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Dear all, I saw this post recently and I just would like to understand correctly. Comparing PM using a 2nd level design is not a problem if PM are of the same order ? So, I suppose it's neither a problem to compare two PM of two experimental conditions from a same run ? For example, one could ask participants to press a button in a slow rate and in a fast rate (cond1 and cond1) and set the intensity of the press into a 2 PM and then compare PM using a paired-t test or other 2nd level ? And is it ok to interpret this as a stronger linear (in the case of a 1st order PM) relationship between the PM and the regions obtained from the 2nd level analysis for a condition versus the other ? Regards, A.O.
[log in to unmask]" type="cite">I would choose either the PM method or the multiple condition method, but not both.
More conditions would mean that you have more points to estimate the line through different levels; however, if you have more conditions, you'll have less trials of each condition and the estimate of each level will be potentially less accurate.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Postdoctoral Research Fellow, GRECC, Bedford VA
Website: http://www.martinos.org/~mclaren
Office: (773) 406-2464
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On Mon, Jul 1, 2013 at 1:37 PM, fMRI <[log in to unmask]> wrote:
Dear Donald,
U recommend doing different conditions ( for example three conditions for the targets that I have). What about the parametric modulations? Can I add them? If yes how I contrast them ? Because in this case I cannot see any parametric effect in a condition since the main aim is to compare the conditions agains each other.
Another question is that does having more conditions ( more than three) explain linearity effects better?
Regards,
AS
On Fri, Jun 28, 2013 at 2:24 PM, fMRI <[log in to unmask]> wrote:
Hi Donald,
How about having a second and third order effects in the parametric modulations. My understanding of these is that they also show whether there is a non linear relation between the BOLD signal and the parametric regressors.
>> The relationship is always linear with the PM. However, you are right that you can model non-linear relationship if you square or cube or transform the PM values before entering them as the PM. You can think of the beta of the PM as the scaling of the PM term - whether is the slope, parabolic, cubic or other effect. Hope that clarifies the issue.
Regards,
AS
On Fri, Jun 28, 2013 at 11:48 AM, fMRI <[log in to unmask]> wrote:
Dear Donald and all,
Would it be correct if I have two sessions with different (experimental designs and thus parametric effects) and compare contrast of parm1 > parm2?
>> If parm1 and parm2 come from different sessions, you have to be careful how you compare them as you might not find differences if the baseline shifts between runs.
The difference is only in how subjects perform a movement.
The other question is : if I have three conditions : mov hard , mid and low.
I am recording subject responses and there are lots of variabilities. I thought to follow some previous papers where they model it as a one condition and including a parametric modulation. Would this be better than specifying three conditions?
In PM, you assume a linear relationship with the PM. If you model it as 3 conditions, then you can test different relationship across the three conditions.
Regards,
AS