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Thank you Donald.. In the second point, I meant the PM modulation (negative linear PPI). Does this mean that as the PM increases the connectivity decreases ? For the two group question, if there are no differences using two sample t tests but there are differences using one sample t tests (for example visually or when looking at common and specific areas), would this be still reportable and acceptable ? Aser On Tue, Oct 20, 2015 at 6:22 PM, MCLAREN, Donald <[log in to unmask]> wrote: > See below. > > On Tue, Oct 20, 2015 at 1:11 PM, Aser A <[log in to unmask]> wrote: > >> Dear Donald and all, >> >> I have two PPI questions: >> >> 1- if I have a PM modulation (e.g. linear changes) and done +1 on the >> subjects levels and then did group analysis using +1 or -1 ? is this >> correct (i.e. do I have to return to the subjects level again and do the >> contrast -1 in order to perform one sample t tests group analysis ? >> > > >> Correct. Both directions at the group level can be tested with the > single contrast from the first level. > >> >> Now the PPI related question here is that what does it mean a negative >> first order linear PPI analysis between ROI (A (seed)) and ROI (B) ? >> > > >> Do you mean for a first level contrast of -1 over one PPI column? If > so, this would mean that the connectivity amplitude is less than during > baseline. > > >> >> 2- The second question is related to the group analysis. If I have two >> groups patients (A) and healthy (B), when performing two sample t tests to >> investigate PPI (A) > PPI (B) I do not get any significant even at very low >> threshdol. Is it always difficult to get differences between group and this >> need very high number of samples ? >> > > >> It depends on the effect size. Some effects will be greater than > others. Without knowing anything about the task or how the task was > modeled, its hard to say if you'd need a large number of subjects or not. > We are working on spatial analysis approaches that would be less dependent > on the actual effect size and more dependent on the spatial distribution of > the effects. > > >> >> Thanks >> >> Aser >> > >