Washington University in St. Louis (WU)
My overarching research aims to understand the organization of the human cerebral cortex and its relevance to behavior and aging. Over the past year, I have focused specifically on cortical thickness, a widely used neuroimaging measure that is often confounded by cortical folding patterns established during development. To address this limitation, I developed and validated a novel, folding-compensated cortical thickness measure that accounts for anatomical variability introduced by folding. This method leverages nonlinear multiple regression with five local curvature descriptors to estimate cortical thickness as it would appear in the absence of folding. By applying the method to Human Connectome Project (HCP) datasets from both young and aging cohorts, I demonstrated its ability to reduce inter-individual variability, preserve biologically meaningful signal, and improve sensitivity to age-related cortical atrophy.