Dear Rik ,
Thank you very much! I am looking for something like this. I tried your trick.
New t map can be explored. The problem is, t values are not correct. I checked
the mail list. Several year ago Stephan ever pointed out that the threshold
should presumably be p<.999, as the thresholding has already been effected when
the map was transformed. I tried 0.99 or the original one and others, but
none lead to a corrected result. In your opinion, what kind of threshold is
correct? (the process to make the new t map: for condition 1, a nii file made
from Save button under threshold none 0.001 and 10 voxel, together
with spmT_0001.img for condition 2 in another model under the same threshold,
expression: i2.*(i1>0) ).
Sorry to back to spm_FcUtil function again. I use your code to make
one-condition contrast successfully in one way anova model with subject effect,
while the results are quite differently from those of one-sample t test and one
way anova without subject effect. eg, for the 4 conditions checked, threshold p
0.001 and 10 voxel is suitable for the former two approaches, while in the one
way anova model with subject effect, the same threshold bring much extensive
signal changes. Besides, one of 4 condition (the third) show no any voxel with
any threshold in the new model. I think this great change may comes from some
settings wrong but not from new variance introduced by the model. I attached the
spm.mat files, hope you point out where the problem is if you don't mind it will
take your addition time.
Best regards,
Pengxu
________________________________
From: Rik Henson <[log in to unmask]>
To: pengxu wei <[log in to unmask]>; "[log in to unmask]"
<[log in to unmask]>
Sent: Sun, April 10, 2011 5:16:09 PM
Subject: RE: [SPM] a simple question about one-way within-subjects ANOVA
>It's really interesting about '"inclusive masking" across these t-tests (ie
>across SPM analyses)' you mentioned.
>
>It seems that inclusive masking can not be made across models through GUI. With
>image calculator common
>areas between 2 t-maps can be made, but this isn't a direct method and need
>transform images several times.
>
>Is there a convenient method to do inclusive masking across models?
I don't think there is a convenient way via the GUI - but it might be relatively
easy for the SPM authors (;-) to allow the user to select a new "SPM.mat" file
when pressing "contrast" in response to the "masking" question that arises when
you review results, ie to bring up a Contrast Manager window from an analysis
different from the one currently being reviewed?
In the meantime (;-), you can use ImCalc to create a masked image, but as you
note, you won't be able to surf the stats in the same way that you can when
reviewing contrasts within the same analysis. One "hack" is described below - ie
to create a new dummy spmT contrast in one analysis, but overwrite it with the
result of your ImCalc masking across multiple analyses:
https://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=ind00&L=SPM&P=R95431&I=-3
Note however that this is a dangerous procedure (if you make a mistake), and you
must ensure that the spmT images are in the same space.
BW,R
-------------------------------------------------------
DR RICHARD HENSON
Assistant Director, Imaging
MRC Cognition & Brain Sciences Unit
15 Chaucer Road
Cambridge, CB2 7EF
England
EMAIL: [log in to unmask]
URL: http://www.mrc-cbu.cam.ac.uk/people/rik.henson/personal
TEL +44 (0)1223 355 294 x522
FAX +44 (0)1223 359 062
MOB +44 (0)794 1377 345
-------------------------------------------------------
________________________________
From: pengxu wei [[log in to unmask]]
Sent: 10 April 2011 02:51
To: Rik Henson; [log in to unmask]
Subject: Re: [SPM] a simple question about one-way within-subjects ANOVA
Dear Rik,
Thank you very much for your patience! Now both your two codes work.
It's really interesting about '"inclusive masking" across these t-tests (ie
across SPM analyses)' you mentioned. It seems that inclusive masking can not be
made across models through GUI. With image calculator common areas between 2
t-maps can be made, but this isn't a direct method and need transform images
several times. Is there a convenient method to do inclusive masking across
models?
Best regards,
Pengxu
________________________________
From: Rik Henson <[log in to unmask]>
To: [log in to unmask]
Sent: Sat, April 9, 2011 10:01:48 PM
Subject: Re: [SPM] a simple question about one-way within-subjects ANOVA
> If you don’t mind deleting your previous contrasts, try loading the SPM.mat
>file, then:
>
> SPM = rmfield(SPM,'xCon');
> SPM.xCon(1) = spm_FcUtil('Set',’Any_name’,'T','c',c',SPM.xX.xKXs);
> spm_contrasts(SPM);
>
> Does that still fail?
From:SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf
Of pengxu wei
Sent: 07 April 2011 13:05
To: [log in to unmask]
Subject: Re: [SPM] a simple question about one-way within-subjects ANOVA
Dear Rik, Donald and Alexa,
Thank you very much for informative suggestions and such an in-depth discussion.
First, the code about spm_FcUtil can't work. It works for other conditions(eg,
if building a model without subject effects). After testing, what I find is, the
code works if we can make a contrast through GUI, but fails if we can't through
GUI.
Infomation that Matlab shows:
Warning:
c is not a proper contrast in +c->Tsp in spm_SpUtil
!!! projecting...
