My friend recently talked with me some questions on mvpa technique. He hopes to use 3dsvm for MVPA analysis but we both are puzzled with some options. I appreciate if anyone can offer advice here.

Our case is an event related fMRI study. Suppose that our experiment consists of 5 balanced runs. In each run of the experiment, there are four events: (1)House, (2)Building, (3)Threat Face, and (4)Scrambled Face. Each event has 45 trials. I know that we should not do the spatial smoothing for the pre-processing. Our specific discussion and question is based on 3dsvm (model training) usage, as following.

3dsvm -alpha a_Run1 \
-trainvol Run1_IM+tlrc \
-trainlabels Run1_categories.1D \
-censor Censor.1D \
-mask Mask+tlrc \
-model Model_Run1 \
-nodetrend \
-bucket Run1_fim

Questions, Run1_IM+tlrc & -trainlabels file
Some experts suggested that "use 3dDeconvolve -stim_times_IM to estimate the amplitude of the response to each individual stimulus. Create a new 3d+time BRIK from these amplitudes and then assign each one to the appropriate stimulus for 3dsvm analysis". We have some questions.

3dDeconvolve -stim_times_IM
We find that when people use "-stim_times_IM" option, they take the assumption "fixed-shape regression", and usually use the function "GAM, BLOCK(d,p) or BLOCK(d)". They do not use the variable-shape regression, such as the TENT(b,c,n). Are we right? If we only can use the "fixed-shape regression" with the 3dDeconvolve -stim_times_IM", then things are complex.

When we use "3dDeconvolve - stim_times_IM" function with the assumption "fixed-shape regression", We perhaps can get 40 trials for the event "House", 38 trials for the event "Building" , 45 trials for the event "Threat Face", and 41 trials for the event "Scrambled Face" (after I excluded the wrong and miss trials), for Run1. In the 3dDeconvolve output, they showed something like Bricks below (I use "3dinfo -verb" command to show),

Sub_Brick #0, "Full_Fstat",
Sub_Brick #1-40: "House #1_Coef, House #2_Coef............House #40_Coef'.
Sub_Brick #41-79: "Building #1_Coef, Building #2_Coef............Building #38_Coef'.
Sub_Brick #80-124: "ThreatFace #1_Coef, ThreatFace #2_Coef............ThreatFace #45_Coef'.
Sub_Brick #125-165: "ScrambledFace#1_Coef, ScrambledFace #2_Coef............ScrambledFace #41_Coef'.
Sub_Brick #166, "Error#0"

As you see, they are list sequentially since 0 to 166. How should I prepare the option " -trainlabels "? Suppose the part of the sequence in a run of the experiment coding is as following (codes: House 1, Building 2, Threat Face 3, Scrambled Face 4): 2 2 1 1 2 4 3 4 4 3 1 2 4 4 3 3 3 2 1 1.....

Does it mean that we just use this 1D data as the run's trainlabels input file here? Or should weI prepare them as 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4 ...., as the Bricks in the "Run1_IM+tlrc " are sequential?

Is that MVPA only can be used to classify two conditions? Then does it mean that we only can compare one condition, against the other three conditions at one time? If we want to classify 1 and 2, then 3 and 4 should all be 999? If so they maybe 1 1 1 1 1 2 2 2 2 2 999 999 999 999 999 999 999 999 999 999? Or, as above, 2 2 1 1 2 999 999 999 999 999 1 2 999 999 999 999 999 2 1 1?

Question, MVPA based on several Regions

If we only want to focus on several special regions, such as temporal lobe (e.g, Hippocampus, parahippocampal cortex...). how can we select the features from the third (5s) or fourth (7.5s) scans? Our question goes back, can we use 3dDeconvolve -stim_times_IM with variable-shape regression? I know if I do the ROI analysis, perhaps I can use the IRF-Shape nonfixed 3dDeconvolve, such as CSPLIN (0,15,7), then select the features from 5s or 7.5s TRs.

Question, Prediction accuracy

After we use the model to predict/Testing the remained data, we can get the prediction accuracy for each subject. Can we just group all the accuracy data and do the one-tailed T test (compared to the chance level 50%) to see whether the MVPA model is good/significant or not? Some people suggested "group-level classifier accuracy was computed using a one-sampled t-test versus chance (50%) and assessed for significance with permutaiton testing". Any good pointers?

Thank you very very much,
Juan Wu