[pymvpa] Group level RDMs (Nick Oosterhof)

Martin Sjøgård sjogard at stud.ntnu.no
Thu Feb 25 15:32:43 UTC 2016

Ok thanks for the suggestion. But does distatis average all subjects' response patterns before then making an RDM for the averaged data, or make one RDM per subject and then average across RDMs? This is crucial, since the former could destroy individual idiosyncratic patterns.

I looked at the script you linked and wasn't sure which. It depends how the ds is constructed.


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Today's Topics:

   1. Re: Group level RDMs (Nick Oosterhof)


Message: 1
Date: Wed, 24 Feb 2016 13:08:05 +0100
From: Nick Oosterhof <n.n.oosterhof at googlemail.com>
To: Development and support of PyMVPA
        <pkg-exppsy-pymvpa at lists.alioth.debian.org>
Subject: Re: [pymvpa] Group level RDMs
Message-ID: <B1FD4AEE-3C0F-43F5-9A82-D5954DFB0CF0 at googlemail.com>
Content-Type: text/plain; charset=utf-8

> On 24 Feb 2016, at 12:49, Martin Sj?g?rd <martinsjogard at gmail.com> wrote:
> When doing RSA, is there a preferred way to construct representational distance matrices at the group level (RDMs averaged across subjects) in pyMVPA? Reading the example data, it seems that it might skip directly to the group RDM?

Indeed taking the average is quite common and would seem a reasonable thing to do.

If you do ROI analysis and if there is sufficient agreement across participants, you may consider using STATIS/DISTATIS [1]. There was a PR long time ago [2] but it seems it  never got in a ready state to be merged in the official master branch. If you're comfortable using Matlab / GNU Octave, DISTATIS functionality is present [3] in CoSMoMVPA (disclaimer: I'm its main developer).

[1] Abdi, H. & Valentin, D. in Encyclopedia of Measurement and Statistics (ed. Salkind, N.) 42?42 (SAGE Publications, 2007).
[2] https://github.com/PyMVPA/PyMVPA/pull/121
[3] http://cosmomvpa.org/matlab/demo_fmri_distatis.html


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