A longitudinal microstructural MRI dataset in healthy C57Bl/6 mice at 9.4 Tesla.
Naila RahmanKathy XuMatthew D BuddeArthur BrownCorey Allan BaronPublished in: Scientific data (2023)
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.
Keyphrases
- contrast enhanced
- white matter
- magnetic resonance imaging
- diffusion weighted imaging
- diffusion weighted
- magnetic resonance
- high resolution
- electronic health record
- big data
- type diabetes
- resting state
- functional connectivity
- skeletal muscle
- insulin resistance
- artificial intelligence
- mesenchymal stem cells
- medical students
- brain injury
- molecular dynamics
- deep learning
- machine learning
- blood brain barrier
- subarachnoid hemorrhage
- room temperature
- wild type