Cerebral aneurysms (CAs) are out-pouching dilations of cerebral arteries caused by local wall weakening and maladaptive remodeling. Though rupture is relatively rare, the post-rupture survival rate is low, due to complications such as vasospasm and stroke. Since the majority of cerebral aneurysms are stable, the ability to predict rupture would both allow early intervention and eliminate unnecessary surgical procedures for stable aneurysms. Many computational models have been developed with the aim of predicting rupture based on correlation with clinically measurable factors, such as aneurysm shape or blood flow dynamics. But, these models are not yet accurate enough for them to have been used in the clinic. A major shortcoming of the current approach is that it does not consider the complex mechanics of rupture but instead tries to leap from shape and/or fluid dynamics directly to rupture risk. In contrast, we will build on our understanding of mechanical heterogeneity and its role in tissue growth, remodeling, and failure. By incorporating heterogeneity into the description of the CA, we will inform future models and enable more accurate assessment of CA rupture risk. We hypothesize that cerebral aneurysms are mechanically heterogeneous, and this heterogeneity is predictive of the rupture potential of the aneurysm. We further hypothesize that the material heterogeneity can be determined from (i) the wall shear stress field caused by blood flow in the aneurysm and (ii) the geometry of aneurysm, both of which can be determined in a clinical setting. We propose a series of novel experiments and computational models aimed at elucidating the role of tissue heterogeneity on cerebral aneurysm growth, remodeling, and rupture. Using freshly excised human aneurysm tissue, we will measure regional tissue-scale mechanical properties, ECM structure and composition, cell organization, and the rupture stress of the aneurysm. Next, we will develop and use computational models to