ABSTRACT
This chapter focuses on functional magnetic resonance imaging (fMRI) data analysis and modeling using statistical learning techniques. fMRI is a powerful technique for mapping brain function by using the blood oxygenation level dependent (BOLD) effect (32); however, the small signal change due to the BOLD effect is very noisy and susceptible to artifacts such as those caused by scanner drift, head motion, and cardiorespiratory effects. Although a task or stimulus can be repeated over and over again, there are limits due to time constraints, habituation effects, etc. Therefore,
refined techniques from statistics, biosignal analysis, and image processing and analysis is required for sensitive and robust detection and characterization of functional activity.
