To address point #1.
The high points at the beginning/end are due to the filtering. In PPI
(and gPPI), they are reversed by:
Yc = Y - X0*inv(X0'*X0)*X0'*Y;
You don't need to adjust for any contrast to reverse this effect. All
you need to do is generate the confounds and remove them after you
compute the eigenvariate/mean of the VOI.
For any VOI, if you extract the raw data, you can compute the
mean/eigenvariate as follows. This will give you the mean/eigenvariate
of the raw unfiltered data.
Alternatively, you can extract all voxels from the VOI, then use:
[m n] = size(y);
if m > n
[v s v] = svd(y'*y);
s = diag(s);
v = v(:,1);
u = y*v/sqrt(s(1));
else
[u s u] = svd(y*y');
s = diag(s);
u = u(:,1);
v = y'*u/sqrt(s(1));
end
d = sign(sum(v));
u = u*d;
v = v*d;
Y = u*sqrt(s(1)/n);
% set in structure
%-----------------------------------------------------------------------
xY.y = y;
xY.yy = transpose(mean(transpose(y))); %average (not in spm_regions)
xY.u = Y; %eigenvariate
xY.v = v;
xY.s = s;
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 Wed, May 8, 2013 at 3:07 PM, Theresa M Desrochers
<[log in to unmask]> wrote:
> Dear Donald (and others),
>
> Thank you for your reply. I think there may be two separate issues here:
>
> 1) When entering an eigenvariate using SPM that has edge effects from
> filtering into a GLM design whether or not those edge effects are accounted
> for by the SPM machinery (perhaps by using the "confounds" that are
> calculated), as the aberrant edges do *not* appear in the SPM generated
> design matrix. I don't think anyone has addressed this yet.
>
> 2) When designing a PPI analysis, when and how to account for noise factors.
> If I understand you correctly, you would have me essentially remove noise at
> the stage of extracting the VOI (and just to be clear this is not the kind
> of "noise" I'm referring to above). In my current PPI design I use the raw
> VOI eigenvariate, and a task variable for the calculation of the PPI. Then
> when I create the subsequent PPI GLM I include 6 motion parameters and the
> average gm, wm, and csf signal. Wouldn't that essentially accomplish the
> same thing? If it doesn't, then perhaps you could explain further? Among
> colleagues that I discussed with here we weren't sure what the purpose would
> be of the F-contrast given my current PPI setup.
>
> Thank you,
>
> ~Theresa
>
> --
> Theresa M. Desrochers, Ph.D.
> Badre Lab @ Brown University
> [log in to unmask]
> office: (401) 863-5197
>
>
> On May 7, 2013, at 11:22 AM, MCLAREN, Donald wrote:
>
> You want to use an omnibus F-contrast. This can be done automatically with
> the gPPI toolbox.
>
> The reason for using the omnibus is that you want to remove the non-task
> elements (e.g. noise covariates) as they are not related to the neural
> signal. The process actually removes the null-space of the contrast you use
> for adjusting.
>
> 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
> =====================
> 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 (773)
> 406-2464 or email.
>
>
> On Tue, May 7, 2013 at 10:36 AM, Theresa M Desrochers
> <[log in to unmask]> wrote:
>>
>> Dear Thilo (and Misun, who replied earlier),
>>
>> Thank you for your replies.
>>
>> Here is what I found in my investigations. The artifact is caused by
>> filtering/whitening. The function that is called to calculate the
>> eigenvariate is spm_regions. In that function first the raw data is
>> extracted on this line:
>> <PastedGraphic-3.tiff>
>> then it is filtered on this line:
>> <PastedGraphic-4.tiff>
>> plotting y before and after filtering, it is easy to see the raw with no
>> artifact followed by the filtered data with artifact.
>>
>> However, later in the function, what are called "confounds" are
>> calculated, and one of the confounds (in structure xY.X0) basically looks
>> like the square "U" shape that is the noise. When I examine the design
>> matrix after creating the GLM for the PPI analysis, I do not see these
>> extreme values at the beginning/end of runs, so I assume that in its
>> construction SPM uses the "confounds" and removes those extreme points. Is
>> this correct? Could someone confirm this?
>>
>> Regarding using an F-contrast, I'm not familiar with them, so I could be
>> misunderstanding but I don't think I want that for a PPI analysis, given
>> that I have task variables that I'm looking for the interaction with this
>> eigenvalue from this ROI. Wouldn't that end up giving me an interaction
>> between task and non-task?
>>
>> Thank you again,
>>
>> ~Theresa
>>
>> --
>> Theresa M. Desrochers, Ph.D.
>> Badre Lab @ Brown University
>> [log in to unmask]
>> office: (401) 863-5197
>>
>>
>> On May 7, 2013, at 10:20 AM, Thilo Kellermann wrote:
>>
>> Dear Theresa,
>>
>> in order to avoid including these outlying values you may try to adjust
>> the values according to an appropriate F-contrast. In order to do so you
>> have to define such an F-contrast which was automatically done in
>> previous SPM versions and known as "effects of interest". In the
>> contrast manager click "Define new contrast" and then check the button
>> "F-contrast". Then go to the line at the bottom "columns for reduced
>> design". Write all indexes of those columns of the design that are NOT
>> interesting - i.e. block or nuisance variables like session intercepts
>> and realignment parameters. If you have e.g. three conditions modeled
>> with the HRF only in one session and included 6 realignment parameters
>> the indexes of the columns of no interest would be: 4 5 6 7 8 9 10 (or
>> 4:10 as Matlab "short cut").
>>
>> After calculating that contrast you should be asked if you would like to
>> adjust the data when you go once again through the steps by clicking the
>> eigenvariate button. Then check "yes" and select the new defined
>> F-contrast for adjustment. Hopefully the data you extract won't show
>> these extreme values at the end of the session anymore.
>>
>> Good luck,
>> Thilo
>>
>> On Mon, 2013-05-06 at 15:35 +0100, Theresa Desrochers wrote:
>>
>> Hi,
>>
>>
>> I am trying to do a VOI time-series extraction as part of a PPI analysis.
>> I am simply trying to extract the eigenvariate of a sphere. No matter how I
>> do this (with a batch or with the eigenvariate button), the last value of
>> each session (run) seems exceedingly high in comparison to the rest of the
>> values. This seems to be true for all sessions, all subjects, and regardless
>> of which VOI (where in the brain) I choose. I am attaching a screen shot of
>> a time course as an example.
>>
>>
>> I've searched the archive and can't seem to locate anyone who's seen
>> anything similar. Does anyone have any insight into this or what I might
>> possibly do to correct it?
>>
>>
>> Thank you,
>>
>> ~Theresa
>>
>>
>>
>>
>
>
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