SPM:
What Donald says below is correct about Yc.
I agree with the comments about SPM.xX.iB and SPM.xX.iG.
As Donald notes SPM.xX.iG is not filled in by SPM as SPM has no
"understanding" of effects of interest vs. effects of no interest.
Columns of the design become effects of no interest by weighting them
as 0 in your contrasts.
Regards,
Darren
On Wed, May 4, 2011 at 8:57 PM, MCLAREN, Donald
<[log in to unmask]> wrote:
> I could be wrong, Karl or Darren please correct me if I am.
>
> X0 is composed of the high-pass filter and constant term. Thus, you
> want to filter these out of the Yc regressor. However, you don't want
> to filter out anything related to neural activity, so you wouldn't
> want to filter your signal and then predict the neural activity from
> the filtered signal, especially filtering frequencies.
>
> 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 Sat, Apr 30, 2011 at 8:03 PM, Alex Fornito <[log in to unmask]> wrote:
>> 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
>>> =====================
>>> 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 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
>>
>>
>>
>>
>
--
Darren Gitelman, MD
Northwestern University
710 N. Lake Shore Dr.
Abbott 11th Floor
Chicago, IL 60611
Ph: (312) 908-8614
Fax: (312) 908-5073
