demo: R code to perform a voxelwise t-test

It's easy to perform a voxelwise t-test (t-test at each voxel individually). Programs for mass-univariate analysis (like SPM and fsl) of course do this (and much more), but sometimes you just want to do a simple t-test across subjects at each voxel.

The demo code linked in this post does a voxelwise t-test in R. It takes as input a set of 3d NIfTI files, where each NIfTI is assumed to come from a different person, each voxel of which contains a statistic describing effect strength (for example, accuracy resulting from a searchlight analysis). The code reads the 3d NIfTI images into a 4d array (people in the fourth dimension), then performs a t-test at each voxel, saving the t-values for each voxel in a new NIfTI image. This figure shows the t-value image produced by the demo code.

UPDATE 4 June 2021:

The original version of this post continues below. I wrote a new version of the code to 1) avoid aaply (memory usage a bit better), and 2) use the RNifti instead of oro.nifti library (RNifti functions can be dramatically faster, and have some nice features; I now use RNifti exclusively for nifti image R input/output). I also added the files to the MVPA Meanderings OSF site, under the "voxelwise" subdirectory (direct link to R script).

Both versions of this code loop through all of the image voxels, which is most assuredly not the most efficient (or foolproof) way to perform a voxelwise t-test (I would recommend 3dttest++ instead, perhaps following 3dbucket; calling afni functions directly from R can make things quite smooth for those of us that are not bash fluent). I rewrote rather than removed this demo, however, because sometimes looping through all the voxels like this is the most straightforward way to get something done. 

Original version:

Here is the key part of the code. The 4d array big contains the 3d statistic images for each person (subjects are the 4th dimension of the array). Then the plyr function aaply calls my little getT function (below and in the code file), which calculates the t-value at each voxel (ie over the subjects), creating a 3d array of t-values.

 # function to calculate the t-test at each voxel and return the t value  
 getT <- function(x) {    
  # can't do a t-test if variance is zero, so check before trying.  
  if (var(x) == 0) {   
   stat <- NA;   
  } else {   
   stat <- t.test(x, alternative="greater", mu=0.5)$statistic;   
  }  
  return(stat)  
 }  
 # plyr function aaply calls getT at each voxel in the 4d array big,  
 # creating a 3d output array of t-values (what getT returns)  
 t.img <- aaply(big, c(1,2,3), getT);