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Dear Mike,<br>
<br>
first of all I am not a neuroscientist but more of a machine learner
(who has had your problem a lot of times). <br>
I know who does just as you describe and just concatenates first and
applies motion correction for the whole session. Sessions are
usually never co-registered just by motion correction even if it is
possible.<br>
There are also others who perform motion correction on each run, and
take one run/session as reference and co-register all others with it
and furthermore co-register between sessions.<br>
<br>
Obviously the better the features/voxels match between runs or
sessions, the better any kind of prediction can be expected to work.<br>
For this reason consider:<br>
Whenever you do some kind of motion correction or co-registration
you interpolate somehow and introduce some kind of smoothing. This
means if you do multiple actions like that it might happen that you
introduce a notable amount of smoothing to all runs which have not
been used as reference. <br>
For this reason there are also 'crazy ones' that take one volume/run
(preferably with the higher contrast so within the very first
volumes of the run which you usually discard later) and then you
select a reference and co-register the other to it. The mean of
these co-registered HC columns is taken as reference for motion
correction for all runs, so every run has 'almost' the same amount
of smoothing-interpolation added to the original data (this has been
never demonstrated though).<br>
<br>
As you see, there are quite a few ways of doing it (maybe others I
have never come across yet). As far as I know there are few/ no
publications out there stating exactly what works best when... (just
every lab has its way of doing it)<br>
I would recommend you to use the method, that makes you feel most
comfortable (and shows best voxel correspondence when having a short
look at what you did)<br>
<br>
Note that between runs/sessions there might also be important
differences which need z-scoring and linear de-trending (easily done
by pymvpa not fsl just before using the classifier)<br>
<br>
If you discover your own way of solving the problem and it works out
well, I encourage you to share the knowledge!!<br>
<br>
LG,<br>
<br>
Susanne<br>
<br>
PS: I just saw, Michael has already answered. As he states, never
think that you will be able to solve everything by pre-processing
:-)<br>
<br>
--------------<br>
<blockquote
cite="mid:CAPYksDPdQ3O9hXt4Y1jCYVdWJ4Tz3E-MSd54HVWf0r4q3GruSQ@mail.gmail.com"
type="cite">Hi all,
<div><br>
</div>
<div>I've used FSL/FEAT before, though never specifically for
preprocessing for PyMVPA, so please forgive the simplicity of
these questions. I have an experimental design with several
short sessions, so for a standard FSL analysis (i.e. univariate)
I've performed motion correction (etc.) and stats on each
session separately, before running a second level analysis on
the FEAT results. (I should also note that our scanner will
output a separate 4D nifti file for each short session.)</div>
<div><br>
</div>
<div>So essentially I'm wondering the best order of events: (a)
when to concatenate the shorter 4D files into one large 4D file,
(b) whether I should run motion correction 1 time (after the
concatenation) or whether it should be run separately for each
session's nifti file, before being run a second time on the
concatenated file. While there is very little head motion within
each session, there looks to be considerably more between
sessions, which probably comes as no surprise. A test of running
motion correction a single time (after concatenation) looks like
it does not perform very well: there is still a large amount of
motion visible to the naked eye.</div>
<div><br>
</div>
<div>As an aside, if I plan to discard some specific volumes, I
also would value any input as to whether it makes more sense to
delete them from the 4D time series (using fslsplit and
fslmerge), or to leave them in and give them their own label
("discard") in the attributes.txt file.</div>
<div><br>
</div>
<div>Best and many thanks,</div>
<div>Mike</div>
<br>
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