 |
 |
Hi there John, > I'm trying to implement a first-level GLM with EVs representing > condition (two levels,negative affect and neutral), activity within a > specific region over that time series (say, avg. amygdala activity), and > their interaction. My main interest is in the interaction effect as a > representation of connectivity between the specific region (amygdala) > and the rest of the brain, as a function of negative affect (what I > believe is often referred to as an analysis of "psychophysiological > interactions"). I haven't seen anything on this listserv or in the FSL > documentation on how specifically to carry this out via FEAT (let me > know if I've missed anything). But I imagine I could carry it out by > extracting post-processed intensity values within the amygdala, dumping > this to a text file and adding it as a column in the design matrix to be > run on that same time series. I could then add an EV for my task to the > same design matrix, and use the GUI option to multiply them together for > the interaction. My questions are: > > 1) Does this approach seem valid? It seems valid indeed. One thing you should think about is to not chose too small a region to extract values from (i.e. not a single voxel). An assumption of the GLM is that there is no error in the independent variables, i.e. that the regressors are "completely known". As soon as you enter a measured time-series as a regressor you will of course violate this to some degree, and chosing a large enough region is a way to at least ensure that the error in the independent variable is a lot smaller than that in the dependent variable. > > 2) Do I need to worry about the different heights of the EVs > representing my task (e.g., 0 to 1) and the amygdala activity (e.g., 0 > to the max. intensity value, in the thousands)? Specifically, I'm > concerned with the interpretability of the EVs if I enter them into a > higher level analysis examining the interaction within and across groups > (e.g. higher-level analyses), as well as contrasting interactions within > and across subjects. If height is an issue here, can you suggest a > correction I could apply to the amygdala EV before entering it into the > analysis that might take care of this? First of all your task should be mean-corrected, i.e. -.5 to .5 rather than 0 to 1. Multiplying the amygdala time-series with a 0--1 task regressor would simply ignore the correaltion in the 0 level of the task, rather than contrasting the correlations as is your intention. I hear that FSL will automatically mean correct your task regressor for you, so this may not be an issue. Just check the finished regressor to see all went to plan. As for scaling and interpretability at a 2nd level, I have never before thought of that issue in the specific context of PPI's. I guess what you want is to be certain you compare like with like. Let us think of the case of just entering a time-series from one region (A) and regressing the rest of the brains voxels on that series. The way to interpret the corresponding beta in that case is "rate of change of area X per unit change of area A". When we extend this to an interaction with some task the interpretation changes to "difference between condition 1 and 2 of the rate of change of area X per unit change of area A". As far as I can understand this is a reasonable thing to compare across subjects. So, the important thing is that you encode the task in the same way for each subject, but that would be equally true if you just wanted to compare a task effect across subjects. Needless to say you should not attempt a contrast to compare the PPI regressor with any other regressor in the model. I guess one additional thing to consider would be to give some thought to how you define the amygdala ROI. Let us say you have delineated the "real" part of amygdala that exhibit a certain effect. Let us then say that you double the size of the ROI, i.e. you "dilute" the effect so that the task induced variance is just half of what it was before. That would amount to doubling the beta at any target region. The same would of course be true for an interaction regressor. Having said that, it is not clear to me just how to achieve that objective. I think you will just have to use your own discretion, and avoid deep-sea fishing by testing different ROIs. Good luck Jesper