Hi Donald,
As far as I can tell, spm_regions filters and whitens the VOI time series,
and adjusts for the null space of the contrast if an adjustment is specified
in xY.Ic. It defines the confound matrix, X0, but does not correct for it.
The following line in spm_peb_ppi corrects for the confounds.
Yc = Y - X0*inv(X0'*X0)*X0'*Y;
PPI.Y = Yc(:,1);
So the above correction does seem to be doing something different to
spm_regions. I'm wondering why the uncorrected data, Y, is used when
generating the ppi interaction term, while the corrected data Yc is to be
used as a covariate of no interest in the PPI model. I would has thought
that Yc should be used to generate the ppi interaction term as well.
In my data, my X0 is a 195 x 7 matrix (I have 195 volumes per session).
Regards,
Alex
On 30/04/2011 02:22, "MCLAREN, Donald" <[log in to unmask]> wrote:
> I'm actually travelling at the moment. However, I know Y has already
> been adjusted as part of the VOI processing.
>
> Can you check what X0 looks like?
>
> 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, Apr 29, 2011 at 2:55 AM, Alex Fornito <[log in to unmask]> wrote:
>> Hi all,
>> I have a question concerning the code used to implement a PPI analysis.
>>
>> In the spm_peb_ppi.m function packaged with SPM8, lines 329-332 remove
>> confounds from the input seed region¹s time course:
>>
>> % Remove confounds and save Y in ouput structure
>> %--------------------------------------------------------------------------
>> Yc = Y - X0*inv(X0'*X0)*X0'*Y;
>> PPI.Y = Yc(:,1);
>>
>> PPI.Y is then to be used as a covariate of no interest when building the PPI
>> regression model. However, on line 488, it is the uncorrected seed time
>> course, Y, that is input to the deconvolution routine and used to generate
>> the ppi term:
>>
>> C = spm_PEB(Y,P);
>> xn = xb*C{2}.E(1:N);
>> xn = spm_detrend(xn);
>>
>> % Setup psychological variable from inputs and contast weights
>> %----------------------------------------------------------------------
>> PSY = zeros(N*NT,1);
>> for i = 1:size(U.u,2)
>> PSY = PSY + full(U.u(:,i)*U.w(:,i));
>> end
>> % PSY = spm_detrend(PSY); <- removed centering of psych variable
>> % prior to multiplication with xn. Based on discussion with Karl
>> % and Donald McLaren.
>>
>> % Multiply psychological variable by neural signal
>> %----------------------------------------------------------------------
>> PSYxn = PSY.*xn;
>>
>> Is there any specific reason why Y, rather than Yc(:,1), is used to compute
>> PSYxn? I thought Yc might be more appropriate (?).
>> Thanks for your help,
>> Alex
>>
>>
>>
>>
>> ------------------------------------------
>>
>> Alex Fornito
>> Research Fellow
>> Melbourne Neuropsychiatry Centre
>> University of Melbourne
>> National Neuroscience Facility
>> Levels 1 & 2, Alan Gilbert Building
>> 161 Barry St
>> Carlton South 3053
>> Victoria, Australia
>>
>> Email: [log in to unmask]
>> Phone: +61 3 8344 1876
>> Fax: +61 3 9348 0469
>>
>>
>
------------------------------------------
Alex Fornito
Research Fellow
Melbourne Neuropsychiatry Centre
University of Melbourne
National Neuroscience Facility
Levels 1 & 2, Alan Gilbert Building
161 Barry St
Carlton South 3053
Victoria, Australia
Email: [log in to unmask]
Phone: +61 3 8344 1876
Fax: +61 3 9348 0469
|