This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The project will develop and test tools based on advanced diffusion MRI methods of diffusion spectral imaging (DSI) to map structural changes in white matter architecture and connectivity in early Alzheimer's dementia. Using diffusion tensor MRI, CIND Investigator Dr. Y Zhang et. al. [Zhang 2007] have recently demonstrated that structural changes with the fibers of the cingulum bundle of the medial temporal lobe have potential as a specific early marker of Alzheimer's disease.
The Aims of this section are: 1. Translate and optimize DSI technology for use in human subjects with cognitive impairment. 2. Devise robust procedures of data reduction and analysis to map the cingulum bundle and quantify it structural integrity. 3. Evaluate the capacity of DSI to differentiate normal from abnormal structure of the cingulum bundle and other major fiber tracts of the temporal lobe in the Alzheimer's population.

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Biotechnology Resource Grants (P41)
Project #
5P41RR023953-02
Application #
7957228
Study Section
Special Emphasis Panel (ZRG1-SBIB-J (40))
Project Start
2009-07-01
Project End
2010-06-30
Budget Start
2009-07-01
Budget End
2010-06-30
Support Year
2
Fiscal Year
2009
Total Cost
$87,907
Indirect Cost
Name
Northern California Institute Research & Education
Department
Type
DUNS #
613338789
City
San Francisco
State
CA
Country
United States
Zip Code
94121
Kuceyeski, A; Shah, S; Dyke, J P et al. (2016) The application of a mathematical model linking structural and functional connectomes in severe brain injury. Neuroimage Clin 11:635-647
Lam, Fan; Liu, Ding; Song, Zhuang et al. (2016) A fast algorithm for denoising magnitude diffusion-weighted images with rank and edge constraints. Magn Reson Med 75:433-40
Pannetier, Nicolas A; Stavrinos, Theano; Ng, Peter et al. (2016) Quantitative framework for prospective motion correction evaluation. Magn Reson Med 75:810-6
Kuceyeski, Amy; Navi, Babak B; Kamel, Hooman et al. (2016) Structural connectome disruption at baseline predicts 6-months post-stroke outcome. Hum Brain Mapp 37:2587-601
Kuceyeski, A F; Vargas, W; Dayan, M et al. (2015) Modeling the relationship among gray matter atrophy, abnormalities in connecting white matter, and cognitive performance in early multiple sclerosis. AJNR Am J Neuroradiol 36:702-9
Friedman, Eric J; Young, Karl; Tremper, Graham et al. (2015) Directed network motifs in Alzheimer's disease and mild cognitive impairment. PLoS One 10:e0124453
Kuceyeski, Amy; Navi, Babak B; Kamel, Hooman et al. (2015) Exploring the brain's structural connectome: A quantitative stroke lesion-dysfunction mapping study. Hum Brain Mapp 36:2147-60
Ma, Chao; Liang, Zhi-Pei (2015) Design of multidimensional Shinnar-Le Roux radiofrequency pulses. Magn Reson Med 73:633-45
Zhao, Bo; Lu, Wenmiao; Hitchens, T Kevin et al. (2015) Accelerated MR parameter mapping with low-rank and sparsity constraints. Magn Reson Med 74:489-98
Lu, Zhao-Hua; Zhu, Hongtu; Knickmeyer, Rebecca C et al. (2015) Multiple SNP Set Analysis for Genome-Wide Association Studies Through Bayesian Latent Variable Selection. Genet Epidemiol 39:664-77

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