Whole brain masks are produced by SPM when estimating a model.  They’re great to look over if you want to check  the extent of participant movement (a quick heuristic is to examine whether movement has been so severe that it has noticeably chopped off bits of the brain, e.g. the cerebellum).

These masks can also be used as large, whole-brain ROIs from which to extract signal to covary out of resting connectivity analyses.  I’ll write more about conducting resting connectivity analyses using SPM, without the need for a dedicated connectivity toolbox, at a later date, but it involves extracting timecourses from the whole brain, white matter, CSF and entering these as nuisance regressors alongside movement paramters and their first derivatives.  I use Marsbar to extract the timecourses from the ROI files saved in the *roi.mat format.

Recently, when combining a few different datasets into one bank of resting connectivity data, I noticed that the whole brain mask aggregated across the large number of participants was dropping out a lot of the brain – not enough to consider excluding individual participants, but cumulatively quite deleterious for the overall mask.  I therefore used Imcalc to generate a binary-thresholded image (thresholded at 0.2) of the SPM-bumdled EPI template.  As you can see below, once you remove the eyeballs, this makes for a nice whole-brain mask.

whole-brain mask image
Whole-brain mask constructed from SPM EPI template

I’ve zipped this mask and made available in roi.mat and .nii format here.

One thought on “Whole Brain Mask

  1. Pingback: Implicit and Explicit Masking in SPM « akira o'connor's research blog

Leave a reply

<a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong> 

required


*