On Tue, 3 Nov 2009 16:37:26 -0600, MCLAREN, Donald
<[log in to unmask]> wrote:
>I'd look at the beta values for both tasks in these regions that you
>expect differences.
>
>If you set up an F contrast:
>1 0 0 0
>0 1 0 0
>
>This will allow you to plot values of both tasks to see how close the
>beta values are. In your comparison between conditions, you are
>comparing the beta values -- not the T-statistics which you see. For
>example, its possible that there is a lot of subthreshold activity
>bilaterally.
>
>As for combining them in a single run -- this would help because you
>don't have to worry about the baseline being different between runs.
>In your case, you can't be sure that differences are due to your task
>or due to a baseline shift.
In generally, combining into one run isn't a good idea, unless it's done very
carefully, because of technical issues like high-pass filtering, the modeling of
the hemodynamic response, etc.
Furthermore, contrasts that take differences _between_ sessions that don't
account for the main effect of session (and hence don't control for baseline
shift) shouldn't be performed anyway.
Best,
S
>Best Regards, Donald McLaren
>=================
>D.G. McLaren
>University of Wisconsin - Madison
>Neuroscience Training Program
>Office: (608) 520-0586
>=====================
>This e-mail contains CONFIDENTIAL INFORMATION which may contain
>PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
>and which is intended only for the use of the individual or entity
>named above. If the reader of the e-mail is not the intended recipient
>or the employee or agent responsible for delivering it to the intended
>recipient, you are hereby notified that you are in possession of
>confidential and privileged information. Any unauthorized use,
>disclosure, copying or the taking of any action in reliance on the
>contents of this information is strictly prohibited and may be
>unlawful. If you have received this e-mail unintentionally, please
>immediately notify the sender via telephone at (608) 520-0586 or
>email.
>
>
>
>On Tue, Nov 3, 2009 at 4:30 PM, Stephen J. Fromm <[log in to unmask]>
wrote:
>> 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.
>>
|