[pymvpa] null classification performance in the presence of strong univariate signal??

[David Soto]

Thanks Nick for the advise, it is helpful and am happy to try the within
subject approach using the PEs from each run as you suggested

however I should say that I still do not get why using the very same input
data, the univariate GLM picks the difference between the*cued and uncued*
conditions but the MVPA seems not

cheers
ds


On Mon, Sep 8, 2014 at 10:31 AM, Nick Oosterhof <nikolaas.oosterhof at unitn.it
> wrote:

> On Sep 8, 2014, at 11:21 AM, David Soto <d.soto.b at gmail.com> wrote:
>
> > There are two experimental conditions  *cued and uncued* and 19 subjects.
> >
> > We therefore have a 4D nii file in which volumes 1-19 are PEs for the
> *cue* classification target and volumes 20-38  are the PEs for the *uncued*
> classification target
>
> If I understand correctly, you have two samples per subject (one for each
> condition), and each value for a chunk corresponds to one subject.
> With those parameters you would be doing between-subject classification.
> Are you sure that is what you want?
>
> I'm asking, because /almost/ all MVPA (hyperalignment (TM) being an
> exception) are doing within-subject analysis. If you have not done so yet,
> I suggest strongly to do within-subject analysis first before trying
> between-subject analysis.
>
> For that you would need more than one sample for each chunk. In fMRI world
> people usually take each run as a chunk; if you have 6 runs, you would have
> 6 chunks. Using the GLM to estimate the response gives 2 samples in each
> chunk (indicated by .sa.targets), and 12 samples in total.
> In this scenario you would analyze  each subject separately.
>
> >
> > The above code gave a Warning re: the zscoring stage becos the number of
> datapoints per chunks was only 2 (in the example above the chunks were the
> subjects)
>
> Yes, with only two values per chunk you cannot z-score, because you're
> losing 2 dfs when estimating mean and standard deviation. The z-scoring is
> more suitable for fMRI time series. When using a GLM, it is a good idea to
> do signal normalization (z-scoring, or dividing the signal at each
> timepoint by the mean over all timepoints - for each run (or chunk)
> separately) /before/ the GLM.
>
>
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