This project combines computer science and neuroscience towards die development of a computerized three dimensional (3D) atlas of the human brain. Our goals are to create a collection of human brain data sets, retaining information about morphometric variability, provide appropriate quantitative visualization and data organization tools and share this data using electronic networks. During the next five years we will generate a morphological collection of sectioned whole human brain using a novel combination of histotechnologies and advanced computer applications. We will obtain very high resolution, color image data by directly digitizing the cryoplaned blockface of human head and brain. Digital serial images will be reconstructed into 3D volumes or retained for the outlining of selected neuranatomical structures. We will refine our current histotechnology so that digital image data from retrieved histological sections may be remapped in register with the directly imaged data sets. This will ultimately provide maximal delineation of morphological subregions. The resultant digital atlas volumes will be placed in a well understood and universally accepted coordinate system to simplify application in basic and clinical neuroscience fields. These will be made freely available to the scientific, clinical and educational communities via electronic network distribution. The significance of this project is expressed in several ways.. First, die project will present very high resolution, 3D imagery of the human brain with intracranial landmarks intact; these data, presented at a resolution level never before available, will further the study of neuroanatomical structures and their spatial relationships within the brain. Secondly, data collection will generate and organize a substantial amount of information on individual brain morphometry and histology applicable to neuroscientific research. Thirdly, by placing digital 3D volume data into a standard coordinate system we provide the framework for multimodality brain mapping useful in a variety of medical imaging applications. Fourth, we will develop and implement a medical informatics approach to die distribution and sharing of a unique and important database of human brain structure. Finally, this research represents the groundwork for development of a complete, standardized 3D atlas of human brain.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
5R01LM005639-04
Application #
2702869
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Bean, Carol A
Project Start
1995-05-01
Project End
2000-04-30
Budget Start
1998-05-01
Budget End
1999-04-30
Support Year
4
Fiscal Year
1998
Total Cost
Indirect Cost
Name
University of California Los Angeles
Department
Type
Other Domestic Higher Education
DUNS #
119132785
City
Los Angeles
State
CA
Country
United States
Zip Code
90095
Daianu, Madelaine; Mezher, Adam; Mendez, Mario F et al. (2016) Disrupted rich club network in behavioral variant frontotemporal dementia and early-onset Alzheimer's disease. Hum Brain Mapp 37:868-83
Daianu, Madelaine; Mendez, Mario F; Baboyan, Vatche G et al. (2016) An advanced white matter tract analysis in frontotemporal dementia and early-onset Alzheimer's disease. Brain Imaging Behav 10:1038-1053
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Moon, Seok Woo; Dinov, Ivo D; Hobel, Sam et al. (2015) Structural Brain Changes in Early-Onset Alzheimer's Disease Subjects Using the LONI Pipeline Environment. J Neuroimaging 25:728-37
Shi, Jie; Stonnington, Cynthia M; Thompson, Paul M et al. (2015) Studying ventricular abnormalities in mild cognitive impairment with hyperbolic Ricci flow and tensor-based morphometry. Neuroimage 104:1-20
Daianu, Madelaine; Jacobs, Russell E; Weitz, Tara M et al. (2015) Multi-Shell Hybrid Diffusion Imaging (HYDI) at 7 Tesla in TgF344-AD Transgenic Alzheimer Rats. PLoS One 10:e0145205
Zhan, Liang; Liu, Yashu; Wang, Yalin et al. (2015) Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition. Front Neurosci 9:257
Ching, Christopher R K; Hua, Xue; Hibar, Derrek P et al. (2015) Does MRI scan acceleration affect power to track brain change? Neurobiol Aging 36 Suppl 1:S167-77
Daianu, Madelaine; Jahanshad, Neda; Nir, Talia M et al. (2015) Rich club analysis in the Alzheimer's disease connectome reveals a relatively undisturbed structural core network. Hum Brain Mapp 36:3087-103

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