> In spm_SpUtil at 327
In spm_FcUtil at 219
from
c =
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
to
c =
0.9583
-0.0417
-0.0417
-0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
0.0417
??? Subscripted assignment between dissimilar structures.
and finally no new contrast is written in.
Though it can not work, it looks like this projecting process transfroms the
original contrast to a new one in which the value of the 1st comlumn near 1, the
other 23 column have the same small net value but the 2nd-4th are negative while
the left 20 positive. It will work, I think, If the sum of these reprojected
values equals zero ( the sum of above values is 1.5004 so that may be why it
fails). Nevertheless, such method is an approximation instead of a real [1 0 0
0], if succeed.
Second, the contrast [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]/20 can't
work, too.
I am thinking what Donald said and materials Rik and Alexa provide, it takes
time.
Any other suggestions?
Best regards,
Pengxu
--- 11年4月7日，周四, MCLAREN, Donald <[log in to unmask]> 写道：
>发件人: MCLAREN, Donald <[log in to unmask]>
>主题: Re: [SPM] a simple question about one-way within-subjects ANOVA
>收件人: [log in to unmask]
>日期: 2011年4月7日,周四,上午2:03
>Alexa,
>
>My statement about the error term being wrong only applies to within-subject
>designs or mixed-designs. If you look at any other statistica program (SAS,
>SPSS, STATA, etc.) anytime that they compute the effect of an individual
>condition, they use a different error term. The reason for this is that
>comparing a condition against 0 is a test of a between-subject effect, while
>comparing condition 1 versus condition 2 is a test of a within-subject effect.
>The test of each effect has its own error term. However, in SPM (FSL, FAST,
>etc.) only 1 error term is generated. For within-subject or mixed-designs, the
>error term in SPM, etc. is the within-subject error term. One cannot partition
>this error term to derive the between-subject error term because the
>between-subject error term is based on the between subject variance not the
>within-subject variance.
>
>Here is a good example, of heights of people:
>
>condition 1: height [ 61 62 59 63]
>condition 2: height + 2 inch board [63 64 61 65]
>
>condition 1 versus 60 is not significant (p=.25)
>condition 2 versus 60 is significant (p=.024)
>
>Now, if we build a big model and ask if condition 1 is different than 0, we get
>p=0, t-stastic=Inf. In other words, by measuring people's height with a board,
>their height without the board becomes significantly taller than 60 inches. To
>me, this demonstrates that SPM's error term is wrong in make conclusions about
>individual conditions within a repeated measures design.
>
>
>If one were to change condition 2: height +2 inch board to [63 64 61 64] (I
>guess the board shrunk before being used on the last person.
>condition 1 versus 60 is not significant (p=.25)
>condition 2 versus 60 is significant (p=.022)
>
>Condition 1, using a paired test (e.g. a one-way ANOVA with 2 condition), is now
>still significant with a p-value of 0.005986. Thus, we would [incorrectly]
>conclude that these people are significant taller than 60 inches. This is a
>result of making a second measurement that doesn't change the first measurement.
>Also, we want to make a inference about the between-subject effect, which should
>not be influenced by other measurements of the same subjects as it is asking
>about the population behavior.
>
>In conclusion, asking about the effect of 1 condition within SPM is invalid
>because it doesn't represent the between-subject effect because it is not using
>the between subject error term as that error term is not currently provided by
>SPM.
>
>Hopefully this clears up my original statement on the validity of asking the
>significance of a single condition or group of condition in a within-subject or
>mixed-design model.
>
>Please feel free to ask any question or suggest comments on the approach or
>statistics.
>
>
>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, Apr 6, 2011 at 9:46 AM, Alexa Morcom <[log in to unmask]> wrote:
>Donald, Rik, Pengxu
>
>I agree about the contrast, and you can work out how to do others using the
>general system described in the excellent Gitelman/ Glascher tutorial for
>Flexible Factorial contrasts (I don't have a direct link, but google it).
>
>I don't think the error term is wrong, though. All weighting the subject effect
>columns does here is to weight the 'contribution' of the effect you are
>interested in proportionately, and appropriately, for all columns to which it
>contributes. It would be incorrect for these subject effects to go into the
>error term, even if it were possible to code it that way. I believe that doing
>this will have just the same effect as what Rik suggests, which might be useful
>if you don't want to use the GUI.
>
>
>cheers
>
>Alexa
>
>On 6 April 2011 14:12, MCLAREN, Donald <[log in to unmask]> wrote:
>Rik and Pengxu,
>
>They don't have to equal 0, for example if you had 4 condition and 20
>subjects, (conditions then subject factors in the design matrix), then
>the contrast for condition 1 would be:
>1 0 0 0 ones(1,20)/20
>While this contrast works, it is not valid for statistical inference
>because you end up using the wrong error term because the error term
>is for the within-subject errors. However, it is useful for getting
>the mean response to condition1 across subjects if you just want to
>plot the results.
>
>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, Apr 6, 2011 at 8:43 AM, Rik Henson <[log in to unmask]>
wrote:
>>
>>
>> You can’t do this via SPM’s GUI, because it forces contrast weights to sum
>> to zero for rank deficient design matrices (inestimable contrasts with
>> respect to columns of interest).
