Dealing with dropouts

Dear community,

we have data in which many subjects have no BOLD activation data in the inferior frontal part of the brain (i.e., a drop out). As a result, some of the voxels in this area get the value of zero at the second-level analysis (i.e., there is no information).

My questions are:

  1. Do I understand it correctly, that even if one subject has a dropout in given voxel, this voxel is not counted at the higher level analysis? it seems like it from the mask that fsl generates…

  2. If this is the case, what are the best ways to deal with it? Could these voxels be saved somehow? I used fMRIprep for preprocessing. Perhaps there is something I could do during the preprocessing stage?

Thank you very much,

Hi @LiorAbramson,

Sounds like that is the case, without having seen any screenshots.

You can use Nilearn to do your modeling and explicitly define your mask.


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