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Dear all, hi!!

   let's say that probably I didnt' quite understand the point of doing two
sessions analysis if overall differences between session are removed. based
on Will Peeny old post (as wrote by Guillaume) it seems like two sessions
analysis "confound" the differences between different acquisitions (stop and
start again the scanner) so it should solve the "baseline problem" that I
although think is modeled by column 3 and 4. and talking about beta
differences, are not supposed to be taken in account in the t-test
statistics? is not the t-test based on beta values (how well your data
follow the model you setted up)? the problem stands in any case: how to
model the design matrix so as to compare the two acquisitions of the same
subject? Jonathan solution number 1 is not the same as doing factorial
design? and in this case what about the baseline issue?

thanks for the help
marta
2009/11/4 Guillaume Sescousse <[log in to unmask]>

> Hi,
> I'm very interested in this issue, related to a new study we are currently
> designing in our lab.
> Like Stephen, I was under the impression that comparisons across sessions
> were valid as long as one modeled session effects with a "constant
> regressor" (based on this old post by Will Penny:
> https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind04&L=SPM&P=R147846&m=9593
> ).
> Did I misinterpret something ?...
> Guillaume
>
> Stephen J. Fromm a écrit :
>
> On Tue, 3 Nov 2009 12:22:12 +0000, Jonathan Peelle <[log in to unmask]>
>> wrote:
>>
>> Jonathan,
>>
>> Maybe I'm interpreting Marta's design matrix schematic incorrectly, but
>> doesn't it look like there's an implicit baseline in each of the sessions?
>>  (The dark bands in the regressors for the conditions.)  If so, that would
>> serve as your "C" below.
>>
>> Of course, everything you wrote stands on its own.
>>
>>
>>
>>> Hi Marta
>>>
>>> The problem is that the effect in which you are interested (right vs.
>>> left ankle dorsiflexion) is also a difference between sessions.  By
>>> modeling session effects (which is the right thing to do) (columns 3
>>> and 4 of your design matrix), you essentially remove any overall
>>> difference between sessions, making it difficult or impossible to pull
>>> out differences in your conditions.  Put another way, your effects of
>>> interest are confounded with session effects.
>>>
>>> If you have a chance to change the design, you could consider one of
>>> the following alternatives:
>>>
>>> 1) Have both conditions of interest in both sessions (i.e. alternate
>>> left and right ankle movements within both sessions).  This way you
>>> have the same number of events of each type but they are not related
>>> to session effects.
>>>
>>> 2) Have some baseline condition you can compare the ankle dorsiflexion
>>> to.  This is no doubt explained in more detail somewhere previously on
>>> the list, but the idea is that it's possible to look at an interaction
>>> across sessions, but not really main effects.  I.e.
>>>
>>> Session 1: condition A and condition C
>>> Session 2: condition B and condition C
>>>
>>> contrast A > B is problematic because of session effects;
>>> contrast: (A > C) > (B > C) would be fine.
>>>
>>> If you are stuck with the data as it stands (and no possibility of
>>> finding a baseline condition to add to the model), I don't know if
>>> there is a particularly good solution.  You can choose not to model
>>> session effects, but this will add noise, and I think be a bit harder
>>> to interpret (e.g., is higher signal in a region actually due to your
>>> task, or could it just be a byproduct of one session by chance having
>>> a different level of activity than another?).
>>>
>>> Hope this helps,
>>> Jonathan
>>>
>>>
>>> On Tue, Nov 3, 2009 at 11:46 AM, Gandolla Marta
>>> <[log in to unmask]> wrote:
>>>
>>>
>>>> Hi everyone,
>>>>
>>>>   I have some problems in two sessions analysis. I want to compare two
>>>> activation maps from the same subject in two different conditions. I
>>>> built
>>>> the design matrix with two sessions (using first level analysis) so I
>>>> ended
>>>> up with four columns as you can see from the first figure of the
>>>> attached
>>>> file. I then did inference analysis with contrast vector of [1 -1 0 0]
>>>> and I
>>>> suppose I shoud get a map with the significative differences between the
>>>>
>>>>
>>> two
>>
>>
>>> conditions.
>>>> my problem is that if I implement this same approach with maps that are
>>>> significantly different for sure (right ankle dorsiflexion and left
>>>> ankle
>>>> dorsiflexion) I get a "difference map" that is not at all as expected.
>>>> so as
>>>> you can see what I'm talking about, the second figure of the attached
>>>> file
>>>> is the result I got.
>>>>
>>>>
>>>
> --
> ___________________________________
>
> Guillaume Sescousse, PhD student
> 'Reward and decision making' group
> Centre de Neuroscience Cognitive
> CNRS UMR5229 - UCB Lyon 1
> 67 Bd Pinel, 69675 Bron, France
>
> tel: 00 33 (0)4 37 91 12 44
> fax: 00 33 (0)4 37 91 12 10
> http://www.cnc.isc.cnrs.fr
> http://www.isc.cnrs.fr/dre
> ___________________________________
>