>>
>>
>>
>> You can however estimate the equivalent contrast via calling SPM’s
>> spm_FcUtil function directly, which will re-parametrise your contrast to
>> make it estimable with respect to all columns in the design matrix, eg:
>>
>>
>>
>> SPM.xCon(end+1) = spm_FcUtil('Set',’Any_name’,'T','c',c',SPM.xX.xKXs);
>>
>> spm_contrasts(SPM);
>>
>>
>>
>> where c is your original contrast vector (eg [1 0 0 0]), which will be
>> reprojected to include contributions from the subject effects. For further
>> information, try Christophe Pallier’s document here:
>>
>>
>>
>> http://www.pallier.org//ressources/glm_anova/glm_anova2.pdf
>>
>>
>>
>> BW,R
>>
>>
>>
>> -------------------------------------------------------
>>
>> Dr Richard Henson
>>
>> Assistant Director, Neuroimaging
>>
>> MRC Cognition & Brain Sciences Unit
>>
>> 15 Chaucer Road
>>
>> Cambridge, CB2 7EF, UK
>>
>>
>>
>> Office: +44 (0)1223 355 294 x522
>>
>> Mob: +44 (0)794 1377 345
>>
>> Fax: +44 (0)1223 359 062
>>
>>
>>
>> http://www.mrc-cbu.cam.ac.uk/people/rik.henson/personal
>>
>> -------------------------------------------------------
>>
>>
>>
>> From: Pengxu Wei [mailto:[log in to unmask]]
>> Sent: 06 April 2011 11:52
>> To: Rik Henson
>> Subject: a simple question about one-way within-subjects ANOVA
>>
>>
>>
>> Dear Dr. Rik Henson,
>>
>>
>>
>> I am reading your paper "ANOVAs and SPM". One of my data is just the same of
>> Figure 5: Design matrix for one-way (1x4) within-subjects ANOVA. The first 4
>> columns are treatment effects and the last 12 are subject effects, except 20
>> instead of 12 subjects in my study.
>>
>>
>>
>> In SPM5 I get this design matrix through 'Flexible factorial'
>> specification. I want to check activation of each of the 4 conditions
>> through this model, and then use inclusive masking or conjunction to explore
>> the activation areas in common between each pair of the 4 treatment effects.
>> But I failed when using the contrast like [1 0 0 0]. Only contrast with a
>> zero sum(eg.1 -1 0 0) can be used. Would you like to show me a clue?
>>
>>
>>
>>
>>
>> Best regards,
>>
>>
>>
>> Pengxu Wei
>> Director, Chinese Medicine and Rehabilitation Dept.
>> National Rehabilitation Hospital,
>> National Research Center for Rehabilitation Technical Aids, China
>
>
>
>--
>Dr. Alexa Morcom
>RCUK Academic Fellow
>Centre for Cognitive & Neural Systems/ Centre for Cognitive Ageing & Cognitive
>Epidemiology
>Psychology, University of Edinburgh
>http://www.ccns.sbms.mvm.ed.ac.uk/people/academic/morcom.html
>
>The University of Edinburgh is a charitable body, registered in Scotland, with
>registration number SC005336
>
>
>The University of Edinburgh is a charitable body, registered in
>Scotland, with registration number SC005336.
>Thank you. But, yes, after running the new code I got:
>
>??? Error using ==> rmfield at 41
>A field named 'xCon' doesn't exist.
Ok - I had assumed your previous error was because you had already performed
some contrasts in your SPM analysis, so the error arose from trying to index the
last contrast in an existing structure. The fact that the 'xCon' field does not
exist means that you hadn't performed any contrasts, so that is not the problem.
So I suggest you skip the first line above, and try the next two? If that fails,
then, without looking at your SPM.mat file in detail, I do not know what is
causing your errors (because the code works for me!).
Anyway, your original purpose was to find activations common to each condition
in a repeated-measures ANOVA, using contrasts like [1 0 0...], [0 1 0...] in a
model with condition and subject effects. If you are prepared to assume a single
error term (though see below), then you could: 1) estimate contrasts of the type
Donald suggested, and use inclusive masking to find common activation, though
you won't get a final corrected p-value based on Random Field Theory for T^n
(n>1) fields (as you would with a conjunction), but you could report each
component threshold (though note that the tests are not independent, given that
they share the same error term, so estimating the combined probability is
difficult), or 2) specify contrasts using the spm_FcUtil function above, which
will actually create orthogonal contrasts (as I think you would get if you tried
to specify a conjunction across contrasts like Donald's), and then use a
conjunction (by selecting all contrasts in using shift-mouse in the Contrast
Manager window).
Having said all this, if you want your error term to be restricted to
between-subject variance only (as Donald recommends), then it might be simpler
to perform separate one-sample t-tests for each contrast, and then use
"inclusive masking" across these t-tests (ie across SPM analyses). Again, you
won't get a final corrected p-value based on Random Field Theory for T^n (n>1)
fields, but you can report the component thresholds (and estimate a combined
p-value, if data are independent).
BW,R
