There are 3 messages totaling 1723 lines in this issue.

Topics of the day:

 1. spm_affreg
 2. PPI question
 3. using mri3dx

----------------------------------------------------------------------

Date:    Fri, 6 May 2011 13:48:29 -0700
From:    Siddharth Srivastava <[log in to unmask]>
Subject: Re: spm_affreg

Hi John, please ignore the first part of my previous mail: made some errors
in
cut and paste. It runs fine now. The question about the shears, however,
still lingers: do I have
to somehow use both Sx and Sy, or as your implementation suggests, only one
would suffice?

thanks again, and sorry for being such a bother...
sid.

On Fri, May 6, 2011 at 12:23 PM, Siddharth Srivastava <[log in to unmask]>wrote:

> Thanks John, for the answers. The code, however exits with "Problem". M0
> and M1 differ.
> Also, why is there just one shear component in the matrix? I have seen
> general shears modeled as [1,h;g,1]: is this superfluous, and just one "h"
> will be sufficient for 2d case?
>
> Thanks,
> Sid.
>
>
>
>
> On Fri, May 6, 2011 at 11:01 AM, John Ashburner <[log in to unmask]>wrote:
>
>> Your model uses 7 parameters, but there are only 6 elements in
>> A(1:2,1:3).  Also, you need to be able to deal with a broader range of
>> angles, so atan2(s,c) is used to compute the angle.  I've tested it
>> with the following code, so hopefully it works for all cases.
>>
>> for i=1:10000
>>  P=randn(1,6)*10;
>>  M0=spm_matrix2D(P);
>>  P1=spm_imatrix2D(M0);
>>  M1=spm_matrix2D(P1);
>>  t=sum(sum((M1-M0).^2));
>>  if t>1e-9, disp('Problem'); break; end
>> end
>>
>> Note that spm_imatrix2D(spm_matrix2D(P)) does not necessarily have to
>> equal P, but the matrices generated from the two parameter sets should
>> be the same.
>>
>> Best regards,
>> -John
>>
>> On 6 May 2011 17:11, Siddharth Srivastava <[log in to unmask]> wrote:
>> > Hi everyone,
>> >       Can someone help me check the 2D versions of the spm_matrix and
>> > spm_imatrix? My
>> > implementation currently is as follows:
>> >
>> > ---2D spm_matrix ---
>> > function [A] = spm_matrix2D(P)
>> > % P = [Tx, Ty, R, Zx, Zy, Sx,Sy]
>> > p0 = [0,0,0,1,1,0,0];
>> > P = [P, p0((length(P)+1):7)]
>> >
>> > A = eye(3);
>> > % T -> R -> Zoom -> Shear
>> >  A =     A * [1,0,P(1); ...
>> >           0,1,P(2); ...
>> >         0,0,   1];
>> >  A =     A * [cos(P(3)), sin(P(3)), 0; ...
>> >           -sin(P(3)),  cos(P(3)), 0; ...
>> >       0,          0,         1];
>> >  A =     A * [P(4), 0, 0; ...
>> >               0,   P(5), 0; ...
>> >           0, 0, 1];
>> >  A =     A * [1, P(6), 0; ...
>> >               P(7)  , 1, 0; ...
>> >           0, 0, 1];
>> > --------------------------------------------------
>> > -----2D spm_imatrix ------------------------
>> > function P =  spm_imatrix2D(M);
>> >
>> >
>> >
>> > R = M(1:2,1:2);
>> > C = chol(R' * R);
>> > P = [M(1:2,3)',0,diag(C)',0,0];
>> > if(det(R) < 0) P(4) = -P(4); end
>> > C = diag(diag(C))\C;
>> > P(6:7) = C([2,3]);
>> >
>> > R0 = spm_matrix2D([0,0,0,P(4:end)]);
>> > R0 = R0(1:2,1:2);
>> > R1 = R/R0;
>> >
>> > th = asin(rang(R1(1,2)));
>> > P(3) = th;
>> >
>> > % There may be slight rounding errors making b>1 or b<-1.
>> > function  a = rang(b)
>> > a = min(max(b, -1), 1);
>> > return;
>> >
>> ---------------------------------------------------------------------------
>> >
>> > I know there is something not correct, since when, for a parameter set
>> 'p',
>> > i do spm_imatrix2D(spm_matrix2D(p)), I do not recover P for all entries,
>> > specially
>> > rotation and shears. C(2) above always evaluates to zero. The result is
>> that
>> > these errors
>> > accumulate (or are not accounted for) on repeated application of this
>> > sequence of
>> > operations in the registration routine.
>> >
>> > Thanks in anticipation,
>> > Sid.
>> >
>> >
>> >
>> > On Fri, Apr 22, 2011 at 9:13 AM, Siddharth Srivastava <[log in to unmask]
>> >
>> > wrote:
>> >>
>> >> Hi John,
>> >>       Thanks a lot for your answer, and it has been very helpful for my
>> >> understanding
>> >> of the details of the implementation, and my own development efforts. I
>> >> have
>> >> 2 very quick questions, though:
>> >>
>> >> 1) the parameter set x(:) that is returned from spm_mireg, and the
