CAREER: Quantifying the Mechanical Role of Matrix-Bound Water in Bone through Multiscale Imaging and Modeling

About This Grant

This Faculty Early Career Development Program (CAREER) award supports research to determine how matrix-bound water contributes to the bone’s ability to carry load and resist fracture. Although bone fractures are common and costly, most tools used to estimate fracture risk rely primarily on measurements of mineral content and overlook the role of water and collagen in the bone matrix. This project will examine how matrix-bound water changes across the bone structure during aging and reduced mechanical use; and identify when and where water loss begins to impact mechanical performance. By combining advanced magnetic resonance imaging, experimental mechanics, and artificial intelligence (AI)–based analysis of spatial patterns, the research will lay the groundwork for imaging and computational models that more accurately reflect how bone fails. In parallel, a remote, scaffolded research program will provide undergraduate students, including students balancing work, caregiving, or remote study, with hands-on experience in biomedical imaging and modeling. Together, these efforts advance the fundamental understanding of bone mechanobiology, while equipping students with practical skills in imaging, data analysis, and computational modeling and advancing the modern engineering workforce. This project will determine how spatially localized changes in matrix-bound water compromise bone mechanical performance using spectroscopy, water-sensitive imaging, and mechanical testing. These measurements will be combined to develop and validate multiscale, imaging-based finite element models that incorporate dynamic, water-sensitive matrix properties. Two coordinated research efforts will be conducted. The first will quantify when and where matrix-bound water loss leads to declines in toughness and viscoelasticity, independent of mineral or collagen degradation, through complementary studies in animal and human bone that establish spatially resolved composition–mechanics relationships. The second will apply artificial intelligence methods to extract texture and heterogeneity features from water-sensitive imaging data and integrate these features into finite element models to improve prediction of local failure risk. Model predictions will be benchmarked against experimental mechanical outcomes and disseminated through an open imaging and modeling atlas. This work advances biomechanics and mechanobiology by defining when matrix hydration changes become mechanically meaningful and by enabling mechanics-based models of bone failure that reflect dynamic matrix composition rather than mineral structure and density alone. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.