[pymvpa] group-level analysis differences vs. pattern classification

Vadim Axel axel.vadim at gmail.com
Mon Sep 23 17:27:07 UTC 2013

Thanks for the answer.
Suppose we trust 100% the conjunction analysis. Why do you think MVPA ROI
analysis result will require more analytic support? If I have an ROI within
a network of regions,  which decodes beyond chance, is it not an evidence
that this region contains the relevant information?

On Mon, Sep 23, 2013 at 4:40 PM, Yaroslav Halchenko
<debian at onerussian.com>wrote:

> On Mon, 23 Sep 2013, Vadim Axel wrote:
> >    Hi,
> >    There are two commonly used approaches to analyze the data of the
> >    experiment below:
> >    Simple design with two conditions (A and B), which �both activate
> large
> >    network of well established regions (e.g. conjunction analysis of A >
> >    baseline and B > baseline). The question is whether we can find neural
> >    correlates of difference between the two.
> the answer I guess is: yes --  we should be able to if "conditions are
> right" (power, etc)
> > Direct group-level analysis
> >    comparison between A and B results in small activations (~5% of volume
> >    comparing to commonality of conjunction analysis) and these
> activations
> >    are located mostly outside the main network, all over the brain.
> Remembering that statistics is there only to help us to support/reject
> our hypotheses, not really to be treated as "the ground truth", you
> might have set up your analysis to include only the "differential"
> activations which are within the main network, since that is where you
> believe activity or relevance is.
> > Given
> >    that the result is dependent on p-value threshold, it looks like a
> >    classical blobology. �Another approach is to select (independently)
> the
> >    ROIs of the common network nodes and to run MVPA.
> or even run MVPA on full brain happen you data has enough power to cope
> with such large initial feature space.
> > With this analysis I
> >    successfully discriminate between the two conditions. So, two people
> >    analyzing the same data can draw absolutely different conclusions: one
> >    would say, that small regions X, Y, Z are the regions, which
> discriminate
> >    between conditions A and B;
> which given your results above would be sensible conclusion imho besides
> that I would have clarified that this set of regions is not necessarily
> exhaustive (thus "the regions" statement might be a bit too strong)
> >  the other, in contrast, would say that since
> >    both A and B activate common network
> depending on what is implied by "activate common network" I might argue
> because it would be hard (if not impossible) to prove null hypothesis
> here that the network is the same for both A and B.
> > , the difference between the two lies
> >    within this network (different patterns of activity). �
> >    What approach is more reliable in your opinion?
> as I stated above (if I got the question right), the 2nd approach would
> require (much) more analysis to support itself.
> --
> Yaroslav O. Halchenko, Ph.D.
> http://neuro.debian.net http://www.pymvpa.org http://www.fail2ban.org
> Senior Research Associate,     Psychological and Brain Sciences Dept.
> Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
> Phone: +1 (603) 646-9834                       Fax: +1 (603) 646-1419
> WWW:   http://www.linkedin.com/in/yarik
> _______________________________________________
> Pkg-ExpPsy-PyMVPA mailing list
> Pkg-ExpPsy-PyMVPA at lists.alioth.debian.org
> http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.alioth.debian.org/pipermail/pkg-exppsy-pymvpa/attachments/20130923/a20c2591/attachment.html>

More information about the Pkg-ExpPsy-PyMVPA mailing list