Thanks, Donald!

How to "have one big design with 5 sessions in it as that is the standard approach"? Do you have some guide for this? Is that achieved by

1. Concatenate all the volumes;
2. Set up the design matrix: each column corresponde to one condition for one session, and set others to 0? Then filtering the regressors maybe a problem.

I just simply want to achieve the fix-effect analysis in FSL: first do 1st level for each session, and then do fix-effect analysis at 2nd level to combine all the first level results.

I know this doesn't have statistical generalizability, but just want to combine the results for some pilot data (repeated many times for a same subject).

Best,

Chao-Gan






On Thu, Oct 31, 2013 at 3:31 PM, MCLAREN, Donald <[log in to unmask]> wrote:
In general, you are not interested in the T-statistic for each subject, so you could just average the contrasts from each session, then you wouldn't need to build the contrast.

When I provided the contrasts, I had thought you had one big design with 5 sessions in it as that is the standard approach. Concatenation means that you combine all the sessions and treat them as a single session. In SPM, you can build a model with multiple sessions - which is different from concatenation and differerent from having each session in its own model.

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Postdoctoral Research Fellow, GRECC, Bedford VA
Website: http://www.martinos.org/~mclaren
Office: (773) 406-2464
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On Thu, Oct 31, 2013 at 3:24 PM, YAN Chao-Gan <[log in to unmask]> wrote:
Thanks, Donald!

That means I need to have a 10-column design matrix upon which I can apply the contrast "1/5 0 1/5 0 1/5 0 1/5 0 1/5 0 0 0 0 0 0", right?

In my case, each session has one directory, under each has the SPM.mat. For each SPM.mat, there are 2 columns for design matrix. How can I combine them to have a 10-column design matrix, thus I can apply the contrast?

Thanks,

Chao-Gan


On Thu, Oct 31, 2013 at 3:19 PM, MCLAREN, Donald <[log in to unmask]> wrote:
You can use a fixed effects analysis to combine them. All you need to do is create a contrast that spans all 5 sessions/runs.
1/5 0 1/5 0 1/5 0 1/5 0 1/5 0 0 0 0 0 0 for 5 sessions with 2 conditions. Just make sure the contrast weights sun to 1 for the positive coefficients and -1 for the negative coefficients.

Concatenate is potentially bad if you don't account for the fact that you have multiple runs. If you properly account for the runs, then its okay; however, its not worth scripting the analysis to get the proper run effects in the HP filter, AR(1) estimation, or HRF.

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Postdoctoral Research Fellow, GRECC, Bedford VA
Website: http://www.martinos.org/~mclaren
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED
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On Thu, Oct 31, 2013 at 1:36 PM, YAN Chao-Gan <[log in to unmask]> wrote:
By the way, how to perform 1st level analysis across sessions?
I.e., I have one subject performed the same task for 5 sessions (or some may call them runs). How can I combine them to have one single t map? Use some fix effect analysis?

I think it's not a good idea to simply concatenate all the 5 sessions, right?

Thanks in advance!

Best,

Chao-Gan


On Thu, Oct 31, 2013 at 1:32 PM, YAN Chao-Gan <[log in to unmask]> wrote:
Thanks Don!

It's a two sample t test, the variance is default to be unequal. Then there should be some special process on SPM.xX.Bcov, right?

Best,

Chao-Gan


On Thu, Oct 31, 2013 at 12:22 PM, MCLAREN, Donald <[log in to unmask]> wrote:
Did you set the variance to be equal?

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Postdoctoral Research Fellow, GRECC, Bedford VA
Website: http://www.martinos.org/~mclaren
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
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On Thu, Oct 31, 2013 at 11:45 AM, YAN Chao-Gan <[log in to unmask]> wrote:
Thanks!

Now I am using SPM.xX.Bcov directly, thus got the same results as spm_results_ui.

TData = (beta_0001*Contrast(1)+beta_0002*Contrast(2))./(sqrt(ResMS*(Contrast*SPM.xX.Bcov*Contrast')));

In the case of 2nd level statistical analysis, there should be no filtering issue, right? Why SPM.xX.Bcov is slightly different from (X'*X)^(-1)?

>>SPM.xX.Bcov
    0.0656         0
         0    0.0438
>>(X'*X)^(-1)
    0.0625         0
         0    0.0455

Is this caused by an whitening issue?

Thanks,

Chao-Gan 







On Thu, Oct 31, 2013 at 10:44 AM, MCLAREN, Donald <[log in to unmask]> wrote:
xX.xKXs   = spm_sp('Set',spm_filter(xX.K,W*xX.X)); % K*W*X

Also,
xX.xKXs.X = full(xX.xKXs.X);
xX.pKX    = spm_sp('x-',xX.xKXs);                        % projector/pseudo-inverse
xX.Bcov         = xX.pKX*xX.V*xX.pKX';  

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Postdoctoral Research Fellow, GRECC, Bedford VA
Website: http://www.martinos.org/~mclaren
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
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On Thu, Oct 31, 2013 at 10:41 AM, YAN Chao-Gan <[log in to unmask]> wrote:
Thanks, Guillaume and Donald! I will read that.

