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 overall goal of the project is to develop a statistical multi-modal population brain atlas construction framework incorporating all the available imaging modalities. The imaging modalities that the framework will support are T1, T2, various diffusion weighted protocols such as DWI and DSI as well as arterial spin labeling perfusion MRI. The atlas construction framework will be used to study the anatomy of the aging brain and impact of various diseases on the aging process. In particular, the generation of probabilistic brain atlases will improve power to identify systematic brain differences between diseases and aging brains by utilizing information of local brain variability. Moreover, a probabilistic brain atlas that includes jointly information from all imaging modalities will allow more comprehensive investigations in brain alterations than conventional methods that use a single image modality or multiple modalities separately. Moreover, the mapping of multimodal image data into a common 3D coordinate space is a prerequisite for many imaging analysis projects of this P41, as it supplies a quantitative spatial reference system in which brain data from multiple subjects and modalities can be compared and correlated.

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Biotechnology Resource Grants (P41)
Project #
5P41RR023953-03
Application #
8170583
Study Section
Special Emphasis Panel (ZRG1-SBIB-J (40))
Project Start
2010-07-01
Project End
2011-06-30
Budget Start
2010-07-01
Budget End
2011-06-30
Support Year
3
Fiscal Year
2010
Total Cost
$142,173
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
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
Raj, Ashish; LoCastro, Eve; Kuceyeski, Amy et al. (2015) Network Diffusion Model of Progression Predicts Longitudinal Patterns of Atrophy and Metabolism in Alzheimer's Disease. Cell Rep :

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