UCSF

Gradient-echo blood oxygen level dependent functional magnetic resonance imaging (BOLD fMRI) is one of the most powerful and widely used tools for studying neural processing in the human brain. However, BOLD fMRI is limited to measuring neural activity indirectly through blood oxygen concentration and is heavily biased toward large draining veins. Although this large vein bias is often ignored when interpreting results of BOLD fMRI experiments, it can lead to inaccurate estimates of the strength, location, and cortical extent of underlying neural activity. In this dissertation, novel methods utilizing the entire complex-valued BOLD signal are proposed. These methods provide robust localization of large veins and quantification of large vein contributions to studies using gradient-echo BOLD fMRI.

The BOLD fMRI signal is complex-valued consisting of both magnitude and phase components. Although most experiments using BOLD fMRI only analyze the magnitude component, the phase component exhibits BOLD activity exclusively from large veins. Thus the phase component can either be used to enhance or suppress BOLD activity from large veins. The work here demonstrates that by using the phase component to enhance magnitude component BOLD activity, functional signal-to-noise ratio (fSNR) is improved equivalent to collecting 115% more data. This improvement is expected to increase at higher field strengths and spatial resolutions. By using the phase component to suppress large vein contributions, it is found that current understanding of semantic category representation may actually reflect vascular differences rather than neural differences across cortex. By suppressing large vein contributions using the methods proposed here, gradient-echo BOLD fMRI can more closely reflect underlying neural activity.