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See comment on microtime below. On Fri, Sep 16, 2011 at 3:05 PM, Darren Gitelman <[log in to unmask]> wrote: > A couple additional answers: > > > On Fri, Sep 16, 2011 at 12:08 PM, Friston, Karl <[log in to unmask]> wrote: >> >> Dear Aize, >> >> >> >> 1. In my understanding, the BOLD signal in a roi is deconvolved (through >> HRF) into neural signal level (xn), filtered by psy task (psy * xn), then >> convolved with HRF, where the eigenvariate will be calculated, this is the >> ppi signal , which will be future fed into GLM to look at the whole brain >> activation influenced by this ROI. Am I right? > > >> >> Yes this is right. I should note that the basic PPI analysis does not >> depend on this deconvolution step. We introduced the deconvolution in the >> days before DCM (which provides an optimal hemodynamic deconvolution). In >> many instances, you may get better results if you simply take the raw >> (mean-corrected) eigenvariate as xn (using the VOI tool in the results >> interface) and multiply it by the (mean corrected) psy factor; particularly >> for slow or block designs. This is because the PPI deconvolution is trying >> to solve a very difficult problem using fairly old methods (Weiner >> filtering) and can sometimes give unstable estimates. > > I agree the concept of PPI is not dependent on deconvolution since it > relates to interactions in factorial designs, however, the need for > deconvolution became apparent when one tried to apply PPI to event related > data. The PPI deconvolution paper (Gitelman et al., Neuroimage, 2003) showed > that forming the interaction term without deconvolution led to errors > particularly for event-related data, but only to a minimal extent with block > design data. The deconvolution step in the script uses empirical Bayes > deconvolution and not Weiner filtering (although it is formally identical to > Weiner filtering if the prior spectral density is assumed to be 1). In any > case I would generally suggest using the PPI machinery to do the > deconvolution. > > >> >> 2. For my data, we have 98 scans per run. For example length(PPI.ppi) = >> 98, but I do know why the length(PPI.xn) = 1568. Any idea? >> >> If I remember, this is an estimate of the underlying neuronal time-series >> in micro-time (with TR/16 time bins). Darren may know - he loved this code J > > I did love it. :). Yes Karl is correct it reflects the conversion to > microtime. The default number of bins per TR = 16 in SPM. This value is > stored in defaults.stats.fmri.fmri_t or in an SPM.mat structure in > SPM.xBF.dt. So 16 * 98 = 1598. If you want to convert PPI.xn back to TR time > you would resample by this vector: > 1:NT:N*NT > > where N = number of scans in the session. > NT = TR/SPM.xBF.t; > > so PPI.xnmacro = PPI.xn(1:NT:N*NT); > > The resulting vector may miss onsets if you are using an event related > design. While you will miss the "onset", you will not miss the HRF associated with the event. > > > Darren >> >> >> >> 3. We have four runs for each subject. It is a kids related study, to look >> at their word comprehension skill. If I overlay the PPI.ppi in left pulvinar >> for these four runs under word condition, the ppi signal looks different, >> please see the attached file. This subject was very still and almost no >> motion inside the scanner, also his performance accuracy is the same across >> runs. Any suggestion? >> >> >> >> I would compare these estimates of xn * psy with those obtained by >> constructing them using an xn that was not deconvolved (as above). If the >> raw PPIs look more stable over sessions, you could proceed with these. >> >> >> >> 4. What is the unit of PPI.ppi ? The original BOLD signal is very high, >> but after PPI, the signal in PPI is with in 2 or 3 >> >> >> >> The units will depend on the units of the HRF assumed during deconvolution >> (because they are neuronal activity times psy units; not BOLD units times >> psy units). I would just call them arbitrary units, because the psy units >> could be anything. >> >> >> >> I hope that this helps - Karl > > > -- > Darren Gitelman, MD > Northwestern University > 710 N. Lake Shore Dr. > Abbott 11th Floor > Chicago, IL 60611 > Ph: (312) 908-8614 > Fax: (312) 908-5073 > > >