NITRC Graph Theory GLM (GTG) MATLAB Toolbox Forum: help

filename error

li nan — Wed, 29 Apr 2020 7:15:06 GMT

Dear professors,
when I do the first step, I had some problems with filename error.After I set parameters ande click 'start',MATLAB was reporting that 'Error using fopen The file name contains characters that are not contained in the filesystem encoding.
Certain operations may not work as expected'.
I try to change all the filename but it did't work.I use matlab R2018A and GTG_beta_045.Here are some pictures
with my work.

RE: how to write design matrix for permutation analysis

Jeffrey Spielberg — Thu, 12 Mar 2020 13:49:10 GMT

[i]Originally posted by li wenlong:[/i][quote]The program will recognize that you have a repeated measure, because the last dimension of your input matrix is 2 (not 1). Therefore, you don't have to explicitly do anything beyond setting up your input matrix correctly. In other words, the toolbox will automatically compute between, within, and (if needed) between x within interactions. Therefore, you should just set up your design/contrast matrices as if you did not have a repeated measure and those effects will be automatically computed.[/quote][quote]
[/quote][quote]If you just want to compare the repeated measure, just create a variable that is one column of all ones and enter use that as your predictor. This will setup a within-subjects comparison (equivalent to a paired t-test) that tests whether the mean of one condition differs from the mean of the other. You don't need a separate 'group' predictor, unless you have different groups. The contrast would just be 1. You also don't need to do the -1 contrast, unless you want a 1-tailed test (you will be given the 1-tailed and 2-tailed p-values). [/quote][quote]
[/quote][quote]
[/quote][quote]
[/quote][quote]Dear Dr. Spielberg,

Forgive my ignorance, I meet the same questions and cannot fully understand what you replied. So, can we give an example：

If the connectivity matrix is 90*90*5*2 , now we set a design matrices:
(5 subjects: sub1/sub2/sub3/sub4/sub5; 2 conditions: A/B )

we plan to compare A > B, should predictors be set like this?

predictors: 1 1 1 1 1
options: Contrasts
contrast: [1 0] (group is 1, intercept is 0)
A < B contrast: [-1 0]

What do these two columns represent? And how should I set the predictors and contrast to find the correction between A > B and scale score?
Best wishes,
Wenlong[/quote]

RE: how to write design matrix for permutation analysis

li wenlong — Wed, 11 Mar 2020 13:22:15 GMT

Dear Dr. Spielberg,

Forgive my ignorance, I meet the same questions and cannot fully understand what you replied. So, can we give an example：

If the connectivity matrix is 90*90*5*2 , now we set a design matrices:
(5 subjects: sub1/sub2/sub3/sub4/sub5; 2 conditions: A/B )

we plan to compare A > B, should predictors be set like this?

predictors: 1 1 1 1 1
options: Contrasts
contrast: [1 0] (group is 1, intercept is 0)
A < B contrast: [-1 0]

What do these two columns represent? And how should I set the predictors and contrast to find the correction between A > B and scale score?
Best wishes,
Wenlong

design matrix in permutation analysis

xiu ling — Fri, 07 Jun 2019 23:50:10 GMT

Dear Jeffrey Spielberg,
Thank you very much for your help. When the design matrix is n*n*number of subjects*conditions, I still cannot figure out the corresponding design matrix and contrast. In your article named "Flexible brain network reconfiguration supporting inhibitory control" which published in 2015 in PNAS, each graph property was entered as a repeated-measure dependent variable into the GTG toolbox, with task condition (incongruent vs. congruent) as the repeated measure. Could you teach me how to write the design matrix and contrast when you analyze the data to compare the two conditions in your article? I mean if you could give me an example like the one below that from NBS.

design matrix:
1 0 0 0 1
0 1 0 0 1
0 0 1 0 1
0 0 0 1 1
1 0 0 0 -1
0 1 0 0 -1
0 0 1 0 -1
0 0 0 1 -1
contrast:
0 0 0 0 1

Thank you again. I am looking forward to hearing from you.

