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Dear David ROI size effect is negligible for spectral DCM. See here (e.g. Fig 6): https://www.sciencedirect.com/science/article/pii/S1053811918307511 I hope this helps. Best wishes Adeel On Wed, 3 Oct 2018, 00:32 Zeidman, Peter, <[log in to unmask]> wrote: > Dear David > > > > 1. In case I'm using differently sized ROIs (e.g. one with 20 > voxels, one with 500), does this strongly affect the estimation of the DCM > parameters due to the difference in signal-to-noise ratio? > > > > > > I don’t think so – the precision of the observation noise is estimated on > a per-region basis (DCM.Ce). However, there may be other effects of ROI > size. For instance, if your effect is small (a few voxels) and you have a > 500 voxel ROI, then the principle mode of variance in that ROI is unlikely > due to be related to your task. That means that the representative > timeseries used in DCM for that region may not reflect your effect of > interest. So try to pick ROIs where most voxels express the effect of > interest. > > > > I understand. I'm just wondering if it will be a problem in a resting > state (spectral) DCM? > > > > > > I’m afraid I don’t know. Perhaps you could try different sizes and report > back. However, to simplify your analysis and avoid post-hoc inferences, I > would recommend not getting bogged down by this. Come up with a principled > way of selecting ROI size – e.g. by convention (e.g. the size of your > smoothing your kernel) or by using an ICA component as a mask or by an > anatomical mask. Then stick to it. > > > > > > 2. Is there a way to calculate contrasts between connectivity > parameters of a PEB? That is, is it possible to compare the connectivity > estimates between conditions, e.g. if there is a difference in the strength > of modulation of some between-region connection between an emotional or > neutral condition? > > > > This feature is on my to-do list! At the moment, the most straight-forward > way is to form regressors which capture the contrast or difference of > interest. Can you give more details as to the regressors in your PEB and > your experimental design? > > > > Do you mean adding regressors within the PEB design matrix? I did not have > a specific design in mind, but an easy one would be a single group, 3 ROIs > and two conditions. So there will be only the regressor with all ones for > the group mean. What regressor to include in order to, for example, > contrast the modulation between the connectivity of ROI 1 to ROI 2 of the > two conditions? In other words is the modulation of connectivity of R1-R2 > stronger in the emotional than in the neutral condition. > > > > Yes you can do this without additional regressors – we call this a > Bayesian contrast. You get the expected value of the parameters from BMA.Ep > and the covariance from BMA.Cp and then compute the probability of a > difference between the two parameters of interest. I’m happy to write a > script to do this – which I’d adapt from spm_dcm_review lines 253-273 – but > I don’t have time today. Perhaps you could get back in touch at such a time > that you want to perform this analysis? > > > > Best > > Peter > > >