4. Functional Magnetic Resonance Imaging

The chapter begins by outlining the prerequisites for running the code used in fMRI analysis and lists the packages required for data preprocessing. It then discusses three example datasets used for demonstrating fMRI techniques, including datasets on visual object recognition and face processing. The chapter covers various preprocessing steps such as slice time correction, motion correction, and registration, highlighting the importance of these steps in ensuring accurate fMRI analysis. It also introduces advanced techniques like spatial smoothing and adaptive segmentation to improve signal detection and reduce noise. Additionally, the chapter discusses the use of the General Linear Model (GLM) for fMRI data analysis and provides examples of signal detection methods, including Bonferroni correction, Random Field Theory, and False Discovery Rate control. The chapter concludes with a discussion on multivariate fMRI analysis and functional connectivity in resting-state fMRI, providing a comprehensive overview of the techniques used in fMRI data analysis.