 |
 |
Dear all, I am currently trying to make a decision on whether or not I should use the automatic outlier de-weighting option in my higher level analyzes. I have just ran Flame1+2 on 24 subjects. The reason I am interested in this approach is for having observed by looking at individual level analyzes outputs that a couple of participants "look quite different", e.g., have deactivations in response to painful stimulation instead of activations (2/24). What I would like to do is to get at the extent to which those deviations (deactivations instead of activations) would qualify as "outliers deviations", and if so, decrease the impact they might have on group statistics. I haven't been able to find any errors on stimulus timing files or uncorrected motion that could explain these widespread deactivations- hence my keeping the subjects in the group analyzes- so I thought perhaps the outlier de-weighting could be a good way to go- if they are indeed outliers. Here are my questions: 1) Based on http://www.fmrib.ox.ac.uk/fsl/feat5/detail.html#higher , it seems that Flame 1+2 would give an indication of whether there are outliers in the data, correct? I don't think the images look "speckled", so my inclination would be to believe there are not based on looking at the higher level images, but is there a more quantitative/objective way to get at that? For what type of information should I be looking for in the feat log files? 2) Is using Flame 1+2 *and* automatic outlier de-weighting redundant in any way? 3) Are there any concerns with adopting the outlier de-weigthing option? In particular, taking into consideration that: a) I have only 24 subjects and b)I'm primarily interested in looking at individual differences in this paradigm? Thank you very much for any feedback, Regina