Huntington's Disease (HD) is an inherited, neurodegenerative disorder. Major research findings describe the progressive nature of neurodegeneration and subtle changes very early during the preclinical phase, e.g. atrophy of the neurostriatum and other subcortical structures and observation of movement abnormalities up to 15 years before clinical symptoms are diagnosed. The need for improved understanding of the time-course of underlying neurobiological and cognitive changes during the prodromal stage, which is essential for the development of new therapies, motivated the longitudinal design of the multi-center PREDICT-HD study. Subjects at risk for HD are imaged and examined repeatedly to study patient-specific trajectories of brain structures and associated cognitive changes. Given the consortium's large database of longitudinal imaging data, there is a clear need to develop sensitive measures describing and characterizing the timing and nature of such changes. This proposal for an Ancillary Study in PREDICT-HD will provide newly developed computational anatomy tools specifically designed for the analysis of anatomical structures in longitudinal image data and for the statistical modeling of spatio-temporal trajectories of morphometric brain measurements. We will provide novel tools for spatio-temporal (4D) analysis of longitudinal neuroimage data, via a shareable computational environment with the PREDICT-HD consortium. The proposed methods are particularly innovative in various analytical and computational aspects: (1) Overcoming limitations of longitudinal studies with its inherent challenges of multiple non-uniformly spaced time points and missing data via continuous modeling;(2) Presenting efficient and robust 4D shape modeling without the need to compute corresponding landmarks across shape-groups;(3) Applying a mathematical concept that mimics biological growth to guarantee smooth 4D shape trajectories, and (4) The joint analysis of multi-object complexes where structures of interest are embedded in their anatomical context. This new resource will significantly enhance image-analysis capabilities of the PREDICT-HD consortium, as the tools will provide a modeling of the time course of pathophysiological processes affecting single or multi-object subcortical structures, offering researchers new insight into the time-course and progression of pathology. This project will provide optimal collaboration between MRI segmentation work at Iowa, our novel 4D shape modeling methodology as well as the expertise on statistical shape analysis and spatiotemporal shape modeling at Utah, combined with biostatistical excellence in longitudinal data analysis of the PREDICT-HD consortium at Iowa. This synergy includes both groups'strong expertise in providing shareable computational resources and training materials. Beyond providing tools, our collaborative efforts will process and analyze the large PREDICT-HD data- base, with up to 351 multi-time point MRI datasets. This will potentially lead to new biomarkers that are crucial to the development of new therapies to prevent onset or slow the progression of symptoms. This resource also serves the general scientific community since it is generic w.r.t. the application domain and freely distributed via NITRC.

Public Health Relevance

Huntington's disease (HD) is a genetic, hereditary disease with 30,000 people in North America affected and150,000 at risk for illness. Previous neuroimaging research showed progressive brain atrophy that begins many years before symptoms are severe enough to ensure reliable diagnosis. In view of developing new drug therapies that may delay or even prevent disease onset or slow down disease progression, it is particularly important to develop sensitive objective biomarkers of such changes. Using longitudinal imaging data from the multi-site PREDICT-HD consortium, we propose new innovative spatio-temporal shape analysis methodology that provides a detailed characterization of the time course of brain changes of individuals. The toolkit and training materials will represent a new resource for the PREDICT-HD consortium but also for the scientific community.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZNS1)
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Sutherland, Margaret L
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University of Utah
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Salt Lake City
United States
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Hong, Sungmin; Fishbaugh, James; Gerig, Guido (2018) 4D CONTINUOUS MEDIAL REPRESENTATION BY GEODESIC SHAPE REGRESSION. Proc IEEE Int Symp Biomed Imaging 2018:1014-1017
Fishbaugh, James; Durrleman, Stanley; Prastawa, Marcel et al. (2017) Geodesic shape regression with multiple geometries and sparse parameters. Med Image Anal 39:1-17
Hong, Sungmin; Fishbaugh, James; Rezanejad, Morteza et al. (2017) Subject-Specific Longitudinal Shape Analysis by Coupling Spatiotemporal Shape Modeling with Medial Analysis. Proc SPIE Int Soc Opt Eng 10133:
Sadeghi, Neda; Gilmore, John H; Gerig, Guido (2017) Twin-singleton developmental study of brain white matter anatomy. Hum Brain Mapp 38:1009-1024
Lourens, Spencer; Zhang, Ying; Long, Jeffrey D et al. (2017) Analysis of longitudinal censored semicontinuous data with application to the study of executive dysfunction: The Towers Task. Stat Methods Med Res 26:865-879
Gerig, Guido; Fishbaugh, James; Sadeghi, Neda (2016) Longitudinal modeling of appearance and shape and its potential for clinical use. Med Image Anal 33:114-121
Muralidharan, Prasanna; Fishbaugh, James; Kim, Eun Young et al. (2016) Bayesian Covariate Selection in Mixed-Effects Models For Longitudinal Shape Analysis. Proc IEEE Int Symp Biomed Imaging 2016:656-659
Kim, Regina E Y; Lourens, Spencer; Long, Jeffrey D et al. (2015) Preliminary analysis using multi-atlas labeling algorithms for tracing longitudinal change. Front Neurosci 9:242
Williams, Janet K; Kim, Ji-In; Downing, Nancy et al. (2015) Everyday cognition in prodromal Huntington disease. Neuropsychology 29:255-67
Kim, Ji-In; Long, Jeffrey D; Mills, James A et al. (2015) Performance of the 12-item WHODAS 2.0 in prodromal Huntington disease. Eur J Hum Genet 23:1584-7

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