A longitudinal microstructural MRI dataset in healthy C57Bl/6 mice at 9.4 Tesla
Abstract Multimodal microstructural MRI has shown increased sensitivity and specificity to changes in various brain disease and injury models in the preclinical setting. Here, we present an in vivo longitudinal dataset, including a subset of ex vivo data, acquired as control data and to investigate...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-02-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-01942-5 |
| _version_ | 1797865622683516928 |
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| author | Naila Rahman Kathy Xu Matthew D. Budde Arthur Brown Corey A. Baron |
| author_facet | Naila Rahman Kathy Xu Matthew D. Budde Arthur Brown Corey A. Baron |
| author_sort | Naila Rahman |
| collection | DOAJ |
| description | Abstract Multimodal microstructural MRI has shown increased sensitivity and specificity to changes in various brain disease and injury models in the preclinical setting. Here, we present an in vivo longitudinal dataset, including a subset of ex vivo data, acquired as control data and to investigate microstructural changes in the healthy mouse brain. The dataset consists of structural T2-weighted imaging, magnetization transfer ratio and saturation imaging, and advanced quantitative diffusion MRI (dMRI) methods. The dMRI methods include oscillating gradient spin echo (OGSE) dMRI and microscopic anisotropy (μA) dMRI, which provide additional insight by increasing sensitivity to smaller spatial scales and disentangling fiber orientation dispersion from true microstructural changes, respectively. The technical skills required to analyze microstructural MRI data are complex and include MRI sequence development, acquisition, and computational neuroimaging expertise. Here, we share unprocessed and preprocessed data, and scalar maps of quantitative MRI metrics. We envision utility of this dataset in the microstructural MRI field to develop and test biophysical models, methods that model temporal brain dynamics, and registration and preprocessing pipelines. |
| first_indexed | 2024-04-09T23:11:03Z |
| format | Article |
| id | doaj.art-a9c4310689cf41488b020659a1a3e157 |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-04-09T23:11:03Z |
| publishDate | 2023-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-a9c4310689cf41488b020659a1a3e1572023-03-22T10:23:00ZengNature PortfolioScientific Data2052-44632023-02-0110111610.1038/s41597-023-01942-5A longitudinal microstructural MRI dataset in healthy C57Bl/6 mice at 9.4 TeslaNaila Rahman0Kathy Xu1Matthew D. Budde2Arthur Brown3Corey A. Baron4Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western OntarioTranslational Neuroscience Group, Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western OntarioDepartment of Neurosurgery, Medical College of WisconsinTranslational Neuroscience Group, Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western OntarioCentre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western OntarioAbstract Multimodal microstructural MRI has shown increased sensitivity and specificity to changes in various brain disease and injury models in the preclinical setting. Here, we present an in vivo longitudinal dataset, including a subset of ex vivo data, acquired as control data and to investigate microstructural changes in the healthy mouse brain. The dataset consists of structural T2-weighted imaging, magnetization transfer ratio and saturation imaging, and advanced quantitative diffusion MRI (dMRI) methods. The dMRI methods include oscillating gradient spin echo (OGSE) dMRI and microscopic anisotropy (μA) dMRI, which provide additional insight by increasing sensitivity to smaller spatial scales and disentangling fiber orientation dispersion from true microstructural changes, respectively. The technical skills required to analyze microstructural MRI data are complex and include MRI sequence development, acquisition, and computational neuroimaging expertise. Here, we share unprocessed and preprocessed data, and scalar maps of quantitative MRI metrics. We envision utility of this dataset in the microstructural MRI field to develop and test biophysical models, methods that model temporal brain dynamics, and registration and preprocessing pipelines.https://doi.org/10.1038/s41597-023-01942-5 |
| spellingShingle | Naila Rahman Kathy Xu Matthew D. Budde Arthur Brown Corey A. Baron A longitudinal microstructural MRI dataset in healthy C57Bl/6 mice at 9.4 Tesla Scientific Data |
| title | A longitudinal microstructural MRI dataset in healthy C57Bl/6 mice at 9.4 Tesla |
| title_full | A longitudinal microstructural MRI dataset in healthy C57Bl/6 mice at 9.4 Tesla |
| title_fullStr | A longitudinal microstructural MRI dataset in healthy C57Bl/6 mice at 9.4 Tesla |
| title_full_unstemmed | A longitudinal microstructural MRI dataset in healthy C57Bl/6 mice at 9.4 Tesla |
| title_short | A longitudinal microstructural MRI dataset in healthy C57Bl/6 mice at 9.4 Tesla |
| title_sort | longitudinal microstructural mri dataset in healthy c57bl 6 mice at 9 4 tesla |
| url | https://doi.org/10.1038/s41597-023-01942-5 |
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