Grant: Project Grant

Cranial sutures are the soft connective tissue joining skull bones allowing for cranial growth and development, and are known to biologically respond to mechanical stimuli resulting in structural changes at the site (e.g. changes in suture width through bone formation); however, the direct quantitative link between mechanical state and structural adaptations is weak. Abnormalities causing the premature fusion of a cranial suture (i.e. craniosynostosis) effects nearly 1:2000 births worldwide, leading to increased intracranial pressure and abnormal cranial shapes requiring correction through invasive surgical procedures. The proposed project aims to develop a foundational link between local mechanical state and structural adaptations of cranial sutures during normal growth and premature fusion. Mouse models exhibiting a genetic mutation leading to prematurely-fusing cranial sutures will be used alongside wild-type control animals to monitor sutures with abnormalities and those under normal growth, respectively. We will utilize dynamic bone labeling and micro-computed tomography X-ray images at various length scales to characterize the structural evolution at suture sites (e.g. changes in width). We will further utilize the images to generate mechanical simulations and link the local mechanical state of sutures to structural progression. Finally, we will link the complex variable sets representing the local mechanical state and structural response through a machine learning approach to determine the best predictors of cranial sutures adaptations with time. Through the advanced characterization techniques to be employed within this project, a fundamental understanding of how cranial sutures under normal growth and those exhibiting premature fusion progress will be achieved, leading to improved predictive capabilities. The project will lay the foundation for future work to establish informed predictive treatment planning leading to improved patient outcomes.