External Email - Use Caution I’ve been using the .annot files generated by group-level GLM results for functional masks for task-related functional connectivity studies. Depending on threshold and contrast, the clusters can be quite large, and I would prefer to use the mris_divide_parcellation utility to break the clusters up into smaller subregions, and generally use the size threshold to make the regions roughly equal size.
One quirk of mris_divide_parcellation is that, because it divides along the longest axis, if I want my final region sizes to be small (relative to the size of the whole cluster), I often find that the subdivisions take on the form of many narrow bands perpendicular to the axis of division. I have attempted to combat this by applying the utility in two passes: in the first pass, I might call: mris_divide_parcellation fsaverage lh mycontrast.annot 1600 mycontrast_1600 This first breaks up large clusters into 1600mm2 subclusters along their longest axes If my goal is to have 400mm regions, I then call mris_divide_parcellation on the new annotation: mris_divide_parcellation fsaverage lh mycontrast_1600.annot 400 mycontrast_400 This has the desired effect of generating roughly 400mm regions that are roughly square, at least, compared to the very narrow regions I would have gotten had I just done a single pass using 400mm subdivisions from the original annotation. The problem I am having is that the subdivisions in the second pass appear to be assigning discontiguous subdivisions of the same source cluster to the same label. An example is attached. The subdivisions have the proportions I’m looking for, but two regions at opposite ends of the same cluster have the same label (there are actually three pairs of noncontiguous regions with the same label in this large cluster). I am wondering if there is any way that this can be avoided? [cid:373867F5-ADB4-4D59-86B1-8384131E3822] /********************************************** * Chris McNorgan * Assistant Professor * Department of Psychology * University at Buffalo, * The State University of New York * http://ccnlab.buffalo.edu/ * Office: 716.645.0236 * Lab: 716.645.0222 **********************************************/
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