Depth-Dependent Analysis of Human Ocular Dominance Columns using fMRI with Phase Regression
Abstract
High-resolution fMRI using gradient-echo blood-oxygen-level-dependent (BOLD) contrast is beneficial for the non-invasive study of neural microcircuits. However, the signal spatial specificity of the BOLD contrast severely limits the ability to localize regions of neural activity at the mesoscopic scale in the cortex due to signal contamination from large veins. Phase regression is a venous bias correction technique that uses the correlation between magnitude and phase data in large veins to estimate and supress their contribution to the BOLD signal. This thesis further investigates the performance of phase regression by examining the laminar BOLD signal in human ocular dominance columns. Phase regression removes the venous bias from pial veins and large intracortical veins, while not removing the venous bias from venous vessel sizes within the cortex running parallel to the cortical surface. This thesis demonstrates improved laminar BOLD signal specificity that will be beneficial in future high-resolution laminar fMRI studies.