AUTHOR=Ma Da , Holmes Holly E. , Cardoso Manuel J. , Modat Marc , Harrison Ian F. , Powell Nick M. , O’Callaghan James M. , Ismail Ozama , Johnson Ross A. , O’Neill Michael J. , Collins Emily C. , Beg Mirza F. , Popuri Karteek , Lythgoe Mark F. , Ourselin Sebastien TITLE=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 JOURNAL=Frontiers in Neuroscience VOLUME=13 YEAR=2019 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2019.00011 DOI=10.3389/fnins.2019.00011 ISSN=1662-453X ABSTRACT=

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.