>> >> "params" that
>> >> spm_affsub3 returns (in spm99, which is also the version that I am
>> looking
>> >> at) ,
>> >> are they equivalent? In other words, If I am collating the parameters
>> from
>> >> the mireg
>> >> routine, would the distribution x(7:12) be used to estimate the icovar0
>> >> entries in affsub3?
>> >>
>> >> 2) also would VG.mat\spm_matrix(x)*VF.mat transform G to F similar to
>> >>      VG.mat\spm_matrix(params)*VF.mat , or do I have to do
>> >>       M = VG.mat\spm_matrix(x(:)')*VF.mat
>> >>       pp = spm_imatrix(M)
>> >>       and use pp(7:12) ?
>> >>
>> >>
>> >>
>> >> Thanks again.
>> >> Sid.
>> >>
>> >> On Thu, Apr 14, 2011 at 2:13 PM, John Ashburner <[log in to unmask]>
>> >> wrote:
>> >>>
>> >>> > I had a look at the code in spm_maff, and
>> >>> > it looks like it also uses the values of isig and mu in the call to
>> >>> > affreg(). My guess is
>> >>> > that during the initial registration on a large data set for
>> estimating
>> >>> > the
>> >>> > prior
>> >>> > distribution of parameters, this will be called with typ = 'none' .
>> Is
>> >>> > this
>> >>> > correct?
>> >>>
>> >>> Once the registration has locked in on a solution, the priors have
>> >>> less influence.  Their main influence is in the early stages of the
>> >>> registration, or when there are relatively few slices in the data.  I
>> >>> don't actually remember if the registration was regularised when I
>> >>> computed their values.
>> >>>
>> >>> >
>> >>> > Further, do we have to do a non-rigid registration for the
>> estimation
>> >>> > (since, in the comments the selected
>> >>> > files are filtered as *seg_inv_sn.mat"? Since only p.Affine is being
>> >>> > used,
>> >>> > can this work with only
>> >>> > an affine / rigid transformation?
>> >>>
>> >>> Only the affine transform in the files is used, so basically yes.  You
>> >>> could just do affine registration.
>> >>>
>> >>> >
>> >>> > I would also be happy if you could also answer my other question
>> about
>> >>> > examining the
>> >>> > effects of the values in isig and mu (what are the units of mu?) on
>> the
>> >>> > transformed image.
>> >>>
>> >>> They are unit-less, and are just a measure of the zooms and shears.
>> >>>
>> >>> > For example,
>> >>> > if i set a mu of [mu1, mu2, ...] and a isig of [sig1, 0, 0...; 0,
>> sig2,
>> >>> > 0,
>> >>> > 0..; ...] (with only diagonal components),
>> >>> > based on the Hencky tensor penalty, it seems that large deviations
>> of
>> >>> > parameter 1  estimate (T - mu) from
>> >>> > mu1 will be penalized. How is the value of isig1 modifying the
>> penalty?
>> >>>
>> >>> Its assuming that the elements of the matrix log of the part that does
>> >>> shears and zooms has a multivariate normal distribution, so the
>> >>> penalty is a kind of negative log likelhood.  The isig is essentially
>> >>> a precision matrix, which is the inverse of a covariance matrix.
>> >>>
>> >>> p(x|mu,S) = 1/sqrt(det(2*pi*S)) * exp(-0.5*(x-mu)'*inv(S)*(x-mu))
>> >>>
>> >>> Taking logs and negating, you get 0.5*(x-mu)'*inv(S)*(x-mu) + const
>> >>>
>> >>> > How
>> >>> > do the off diagonal terms affect
>> >>> > the penalty?
>> >>>
>> >>> The inverse of isig would just encode the covariance between the
>> >>> various shears and zooms.
>> >>>
>> >>> >
>> >>> > Could you also point me to a reference where I can learn more about
>> >>> > Hencky
>> >>> > strain tensor (specifically
>> >>> > what information it carries in the context of image processing etc.)
>> >>>
>> >>> I don't know if this is of any use:
>> >>>
>> >>> http://en.wikipedia.org/wiki/Deformation_(mechanics)
>> >>>
>> >>> Best regards,
>> >>> -John
>> >>>
>> >>>
>> >>> > On Thu, Apr 14, 2011 at 10:17 AM, John Ashburner <
>> [log in to unmask]>
>> >>> > wrote:
>> >>> >>
>> >>> >> They were computed by affine registering loads of (227) images to
>> MNI
>> >>> >> space.  This gave the affine transforms, which were then used to
>> build
>> >>> >> up a mean and inverse covariance matrix.  In the comments of
>> >>> >> spm_maff.m, you should see the following...
>> >>> >>
>> >>> >> %% Values can be derived by...
>> >>> >> %sn = spm_select(Inf,'.*seg_inv_sn.mat$');
