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I haven't thoroughly tested this, but if you set iG to be the movement
parameters columns. Then still adjust for the null space (motion
parameters). You will get the best of both. If you don't adjust for
the null space, then movement will contribute to the deconvolution. In
summary, if you have iG be the motion parameter columns AND adjust for
the null-space (motion columns), then:

Y will have the effect of motion removed. Yc (removal of motion twice)
will be minimally different since Y is orthogonal to the motion
parameters. So, you have motion removed from both the autocorrelation
estimate, the Y for deconvolution, and Yc.

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 Thu, May 5, 2011 at 9:48 AM, Torben Ellegaard Lund
<[log in to unmask]> wrote:
> Hi Alex
>
>
> Den Uge:18 05/05/2011 kl. 14.15 skrev Alex Fornito:
>
>> Hi Torben,
>> Sorry, I just wanted to clarify: do you mean leaving SPM.xX.iG empty? It
>> seems like this is done by default by spm_fmri_design (?)
>
> leaving it empty will include all user defined regressors in the effects of interest F-test which determines the mask where the serial correlation is estimated. I think it would make sense if the motion regressors did not go into this test, just like the DCS HP-filter does not go into the F-contrast.
>
>> Are you suggesting manually entering motion parameters into this field?
>
> Yes, or their indices that is. It should be done after specification, but before model estimation. You can check if SPM recognized your changes by looking at the automatically generated effects of interest F-contrast.
>
>
> Best
> Torben
>
>
>
>
>>
>> On 05/05/2011 18:16, "Torben Ellegaard Lund" <[log in to unmask]> wrote:
>>
>>> Just as a kind reminder. Leaving SPM.xX.iG is a bit unfortunate, because
>>> voxels where motion artefacts are significant then constitutes a large part of
>>> the F-test derived mask used to estimate serial correlations. If you are picky
>>> about this you can change it yourselves, but it is not as easy as it should
>>> be.
>>>
>>>
>>> Best
>>> Torben
>>>
>>>
>>>
>>> Den Uge:18 05/05/2011 kl. 05.03 skrev Alex Fornito:
>>>
>>>> Ahh, I see. I was assuming that SPM.xX.iG contained the indices to the
>>>> nuisance covariates in the design matrix, but you're right - spm_fMRI_design
>>>> leaves it empty.
>>>>
>>>> Thanks for your help!
>>>> A
>>>>
>>>>
>>>> On 05/05/2011 12:54, "MCLAREN, Donald" <[log in to unmask]> wrote:
>>>>
>>>>> I agree that nuisance covariates, such as motion should be removed;
>>>>> however, motion covariates are not generally stored in X0 in my
>>>>> reading of the SPM code (spm_fMRI_design, spm_regions). They should be
>>>>> removed in the spm_regions filtering based on the null-space of the
>>>>> contrast.
>>>>>
>>>>> As for the motion parameters being in the model, if they were in the
>>>>> standard analysis, they should be included in the PPI analysis. My
>>>>> gPPI code will do this automatically as well.
>>>>>
>>>>> X0 is made of SPM.xX.iB (block effects -- so removing the mean) and
>>>>> SPM.xX.iG (nuisance variable that is empty as far as I can tell (line
>>>>> 369 of spm_fMRI_design)) and the high-pass filtering (K.X0).
>>>>>
>>>>> 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 Wed, May 4, 2011 at 10:16 PM, Alex Fornito <[log in to unmask]>
>>>>> wrote:
>>>>>> Hi Donald,
>>>>>> Thanks for your response. When you say "you don't want
>>>>>> to filter out anything related to neural activity", I'm presuming that you
>>>>>> are referring to the deconvolved neural signal?
>>>>>>
>>>>>> If so, that makes sense. However, X0 also contains user-specified nuisance
>>>>>> covariates. I would have thought it would make sense to remove those,
>>>>>> depending on what they were (e.g., movement parameters)? I guess they can
>>>>>> always be entered into the PPI regression model...
>>>>>>
>>>>>>
>>>>>> On 05/05/2011 11:57, "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
>>>>>>> =====================
>>>>>>> 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 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 region’s 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
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>> ------------------------------------------
>>>>>>
>>>>>> 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
>>>
>>>
>>
>>
>> ------------------------------------------
>>
>> 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
>>
>>
>>
>