Steve,

You are right on the contrast. However, one needs to make sure they do not interpret the statistics from the contrast. That is to say, you should not interpret the mean pre contrast as regions that are significantly positive/negative from this model. It is fine to use the contrast as a mask for positive versus negative areas with a p-value of .5.

The reason you can not interpet the regions as significantly positive/negative is that the statistics for between-subject effects (e.g. mean pre or mean post in this model) is that the error term for the paired t-test is the within-subject error.

Best Regards, Donald McLaren
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Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
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On Tue, Dec 6, 2011 at 4:25 AM, Stephen Smith <[log in to unmask]> wrote:

Hi - - if you expand on how this modelling is working you'll see (e.g. for two subjects):

S1pre = PE1 + PE2

S2pre = PE1 + PE3

S1post = -PE1 + PE2

S2post = -PE1 + PE3

so if you want MEANpre = (S1pre+S2pre)/2

that would be (2*PE1 + PE2 + PE3) / 2 =  PE1 + PE2/2 + PE3/2

ie a contrast of [1 0.5 0.5]

I *think* this is right?

Cheers.

On 5 Dec 2011, at 18:53, Lorena Jimenez-Castro wrote:

Dear FSL experts,

I have sent you an email last week and I have not received a reply yet, So I would appreciate any advice you could give me regarding my previous post (Please see the email below)

Thank you very much

Lorena








Subject: GLM

From: Lorena Jimenez-Castro <[log in to unmask]>

Reply-To: FSL - FMRIB's Software Library <[log in to unmask]>

Date: Mon, 28 Nov 2011 22:37:43 +0000

Reply

Dear FSL experts,

I would like to verify if I am on  the right track setting up a design for a resting state seed based analysis on a group of 13 subjects with pre and post treatment data. I am trying to set up the design matrix and design contrast to run the higher-level analysis using FEAT. I know I have to use “one group paired t test†but also I want to use “contrast masking†to mask the positive and negative co-activation, that is why I also need to get from the pre and post groups the correspond negative co-activation mean and positive co-activation mean.

I would like to know if the following designs are correct:

Design matrix:

subject group EV1 EV2 EV3 EV4  .  .  . EV13     EV14
1 1          1 1  0  0           0 0
2 1          1 0  1  0           0 0
3 1          1 0  0  1           0 0
.
.
13 1        1 0 0 0           0 1
14 1       -1 1 0 0           0 0
15 1       -1 0 1 0           0 0
.
.
26 1       -1 0 0 0            0 1


Design Contrast:

                                                         EV1 EV2 EV3                            EV12 EV13 EV14
c1: preTx_ positive correlation > post_tx     1  0 0   .  .  .  ..          0          0        0
C2:post_tx positive correlation > pre_tx -1  0 0                  0          0        0
C3:pre_tx positive correlation mean          1  1 1                   1          1         1
C4:post_tx positive correlation mean         -1  1 1                   1          1         1
C5: pre_tx negative correlation mean  1  -1 -1                  -1         -1        -1
C6: post_tx negative correlation mean  -1  -1 -1                  -1         -1        -1
C7: preTx_ negative correlation > post_tx   -1  0 0                   0          0          0
C8: post_tx negative correlation > pre_tx   1  0 0                   0          0          0



Contrast masking:

C1 with C3>0 = real preTx_ positive correlation > post_tx
C2 with C4>0 =real  post_tx positive correlation > pre_tx
C7 with C5>0 = real  preTx_ negative correlation > post_tx
C8 with C6>0= real  post_tx negative correlation > pre_tx

Am I doing the right thing?

Any hint on this would be greatly appreciated

Thanks

Lorena

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