>> >>> >> %X  = zeros(size(sn,1),12);
>> >>> >> %for i=1:size(sn,1),
>> >>> >> %    p  = load(deblank(sn(i,:)));
>> >>> >> %    M  = p.VF(1).mat*p.Affine/p.VG(1).mat;
>> >>> >> %    J  = M(1:3,1:3);
>> >>> >> %    V  = sqrtm(J*J');
>> >>> >> %    R  = V\J;
>> >>> >> %    lV      =  logm(V);
>> >>> >> %    lR      = -logm(R);
>> >>> >> %    P       = zeros(12,1);
>> >>> >> %    P(1:3)  = M(1:3,4);
>> >>> >> %    P(4:6)  = lR([2 3 6]);
>> >>> >> %    P(7:12) = lV([1 2 3 5 6 9]);
>> >>> >> %    X(i,:)  = P';
>> >>> >> %end;
>> >>> >> %mu   = mean(X(:,7:12));
>> >>> >> %XR   = X(:,7:12) - repmat(mu,[size(X,1),1]);
>> >>> >> %isig = inv(XR'*XR/(size(X,1)-1))
>> >>> >>
>> >>> >> You may be able to re-code this to work with your 2D transforms.
>>  Note
>> >>> >> that I only use the components that describe shears and zooms.
>>  Also,
>> >>> >> if I was re-coding it all again, I would probably do things
>> slightly
>> >>> >> differently, using the following decomposition:
>> >>> >>
>> >>> >> J=M(1:3,1:3);
>> >>> >> L=real(logm(J));
>> >>> >> S=(L+L')/2; % Symmetric part (for zooms and shears, which should be
>> >>> >> penalised)
>> >>> >> A=(L-L')/2; % Anti-symmetric part (for rotations)
>> >>> >>
>> >>> >> I might even base the penalty on lambda(2)*trace(S)^2 +
>> >>> >> lambda(1)*trace(S*S'), which is often used as a measure of "elastic
>> >>> >> energy" for penalising non-linear registration.  Of course, there
>> >>> >> would also be a bit of annoying extra stuff to deal with due to MNI
>> >>> >> space being a bit bigger than average brains are.  Figuring out the
>> >>> >> mean would require (I think) an iterative process similar to the
>> one
>> >>> >> described in "Characterizing volume and surface deformations in an
>> >>> >> atlas framework: theory, applications, and implementation", by
>> Woods
>> >>> >> in NeuroImage (2003).
>> >>> >>
>> >>> >> Best regards,
>> >>> >> -John
>> >>> >>
>> >>> >> On 14 April 2011 17:15, Siddharth Srivastava <[log in to unmask]>
>> wrote:
>> >>> >> > Hi all,
>> >>> >> >      My question pertains specifically to the values of isig and
>> mu
>> >>> >> > that
>> >>> >> > are
>> >>> >> > incorporated
>> >>> >> > in the code, and I wish to disentangle the effect that these
>> numbers
>> >>> >> > have on
>> >>> >> > individual parameters
>> >>> >> > during the optimization stage. I am trying to map it to a 2D
>> case,
>> >>> >> > and
>> >>> >> > these
>> >>> >> > numbers
>> >>> >> > will have to be calculated ab-initio for my case. Before I begin
>> >>> >> > with
>> >>> >> > estimating the prior distribution
>> >>> >> > for these parameters, I need to check the effect that they have
>> on
>> >>> >> > the
>> >>> >> > registered images. Regarding this
>> >>> >> > 1) Can someone suggest a way I can examine the effect on each
>> >>> >> > parameter
>> >>> >> > separately, if it is
>> >>> >> >      at all possible.
>> >>> >> > 2) Some advise on how to estimate these parameters, if it has
>> been
>> >>> >> > done
>> >>> >> > for
>> >>> >> > images other
>> >>> >> >      than brain images, will be really helpful for me.
>> >>> >> >
>> >>> >> > Thanks,
>> >>> >> > Sid.
>> >>> >> >
>> >>> >> > On Mon, Mar 28, 2011 at 7:04 PM, Siddharth Srivastava
>> >>> >> > <[log in to unmask]>
>> >>> >> > wrote:
>> >>> >> >>
>> >>> >> >> Dear John,
>> >>> >> >>           Thanks for your answer. I was able to correctly run my
>> 2D
>> >>> >> >> implementation
>> >>> >> >> based on your advise. However, I am am not very confident of my
>> >>> >> >> results
>> >>> >> >> and wanted to
>> >>> >> >> consult you on certain aspects of my mapping from 3D to 2D.
>> >>> >> >>            1) In function reg(..), the comment says that the
>> >>> >> >> derivatives
>> >>> >> >> w.r.t rotations and
>> >>> >> >> translations is zero. Consequently, I ran the indices from 4:7.
>> >>> >> >> According
>> >>> >> >> to your previous
>> >>> >> >> reply, rotations will be lumped together with scaling and affine
>> in
>> >>> >> >> 4
>> >>> >> >> parameters, and
>> >>> >> >> translations, independently, in the last two. I understand that
>> in
>> >>> >> >> this
>> >>> >> >> case, the indices
>> >>> >> >> run over parameters that have been separated into individual
