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

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...

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Published inFrontiers 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
LanguageEnglish
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
Online AccessGet full text

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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 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.
AbstractList 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.
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.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.
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.
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 analyse 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 grey 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 emphasise the importance of longitudinal analysis for in vivo data analysis.
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.
Author Holmes, Holly E.
Johnson, Ross A.
Beg, Mirza F.
O’Callaghan, James M.
Powell, Nick M.
Ourselin, Sebastien
Cardoso, Manuel J.
Modat, Marc
Popuri, Karteek
Collins, Emily C.
Lythgoe, Mark F.
Ma, Da
Ismail, Ozama
Harrison, Ian F.
O’Neill, Michael J.
AuthorAffiliation 1 Translational Imaging Group, Centre for Medical Image Computing, University College London , London , United Kingdom
3 School of Engineering Science, Simon Fraser University , Burnaby, BC , Canada
5 Tailored Therapeutics, Eli Lilly and Company, Lilly Corporate Center , Indianapolis, IN , United States
4 School of Biomedical Engineering and Imaging Sciences, King’s College London , London , United Kingdom
2 Centre for Advanced Biomedical Imaging, University College London , London , United Kingdom
6 Eli Lilly & Co. Ltd., Erl Wood Manor , Windlesham , United Kingdom
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– name: 6 Eli Lilly & Co. Ltd., Erl Wood Manor , Windlesham , United Kingdom
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ContentType Journal Article
Copyright 2019. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright © 2019 Ma, Holmes, Cardoso, Modat, Harrison, Powell, O’Callaghan, Ismail, Johnson, O’Neill, Collins, Beg, Popuri, Lythgoe and Ourselin. 2019 Ma, Holmes, Cardoso, Modat, Harrison, Powell, O’Callaghan, Ismail, Johnson, O’Neill, Collins, Beg, Popuri, Lythgoe and Ourselin
Copyright_xml – notice: 2019. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Keywords atlas-based segmentation
treatment effect
volumetric
in vivo
disease progression
ex vivo
longitudinal
structural parcellation
Language English
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This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience
Reviewed by: Peixin Zhu, Harvard University, United States; Helene Benveniste, Yale University, United States
Joint senior authors
Edited by: Dongdong Lin, Mind Research Network (MRN), United States
Joint first authors
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SSID ssj0062842
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Snippet Brain volume measurements extracted from structural MRI data sets are a widely accepted neuroimaging biomarker to study mouse models of neurodegeneration....
Brain volume measurements extracted from structural MRI data sets are a widely-accepted neuroimaging biomarker to study mouse models of neurodegeneration....
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SubjectTerms Animal models
Atrophy
Automation
Brain
Classification
Datasets
Disease
disease progression
ex vivo
in vivo
longitudinal
Magnetic resonance imaging
Morphology
Neurodegeneration
Neurodegenerative diseases
Neuroimaging
Neuroscience
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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
URI https://www.ncbi.nlm.nih.gov/pubmed/30733665
https://www.proquest.com/docview/2306534083
https://www.proquest.com/docview/2229075279
https://pubmed.ncbi.nlm.nih.gov/PMC6354066
https://doaj.org/article/9b84e61895284ebd9c09e6d1b92b0f14
Volume 13
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