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Analysis of fMRI Data

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Modern Psychometrics with R

Part of the book series: Use R! ((USE R))

Abstract

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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Notes

  1. 1.

    See http://dx.doi.org/10.7910/DVN/ELLLZM

  2. 2.

    This is certainly not the best way of doing it since ROIs can have irregular (non-spherical) shapes. A better alternative is to import the parcellation boundaries of the ROIs into R, if available, and assign the voxels accordingly.

  3. 3.

    If we are not interested in inference on single subjects, autocorrelation is often ignored, and a simple lm call does the job.

  4. 4.

    The reader can change nruns argument for fitting a different number of runs.

  5. 5.

    A width of 5 led to a reasonable number of clusters in our example. Typically we would use a width of 1 such that we get completely contiguous clusters.

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Authors and Affiliations

  1. Department of Psychology, Harvard University, Cambridge, MA, USA

    Patrick Mair

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  1. Patrick Mair
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Mair, P. (2018). Analysis of fMRI Data. In: Modern Psychometrics with R. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-319-93177-7_14

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