The overall hypothesis of this application is that white matter (WM), especially the limbic tracts, is one of the primary targets of Alzheimer's disease (AD) and diffusion tensor imaging (DTI) can sensitively detect changes in these WM tracts. The goal is to develop quantitative DTI image analysis techniques to detect WM abnormalities in AD. AD is the most common cause of dementia. Although the primary pathology is cortical neuronal cell degeneration, increasing evidence indicates a preponderance of WM pathology over gray matter pathology. Moreover, WM alterations could be an indirect indicator of nerve cell loss, since the volume of a nerve cell is much smaller than its myelinated fiber. Therefore, WM seems to be a good focus for both early diagnosis and monitoring of disease progression. MRI is a non-invasive technique that is widely available in the United States, which has great potential as an imaging biomarker. However, conventional MRI cannot provide contrasts to differentiate various WM structures and has low sensitivity and specificity for detecting changes in specific WM structures. DTI is a method that has the potential to detect abnormalities in specific white matter structures. Using this method can increase the sensitivity and specificity to detect WM abnormalities, compared to conventional MRI analysis. However, quantification techniques for DTI data have not been well-developed and it has been difficult to fully exploit the WM anatomical information revealed by DTI. This application is based on two technical innovations we have developed: one is a white matter brain atlas (JHU-DTI-MNI) in stereotaxic coordinates that contains detailed white matter maps based on diffusion tensor imaging;and the other is the state-of-the-art computational neuroanatomy technology based on the highly-elastic non-linear brain normalization method (LDDMM), which can preserve WM fiber connectivity in the transformation process.
In Aim 1, we will extend this effort to 1) optimize the white matter atlas for the elderly population, and 2) test our advanced cost functions of LDDMM to improve the normalization quality. After the optimization, we will apply these techniques to detect WM abnormalities in AD and Mild Cognitive Impairment (MCI) patients by cross-sectional analysis (Aim 2) and longitudinal analysis (Aim 3). An existing longitudinal MRI and clinical database acquired at Johns Hopkins University is available for this study. The data were acquired every four months for one year from the same subjects.
In Aim 2, normalized DTI data from AD and MCI patients were compared to age-matched controls to detect disease-specific alterations in morphology and MR parameters (diffusion constant, diffusion anisotropy, and T2).
In Aim 3, we will characterize disease progression-related changes in morphology and MR parameters by a longitudinal analysis. In summary, we propose a new image-analysis method for a comprehensive WM survey that will detect disease-specific changes and disease progression-specific changes in MCI and AD.

Public Health Relevance

We will develop new imaging biomarkers for Alzheimer's disease. Our image-analysis method based on probabilistic white matter atlas and white matter fiber direction-oriented transformation enable us to comprehensively survey the white matter structures to detect disease specific and time-dependent alteration of the brain.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Exploratory/Developmental Grants (R21)
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Special Emphasis Panel (ZRG1-CNN-L (03))
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Hsiao, John
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Johns Hopkins University
Schools of Medicine
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Nowrangi, Milap A; Okonkwo, Ozioma; Lyketsos, Constantine et al. (2015) Atlas-based diffusion tensor imaging correlates of executive function. J Alzheimers Dis 44:585-98
Liang, Zifei; He, Xiaohai; Ceritoglu, Can et al. (2015) Evaluation of Cross-Protocol Stability of a Fully Automated Brain Multi-Atlas Parcellation Tool. PLoS One 10:e0133533
Djamanakova, Aigerim; Tang, Xiaoying; Li, Xin et al. (2014) Tools for multiple granularity analysis of brain MRI data for individualized image analysis. Neuroimage 101:168-76
Zhang, Yajing; Zhang, Jiangyang; Hsu, Johnny et al. (2014) Evaluation of group-specific, whole-brain atlas generation using Volume-based Template Estimation (VTE): application to normal and Alzheimer's populations. Neuroimage 84:406-19
Zhang, Yajing; Chang, Linda; Ceritoglu, Can et al. (2014) A Bayesian approach to the creation of a study-customized neonatal brain atlas. Neuroimage 101:256-67
Oishi, Kenichi; Faria, Andreia V; Yoshida, Shoko et al. (2013) Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging. Int J Dev Neurosci 31:512-24
Djamanakova, Aigerim; Faria, Andreia V; Hsu, John et al. (2013) Diffeomorphic brain mapping based on T1-weighted images: improvement of registration accuracy by multichannel mapping. J Magn Reson Imaging 37:76-84
Oishi, Kenichi; Mielke, Michelle M; Albert, Marilyn et al. (2012) The fornix sign: a potential sign for Alzheimer's disease based on diffusion tensor imaging. J Neuroimaging 22:365-74
Unschuld, Paul G; Joel, Suresh E; Pekar, James J et al. (2012) Depressive symptoms in prodromal Huntington's Disease correlate with Stroop-interference related functional connectivity in the ventromedial prefrontal cortex. Psychiatry Res 203:166-74
Faria, Andreia V; Joel, Suresh E; Zhang, Yajing et al. (2012) Atlas-based analysis of resting-state functional connectivity: evaluation for reproducibility and multi-modal anatomy-function correlation studies. Neuroimage 61:613-21

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