Default mask vs. input mask

Hi all,

I'm not sure what the default mask is.  Is this an "or" mask of the subejcts in the dataset, or not a mask at all?  Also what space is it in?  I'd like to limit the number of voxels in the analysis to just those where I have brain data for all subjects.  Do you have any recommendations?



Hi Dr. Hoptman,

It's nice seeing you here. Hope all is well!

Let me try to address your questions.  I assume you are using  DPARSF Advaced Edition( DPARSFA).

The default mask of DPARSFA is generated based on SPM5's apriori mask (brainmask.nii) and thresholded at 50%. It's in MNI-152 space.

 DPARSFA supports user-defined mask. What you need to do is to generate your mask with your data and specify the path to the mask file. Your mask should be in MNI space as well. Don't worry about matching the resolution because  DPARSFA resample the masks automatically for you.

Hope these helps.




Many thanks to Yang for the explanation!

That's correct. And in the future release, there will be a module to generate a GROUP-BASED mask, as in

Di Martino, A., Yan, C.G., Li, Q., Denio, E., Castellanos, F.X., Alaerts, K., Anderson, J.S., Assaf, M., Bookheimer, S.Y., Dapretto, M., Deen, B., Delmonte, S., Dinstein, I., Ertl-Wagner, B., Fair, D.A., Gallagher, L., Kennedy, D.P., Keown, C.L., Keysers, C., Lainhart, J.E., Lord, C., Luna, B., Menon, V., Minshew, N.J., Monk, C.S., Mueller, S., Muller, R.A., Nebel, M.B., Nigg, J.T., O'Hearn, K., Pelphrey, K.A., Peltier, S.J., Rudie, J.D., Sunaert, S., Thioux, M., Tyszka, J.M., Uddin, L.Q., Verhoeven, J.S., Wenderoth, N., Wiggins, J.L., Mostofsky, S.H., Milham, M.P., 2013. The Autism Brain Imaging Data Exchange: towards large-scale evaluation of the intrinsic brain architecture in Autism. Mol Psychiatry in press.

Yan, C.G., Craddock, R.C., Zuo, X.N., Zang, Y.F., Milham, M.P., 2013. Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes. Neuroimage 80, 246-262.

For each subject, an EPI mask will be created and normalized to MNI space. Then users can specify a threshold (e.g., 0.9) on the group average to create a group mask. This is one step of the QC tool, thus those subjects have very bad coverage (or very bad spatial normalization) can be excluded.



Great, thanks!