Iris Asllani1, Christian Habeck, Ronald Lazar, Randolph Marshall
1Columbia Universtiy, New York, NY, United States
One of the most significant challenges in stroke neurology is predicting outcomes. While fMRI has played a key role in our understanding of how stroke affects brain function and cognition, translation of imaging data into clinically useful outcomes has been largely ineffectual. This is mainly due to inherent limitations of task-based BOLD fMRI and to a lack of integration of imaging data with other physiological variables. In this study we address these shortcomings by: 1) Acquiring resting functional connectivity networks of cerebral blood flow (CBF) using arterial spin labeling (ASL) perfusion fMRI. 2) Assessing how focal injury affects the integrity of these networks. 3) Investigating the relationship between these networks and other physiological correlates of disease. We focus on carotid occlusive disease as an ideal model for testing perfusion based functional networks.