UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Fully-Automated mu MRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome

Powell, NM; Modat, M; Cardoso, MJ; Ma, D; Holmes, HE; Yu, Y; O'Callaghan, J; ... Ourselin, S; + view all (2016) Fully-Automated mu MRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome. PLoS ONE , 11 (9) , Article e0162974. 10.1371/journal.pone.0162974. Green open access

[thumbnail of Main article]
Preview
Text (Main article)
Powell Fully-Automated mu MRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome.pdf

Download (2MB) | Preview
[thumbnail of Supporting information 1: Group-wise registration assessment]
Preview
Text (Supporting information 1: Group-wise registration assessment)
Powell Fully-Automated mu MRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome - S1 Group-wise registration assessment.pdf

Download (217kB) | Preview
[thumbnail of Supporting information 2: Hyperintense rim]
Preview
Text (Supporting information 2: Hyperintense rim)
Powell Fully-Automated mu MRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome - S2 Hyperintense rim.pdf

Download (75kB) | Preview
[thumbnail of Supporting information 3: Effect sizes]
Preview
Text (Supporting information 3: Effect sizes)
Powell Fully-Automated mu MRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome - S3 Effect sizes.pdf

Download (333kB) | Preview
[thumbnail of Supporting information 4: VBM results]
Preview
Text (Supporting information 4: VBM results)
Powell Fully-Automated mu MRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome - S4 VBM results.pdf

Download (584kB) | Preview
[thumbnail of Supporting information 5: Cohort 1 and 2 parcellation volumes]
Preview
Text (Supporting information 5: Cohort 1 and 2 parcellation volumes)
Powell Fully-Automated mu MRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome - S5 Cohort 1 and 2 parcellation volumes.pdf

Download (166kB) | Preview

Abstract

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–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.

Type: Article
Title: Fully-Automated mu MRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pone.0162974
Publisher version: http://dx.doi.org/10.1371/journal.pone.0162974
Language: English
Additional information: Copyright: © 2016 Powell et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Keywords: Science & Technology, Multidisciplinary Sciences, Science & Technology - Other Topics, Voxel-based Morphometry, Magnetic-resonance Images, Brain Images, In-vivo, Cerebellar Phenotypes, Hippocampal Volume, Segmentation, Atlas, Mice, Registration
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Department of Neuromuscular Diseases
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > UK Dementia Research Institute
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Department of Imaging
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Experimental and Translational Medicine
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/1519966
Downloads since deposit
170Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item