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dc.contributor.authorPowell, NM
dc.contributor.authorModat, M
dc.contributor.authorCardoso, MJ
dc.contributor.authorMa, D
dc.contributor.authorHolmes, HE
dc.contributor.authorYu, Y
dc.contributor.authorO'Callaghan, J
dc.contributor.authorCleary, JO
dc.contributor.authorSinclair, B
dc.contributor.authorWiseman, FK
dc.contributor.authorTybulewicz, VLJ
dc.contributor.authorFisher, EMC
dc.contributor.authorLythgoe, MF
dc.contributor.authorOurselin, S
dc.date.accessioned2020-12-21T01:51:54Z
dc.date.available2020-12-21T01:51:54Z
dc.date.issued2016-09-22
dc.identifierpii: PONE-D-16-21927
dc.identifier.citationPowell, N. M., Modat, M., Cardoso, M. J., Ma, D., Holmes, H. E., Yu, Y., O'Callaghan, J., Cleary, J. O., Sinclair, B., Wiseman, F. K., Tybulewicz, V. L. J., Fisher, E. M. C., Lythgoe, M. F. & Ourselin, S. (2016). Fully-Automated mu MRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome. PLOS ONE, 11 (9), https://doi.org/10.1371/journal.pone.0162974.
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/11343/256691
dc.description.abstractWe describe a fully automated pipeline for the morphometric phenotyping of mouse brains from μMRI data, and show its application to the Tc1 mouse model of Down syndrome, to identify new morphological phenotypes in the brain of this first transchromosomic animal carrying human chromosome 21. We incorporate an accessible approach for simultaneously scanning multiple ex vivo brains, requiring only a 3D-printed brain holder, and novel image processing steps for their separation and orientation. We employ clinically established multi-atlas techniques-superior to single-atlas methods-together with publicly-available atlas databases for automatic skull-stripping and tissue segmentation, providing high-quality, subject-specific tissue maps. We follow these steps with group-wise registration, structural parcellation and both Voxel- and Tensor-Based Morphometry-advantageous for their ability to highlight morphological differences without the laborious delineation of regions of interest. We show the application of freely available open-source software developed for clinical MRI analysis to mouse brain data: NiftySeg for segmentation and NiftyReg for registration, and discuss atlases and parameters suitable for the preclinical paradigm. We used this pipeline to compare 29 Tc1 brains with 26 wild-type littermate controls, imaged ex vivo at 9.4T. We show an unexpected increase in Tc1 total intracranial volume and, controlling for this, local volume and grey matter density reductions in the Tc1 brain compared to the wild-types, most prominently in the cerebellum, in agreement with human DS and previous histological findings.
dc.languageEnglish
dc.publisherPUBLIC LIBRARY SCIENCE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleFully-Automated mu MRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome
dc.typeJournal Article
dc.identifier.doi10.1371/journal.pone.0162974
melbourne.affiliation.departmentRadiology
melbourne.affiliation.facultyMedicine, Dentistry & Health Sciences
melbourne.source.titlePLoS One
melbourne.source.volume11
melbourne.source.issue9
dc.rights.licenseCC BY
melbourne.elementsid1103887
melbourne.contributor.authorCleary, Jon
dc.identifier.eissn1932-6203
melbourne.accessrightsOpen Access


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