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Publication : Study the Longitudinal <i>in vivo</i> and Cross-Sectional <i>ex vivo</i> Brain Volume Difference for Disease Progression and Treatment Effect on Mouse Model of Tauopathy Using Automated MRI Structural Parcellation.

First Author  Ma D Year  2019
Journal  Front Neurosci Volume  13
Pages  11 PubMed ID  30733665
Mgi Jnum  J:311221 Mgi Id  MGI:6766311
Doi  10.3389/fnins.2019.00011 Citation  Ma D, et al. (2019) Study the Longitudinal in vivo and Cross-Sectional ex vivo Brain Volume Difference for Disease Progression and Treatment Effect on Mouse Model of Tauopathy Using Automated MRI Structural Parcellation. Front Neurosci 13:11
abstractText  Brain volume measurements extracted from structural MRI data sets are a widely accepted neuroimaging biomarker to study mouse models of neurodegeneration. Whether to acquire and analyze data in vivo or ex vivo is a crucial decision during the phase of experimental designs, as well as data analysis. In this work, we extracted the brain structures for both longitudinal in vivo and single-time-point ex vivo MRI acquired from the same animals using accurate automatic multi-atlas structural parcellation, and compared the corresponding statistical and classification analysis. We found that most gray matter structures volumes decrease from in vivo to ex vivo, while most white matter structures volume increase. The level of structural volume change also varies between different genetic strains and treatment. In addition, we showed superior statistical and classification power of ex vivo data compared to the in vivo data, even after resampled to the same level of resolution. We further demonstrated that the classification power of the in vivo data can be improved by incorporating longitudinal information, which is not possible for ex vivo data. In conclusion, this paper demonstrates the tissue-specific changes, as well as the difference in statistical and classification power, between the volumetric analysis based on the in vivo and ex vivo structural MRI data. Our results emphasize the importance of longitudinal analysis for in vivo data analysis.
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