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

• Published in: Frontiers in neuroscience Vol. 13; p. 11
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 24.01.2019; Frontiers Media S.A
• Subjects: Animal models; Atrophy; Automation; Brain; Classification; Datasets; Disease; disease progression; ex vivo; in vivo; longitudinal; Magnetic resonance imaging; Morphology; Neurodegeneration; Neurodegenerative diseases; Neuroimaging; Neuroscience; NMR; Nuclear magnetic resonance; Pathology; Rodents; Statistics; structural parcellation; Studies; Substantia alba; Substantia grisea; Tau protein; treatment effect; University colleges; Volumetric analysis; United Kingdom > UK; United States > US; atlas-based segmentation; treatment effect; volumetric; in vivo; disease progression; ex vivo; longitudinal; structural parcellation
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2019.00011

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 or 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 and single-time-point 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 to , 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 data compared to the data, even after resampled to the same level of resolution. We further demonstrated that the classification power of the data can be improved by incorporating longitudinal information, which is not possible for 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 and structural MRI data. Our results emphasize the importance of longitudinal analysis for data analysis.