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Hi Pia, > I extracted from the maxima, the t-value of each covariate for each > condition and the degrees of freedom of the t statistics. > > And use the below formula to calculate the r Pearson correlations: > > r = (t^2/(t^2+df-1))^0.5 I think you're on the right track, except that the formula should be r = (t^2/(t^2+df))^0.5 The (n-2) that commonly appears is actually the df, and not df-1, since the correlation model includes a mean/intercept as well as the regressor of interest. > The formula obviously (as everything is squared) gives only positive > correlations, even tough I know that the covariates in some cases were > negatively correlated with the BOLD. Is there anyway to obtained back > this information, I could always use the signs of the t statistics maybe? This should be fine, and is in fact the approach taken in cg_spmT2x.m of the vbm2 toolbox (I think a similar function is in Volkmar's volumes toolbox. The relevant code fragment is: t2x = sign(Z).*(1./((df(2)./((Z.*Z)+eps))+1)).^0.5; http://dbm.neuro.uni-jena.de/vbm/vbm2-for-spm2/ http://sourceforge.net/projects/spmtools/ > Finally, doing the procedure above I obtained relatively very week > correlations for all subjects and all conditions: r < 0.1; even though > on a second level analysis they were significant different than zero, > would such a weak correlation make sense or do they look too much like > noise. Maybe try with the corrected formula, and/or use one of the toolbox versions, though I wouldn't expect the difference to be huge. Perhaps it's just that a weak correlation found in all subjects can be significant at the second level, though I'm not sure about that... Hope this helps, Ged.