Modying Maris et al. (2007) for acute animal recordings

[Eric Maris]

Dear Adrian,



I am wondering if any of you have any advice or experience adapting the
methods of Maris et al. (2007) to acute animal recordings.



The basic problem is that we sample different locations on different days,
each yielding different trial numbers, and they certainly aren't 'the same
trial'.



The simplest way to deal with this, as I see it, is as follows:



Carry out Maris et al. (2007) methods within each session, clustering across
time (we are working with dynamic spectra and coherencies), frequency, and
space. To correct for the multiple recording sessions, use a crude
bonferonni-corrected P-value as the threshold for the Monte-Carlo P value
(prcit/nsess) for identifying significant clusters. ((or correct the
critical value for establishing what the empirical clusters are initially))



Is this unsound in any way? Any alternatives?

The obvious drawbacks are having to introduce bonferroni-correction, as well
as losing sensitivity to weak effects seen in spatial proximity across
different sessions (i.e. the same guide tube position over many days).





A lot can be said in response to your questions. Actually, it's on my Todo
list to write a methodological paper about some of the issues that you
raise. These are the main points I can think about:



1.	 As your unit of observation, you can choose for trials as well as
sessions. If you choose for sessions, then your dependent variable is an
average over the trials within every session.
2.	 If your unit of observation is trials AND if there is heterogeneity
across the different sessions, then you should incorporate the variable
SESSION as a blocking variable. The theory of permutation tests also applies
in the context of blocking variables. I have added the blocking variable
option in the statfuns for testing regression coefficients. This was for a
joint project with Vladimir Litvak, and the paper should be somewhere under
review now. I have not yet added it to the indepsamplesT and depsamplesT
statfuns, but that shouldn't be too much of an effort.
3.	 The presence of effects that are confined to a particular subvolume
of the brain tissue from which you sample, is a tricky one. Clustering in
space definitely is an option, but the current Fieldtrip code cannot deal
with the fact that the different recording sites were only partly sampled
concurrently (because the probes are lowered to different locations on
different sessions), at least not in a straightforward fashion. This differs
from extracranial recordings in which there is typically only a single
recording session with a single channel configuration.



With good Matlab programming skills, it is definitely possible to make the
required changes to the Fieldtrip code.





Good luck,





dr. Eric Maris
Donders Institute for Brain, Cognition and Behavior

Center for Cognition and F.C. Donders Center for Cognitive Neuroimaging

Radboud University
P.O. Box 9104
6500 HE Nijmegen
The Netherlands
T:+31 24 3612651
F:+31 24 3616066
E: e. <mailto:e.maris at donders.ru.nl> maris at donders.ru.nl



MSc Cognitive Neuroscience:  <http://www.ru.nl/master/cns/>
www.ru.nl/master/cns/







Any input on this would be greatly appreciated,

Thanks in advance.

--
Adrian M. Bartlett, BA
Neuroscience Graduate Diploma Program
Graduate Program in Psychology
Perception & Plasticity Laboratory
Centre for Vision Research
York University, Toronto, ON, Canada
--

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----------------------------------
The aim of this list is to facilitate the discussion between users of the FieldTrip  toolbox, to share experiences and to discuss new ideas for MEG and EEG analysis. See also http://listserv.surfnet.nl/archives/fieldtrip.html and http://www.ru.nl/neuroimaging/fieldtrip.
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