Machine Learning and Large-scale Imaging analytics for dimensional representations of brain trajectories in aging and preclinical Alzheimer's Disease: The brain aging chart and the iSTAGING consortium

$243,750RF1FY2023AGNIH

University Of Pennsylvania, Philadelphia PA

Investigators

Linked publications & trials

Paper 39638794 Paper 39314511 Paper 39147830 Paper 39143319 Paper 39070036 Paper 38948238 Paper 38942983 Paper 38718880 Paper 38650741 Paper 38521789 Paper 38393899 Paper 38353984 Paper 38313197 Paper 38234857 Paper 38191573 Paper 38127979 Paper 37662256 Paper 37398441 Paper 37084926 Paper 36845412 Paper 36731813 Paper 36716365 Paper 36702211 Paper 36690972 Paper 36502764 Paper 36115133 Paper 35985823 Paper 35689029 Paper 35611306 Paper 35599493 Paper 35421567 Paper 35262657 Paper 35142033 Paper 34871931 Paper 34862382 Paper 34826797 Paper 34818611 Paper 34740075 Paper 34564904 Paper 34455822 Paper 34155395 Paper 33683313 Paper 32920988 Paper 32860881 Paper 32591831 Paper 32201044 Paper 32109935 Paper 32095790 Paper 31821869 Paper 31803346 Paper 31759575 Paper 31756399 Paper 31525565 Paper 31408137 Paper 31268512 Paper 31201098 Paper 31127775 Paper 31071473 Paper 30936201 Paper 30927602 Paper 30820858 Paper 30741945 Paper 30688979 Paper 30580127 Paper 30474995 Paper 30296563 Paper 30211609 Paper 30169897 Paper 30077821 Paper 30076276 Paper 29524624 Paper 29518572 Paper 29487798 Paper 29440536 Paper 29324756 Paper 29168904 Paper 29154276 Paper 29107121 Paper 29075681 Paper 28414186

Abstract

Abstract The parent grant has been building a truly unique database of MRI, clinical and cognitive data from over 65,000 individuals and 16 distinct studies. It has been constructing machine learning models and biomarkers aiming to capture the heterogeneity of brain aging and derive predictive models of cognitive decline. The current supplement will build the foundation for making these pipelines available very easily through cloud computing. More specifically, the focus of this supplement will be to build the infrastructure for a web-based portal running n the AWS cloud, which will allow for seamless âdrag-and-dropâ of MRIs for application of the pipelines developed by the parent program. This will facilitate the wide access of these pipelines without the need to set up local software and familiarize with complex preprocessing of MRI datasets and machine learning pipelines.

View original record on NIH RePORTER →