help > NBS- no significant result interpretation and design matrix cross-validation
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Apr 20, 2018  03:04 PM | hex_dex - AUTH
NBS- no significant result interpretation and design matrix cross-validation
Hello! I am new to NBS and though I find it an amazing tool I still am not sure I completely do understand how to set it. I have the eeg data from 2 groups (A & B) which represent different activities and for each group I have 2 time points (pre & post). For each group I have the eeg of 20 people so in total, I have 40 sets of 2 time points. My connectivity matrix is 22*22*80 and my design matrix is 80*42. Specifically, my design matrix is 

  Time    Time*Group   Regression#1   Regression#2   ...  Regression #39 Regression#40
line1     0 0                      1 0  0 0
line2     1 1 1 0  0 0
line3     0 0 0 1  0 0
line 4    1 1 0 1  0 0
         . .
       . .
     . .
line 37   0 1 0 0  1 0
line 38   1 0 0 0  1 0
line 39   0 1 0 0  0 1
line 40   1 0 0 0  0 1

I want a time x group analysis and my contrast matrix is [ 0 1 0 0 ...0] which consists of 42 columns. I run an F-test. However I take no significant results and I think I am doing something wrong. Is my design matrix ok? Do you see anything wrong?

Best regards,
Katerina
Apr 22, 2018  12:04 AM | Andrew Zalesky
RE: NBS- no significant result interpretation and design matrix cross-validation
Hi Katerina,
I don't think that your Time*Group column is correct. The Time* Group column should be the multiplication of the Group and Time columns. Let's assume line 1-4 is labelled group -1 and line 37-40 is labelled group 1. In this case,

       GROUP   TIME  GROUP*TIME
line1     -1      0    0
line2     -1      1      -1
line3     -1      0     0
line4     -1      1      -1
line37   1        0      0
line38   1       1        1
line39   1       0       0
line40   1       1       1

Remember to remove the Group column from the design matrix. I have only shown it here so you can see how the Group and Time columns are multiplied to get the interaction effect.

Andrew

Originally posted by hex_dex:
Hello! I am new to NBS and though I find it an amazing tool I still am not sure I completely do understand how to set it. I have the eeg data from 2 groups (A & B) which represent different activities and for each group I have 2 time points (pre & post). For each group I have the eeg of 20 people so in total, I have 40 sets of 2 time points. My connectivity matrix is 22*22*80 and my design matrix is 80*42. Specifically, my design matrix is 

  Time    Time*Group   Regression#1   Regression#2   ...  Regression #39 Regression#40
line1     0 0                      1 0  0 0
line2     1 1 1 0  0 0
line3     0 0 0 1  0 0
line 4    1 1 0 1  0 0
         . .
       . .
     . .
line 37   0 1 0 0  1 0
line 38   1 0 0 0  1 0
line 39   0 1 0 0  0 1
line 40   1 0 0 0  0 1

I want a time x group analysis and my contrast matrix is [ 0 1 0 0 ...0] which consists of 42 columns. I run an F-test. However I take no significant results and I think I am doing something wrong. Is my design matrix ok? Do you see anything wrong?

Best regards,
Katerina
Apr 22, 2018  08:04 PM | hex_dex - AUTH
RE: NBS- no significant result interpretation and design matrix cross-validation
Dear Andrew,

thank you very much for your response. I prepared the design matrix under your instructions and although I still don't get any significant results, the p-values appear improved now.:-)

Best regards,
Katerina
Apr 22, 2018  11:04 PM | Andrew Zalesky
RE: NBS- no significant result interpretation and design matrix cross-validation
Hi Katerina,

this is possible and a valid result. You might want to experiment with different thresholds.

Andrew
Originally posted by hex_dex:
Dear Andrew,

thank you very much for your response. I prepared the design matrix under your instructions and although I still don't get any significant results, the p-values appear improved now.:-)

Best regards,
Katerina
Apr 23, 2018  10:04 AM | hex_dex - AUTH
RE: NBS- no significant result interpretation and design matrix cross-validation
Hi Andrew,

once again thank you. I experimented with loads of thresholds and I seem to get a graph for a threshold of 1.18. Does this look a valid threshold or is it too low?
Katerina


Originally posted by Andrew Zalesky:
Hi Katerina,

this is possible and a valid result. You might want to experiment with different thresholds.

Andrew