[pymvpa] Analysing data from a 1-run block design

Yaroslav Halchenko debian at onerussian.com
Tue Mar 31 12:39:57 UTC 2015

On Tue, 31 Mar 2015, Jan Derrfuss wrote:

> Hi Nick,

> Thank you very much for your quick reply!

> >>I was wondering if there's a recommended way to analyse fMRI data
> >>from a block design where all data were acquired in one run. I've
> >>modelled each block individually and now have one parameter
> >>estimate for each block. There were two conditions (A and B) with 6
> >>blocks each. Conditions alternated (A - B - A - ... - B) and were
> >>separated by 15-s rest phases. Half of the participants started
> >>with A, the other half with B.

> >>In particular, I was wondering a) how much of a problem the single
> >>run is,

> >Not ideal, but also not necessarily a game-breaker.
> Cool.

> >>b) how chunks should be assigned,
> >You mention 15-s rest phases. Does that mean the design was:
> >R A B R A B R A B R A B R A B R A B R

phew -- better, otherwise you would have not known either you are
classifying A vs B or a condition-after-the-R vs condition-before-the-R.

But in any case, with such non-randomized design you must be more
cautious/careful about interpreting the results.  Any physiological
rhythm of the same 60 sec period is your confound now.  My tale of
caution would be the analysis of the generously shared T1 MR data from
turtle brains [1] which had similar non-randomized design:  whole
brain classification analysis resulted in significantly above chance
performance.  But 1. it was very unlikely, 2.  there were neither
corresponding clear localization of the effects nor correspondence to
ERP recording site which showed the relevant activation.

So unfortunately I had to conclude that it indeed just was a
confound driving the results.  Not sure how any magical
filtering/detrending or alternative analysis could help here really,
besides may be classification across subjects.  But for that, depending
on what effects you are looking after, you might need fancier alignment
procedures etc.  But I will be happy to be proven wrong.

[1] http://dx.doi.org/10.1016%2Fj.neuroimage.2009.06.017

Yaroslav O. Halchenko, Ph.D.
http://neuro.debian.net http://www.pymvpa.org http://www.fail2ban.org
Research Scientist,            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        

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