hi again!!

   the thing is: if a have two acquisitions from the same subject, may I
model them with factorial design separating the two acquisitions with the
"factor" (see attached file for the design matrix obtained)? and then it can
be used a [1 -1 0] contrast vector so as to see the differences. but I'm not
sure it is a right thing to do, the uotput map it's not convincent... I
suppose that the two sessions approach is more correct, but I can't manage
to model the design matrix in the right way...


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:
> ).
> 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
> ___________________________________