Fully-automated |JMRI morphometric phenotyping of the Tc1 mouse model of down syndrome

Nick M. Powell, Marc Modat, M. Jorge Cardoso, Da Ma, Holly E. Holmes, Yichao Yu, James O'Callaghan, Jon O. Cleary, Ben Sinclair, Frances K. Wiseman, Victor L.J. Tybulewicz, Elizabeth M.C. Fisher, Mark F. Lythgoe, Sébastien Ourselin

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11 Citations (Scopus)


We 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-advanta-geous 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.

Original languageEnglish
Article numbere0162974
Number of pages23
JournalPLoS ONE
Issue number9
Publication statusPublished - Sept 2016
Externally publishedYes

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