Hi Donald, what's K in your expression? Seems SPM.xX.K is a struct which cannot be multiplied:

>> SPM.xX.K

ans = 

    HParam: 128
       row: [1x215 double]
        RT: 1.4000
        X0: [215x4 double]

Best,

Chao-Gan
 


On Thu, Oct 31, 2013 at 10:31 AM, MCLAREN, Donald <[log in to unmask]> wrote:
Try using:
pinv(K*W*X)*xX.V*pinv(K*W*X)' instead of (X'*X^(-1))

Then you should get the correct solution.

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Postdoctoral Research Fellow, GRECC, Bedford VA
Website: http://www.martinos.org/~mclaren
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
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On Wed, Oct 30, 2013 at 11:03 PM, YAN Chao-Gan <[log in to unmask]> wrote:
And here is an example based on 1st level results. The differences in T values are pretty big, although the pattern is similar.

I think I missed something in the code?
TData = (beta_0001*Contrast(1)+beta_0002*Contrast(2))./(sqrt(ResMS*(Contrast*(X'*X)^(-1)*Contrast')));

Thanks,

Chao-Gan


On Wed, Oct 30, 2013 at 10:50 PM, YAN Chao-Gan <[log in to unmask]> wrote:
Dear SPM experts,

Recently I would like to calculate the values based on the estimated files (skip spm_results_ui), I followed the formulas in Page 20 of www.fil.ion.ucl.ac.uk/~mgray/Presentations/2nd level analysis- Design contrast & inference.ppt

X=SPM.xX.X;
Contrast=[1 -1];
TData = (beta_0001*Contrast(1)+beta_0002*Contrast(2))./(sqrt(ResMS*(Contrast*(X'*X)^(-1)*Contrast')));

However, the results (Left in the attached figure) is slightly different from the results generated by spm_results_ui (Right in the attached figure). Furthermore, seems this difference is even bigger when applied to 1st level results (maybe due to the hidden high-pass filtering regressors?)

I am wondering if the differences are caused simply by data precision, or I made something wrong in the above code?

Thanks a lot for your time and help in advance!

Best,

Chao-Gan


--
Chao-Gan YAN, Ph.D.
Research Scientist
The Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962
Center for the Developing Brain, Child Mind Institute, 445 Park Avenue, New York, NY 10022
The Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, New York University Child Study Center, New York, NY 10016
-
Initiating + Service Node
The R-fMRI Network (RFMRI.ORG)



--
Chao-Gan YAN, Ph.D.
Research Scientist
The Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962
Center for the Developing Brain, Child Mind Institute, 445 Park Avenue, New York, NY 10022
The Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, New York University Child Study Center, New York, NY 10016
-
Initiating + Service Node
The R-fMRI Network (RFMRI.ORG)




--
Chao-Gan YAN, Ph.D.
Research Scientist
The Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962
Center for the Developing Brain, Child Mind Institute, 445 Park Avenue, New York, NY 10022
The Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, New York University Child Study Center, New York, NY 10016
-
Initiating + Service Node
The R-fMRI Network (RFMRI.ORG)




--
Chao-Gan YAN, Ph.D.
Research Scientist
The Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962
Center for the Developing Brain, Child Mind Institute, 445 Park Avenue, New York, NY 10022
The Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, New York University Child Study Center, New York, NY 10016
-
Initiating + Service Node
The R-fMRI Network (RFMRI.ORG)




--
Chao-Gan YAN, Ph.D.
Research Scientist
The Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962
Center for the Developing Brain, Child Mind Institute, 445 Park Avenue, New York, NY 10022
The Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, New York University Child Study Center, New York, NY 10016
-
Initiating + Service Node
The R-fMRI Network (RFMRI.ORG)



--
Chao-Gan YAN, Ph.D.
Research Scientist
The Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962
Center for the Developing Brain, Child Mind Institute, 445 Park Avenue, New York, NY 10022
The Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, New York University Child Study Center, New York, NY 10016
-
Initiating + Service Node
The R-fMRI Network (RFMRI.ORG)




--
Chao-Gan YAN, Ph.D.
Research Scientist
The Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962
Center for the Developing Brain, Child Mind Institute, 445 Park Avenue, New York, NY 10022
The Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, New York University Child Study Center, New York, NY 10016
-
Initiating + Service Node
The R-fMRI Network (RFMRI.ORG)




--
Chao-Gan YAN, Ph.D.
Research Scientist
The Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962
Center for the Developing Brain, Child Mind Institute, 445 Park Avenue, New York, NY 10022
The Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, New York University Child Study Center, New York, NY 10016
-
Initiating + Service Node
The R-fMRI Network (RFMRI.ORG)
http://rfmri.org/yan
http://scholar.google.com/citations?user=lJQ9B58AAAAJ&hl=en