I created a script to perform a searchlight analysis on my fMRI dataset, but I had difficulty picking the most relevant analyses.
I read Etzel et al. (2013) paper who argued for: (a) doing a searchlight analysis varying the searchlight size, the kernel and the test statistics ; (b) doing a ROI (preferably on a new dataset) in the region of the resulted searchlight to check if all voxels are relevant; (c ) doing a ‘lesioning study’ i.e. looking at the decoding accuracy when the region identified by the searchlight is removed. All these options seem interesting to me, but I think I will run out of time if I apply all of them… Which one do you think I should best focus on?
I also read Stelzer et al. (2013) who argued for using permutation-analysis on each subject and then applying a bootstrap procedure for group-level inferences. The thing is that I currently plan to perform a leave-one-subject-out cross-validation procedure on my dataset given that there were too many labels and too few datapoints for each subject individually to run a searchlight (I for instance have only 10 occurences of one specific label in one run for some subjects, so doing a leave-one-run-out CV would have been difficult). I would like to do a permutation analysis, but I do not know how I should proceed. Would it be better to change my plans and apply a subject-level searchlight, or can I do something similar on the group level?
Your help is greatly appreciated !