Analysis of fMRI Data

fMRI stands for “functional magnetic resonance imaging” and represents a noninvasive, indirect method for measuring neural activity over time. Such brain scans result in large, complex, and noisy data, which makes data analysis challenging. The first part of the chapter focuses on data preparation and visualization techniques. This is followed by standard univariate linear modeling approaches, for which some effort needs to go into the computation of the expected BOLD signal and the design matrix specification. After fitting the regression models, a huge multiple testing problem arises. A corresponding section focuses on the false discovery rate, Gaussian random fields, and permutation tests including cluster-based thresholding. The last few sections describe specific multivariate methods popular in fMRI: independent component analysis, representational similarity analysis, and connectivity analysis.

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