Best wishes,
xiuling

RE: how to write design matrix for permutation analysis

Jeffrey Spielberg — Mon, 03 Jun 2019 13:36:28 GMT

You don't have to account for the repeated measure in the design matrix - the toolbox will recognize that there is a repeated measure because of the dimensions of your input data and automatically set up between, within, and (if needed) between x within interactions. Therefore, you should just set up your design/contrast matrices as if you did not have a repeated measure and those effects will be automatically computed.

how to write design matrix for permutation analysis

xiu ling — Fri, 31 May 2019 11:40:03 GMT

Hi Dr. Spielberg,

Thank you for developing this tool!
I'm having some difficulty at the run permutation analysis stage.

If the format of connectivity matrix is n(ROI)*n(ROI)*number of subject*2(condition), how should I write the design matrix and contrast vector in the stage of run permutation analysis to contrast the graph properties of 2 conditions.

I am looking forward to hearing from you. Thank you for your help.
xiuling

error opening/runing permutation analyses

li wenlong — Tue, 22 May 2018 2:33:35 GMT

Dear experts,

There are some errors when I open or run the stage 4, my computer system is windows 10 and GTG is GTG_beta_0.45.

1.Using matlab R2017a or matlab R2014b,I can not open the GUI fully(screenshot attached).

2.Using matlab R2012a, I can open the GUI fully but can not use parallel processing. I know that PCT was not available until matlab 2013, but it doesn't work even if PCT Workers is set to 1, the following is Matlab error message(PCT Workers=1):

Undefined variable "parallel" or class "parallel.internal.convertStringToChar".

Error in gcp (line 38)
convertedArgs = parallel.internal.convertStringToChar(varargin);
Error in GTG_runpermanalyses_GUI>Start_pushbutton_Callback (line 1728)
if isempty(gcp('nocreate'))
Error in gui_mainfcn (line 96)
feval(varargin{:});
Error in GTG_runpermanalyses_GUI (line 98)
gui_mainfcn(gui_State, varargin{:});
Error in
@(hObject,eventdata)GTG_runpermanalyses_GUI('Start_pushbutton_Callback',hObject,eventdata,guidata(hObject))
Error while evaluating uicontrol Callback

RE: ROC curve analysis

Jeffrey Spielberg — Mon, 27 Nov 2017 16:42:45 GMT

You will want to extract the clustering coefficient values for each ROI (for each participant). These are in the X_propcalc.mat file (or the X_permout.mat file). You will need to figure out the index # for the ROIs you want. To do this, look in out.ROI_labels and find the lines corresponding to the two ROIs. The line numbers will be the index values you need. Note that this is not necessarily the same as the value of that ROI in the original atlas you used. For example, if the label for the dlPFC ROI was on line 30 of out.ROI_labels, the values you want can be extracted by running:

dlPFC_CCvals = out.fullmat_graph_meas.clust_coef_pos(:,30,:);

This will give you an n x 1 vector containing the CC values for the dlPFC ROI (if you want the Zen-Horvath CC, use clust_coef_ZH_pos instead of clust_coef_pos).

ROC curve analysis

Hua Yu — Sun, 26 Nov 2017 14:00:50 GMT

Dear experts,

Recently, I made a two-sample t test to compare the clustering coefficient between the patients' and HCs' groups, and I found some group difference in two nodes , the DLPFC and vmPFC. Now I want to extract the value to do some simple ROC curve analysis to see if these group differences could clarify the two groups. Do you know what kind of value should I use?

Looking forward for your reply,

Thanks a lot,

Hua

RE: covariates

Heena Manglani — Fri, 17 Nov 2017 20:47:44 GMT

Commenting out matlabpool and parpool has not worked. Do you have other suggestions I could try?

Thanks,
Heena