Dysfunction of the cerebellum is associated with a wide variety of diseases that have significant morbidity yet inadequate therapy. Strokes, tumors, infection, autoimmune disease, medication side effects, and chronic alcoholism can all affect the cerebellum, as can more specific diseases of the cerebellum such as olivopontine cerebellar degeneration and cerebellar ataxia. Through the study of cerebellar ataxia, which involves pronounced atrophy in distinct patterns associated with specific genetic defects, we will be able to establish precise relationships between patterns of cerebellar atrophy and patterns of motor and cognitive deficits. Eventually, it will become possible to predict the functional impacts of cerebellar degeneration when observed in clinical assessments and thereby to suggest appropriate targeted therapies. Prior research has enabled automated analysis of regional atrophy in the cerebellum, a task that had previously been carried out manually. The proposed research will advance this capability to include joint, automated segmentation and analysis of both white matter and gray matter in cerebellum and nearby regions using multimodal imaging data. Specifically, we will: 1) Implement and optimize gray matter and white matter segmentation methods in the cerebellum and vicinity using multimodal images;2) Develop methods to quantify regional cerebellar shapes in individuals and to define statistical shape variation across populations;3) Carry out a pilot study on gray matter and white matter structure and relate these to motor and cognitive functional performance. Specific hypotheses related to patterns of degeneration and corresponding functional deficits in SCA2, SCA3, and SCA6 will be tested, and a preliminary map of the topography of functional scores on a low- dimensional shape space incorporating normal and ataxia subjects will be produced. This new capability will yield a novel staging chart for cerebellar function for use in diagnosis, prognosis, and treatment assessment. The overall goal of the proposed research is to develop automated image and statistical analysis methods that can analyze the patterns of atrophy and associated motor and cognitive deficits in populations and individuals due to cerebellar dysfunction. Methods that are developed will be made available as open source software within a previously established and very extensive software collection already available on the NITRC website. The statistical atlas of our subjects will also be made available together with software for positioning individuals within this space and for generating new atlases on new data collections. Through our efforts in technology development as well as our pilot study demonstrating utility of this analytic approach, new discoveries related to the cerebellum in diverse diseases carried out by many labs will become possible. Such investigations can then lead to new treatments for cerebellar disease and to targeted therapies in those suffering from ataxia and other diseases with cerebellum involvement.

Public Health Relevance

Project Narrative Dysfunction of the cerebellum can be caused by strokes, tumors, infection, autoimmune disease, medication side effects, chronic alcoholism, and genetic defects. This research focuses on the study of cerebellar ataxia in order to relate specific patterns of tissue loss to corresponding losses in functions such as coordination, gait, speech, eye movement, and cognition. The computer methods developed in this research will provide researchers and clinicians new information about the physical effects of cerebellar disease in individuals and groups of people in order to better monitor current treatments and to help develop new treatments.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
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Special Emphasis Panel (ZRG1-NT-L (09))
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Babcock, Debra J
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Johns Hopkins University
Engineering (All Types)
Schools of Engineering
United States
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Yang, Zhen; Abulnaga, S Mazdak; Carass, Aaron et al. (2016) Landmark Based Shape Analysis for Cerebellar Ataxia Classification and Cerebellar Atrophy Pattern Visualization. Proc SPIE Int Soc Opt Eng 9784:
Huo, Yuankai; Asman, Andrew J; Plassard, Andrew J et al. (2016) Simultaneous total intracranial volume and posterior fossa volume estimation using multi-atlas label fusion. Hum Brain Mapp :
Huo, Yuankai; Plassard, Andrew J; Carass, Aaron et al. (2016) Consistent cortical reconstruction and multi-atlas brain segmentation. Neuroimage 138:197-210
Bilgel, Murat; Prince, Jerry L; Wong, Dean F et al. (2016) A multivariate nonlinear mixed effects model for longitudinal image analysis: Application to amyloid imaging. Neuroimage 134:658-70
Yang, Zhen; Ye, Chuyang; Bogovic, John A et al. (2016) Automated cerebellar lobule segmentation with application to cerebellar structural analysis in cerebellar disease. Neuroimage 127:435-44
Ye, Chuyang; Prince, Jerry L (2016) A Bayesian approach to fiber orientation estimation guided by volumetric tract segmentation. Comput Med Imaging Graph 54:35-47
Onyike, Chiadi U (2016) Psychiatric Aspects of Dementia. Continuum (Minneap Minn) 22:600-14
Huo, Yuankai; Carass, Aaron; Resnick, Susan M et al. (2016) Combining Multi-atlas Segmentation with Brain Surface Estimation. Proc SPIE Int Soc Opt Eng 9784:
Liu, Luoluo; Glaister, Jeffrey; Sun, Xiaoxia et al. (2016) Segmentation of Thalamus from MR images via Task-Driven Dictionary Learning. Proc SPIE Int Soc Opt Eng 9784:
Ye, Chuyang; Zhuo, Jiachen; Gullapalli, Rao P et al. (2016) Estimation of fiber orientations using neighborhood information. Med Image Anal 32:243-56

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