>> >>> >> >> components
>> >>> >> >> (similar to
>> >>> >> >> the form accepted by spm_matrix). Is this correct for the
>> function
>> >>> >> >> reg(..)?  In fact, I went back to the
>> >>> >> >> old spm code (spm99), and found parameters pdesc and free, and
>> got
>> >>> >> >> more
>> >>> >> >> confused as to when I should
>> >>> >> >> use the 6 parameter, and when to use separate parameters (as if
>> I
>> >>> >> >> was
>> >>> >> >> passing them to spm_matrix).
>> >>> >> >> So, when the loop runs over p1 and perturbs p1(i) by ds, does it
>> >>> >> >> run
>> >>> >> >> over
>> >>> >> >> translations/rotations etc
>> >>> >> >> or does it run over the martix elements that define these
>> >>> >> >> transformations
>> >>> >> >> (2x2 + 1x2)?
>> >>> >> >>
>> >>> >> >>            2) In function penalty(..), I have used unique
>> elements
>> >>> >> >> as
>> >>> >> >> [1,3,4], and my code looks like
>> >>> >> >> T = my_matrix(p)*M;
>> >>> >> >> T = T(1:2,1:2);
>> >>> >> >> T = 0.5*logm(T'*T);
>> >>> >> >> T
>> >>> >> >> T = T(els)' - mu;
>> >>> >> >> h = T'*isig*T
>> >>> >> >>
>> >>> >> >> Assuming an application specific isig, is this correct?
>> >>> >> >>          3) I have not yet incorporated the prior information in
>> my
>> >>> >> >> code,
>> >>> >> >> But for testing purposes,
>> >>> >> >> can you suggest some neutral values  for mu and isig that I can
>> try
>> >>> >> >> to
>> >>> >> >> concentrate on the
>> >>> >> >> accuracy of the implementation minus the priors for now?
>> >>> >> >>           4) My solution curently seems to oscillate around an
>> >>> >> >> optimum.
>> >>> >> >> I
>> >>> >> >> am hoping that after
>> >>> >> >> I have removed any errors after your replies, it will converge
>> >>> >> >> gracefully.
>> >>> >> >>
>> >>> >> >>           Thanks again for all the help.
>> >>> >> >>            Siddharth.
>> >>> >> >>
>> >>> >> >>
>> >>> >> >> On Fri, Mar 18, 2011 at 2:05 AM, John Ashburner
>> >>> >> >> <[log in to unmask]>
>> >>> >> >> wrote:
>> >>> >> >>>
>> >>> >> >>> A 2D affine transform would use 6 parameters, rather than 7: a
>> 2x2
>> >>> >> >>> matrix to encode rotations, zooms and shears, and a 2x1 vector
>> for
>> >>> >> >>> translations.
>> >>> >> >>>
>> >>> >> >>> Best regards,
>> >>> >> >>> -John
>> >>> >> >>>
>> >>> >> >>> On 18 March 2011 05:05, Siddharth Srivastava <[log in to unmask]
>> >
>> >>> >> >>> wrote:
>> >>> >> >>> > Dear list users,
>> >>> >> >>> >           I am currently trying to understand the
>> implementation
>> >>> >> >>> > in
>> >>> >> >>> > spm_affreg, and
>> >>> >> >>> > to make matters simple, I tried to work out a 2D version of
>> the
>> >>> >> >>> > registration
>> >>> >> >>> > procedure. I struck a roadblock, however...
>> >>> >> >>> >
>> >>> >> >>> > The function make_A creates part of the design matrix. For
>> the
>> >>> >> >>> > 3D
>> >>> >> >>> > case,
>> >>> >> >>> > the
>> >>> >> >>> > 13 parameters
>> >>> >> >>> > are encoded in columns of A1, which then is used to generate
>> AA
>> >>> >> >>> > +
>> >>> >> >>> > A1'
>> >>> >> >>> > A1. AA
>> >>> >> >>> > is 13x13, and everything
>> >>> >> >>> > works. For the 2D case, however, there will be 7+1
>> parameters( 2
>> >>> >> >>> > translations, 1 rotation, 2 zoom, 2 shears and 1 intensity
>> >>> >> >>> > scaling).
>> >>> >> >>> > The A1 matrix for 2D, then,  will be (in make_A)
>> >>> >> >>> > A  = [x1.*dG1 x1.*dG2 ...
>> >>> >> >>> >       x2.*dG1 x2.*dG2 ...
>> >>> >> >>> >       dG1     dG2  t];
>> >>> >> >>> >
>> >>> >> >>> > which is (number of sampled points) x 7. The AA matrix (in
>> >>> >> >>> > function
>> >>> >> >>> > costfun)
>> >>> >> >>> > is 8x8
>> >>> >> >>> > so, which is the parameter I am missing in modeling a 2D
>> >>> >> >>> > example? I
>> >>> >> >>> > hope I
>> >>> >> >>> > have got the number of parameters
>> >>> >> >>> > right for the 2D case? Is there an extra column that I have
>> to
>> >>> >> >>> > add
>> >>> >> >>> > to
>> >>> >> >>> > A1 to
>> >>> >> >>> > make the rest of the matrix computation
>> >>> >> >>> > consistent?
>> >>> >> >>> >
>> >>> >> >>> > Thanks for the help
>> >>> >> >>> >
>> >>> >> >>
>> >>> >> >
>> >>> >> >
>> >>> >
>> >>> >
>> >>
>> >
>> >
>>
>
>

