[pymvpa] Fast event-related data and PyMVPA

[Kimberly Zhou]

Hi Everyone,

I searched for this in the list archives but didn't see anything regarding
this so here goes...

Has there been any success using PyMVPA with fast event-related data?

Looking at the sensitivity map in AFNI (mapped back into original space with
map2nifti), the high sensitivity areas look completely random. They seem to
mainly be on the edges of grey matter/in spaces. I wonder if this is
because, with fast event-related data, there is just too little information
for the classifier, or is there something wrong with what I have been doing?

We used 3dDeconvolve's -stim_times_IM option to estimate the amplitude of
the response to each individual stimulus, plus this in PyMVPA:

from mvpa.suite import *

datasetfile = 'allstim.nii.gz'
attrfile = 'attr.txt'
attr = SampleAttributes(attrfile)
ds = fmri_dataset(datasetfile, targets = attr.targets, chunks = attr.chunks,
mask ='resampledmask.nii.gz')

zscore(ds, chunks_attr='chunks')
#no detrending...done in afni

clf = LinearCSVMC()
fsel = SensitivityBasedFeatureSelection(OneWayAnova(),
FractionTailSelector(0.08, mode='select', tail='upper'))
fclf = FeatureSelectionClassifier(clf, fsel)

sclf = SplitClassifier(fclf, enable_ca=['stats'])
cv_sensana = sclf.get_sensitivity_analyzer()
sens = cv_sensana(ds)

sens_comb = sens.get_mapped(maxofabs_sample())
map2nifti(avgds, sens_comb).to_filename('test.nii.gz')

Anybody have any ideas or info...?
Thanks,
Kim
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