scripting help for 2 within subject factor design

Payal Arya

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May 24, 2019, 1:43:15 PM5/24/19

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Hi

I am new user for GLM_flex as well as Matlab. looking at the example onthe google groups. I have tried to write the script for my 2 within subject factor design.

Briefly I have age group and song type as two within subjectfactors.

I have one group of 16 birds which were scanned at different age during their development. At every scan day, they were played two types of songs. making song as secondwithin subject factor.

I used SPM for 1st level analysis and generated two t-contrast images per subject at every age. for example Bird1 imaged at 55day - song played- TUT1 and TUT2. The two contrast images are- TUT1-Rest,TUT2-Rest.Same bird is then scanned at day 90 and two t-contrast images are generated.

Now I am trying to use GLM_FlexFast4 for analysing 2nd level anova for main effect of age , main effect of song played and interaction between two factors.

Following is the script I wrote looking at other examples.

I am stuck at defining which files are for which factor in data structure .Please can someone help me.

Scans={

'G02_55_TUT1.nii';

'G02_55_TUT2.nii';

'G02_90_TUT1.nii';

'G02_90_TUT2.nii'} just for illuastrative purpose how scans are named for each subject

Scans = dir_wfp('/home/gobeslab/maledataset/2nd level/GLM_flex/2x2within/*.nii');
delete *.mat *.nii
clear dat
dat.fn = [];
dat.Group=[];
dat.Factor1 = []; % 2 levels
dat.Factor2 = []; % 2 levels
dat.SS = []; % subject

for ii = 1:numel(Scans)
    [a b c] = fileparts (lower(Scans{11}));
    tmp = regexp(b, '_','split');

    dat.fn{ii} = Scans{ii};
    dat.SS {ii,1} = tmp{1} ;
    dat.Factor1 {ii,1} = tmp{2};
    dat.Factor2 {ii,1} = tmp{3};
end

dat.Group = cell(64,1);
dat.Factor1 =
clear I;
I.Scans = dat.fn ;
I.Model = 'Factor1 * Factor2 + random(SS|Factor1*Factor2)';
I.Data = dat;
I.outputDir = ['/home/gobeslab/maledataset/2nd level/GLM_flex/2x2within'] ;

I.RemoveOutliers = 0;
I.DoOnlyAll = 1;
I.estSmooth = 1;

I = GLM_Flex_Fast4(I)

thank you

Payal

Aaron Schultz

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May 24, 2019, 1:53:14 PM5/24/19

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Let's focus on the example you provided, dat would then be organized as follows:

dat = [];
dat.fn = {
'G02_55_TUT1.nii';
'G02_55_TUT2.nii';
'G02_90_TUT1.nii';
'G02_90_TUT2.nii'};
dat.Factor1 = {'55'
'55'
'90'
'90'};
dat.Factor2 = {'TUT1'
'TUT2'
'TUT1'
'TUT2'};
dat.SS = {'G02'
'G02'
'G02'
'G02'};

dat is effectively a table ( you can also use the matlab table object
instead of data structure if you prefer), if the first image is
G02_55_TUT1.nii, then we need to specify on the same row what subject
this is, what level of factor1 and what level of factor2.

If you repeated the above for each bird (remember to use full
filepaths for the file names), you would then be set to run:

I = [];

I.Scans = dat.fn ;
I.Model = 'Factor1 * Factor2 + random(SS|Factor1*Factor2)';
I.Data = dat;
I.outputDir = ['/home/gobeslab/maledataset/2nd level/GLM_flex/2x2within'] ;

I.RemoveOutliers = 0;
I.DoOnlyAll = 1;
I.estSmooth = 1;

I = GLM_Flex_Fast4(I)

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Payal Arya

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May 24, 2019, 3:37:18 PM5/24/19

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Hi Aaron

thanks for your input. I was able to run the script. After the script ran, I got a message saying writing out mat files and image files.

But I cannot see where it has written the output files.

I had mentioned the full path to the output directory.

Thanks for you help.