------------------------------

Date:    Sat, 7 May 2011 07:14:27 +1000
From:    Alex Fornito <[log in to unmask]>
Subject: Re: PPI question

Actually, sorry one more question:

If Y in spm_peb_ppi is already frequency filtered by spm_regions, then the
only difference (barring any additional confounds specified with xX.iG)
between Y and Yc is the additional removal of block effects.

Since the ppi interaction term is generated from Y and not Yc, I am
presuming then that is solely because it is not desirable to deconvolve a
time course with block effects removed. Is this correct?


On 07/05/2011 04:07, "MCLAREN, Donald" <[log in to unmask]> wrote:

> The null-space of the contrast are any columns of the contrast (which
> must be an F-contrast) that are 0. Thus, you remove the effects of
> motion when you do the adjustment and don't need to worry about them
> being included in the deconvolved data. The code that I have supplied
> has N rows for N conditions of interest after I talked to Darren about
> the ideal contrast to use for adjustment.
>
> As for removing the frequencies, I think it is probably fine to remove
> them, I was just under the impression that they weren't being removed
> because of me commenting out that line during testing of spm_regions.
> I would go with the SPM defaults until their effect is tested. If you
> don't set a high-pass filter, then you won't remove the frequencies,
> so you could test it that way as well.
>
> 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 Thu, May 5, 2011 at 9:46 PM, Alex Fornito <[log in to unmask]> wrote:
>> 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
>>> 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 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
>>
>>
>>
>>
>


------------------------------------------

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  

------------------------------

Date:    Fri, 6 May 2011 16:17:37 -0500
From:    John Fredy <[log in to unmask]>
Subject: using mri3dx

hello all,

I am trying to show some activations in the spm5 template in mri3dx
(template spm5 command) but the activations appear more higher than the
template. Is necessary any conversion to show the activations?

I think that maybe the mri3dX uses tailarach space, I can convert my
normalized activations to tailarach space?

Thanks in advance

------------------------------

End of SPM Digest - 6 May 2011 (#2011-133)
******************************************