Hi list,<div><br></div><div>I have an experiment with 4 scanning runs, and within each run I have trials where I present a stimulus (S1) for 1 TR (2s), then there is 4 TR fixation, and then the 2nd stimulus (S2) for 1 TR, then a 4 TR fixation. So the order is S1(2s),fix(8s),S2(2s),fix(8s), repeated until the end of the scanning run (~10 min). Thus, the trials are separated by 4 TR / 8 s. I would like to decode an aspect of S1 by averaging over activity in the 4 volumes that follow S1, completely ignoring S2, so really the data that I am interested in is separately by a 6 TR / 12 s interval.</div>
<div><br></div><div>The natural way to do this in PyMVPA seems to be that you treat each scanning run as a "chunk," use ERNiftiDataset to set up a boxcar average over the 4 timepoints of interest for each event, and then do something like leave-one-out with the runs for cross-validation.</div>
<div><br></div><div>1.) Would it be possible / advisable for me to do a leave-one-out cross-validated design, where I leave out single trials instead of entire runs, or is this problematic? My worry is that my manipulation is rather subtle, so if I have to leave out 1/4 of my data for training on each fold, my classifier may not learn very well. </div>
<div><br></div><div>I understand that one concern is that the training data should be independent from the transfer data, and I suppose that if I do a leave-one-trial-out analysis, there will be a high temporal correlation between training data in adjacent trials and the transfer trial. However, class 1 is no more likely to follow another class 1 trial than it is to follow a class 2 trial. Does this alleviate the worry? What if I left out only the adjacent trials from the training set?</div>
<div><br></div><div>2.) If this is not especially problematic on a basic level, I would appreciate advice on the optimal way to go about setting up my splitter in PyMVPA. Should I treat each trial as its own chunk? That has the feeling of being stupid or kludgy, but I can't see why it would matter on the face of it. </div>
<div><br></div><div>Thanks in advance for any advice on one or both of these problems.</div><div><br></div><div>Best,</div><div>Stan</div>