[pymvpa] detecting changes between fMRI sessions: a new mapper?
debian at onerussian.com
Wed Sep 17 18:14:49 UTC 2008
So I see 2 questions here actually:
1. technical -- how to combine features of two (or more datasets) into 1
dataset. For now we have some way to ease the life of a user:
MetaDataset, which can join features of multiple datasets. Imho it is
just a temporary solution since
* it is not a full-featured dataset since is not really a
Dataset-derived class, so many things could go wrong
* imho there should be some mapper (come up with nicer names than
MetaMapper, CombinedDatasetsMapper, etc), which would do similar
thing -- take multiple data(set)s and combine into a single blob.
But it raises another aspect which we haven't fully discussed yet:
interaction between Mappers and Datasets. For now forward/reverse
operate on data, although I believe I made them also digest
datasets's .samples if dataset is given. .reverse is always spitting
out .data. But now we have also .train method (optional) so some
mappers have it and it takes dataset... .reverse for "MetaMapper"
should spit out corresponding Dataset, thus we just need to agree
upon something... or just let it go... what am I talking about
anyways? :-) sorry for the moot
2. neuroscientific -- how to contrast two sessions... That is imho more
complicated. I had a limited experience with 1 dataset where
different conditions were collected in different runs... so the
problem was to remove such 'run-specificity' so clf don't rely on
simple misalignment or trend to classify samples. apparently detrend
+ zscoring helped -- at the end sensitivity maps were quite sensible.
In Emanuele's example situation is more interesting, although could
be done indeed just simple way (in addition to suggested): double
number of labels -- ie. dog1 (for session 1) dog2(for session 2).
Do regular analysis, and then look at the sensitivity maps between
dog1-vs-dog2. If dog category wasn't trained for at home -- I hope
that generalization and sensitivity would be at-chance. If it was
trained at home -- should be sensible...
noone would know which one is the best way to proceed unless tried
them all I guess ;-)
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