Thanks, Yaroslav. This is a very good point. I'll keep it in mind for
the actual experiment. Right now, I'm just analysing old data as a proof
of principle.
Interesting article, by the way. I had no idea that turtle brains are
so, well, robust. If somebody else is interested: "Because of the
turtle's resistance to anoxia, it is not only possible to record
apparently normal brain electrical activity in vitro, but to record this
activity from an intact brain in which most, if not all, of the blood
has been removed following cardiac perfusion with aCSF. Moreover, since
the eyes can be left attached to this “bloodless” brain in vitro, it
becomes possible to study neuronal current MRI signals evoked by natural
sensory stimulation."
Best,
Jan
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.
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