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. Over the next twenty-five years, the proportion of the population over age 65 will increase 76%;therefore understanding both the normal and pathological processes involved in the aging of the human brain is of the highest public health priority. We develop a computational method that provides estimates of the """"""""brain age"""""""" of individuals that is based solely on a high resolution Magnetic Resonance Image (MRI) of the brain of the individual, and is blinded to his or her true chronological age. To develop quantitative neuroanatomical markers for dementia, structural MR brain images must be automatically segmented and deformably registered to provide relative volumes for many brain regions as well as other morphometric information. Historically this automatic segmentation and registration has been subject to a lot of variability, but we have developed a pipeline to perform these more reliably. The processing time for a single brain is roughly 1.5 hours on a single core of a quad-core Xeon 2.8Ghz processor. We wish to process several hundred brains. 250 are available from the Pittsburgh Concussion Study, 700 from the Alzheimer Disease Research Center, and other projects have produced large collections of structural scans to be segmented and registered. This start-up grant would allow us to process at least a large fraction of this supply of scans, provide more accurate estimates of processing time, and provide us a good statistical sample of segmentation and registration results to demonstrate the reliability of the pipeline.
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