Following is the script I used

Scans = dir_wfp('/home/gobeslab/maledataset/2nd level/GLM_flex/2x2within/*.nii');
delete *.mat *.nii
clear dat
dat.fn = [];
dat.Group=[];
dat.Factor1 = []; % 2 levels
dat.Factor2 = []; % 2 levels
dat.SS = []; % subject

for ii = 1:numel(Scans)
[a b c] = fileparts (lower(Scans{11}));
tmp = regexp(b, '_','split');

dat.fn{ii} = Scans{ii};
dat.SS {ii,1} = tmp{1} ;
dat.Factor1 {ii,1} = tmp{2};
dat.Factor2 {ii,1} = tmp{3};
end

dat.fn = {'/home/gobeslab/maledataset/2nd level/GLM_flex/G02_55_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/G02_55_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/G02_90_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/G02_90_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/G07_55_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/G07_55_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/G07_90_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/G07_90_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/G08_55_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/G08_55_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/G08_90_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/G08_90_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/R143_55_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/R143_55_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/R143_90_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/R143_90_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/R144_55_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/R144_55_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/R144_90_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/R144_90_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/R145_55_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/R145_55_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/R145_90_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/R145_90_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S46_55_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S46_55_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S46_90_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S46_90_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S47_55_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S47_55_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S47_90_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S47_90_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S56_55_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S56_55_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S56_90_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S56_90_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S63_55_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S63_55_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S63_90_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S63_90_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S64_55_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S64_55_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S64_90_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S64_90_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S71_55_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S71_55_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S71_90_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S71_90_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S75_55_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S75_55_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S75_90_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S75_90_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S79_55_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S79_55_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S79_90_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S79_90_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S85_55_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S85_55_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S85_90_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/S85_90_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/Y98_55_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/Y98_55_TUT2.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/Y98_90_TUT1.nii'
'/home/gobeslab/maledataset/2nd level/GLM_flex/Y98_90_TUT2.nii'}

dat.Factor1 = { '55'
'55'
'90'
'90'

'55'
'55'
'90'
'90'
'55'
'55'
'90'
'90'
'55'
'55'
'90'
'90'
'55'
'55'
'90'
'90'
'55'
'55'
'90'
'90'
'55'
'55'
'90'
'90'
'55'
'55'
'90'
'90'
'55'
'55'
'90'
'90'
'55'
'55'
'90'
'90'
'55'
'55'
'90'
'90'
'55'
'55'
'90'
'90'
'55'
'55'
'90'
'90'
'55'
'55'
'90'
'90'
'55'
'55'
'90'
'90'

'55'
'55'
'90'
'90'};

dat.Factor2 = { 'TUT1'
'TUT2'
'TUT1'
'TUT2'

'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'
'TUT1'
'TUT2'

'TUT1'
'TUT2'
'TUT1'
'TUT2'
};

dat.SS = { 'G02'
'G02'
'G02'
'G02'

'G07'
'G07'
'G07'
'G07'
'G08'
'G08'
'G08'
'G08'
'R143'
'R143'
'R143'
'R143'
'R144'
'R144'
'R144'
'R144'
'R145'
'R145'
'R145'
'R145'
'S46'
'S46'
'S46'
'S46'
'S47'
'S47'
'S47'
'S47'
'S56'
'S56'
'S56'
'S56'
'S63'
'S63'
'S63'
'S63'
'S64'
'S64'
'S64'
'S64'
'S71'
'S71'
'S71'
'S71'
'S75'
'S75'
'S75'
'S75'
'S79'
'S79'
'S79'
'S79'
'S85'
'S85'
'S85'
'S85'
'Y98'
'Y98'
'Y98'
'Y98'

}

clear I;
I.Scans = dat.fn ;
I.Model = 'Factor1 * Factor2 + random(SS|Factor1*Factor2)';
I.Data = dat;
I.outputDir = '/home/gobeslab/maledataset/2nd level/GLM_flex/2x2within' ;

I.RemoveOutliers = 0;
I.DoOnlyAll = 1;
I.estSmooth = 1;

I = GLM_Flex_Fast4(I);

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Aaron Schultz

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May 24, 2019, 3:50:15 PM5/24/19

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They should have been written to I.outputDir.

To view this discussion on the web visit https://groups.google.com/d/msgid/fmri_matlab_tools/CALHNH7m2-ndWEpvTn6Mp_3BR%3DpT2twYJsRB%2BYH6Sd%2BSMmFCgCg%40mail.gmail.com.

Payal Arya

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May 24, 2019, 4:17:49 PM5/24/19

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Hi Aaron

Thanks for your reply..

I figured it out.

It was a silly error in the beginning of the script which deleted the mat file after analysis.

Thanks for your tool.i could run the two factor model..

I just have one question about the post hoc contrast.

The interaction between two factor was significant in my study. Now I want to which pairs are significant.can that be done in this tool?

