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 or cerebellar ataxia. These conditions may be associated with a host of disabling medical conditions, including gait and balance problems, slurred or slow speech, blurred or double vision, tremor, and mild dementia. Indeed, cerebellar dysfunction can cause considerable degradation in the quality of life and may also be life-shortening. There is increasing evidence that regional changes in the cerebellum can be identified with specific behavioral, motor, and cognitive deficits in patients. In order to study these relationships and then to make clinical protocols available, it is necessary to develop, and test automatic procedures for analysis of the cerebellar anatomy from magnetic resonance images. The proposed research program addresses this need. Specifically, we propose to (1) optimize imaging and tissue classification methods for magnetic resonance imaging of the cerebellum;(2) develop and evaluate automatic cerebellar labeling methods;(3) investigate and model shape differences of cerebellar regions; and (4) conduct a pilot study of regional cerebellum volumes in normal subjects and cerebellar ataxia patients. The proposed research program is novel in several ways. Tissue classification and segmentation will use multiple data sets for superresolution and will incorporate topology-preserving deformable models and kernel density prior shape models. Labeling (parcellation) will incorporate topology-preserving, multiple-atlas deformable registration using multiparametric features. Since our clinical application is to diseases associated with cerebellar atrophy, our methods must work on both normal and atrophied cerebella. Our pilot study on spinocerebellar ataxia (specifically SCA6) provides a unique opportunity to study a cerebellar-specific disease and to learn about regional atrophy and its correlation with neurological deficits. It is expected that the automatic procedures developed herein will not only facilitate large-scale neuroscientific studies of the structure/function relationships in the cerebellum, but will also provide the basis for a clinical tool to assist in treatment and management of all types of cerebellar diseases.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-SBIB-S (02))
Program Officer
Babcock, Debra J
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Johns Hopkins University
Engineering (All Types)
Schools of Engineering
United States
Zip Code
Glaister, Jeffrey; Carass, Aaron; NessAiver, Tziona et al. (2017) Thalamus segmentation using multi-modal feature classification: Validation and pilot study of an age-matched cohort. Neuroimage 158:430-440
Rentiya, Zubir; Khan, Noore-Sabah; Ergun, Ezgi et al. (2017) Distinct cerebellar regions related to motor and cognitive performance in SCA6 patients. Neuropsychologia 107:25-30
Ye, Chuyang; Prince, Jerry L (2017) Probabilistic tractography using Lasso bootstrap. Med Image Anal 35:544-553
Huo, Yuankai; Asman, Andrew J; Plassard, Andrew J et al. (2017) Simultaneous total intracranial volume and posterior fossa volume estimation using multi-atlas label fusion. Hum Brain Mapp 38:599-616
Plassard, Andrew J; Landman, Bennett A (2017) Multiprotocol, multiatlas statistical fusion: theory and application. J Med Imaging (Bellingham) 4:034002
Ye, Chuyang; Prince, Jerry L (2017) Fiber Orientation Estimation Guided by a Deep Network. Med Image Comput Comput Assist Interv 10433:575-583
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:
Ye, Chuyang; Zhuo, Jiachen; Gullapalli, Rao P et al. (2016) Estimation of fiber orientations using neighborhood information. Med Image Anal 32:243-56
Onyike, Chiadi U (2016) Psychiatric Aspects of Dementia. Continuum (Minneap Minn) 22:600-14
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 134:658-670

Showing the most recent 10 out of 79 publications