Hi Donald, Torben and Darren,
Thanks for your responses.
Form each of your responses, it seems like best practice would be to remove
motion (and/or other confounds) from the VOI time series prior to
deconvolution.
It also seems like you are recommending NOT to frequency filter the VOI time
series.
In my reading though, spm_regions does this by default with the call to
spm_filter (line 135). So, if spm_regions is used to extract a VOI time
series, the time series that is input to spm_peb_ppi will already frequency
filtered. So it seems like, in standard practice, the deconvolution is
applied to frequency-filtered data. In this case, if xX.iG is empty, the
only difference between Y and Yc in spm_peb_ppi is the additional removal of
block effects (xX.iB).
Based on the current chain of emails, it seems you are recommending that a
confound-corrected, whitened, but non-frequency filtered time course should
be input to spm_peb_ppi. IS that correct, or am I missing something?
Finally, can I clarify exactly what the null space of the contrast is? Most
of the time in my analyses I don't get the option to adjust for it, so xY.Ic
is empty.
Thanks again for all your help,
A
On 06/05/2011 02:18, "MCLAREN, Donald" <[log in to unmask]> wrote:
> 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
> =====================
> This e-mail contains CONFIDENTIAL INFORMATION which may contain
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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 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
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> ------------------------------------------
>>>>>>>
>>>>>>> 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
|