Longitudinal characterization of brain atrophy of a Huntington's disease mouse model by automated morphological analyses of magnetic resonance images

Jiangyang Zhang; Qi Peng; Qing Li; Neda Jahanshad; Zhipeng Hou; Mali Jiang; Naoki Masuda; Douglas R Langbehn; Michael I Miller; Susumu Mori; Christopher A Ross; Wenzhen Duan

doi:10.1016/j.neuroimage.2009.10.027

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Longitudinal characterization of brain atrophy of a Huntington's disease mouse model by automated morphological analyses of magnetic resonance images

Journal article

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Longitudinal characterization of brain atrophy of a Huntington's disease mouse model by automated morphological analyses of magnetic resonance images

Jiangyang Zhang, Qi Peng, Qing Li, Neda Jahanshad, Zhipeng Hou, Mali Jiang, Naoki Masuda, Douglas R Langbehn, Michael I Miller, Susumu Mori, …

NeuroImage (Orlando, Fla.), Vol.49(3), pp.2340-2351

02/01/2010

DOI: 10.1016/j.neuroimage.2009.10.027

PMID: 19850133

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Abstract

Mouse models of human diseases play crucial roles in understanding disease mechanisms and developing therapeutic measures. Huntington's disease (HD) is characterized by striatal atrophy that begins long before the onset of motor symptoms. In symptomatic HD, striatal volumes decline predictably with disease course. Thus, imaging based volumetric measures have been proposed as outcomes for presymptomatic as well as symptomatic clinical trials of HD. Magnetic resonance imaging of the mouse brain structures is becoming widely available and has been proposed as one of the biomarkers of disease progression and drug efficacy testing. However, three-dimensional and quantitative morphological analyses of the brains are not straightforward. In this paper, we describe a tool for automated segmentation and voxel-based morphological analyses of the mouse brains. This tool was applied to a well-established mouse model of Huntington's disease, the R6/2 transgenic mouse strain. Comparison between the automated and manual segmentation results showed excellent agreement in most brain regions. The automated method was able to sensitively detect atrophy as early as 4 weeks of age and accurately follow disease progression. Comparison between ex vivo and in vivo MRI suggests that the ex vivo end-point measurement of brain morphology is also a valid approach except for the morphology of the ventricles. This is the first report of longitudinal characterization of brain atrophy in a mouse model of Huntington's disease by using automatic morphological analysis.

Biomarker

Automated morphological analysis

Huntington's disease

MRI

Brain atrophy

Details

Title: Subtitle: Longitudinal characterization of brain atrophy of a Huntington's disease mouse model by automated morphological analyses of magnetic resonance images
Creators: Jiangyang Zhang - Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
Qi Peng - Division of Neurobiology, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
Qing Li - Division of Neurobiology, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
Neda Jahanshad - Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
Zhipeng Hou - Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
Mali Jiang - Division of Neurobiology, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
Naoki Masuda - Division of Neurobiology, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
Douglas R Langbehn - Department of Psychiatry, University of Iowa, The Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA 52242, USA
Michael I Miller - Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
Susumu Mori - Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
Christopher A Ross - Division of Neurobiology, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
Wenzhen Duan - Division of Neurobiology, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
Resource Type: Journal article
Publication Details: NeuroImage (Orlando, Fla.), Vol.49(3), pp.2340-2351
DOI: 10.1016/j.neuroimage.2009.10.027
PMID: 19850133
NLM abbreviation: Neuroimage
ISSN: 1053-8119
eISSN: 1095-9572
Publisher: Elsevier Inc
Grant note: DOI: 10.13039/100005725, name: CHDI Inc Foundation; DOI: 10.13039/100000065, name: NINDS, award: NS16375; name: NIH, award: EB003543; name: NIH, award: ES012665; name: NIH, award: NS065306
Language: English
Date published: 02/01/2010
Academic Unit: Psychiatry; Iowa Neuroscience Institute
Record Identifier: 9984004194202771

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