UC Berkeley

fMRI is a proven technique for recording brain activity. Although it measures a signal that is coupled with neural firing through a complex chain of events, it is the only method by which we can non-invasively observe an entire healthy brain at millimeter scale resolution. The technique of fMRI is now fairly well known, yet there are still many gaps in the tools we use to analyze it and our understanding of its underpinnings. Indeed, the complexity and depth of the data is the source of many of its shortcomings.

In this dissertation, I detail a set of tools I developed to make better use of fMRI data. In chapter 2, I describe a tool that I created to visualize fMRI data. It provides an intuitive and powerful interface to explore and share your data with others. In chapter 3, I created a device that significantly improves the performance of fMRI by virtually eliminating head motion. This plastic insert relies on commodity 3D printing technology to create personalized helmets that address many of the shortfalls of fast scan sequences. Finally, in chapter 4, I utilize these fast scan sequences to investigate the temporal response of hemodynamics. I show that BOLD timecourses have very little high frequency power, but the high dimensional nature of fMRI can be used to perform classification extremely quickly.