Please see inline responses below.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Office: (773) 406-2464
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On Fri, Sep 9, 2011 at 7:42 PM, Ce Mo <[log in to unmask]> wrote:
> Dear All,
>
> This is my first time performing PPI analysis, and I would really appreciate it if you could provide me with some answers.
>
> Suppose I have two psychological factors A and B, each of which has 2 levels, say A1 A2 B1 B2 respectively. Moreover, I also specified a source seed region R. My objective is to identify the conjunction of regions whose activation can be interpreted as the result of interaction between the source region R and 2 psychological factors A and B. I did a little thinking myself, and came up with 2 possible solutions.
Let's say you have 4 conditions and make it easier and because you
should have modelled it as four conditions in your activation
analysis.
>
>
> 1) Generate 2 individual PPI structures, and thus I got 2 psychophysiological interactions of interest R*A and R*B. Now I combine the 2 factors, the 2 Interactions and R in one single GLM? That is,
>
> y = b1R*A + b2R*B + R + A + B
I assume you mean y=R*A+R*B+A+B and b1 and b2 are the beta values of interest.
You haven't described what A or B represents, but for now I will
assume that A means the difference between A1 and A2 and the same for
B.
My personal suggestion is why stop there, you should model all four conditions.
y=R*A1+ R*A2 + R*B1 + R*B2 +R +A1+A2+B1+B4
In fact, this is exactly what the automated gPPI toolbox does
(http://brainmap.wisc.edu/PPI).
>
> and perform a conjunction of the first two regressors, in other words, conjunction of T contrast [1 0 0 0 0] and [0 1 0 0 0] in the first level. And then take the results into 2nd_level using within subject ANOVA and perform conjunction of contrasts [1 0] and [0 1]
You can't take conjunctions from the first level and use them in the
second level. If you are truly interested in the conjunction -- making
the claim that there is overlap -- then you want to demonstrate it in
the single subject maps.
Contrasts for interaction of A would be [1 -1 0 0 0 0 0 0 0] and B
would be [0 0 1 -1 0 0 0 0]. If you only want this direction of the
test (e.g. A1>A2 and B1>B2), then use a t-test. Otherwise you want to
use an F-test and form the conjunction.
Now that you have single subject conjunctions. Threshold (peak_nii
(http://www.martinos.org/~mclaren/ftp/Utilities_DGM) and convert them
to binary values (imcalc:i1>0) for each subject. Then add up all the
subjects and you will be able to state how many subject overlap at
each voxel.
Taking con_ images to the second level for each contrast and then
doing a conjunction at the second level asks a different question.
Whether the locations of significant group amplitudes overlap, which
doesn't say that individuals overlap because of the statistic being
computed. group mean/standard deviation of the group. If the standard
deviation is lower, for the same mean, then the activation area will
likely be bigger just based on that alone.
>
> The problem of this approach is that I don't know whether the 1st level GLM formed in this way is valid. It seems reasonable to me but I have never seen anybody did this before in published works...
A paper is currently under review on the method I mentioned.
>
> 2) Set up 2 separate GLMs in the traditional PPI fashion, that is,
>
> y = b1R*A + R + A with contrast [1 0 0]
> y = b2R*B + B + R with contrast [1 0 0]
This is bad as your only modelling some of the data which could lead
to errors in model fitting.
>
> and then taking the results into a common 2nd_level ANOVA analysis and perform conjunction in the same manner described above.
I think its fine to take contrasts to the second level, you just need
to be careful about how you interpret the conjunctions using group
maps.
>
> The problem concerning this method is that I wonder whether the results of 2 different 1st level GLMs can be put into 1 common Group Analysis.
Not sure, but since I know there are problems with modelling only some
of the data, I'd avoid using that method.
>
> If both methods are invalid, is there a third method to solve my problem?
See the gPPI toolbox.
> Any help and advice will be greatly appreciated Many thanks in advance!
>
> P.S. One more question about the underlying principle of PPI. Let's say I coded 2 factor levels A1 as 1 and A2 as -1. And I have a source region, say R. If the beta corresponding to the PPI regressor is positive(tested using T contrast [1 0 0]), then the result can be interpreted as the activity of R is stronger in the context of A1 than it is in the context of A2. Why is that?
The interpretation is that the activity of R during A1 leads to a
larger change in region B than A2. If R is +, then its a larger
increase, if R is -, then its a larger decrease. An illustration can
be found in Karl's 1997 paper. Also, you can see the effect by drawing
two lines, one with a slope of 3 and the other a slope of 1. The lines
intersect at X=0.
>
> Best Regards
> Ce
>
Hope you find this useful.
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