The long term goal of Computational Anatomy (CA) is to create algorithmic tools that aid basic and clinical neuroscientists in the analysis of variability in anatomical structures at different scales. The difficulty is the complexity of anatomical substructures and the large variation across subjects. It is proposed to develop an open-source pipeline for 3D statistical shape analysis of anatomical variations from a population of anatomical structures. The overall aim is to integrate 3D Slicer application and ITK software library with the statistical shape analysis pipeline being disseminated by the Biomedical Informatics Research Network and thus enable the wider neuroimaging community to efficiently analyze anatomical variations in disease.
The first aim i s to standardize shape deformation vectors generated by several CA methods such as the Large Deformation Diffeomorphic Metric Mapping (LDDMM) developed at the Center for Imaging Science at Johns Hopkins University and the Finite Element Method for Deformable Registration (FEMDR) used in ITK. This will allow shape vectors to be used by both global metric classifier analysis in classifying diseased shapes and Gaussian Random Field (GRF) model analysis in localizing shape changes in disease. The two methods will be unified to provide a new metric classifier based on the data generated by GRF. In the final stage, hypothesis testing will be used to correlate global metric classification with localized shape changes.
The second aim i s to construct anatomical atlases needed for analysis of shape vectors. These atlases will be generated from segmented hippocampal and amygdala structures in already acquired populations of children, adolescents and young adults in neuroimaging studies of major depression disorder (MDD) at Washington University at St Louis. As a major public health burden, MDD provides the biological testbed for the pipeline from which probabilistic atlases will be generated.
The third aim i s to integrate the software libraries with the pipeline by leveraging the power and flexibility of the 3D Slicer software and ITK libraries developed by NA-MIC, Kitware and others.
The fourth aim i s to implement modules for visualization of the analysis of shape vectors in 3D Slicer. The fifth aim is to implement a stand-alone version of Medical Reality Markup Language (MRML) independent of 3D Slicer. This will allow for the propagation of MRML as a standard format for future neuroimaging applications. The shape analysis pipeline will be disseminated for use in neuroimaging studies of psychiatric disorders under the auspices of NA-MIC. PUBLIC HEALTH REVELANCE: This multidisciplinary, multi-institutional investigation, based on powerful computational anatomy and computer science software, has a strong potential to add significantly to the etiology of neurodevelopmental and neurodegeneration disorders. The driving biological motivation comes from complementary neuroimaging studies of early onset major depression disorder given the considerable public health burden of depression worldwide. The increased importance of early onset illness combined with the application to a population-based sample of twin pairs appears as an attractive model for statistical shape analysis software for the neuroscience community.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB008171-02
Application #
7825284
Study Section
Special Emphasis Panel (ZRG1-BST-E (50))
Program Officer
Cohen, Zohara
Project Start
2009-05-01
Project End
2013-02-28
Budget Start
2010-03-01
Budget End
2011-02-28
Support Year
2
Fiscal Year
2010
Total Cost
$458,488
Indirect Cost
Name
Johns Hopkins University
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21218
Tward, Daniel; Miller, Michael; Trouve, Alain et al. (2017) Parametric Surface Diffeomorphometry for Low Dimensional Embeddings of Dense Segmentations and Imagery. IEEE Trans Pattern Anal Mach Intell 39:1195-1208
Tang, Xiaoying; Qin, Yuanyuan; Zhu, Wenzhen et al. (2017) Surface-based vertexwise analysis of morphometry and microstructural integrity for white matter tracts in diffusion tensor imaging: With application to the corpus callosum in Alzheimer's disease. Hum Brain Mapp 38:1875-1893
Tang, Xiaoying; Varma, Vijay R; Miller, Michael I et al. (2017) Education is associated with sub-regions of the hippocampus and the amygdala vulnerable to neuropathologies of Alzheimer's disease. Brain Struct Funct 222:1469-1479
Tang, Xiaoying; Qin, Yuanyuan; Wu, Jiong et al. (2016) Shape and diffusion tensor imaging based integrative analysis of the hippocampus and the amygdala in Alzheimer's disease. Magn Reson Imaging 34:1087-99
Faria, Andreia V; Ratnanather, J Tilak; Tward, Daniel J et al. (2016) Linking white matter and deep gray matter alterations in premanifest Huntington disease. Neuroimage Clin 11:450-60
Tang, Xiaoying; Holland, Dominic; Dale, Anders M et al. (2015) APOE Affects the Volume and Shape of the Amygdala and the Hippocampus in Mild Cognitive Impairment and Alzheimer's Disease: Age Matters. J Alzheimers Dis 47:645-60
Matsui, Joy T; Vaidya, Jatin G; Wassermann, Demian et al. (2015) Prefrontal cortex white matter tracts in prodromal Huntington disease. Hum Brain Mapp 36:3717-32
Tang, Xiaoying; Holland, Dominic; Dale, Anders M et al. (2015) Baseline shape diffeomorphometry patterns of subcortical and ventricular structures in predicting conversion of mild cognitive impairment to Alzheimer's disease. J Alzheimers Dis 44:599-611
Miller, Michael I; Younes, Laurent; Ratnanather, J Tilak et al. (2015) Amygdalar atrophy in symptomatic Alzheimer's disease based on diffeomorphometry: the BIOCARD cohort. Neurobiol Aging 36 Suppl 1:S3-S10
Tang, Xiaoying; Holland, Dominic; Dale, Anders M et al. (2015) The diffeomorphometry of regional shape change rates and its relevance to cognitive deterioration in mild cognitive impairment and Alzheimer's disease. Hum Brain Mapp 36:2093-117

Showing the most recent 10 out of 30 publications