Thanks

Payal

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Aaron Schultz

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May 28, 2019, 12:54:24 PM5/28/19

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You can prescribe post hocs, for any simple effect (effect of one
variable within one level of another variable)

I.PostHocs = {'Factor1$55|dat.Factor2$TUT1 #
Factor1$55|dat.Factor2$TUT2' 'PostHoc1';
'Factor1$90|dat.Factor2$TUT1 #
Factor1$90|dat.Factor2$TUT2' 'PostHoc2';
'Factor1$55|dat.Factor2$TUT1 #
Factor1$90|dat.Factor2$TUT1' 'PostHoc1';
'Factor1$55|dat.Factor2$TUT2 #
Factor1$90|dat.Factor2$TUT2' 'PostHoc2'};

The above will give you the pairwise comparisons for each of the 4 conditions.

PostHoc1 = difference in TUT within 55
PostHoc2 = difference in TUT within 90
PostHoc3 = difference in Time within TUT1
PostHoc4 = difference in Time within TUT2

Plotting the interaction effect will also make it fairly easy to see
the simple effects.

Also, plotting post hocs in FIVE is not enabled at this time.

-Aaron

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Payal Arya

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May 28, 2019, 3:38:01 PM5/28/19

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Hi Aaron

thank you for your reply. I was able to to run the posthocs.

I am not sure about the alpha levels. So just confirming once. When I change p =0.05 in the interface, is it showing the me clusters which are passed the FWE correction at p =0.05?

Thanks

Payal

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Aaron Schultz

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May 28, 2019, 3:41:36 PM5/28/19

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p=0.05 means the threshold is p<0.05 uncorrected for every voxel.
FIVE does not do any cluster-wise statistics. If smoothness estimates
were generated you can hit the FWE button to derive the FWE corrected
threshold for the alpha value specified in the menubar options.
Similarly you can do FDR correction (does not require smoothness
estimates).

-Aaron

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Payal Arya

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May 28, 2019, 3:54:41 PM5/28/19

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On Tuesday, May 28, 2019 at 3:41:36 PM UTC-4, Aaron Schultz wrote:

p=0.05 means the threshold is p<0.05 uncorrected for every voxel.
FIVE does not do any cluster-wise statistics. If smoothness estimates
were generated you can hit the FWE button to derive the FWE corrected
threshold for the alpha value specified in the menubar options.
Similarly you can do FDR correction (does not require smoothness
estimates).

-Aaron

On Tue, May 28, 2019 at 3:38 PM Payal Arya <pa...@wellesley.edu> wrote:
>
> Hi Aaron

thanks again for the support.

I did as you suggested ,I keep gettingawarning message in the matlab command window

" Warning: No voxels beyond the corrected threshold of 4.863

In FIVE/correctThresh (line 3992) "

I am not sure what this means?

thanks

Payal

Aaron Schultz

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May 28, 2019, 3:58:38 PM5/28/19

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That means that no voxel survives full FWE correction, which would
require a t-value greater than 4.863.

This can be adjusted to some extent by masking the data, though in
this case the mask should be applied from the outset of the analysis
so that only voxels within the mask are analyzed. That will lower the
number of voxels being tested are should result in a lest stringent
FWE correction.

-Aaron

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Payal Arya

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May 29, 2019, 10:31:55 AM5/29/19

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Hi Aaron

As you said a mask would help, would it be possible to do post-hoc t-tests using ROI based on anatomical structures and then do FWe correction?

thanks

Payal

Aaron Schultz

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May 29, 2019, 8:11:44 PM5/29/19

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That could be done but would require different code and decisions about whether to average time series or statistics.

-Aaron

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Payal Arya

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May 30, 2019, 4:27:27 PM5/30/19

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Hi Aaron

thank you so much for your support. when you said I could use a mask at the outset of the analysis, that would be doable in the current code? But it is not advisable to do the analysis on small ROI mask while setting up the model?

Thank you

Payal

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Aaron Schultz

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May 30, 2019, 4:45:19 PM5/30/19

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You shouldn't be using masks to p-hack. Using a gray matter mask for
example would make sense, and would prevent testing of voxels that are
in areas that would not be interpreted as real effects.

-Aaron

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Payal Arya

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Jun 3, 2019, 10:54:15 AM6/3/19

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Hi Aaron

Bird brain does not have clear white matter and gray matter boundaries like human brain does. Bird brain has certain nuclei in the brain which we identify in the atlas and basically looking for activation in those nuclei. I do have a mask which has those nuclei defined in it. I would like to try doing the analysis using that mask. Is it possible in GLM_Flexfast4. I have attached the nuclei segmentation along with this email just for your reference.

thank you for your help.

Payal

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map_za_ReSlice__ReSlice.nii

Aaron Schultz

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Jun 3, 2019, 12:38:03 PM6/3/19

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Ok, It looks like this is a label mask. You should turn it into a
binary mask, and then you can set I.Mask =
'/path/to/map_za_ReSlice__ReSlice.nii', and it should run just fine.

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