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. 2014 Feb 28;9(2):e89470.
doi: 10.1371/journal.pone.0089470. eCollection 2014.

Analytic programming with FMRI data: a quick-start guide for statisticians using R

Ani Eloyan  1 , Shanshan Li  2 , John Muschelli  1 , Jim J Pekar  3 , Stewart H Mostofsky  4 , Brian S Caffo  1
Affiliations

Analytic programming with FMRI data: a quick-start guide for statisticians using R

Ani Eloyan et al. PLoS One. .

Abstract

Functional magnetic resonance imaging (fMRI) is a thriving field that plays an important role in medical imaging analysis, biological and neuroscience research and practice. This manuscript gives a didactic introduction to the statistical analysis of fMRI data using the R project, along with the relevant R code. The goal is to give statisticians who would like to pursue research in this area a quick tutorial for programming with fMRI data. References of relevant packages and papers are provided for those interested in more advanced analysis.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Anatomy of the HRF.
Figure 2
Figure 2. Resting state fMRI image for 1 subject.
The top panel shows one axial slice from the 3D image acquared at each of the 152 time points. formula image denotes a voxel in the brain with coordinates [46, 64, 37]. The plot below shows the intensity of the image at voxel formula image (on the y-axis) at each time point (the x-axis corresponds to time).
Figure 3
Figure 3. A slice of the fMRI image plotted by using the image() function in R (left) along with the corresponding smoothed slice (right).
Here the red color corresponds to high intensity values, followed by yellow and white as the values decrease.
Figure 4
Figure 4. Stimulus vectors for an event-related design (top) and a block design (bottom).
The spikes correspond to the onsets of stimuli. There is a sustained period of activity/task in the block design. In both cases, the spacings between events can be unequal.
Figure 5
Figure 5. P-value histogram from a group-level test.
Figure 6
Figure 6. A slice of the thresholded output map overlaid on a template brain using the image() function.
Figure 7
Figure 7. Permutation histograms of t-statistics for two voxels.
Figure 8
Figure 8. The default brain network obtained via fastICA and overlaid on a template brain.
See this image and copyright information in PMC

References

    1. R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available: http://www.R-project.org/. ISBN 3-900051-07-0.
    1. Avants BB, Tustison N, Song G (2009) Advanced normalization tools (ANTS). Insight J.
    1. Bordier C, Dojat M, Lafayede Micheaux P (2009) AnalyzeFMRI: an R package to perform statistical analysis on fMRI datasets. Software: R Package, AnalyzeFMRI, version: 1–1.
    1. Whitcher B, Schmid V, Thornton A (2011) oro.nifti: Rigorous-NIfTI input/output. R package version 02 6.
    1. Whitcher B (2010) oro.dicom: Rigorous-DICOM input/output. R package version 02 7.

Publication types