From the email you linked to:

you would
first obtain the individual contrasts for each of the canonical and
derivative terms from each subject (two contrast volumes per subject),
as before. Then, instead of entering these volumes directly into a
second level analysis, you would compute, for each subject, a single
volume estimating the "amplitude" of the effects at each voxel =
sign(V1).*sqrt(V1.^2+V2.^2), where V1 is the canonical effect contrast
volume, and V2 is the temporal derivative effect contrast volume.
These "amplitude" effects estimate the amplitude of the peak response
irrespective of the delay at which it occurrs (within a reasonable
range). Last you would enter these "amplitude" volumes in a simple
second-level t-test for population inferences.

On 03/13/2012 02:07 PM, Laura Tully wrote:

Thanks Jonathan, that does help. I'm now trying to work out how to define the appropriate t-contrasts in order to compute the "amplitude" at each voxel as described by Sue Gabriele here (and discussed in Calhoun et al. 2004):
https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=SPM;7fba87b3.0811

I think that I need to define t-contrasts at the individual level for each of the three basis functions (canonical, time, dispersion) for all of my conditions [e.g. 1 0 0; 0 1 0; 0 0 1] Is that correct? The bit that I get stuck on is what to do next... according to Calhoun et al (2004) it looks like they created paired difference maps between conditions as well [CondA(allterms)-CondA(derivatives)] - [condB(allterms)-CondB(derivatives)] but I'm not quite sure how to do this, or how it relates to the "amplitude" computation that is discussed in the paper. Any light you could shed on this issue would be most appreciated!

Laura.

On Tue, Mar 13, 2012 at 12:45 PM, Jonathan Peelle <[log in to unmask] <mailto:[log in to unmask]>> wrote:

   Dear Laura,

   > could someone clarify for me what the betas produced using time and
   > dispersion derivatives are? Is it that the first is canonical
   only, the
   > second is canonical+time, and the third is
   canonical+time+dispersion, OR is
   > it canonical only, time only, and dispersion only?

   It's the latter—canonical only, time only, and dispersion only. When
   you estimate a model in SPM, the beta reflects the contribution of
   that model in your design matrix: beta_0001 is the first column,
   beta_0002 the second column, etc.

   Hope this helps!

   Best regards,

   Jonathan

   --
   Dr. Jonathan Peelle
   Center for Cognitive Neuroscience and
   Department of Neurology
   University of Pennsylvania
   3 West Gates
   3400 Spruce Street
   Philadelphia, PA 19104
   USA
   http://jonathanpeelle.net/




--
Laura Tully
Social Neuroscience & Psychopathology
Harvard University
840 William James Hall
33 Kirkland St
Cambridge, MA 02138

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