[pymvpa] hyperalignment inquiry

[David Soto]

hi,

in my experiment I have 28 betas in condition A and 28 parameter estimate
images and 28  in condition B for each subject (N=16 in total).

i have performed across-subjects SVM-based searchlight classification using
MNI-registered individual beta images and I would like to repeat and
confirm my results using searchlight based on hyperaligned data.

i am not aware of any paper using hyperaligment on  beta images but I think
this should be possible, any advise please would be nice

i've created individual datasets concatenating the 28 betas in condition A
and the 28 in condition (in the actual experiment condition A and B can
appear randomly on each trial). I have 16 nifti datasets, one per subject,
with each in individual native anatomical space. In trying to get a dataset
in the same format as in the hyperlignment tutorial I use fmri_dataset on
each individual wholebrain 48 betas  and then try to merged then all
i.e. ds_merged
= vstack((d1, d2, d3, d4, d5, d6, d7, d8, d9, d10, d11, d12, d13, d14,
d15,d16)) but this gives the following error pasted at the end,
which I think it is becos the number of voxels is different across
subjects. This is one issue.

Another is that the function vstack does appear to produce the list of
individual datasets that is in the hyperligment tutorial dataset, but a
list of individual betas, I would be grateful to receive some tips.

thanks!
david
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-64-2fef46542bfc> in <module>()
     19 h5save('/home/dsoto/dsoto/fmri/wmlearning/h5.hdf5', [d1,d2])
     20 #ds_merged = vstack((d1, d2, d3, d4, d5, d6, d7,d8,d9, d10, d11,
d12, d13, d14, d15, d16))
---> 21 ds_merged = vstack((d1, d2))

/usr/local/lib/python2.7/site-packages/mvpa2/base/dataset.pyc in
vstack(datasets, a)
    687                              "datasets have varying attributes.")
    688     # will puke if not equal number of features
--> 689     stacked_samp = np.concatenate([ds.samples for ds in datasets],
axis=0)
    690
    691     stacked_sa = {}

ValueError: all the input array dimensions except for the concatenation
axis must match exactly
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