Dear Bob,

> From what I understand, during second-level estimation (e.g., one-sample
> t-test) SPM performs the test only for voxels in which ALL subjects have
> data. In areas of the brain in which signal quality is highly variable from
> subject-to-subject (e.g., high susceptibility areas such as ventral frontal
> and temporal), this procedure is quite problematic, especially for large
> samples. Has any one customized the SPM algorithm to bypass the all-or-none
> exclusion procedure? I imagine this would require also producing a
> degrees-of-freedom image (e.g., to use when reporting statistics).

I'm not aware of anyone who has dealt with this, although some folks
(like Donald) are working on solving this in an elegant fashion.

In the meantime, I imagine you could get around this by adjusting the
threshold SPM uses on the 1st level to be less restrictive to the
voxels it includes in the analysis; see, for example:

If there are subjects for whom you don't think you have useful data,
you could include additional regressors in your second level design to
remove their contribution, which should also appropriately adjust the
degrees of freedom (although this would obviously get tricky if it
varied across voxels/regions that you are interested in).  If there
are specific areas you care about, you could also extract the values
and do statistics outside of SPM, which may offer you some additional
flexibility in how you model things.

Best regards,

Dr. Jonathan Peelle
Department of Neurology
University of Pennsylvania
3 West Gates
3400 Spruce Street
Philadelphia, PA 19104