Author name cluster

Christopher A. Ross

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5 papers

1 author row

YNICL Journal 2016 Journal Article

Linking white matter and deep gray matter alterations in premanifest Huntington disease

Andreia V. Faria
J. Tilak Ratnanather
Daniel J. Tward
David Soobin Lee
Frieda van den Noort
Dan Wu
Timothy Brown
Hans Johnson

Huntington disease (HD) is a fatal progressive neurodegenerative disorder for which only symptomatic treatment is available. A better understanding of the pathology, and identification of biomarkers will facilitate the development of disease-modifying treatments. HD is potentially a good model of a neurodegenerative disease for development of biomarkers because it is an autosomal-dominant disease with complete penetrance, caused by a single gene mutation, in which the neurodegenerative process can be assessed many years before onset of signs and symptoms of manifest disease. Previous MRI studies have detected abnormalities in gray and white matter starting in premanifest stages. However, the understanding of how these abnormalities are related, both in time and space, is still incomplete. In this study, we combined deep gray matter shape diffeomorphometry and white matter DTI analysis in order to provide a better mapping of pathology in the deep gray matter and subcortical white matter in premanifest HD. We used 296 MRI scans from the PREDICT-HD database. Atrophy in the deep gray matter, thalamus, hippocampus, and nucleus accumbens was analyzed by surface based morphometry, and while white matter abnormalities were analyzed in (i) regions of interest surrounding these structures, using (ii) tractography-based analysis, and using (iii) whole brain atlas-based analysis. We detected atrophy in the deep gray matter, particularly in putamen, from early premanifest stages. The atrophy was greater both in extent and effect size in cases with longer exposure to the effects of the CAG expansion mutation (as assessed by greater CAP-scores), and preceded detectible abnormalities in the white matter. Near the predicted onset of manifest HD, the MD increase was widespread, with highest indices in the deep and posterior white matter. This type of in-vivo macroscopic mapping of HD brain abnormalities can potentially indicate when and where therapeutics could be targeted to delay the onset or slow the disease progression.

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YNICL Journal 2013 Journal Article

Gross feature recognition of Anatomical Images based on Atlas grid (GAIA): Incorporating the local discrepancy between an atlas and a target image to capture the features of anatomic brain MRI

Yuan-Yuan Qin
Johnny T. Hsu
Shoko Yoshida
Andreia V. Faria
Kumiko Oishi
Paul G. Unschuld
Graham W. Redgrave
Sarah H. Ying

We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas-image misregistration, is used to capture the anatomical features of target images. As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimer's disease, Huntington's disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.

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YNIMG Journal 2012 Journal Article

Spatiotemporal mapping of brain atrophy in mouse models of Huntington's disease using longitudinal in vivo magnetic resonance imaging

Manisha Aggarwal
Wenzhen Duan
Zhipeng Hou
Neal Rakesh
Qi Peng
Christopher A. Ross
Michael I. Miller
Susumu Mori

Mouse models of Huntington's disease (HD) that recapitulate some of the phenotypic features of human HD, play a crucial role in investigating disease mechanisms and testing potential therapeutic approaches. Longitudinal studies of these models can yield valuable insights into the temporal course of disease progression and the effect of drug treatments on the progressive phenotypes. Atrophy of the brain, particularly the striatum, is a characteristic phenotype of human HD, is known to begin long before the onset of motor symptoms, and correlates strongly with clinical features. Elucidating the spatial and temporal patterns of atrophy in HD mouse models is important to characterize the phenotypes of these models, as well as evaluate the effects of neuroprotective treatments at specific time frames during disease progression. In this study, three dimensional in vivo magnetic resonance imaging (MRI) and automated longitudinal deformation-based morphological analysis was used to elucidate the spatial and temporal patterns of brain atrophy in the R6/2 and N171-82Q mouse models of HD. Using an established MRI-based brain atlas and mixed-effects modeling of deformation-based metrics, we report the rates of progression and region-specificity of brain atrophy in the two models. Further, the longitudinal analysis approach was used to evaluate the effects of sertraline and coenzyme Q10 (CoQ10) treatments on progressive atrophy in the N171-82Q model. Sertraline treatment resulted in significant slowing of atrophy, especially in the striatum and frontal cortex regions, while no significant effects of CoQ10 treatment were observed. Progressive cortical and striatal atrophy in the N171-82Q mice showed significant positive correlations with measured functional deficits. The findings of this report can be used for future testing and comparison of potential therapeutics in mouse models of HD.

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YNIMG Journal 2011 Journal Article

Structural MRI detects progressive regional brain atrophy and neuroprotective effects in N171-82Q Huntington's disease mouse model

Yong Cheng
Qi Peng
Zhipeng Hou
Manisha Aggarwal
Jiangyang Zhang
Susumu Mori
Christopher A. Ross
Wenzhen Duan

Huntington's disease (HD) displays progressive striatal atrophy that occurs long before the onset of clinical motor symptoms. As there is no treatment for the disease once overt symptoms appear, it has been suggested that neuroprotective therapy given during this presymptomatic period might slow progression of the disease. This requires biomarkers that can reliably detect early changes and are sensitive to treatment response. In mouse models of HD, structural MRI measures have been shown to detect disease onset. To determine whether such measures could also be suitable biomarkers for following responses to treatment, we used T2-weighted MR imaging combined with automated morphological analyses and characterized changes in regional brain volumes longitudinally in the N171-82Q HD mouse model in a preclinical trial. We report here that N171-82Q HD mice exhibit adult-onset and progressive brain atrophy in the striatum and neocortex as well as in whole brain; the progressive atrophy in striatum and neocortex is positively correlated with motor deficits. Most notably, MRI also detected neuroprotective effects of sertraline treatment, a neuroprotective agent confirmed in our previous studies. Our present studies provide the first evidence that longitudinal structural MRI measures can detect the therapeutic effect in HD mice, suggesting that such measures in brain could be valuable biomarkers in HD clinical trials.

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YNIMG Journal 2010 Journal Article

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

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.

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