I have something unexpected with my MVPA analysis results but cannot found where it come from. Maybe I can find some help here.
I run a whole brain searchlight MVPA analyze on fMRI dataset (using Nilearn:
searchlight = nilearn.decoding.SearchLight(mask_img[sub],
scoring = None,
cv=single_split[sub])). #leave 2 runs out
The experimental protocol included 2 Conditions (A, B) and 2 classes by condition. I run different leave2runs out analyses at individual level then [accuracy maps – 0.5] were entered in SnPM (5000 permutations, 0.05 FWE-cluster corrected) for group level analyses.
- Intracondition decoding : in each condition I trained the classifier to decode the two classes. The results were very powerful, and I have observed common regions that decode the classes in each of the 2 intracondition decoding (A, B).
- Cross-condition decoding : (I trained the classifier on one condition and tested on another one). I run the analysis and find significant below chance decoding in some regions (mostly the common regions of intracondition decodings) and nothing significant above chance. I was expected the exact opposite!
- I thought that perhaps the decoding relationship was strictly opposite in one condition compared to the other. To better understand I performed a univariate analysis that confirmed that the beta values in these regions increase in all condition for the class1 compared to the class2. So, in the same direction (however it’s more related to the intensity of the signal than in spatial representation in that case)
I have the impression that something is wrong, I can’t believe that the brain decodes in a totally opposite way in one condition compared to another and in the expected regions. The results of the univariate contrasts (Class1>Class2) reinforce this feeling.
Have you ever had these kinds of results? Do you have any idea what I could test to better understand what is going on? Maybe plot some of the outputs of the decoding, but I don’t know which ones can help me?
Thanks for your help,