Dear experts, I got some weird results when running MVPA (to distinguish two different stimulus categories) on two fMRI datasets with different smoothing using three different ROIs. I would like to know your opinion on why this could happen. I used linear SVM.The two datasets are from the same data acquisition but with different spatial smoothing: (1) without any spatial smoothing and (2) Gaussian smoothing with sigma=5mmThree ROIs: (A) brain area A containing 357 voxels, (B) brain area B containing 30432 voxels, and (C) brain areas A+B containing 357+30432=30789 voxels. The classification accuracies when using dataset#1: 0.750 for A, 0.792 for B, 0.792 for A+B The classification accuracy when using dataset#2: 0.875 for A, 0.667 for B, 0.583 for A+B so, using unsmoothed data, combining A and B did not change the classification accuracy. However, using smoothed data, combining A and B reduced the classification accuracy considerably and the accuracy was not significantly higher than chance level any more (all other accuracies were significantly higher than chance level according to permutation test). I would be grateful if anyone could let me know your thoughts why changing the ROI size has different effect on smoothed and unsmoothed data. Best,Meng
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