Hi Laura,

1. Is there a paper that I could cite that recommends using mean cortical thickness rather than ICV? 

 

If it helps, we used cortical thickness and area as covariate for the respective analysis of regional thickness and area. Brain volume, which is more closely related to ICV, correlates well with area, but not with thickness. We computed a global thickness average by weighting the thickness of each region by their respective areas. The paper is this: http://surfer.nmr.mgh.harvard.edu/ftp/articles/Winkler2010_Neuroimage.pdf

 

1. Would the same logic be applied to surface area analyses? i.e. would it make more sense to use mean surface area as a covariate in surface area analyses? If so, which mean surface area calculation should be used? mri_anatomical_stats can produce both pial and white matter mean surface area stats. 

Yes, I think so. It seems more logical to have a global measurement of area in the model than a measurement of brain volume. On the other hand, area and thickness are not correlated one to another (as shown in the paper above and also in Panizzon et al, 2009, in Cereb Cortex). I don't think there is a clear answer on which, pial or white, should be used. I'd probably go with the white, as I think it may be more robust to image quality, but I admit this is a rather weak justification and if the images are good, perhaps the pial could be just as good, despite the fact that it somewhat depends on the white for its construction.

 

1. Is there a way to run mri_anatomical_stats on multiple subjects at once and write to a tablefile (similar to asegstats2table output)? 

I think you can use aparcstats2table, then add up all regions in a spreadsheet (or even with awk/gawk). Alternatively, you can use "grep" to pick the WhiteSurfArea for each hemisphere from the ?h.aparc.stats file for each subject.

Hope this helps!

All the best,

Anderson