Hill|Biomedical Research Imaging Center, University of North Carolina at Chapel Hill|Department of Neurology, University of North Carolina at Chapel Hill|Department of Biomedical Engineering, University of North Carolina at Chapel Hill|Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill
Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC
Introduction:
Resting-state functional connectivity (rsFC) in awake rodent fMRI is often hindered by stress induced by acoustic noise, which necessitates extensive habituation or light sedation, as well as by motion artifacts and susceptibility-induced distortions near tissue-air boundaries. We introduce Steady-State On-the-Ramp Detection of INduction-decay with Oversampling (SORDINO), a silent, motion-resistant imaging sequence enabling high-quality fMRI in awake mice.
In this study, we leverage SORDINO to perform stress-minimized resting-state fMRI in awake mice. We develop an optimized preprocessing pipeline, tailored to SORDINO's unique non-Cartesian 3D radial trajectory and Zero-Echo Time (ZTE)-based contrast. By addressing challenges related to acoustic noise, motion artifacts, and susceptibility distortions, SORDINO offers a silent and motion-resistant framework for high-quality brain connectivity mapping in awake animal models.
In this study, we leverage SORDINO to perform stress-minimized resting-state fMRI in awake mice. We develop an optimized preprocessing pipeline, tailored to SORDINO's unique non-Cartesian 3D radial trajectory and Zero-Echo Time (ZTE)-based contrast. By addressing challenges related to acoustic noise, motion artifacts, and susceptibility distortions, SORDINO offers a silent and motion-resistant framework for high-quality brain connectivity mapping in awake animal models.
Methods:
All animals (n=25) were implanted with a custom-built headplate, serving as both a head-fixation device and RF transceiver coil, followed by a 1-week recovery period. Mice were habituated over 5 days to head-fixation on a custom MR-compatible treadmill to minimize stress. SORDINO-fMRI data were acquired on a 9.4-T Bruker BioSpec system using the headplate coil. High-resolution anatomical images (matrix size = 160³, FOV = 25.6 mm³) and low-resolution functional scans (matrix size = 64³, volume sampling time = 2 s, 900 volumes) were collected.
Data preprocessing included NUFFT-based reconstruction, spoke-timing correction, motion correction, nuisance regression (28 parameters), temporal filtering (0.01–0.15 Hz), and spatial smoothing (FWHM = 0.6 mm). Functional images were aligned to a population-averaged template registered to the Allen Mouse Brain Common Coordinate Framework (CCF). Quality control metrics included frame-wise displacement, DVARS, tSNR, and functional specificity.
Functional connectivity analysis used Independent Component Analysis (ICA) to identify intrinsic brain functional networks. ROI-based connectivity matrices were computed using Fisher-transformed correlations, with significant edges identified by permutation testing. Anatomical connectivity matrices were derived from the Allen Institute neuron trace database. Network modules were classified using Louvain community detection, hierarchical clustering, PCA, and DBSCAN to identify robust connectivity structures.
Data preprocessing included NUFFT-based reconstruction, spoke-timing correction, motion correction, nuisance regression (28 parameters), temporal filtering (0.01–0.15 Hz), and spatial smoothing (FWHM = 0.6 mm). Functional images were aligned to a population-averaged template registered to the Allen Mouse Brain Common Coordinate Framework (CCF). Quality control metrics included frame-wise displacement, DVARS, tSNR, and functional specificity.
Functional connectivity analysis used Independent Component Analysis (ICA) to identify intrinsic brain functional networks. ROI-based connectivity matrices were computed using Fisher-transformed correlations, with significant edges identified by permutation testing. Anatomical connectivity matrices were derived from the Allen Institute neuron trace database. Network modules were classified using Louvain community detection, hierarchical clustering, PCA, and DBSCAN to identify robust connectivity structures.
Results:
The implanted custom headplate efficiently stabilized mice during awake imaging (Fig1A). Anatomical and functional SORDINO images captured high-contrast rodent brain structures without distortions, minimizing T2*-related artifacts through zero-echo time imaging (Fig1B). Quality metrics, including frame-wise displacement (FD) and DVARS, assessed motion effects on signal stability (Fig1C-D). After regressing out motion parameters, overall DVARS decreased, and no significant linear relationship between FD and DVARS was observed, indicating effective motion correction. Functional specificity, evaluated via seed-based connectivity at the primary sensory cortex (S1), showed 78% specific functional connectivity (FC) and 22% unspecific FC, with occasional high S1-retrosplenial cortex (RSC) connectivity in some subjects (Fig 2A). Independent Component Analysis with 20 components successfully identified robust large-scale networks, including the Default Mode Network (DMN), Lateral Cortical Network (LCN), and Salience Network (SN), consistent with awake BOLD-EPI studies (Fig2B). Finally, seed-based connectivity analysis revealed functional modules comparable to anatomical connectivity patterns derived from the Allen Institute's publicly available data (Fig2C).
Conclusions:
SORDINO enables stress-free, high-quality resting-state fMRI in awake mice, overcoming challenges of acoustic noise, motion, and susceptibility artifacts. This robust, silent method provides reliable brain connectivity mapping, advancing awake animal neuroimaging studies.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Novel Imaging Acquisition Methods:
Non-BOLD fMRI 1
Keywords:
ANIMAL STUDIES
FUNCTIONAL MRI
HIGH FIELD MR
Other - Zero Echo Time Imaging (ZTE)
1|2Indicates the priority used for review
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