[pymvpa] Group level searchlight

[Pegah Kassraian Fard]

More concretely, I would be very interested in a non-parameteric test:
Permutation of train labels followed by cluster size correction, as for
instance presented in the paper "Statistical inference and multiple testing
correction in classification-based multi-voxel pattern analysis (MVPA):
random permutations and cluster size control":
<https://www.ncbi.nlm.nih.gov/pubmed/23041526>

(A) Whole brain searchlight decoding using a permutation of the training
label on a single-subject level. The whole procedure was repeated Mtimes (M
≥ 100) per subject for all N subjects, resulting in a pool of N × M
single-subject chance accuracy maps. (B) One chance accuracy map was
randomly selected per subject for 105 times. The selection of N maps (for N
subjects) was averaged to one group accuracy map, resulting in a pool of
105 group chance accuracy maps. (C) Voxel-wise obtained empirical chance
distributions, given the pool of 105 group chance accuracy maps. (D) The
empirical chance distributions allowed the determination of a voxel-wise
threshold which was used for a cluster search in the group chance accuracy
maps.

On Fri, Oct 20, 2017 at 1:28 PM, Pegah Kassraian Fard <pegahkf at gmail.com>
wrote:

> Hi,
>
> I have been successfully running a searchlight analysis based on linear
> SVM for N different subjects - individually. Now I want to combine this
> information to plot searchlight results on the 2nd-level, i.e. group wise,
> correcting for the respective possible errors. How exactly can I do that?
> Is there an according pipeline available?
>
> Best regards,
> Pegah
>
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