gift
Figure window was quit
Error using icatb_setup_analysis (line 77)
Output directory is not selected
Error in icatb_enterParametersGUI (line 17)
icatb_setup_analysis;
Error in gift>groupAnalysis_Callback (line 87)
icatb_enterParametersGUI;
Error in gui_mainfcn (line 95)
feval(varargin{:});
Error in gift (line 30)
gui_mainfcn(gui_State, varargin{:});
Error while evaluating UIControl Callback
Figure window was quit
Figure window was quit
Error using icatb_select_data>listCallback (line 344)
Data for dataset 2 is not selected
Figure window was quit
Error using icatb_select_data>listCallback (line 344)
Data for dataset 2 is not selected
Figure window was quit
Error using icatb_select_data>listCallback (line 344)
Data for dataset 3 is not selected
Figure window was quit
Error using icatb_select_data>listCallback (line 344)
Data for dataset 3 is not selected
Figure window was quit
Error using icatb_select_data>listCallback (line 344)
Data for dataset 2 is not selected
Error using icatb_select_data>listCallback (line 344)
Data for dataset 2 is not selected
Figure window was quit
Error using icatb_select_data>listCallback (line 344)
Data for dataset 1 is not selected
Creating Mask
Default mask includes voxels >= mean. Using first file of each subject to create default mask ...
Done Creating Mask
Parameters are saved in C:\Users\gehan\Documents\MRI\gift\test1_ica_parameter_info.mat
Please run the analysis using the same parameter file
GETTING DATA REDUCTION PARAMETERS----------------------
Reduction step 1 starts with 11 groups and gets reduced to 11 groups
-New Group #1: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #2: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #3: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #4: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #5: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #6: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #7: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #8: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #9: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #10: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #11: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
Reduction step 2 starts with 11 groups and gets reduced to 1 groups
-New Group #1: 11 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 38 principal components
The new group will have a total of 418 stacked principal components
This group will then be reduced to 25 principal components
END GETTING DATA REDUCTION PARAMETERS----------------------
Checking to make sure parameters are correct...
Checking mask
Checking principal component parameters
Done with parameter error check
STARTING DATA REDUCTION (PRINCIPAL COMPONENTS ANALYSIS)
--Extracting principal components for data reduction( time #1 )
--Doing pca on Subject #1 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #2 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #3 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #4 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #5 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #6 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #7 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #8 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #9 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #10 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #11 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Extracting principal components for data reduction( time #2 )
Loading data-set 1 ...
Loading data-set 2 ...
Loading data-set 3 ...
Loading data-set 4 ...
Loading data-set 5 ...
Loading data-set 6 ...
Loading data-set 7 ...
Loading data-set 8 ...
Loading data-set 9 ...
Loading data-set 10 ...
Loading data-set 11 ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
79.9882% of (non-zero) eigenvalues retained.
Done with data reduction( time # 2)
ENDING DATA REDUCTION (PRINCIPAL COMPONENTS ANALYSIS)
Using skewness of the distribution to determine the sign of the components ...
Changing sign of component 1
Changing sign of component 3
Changing sign of component 5
Changing sign of component 7
Changing sign of component 8
Changing sign of component 9
Changing sign of component 10
Changing sign of component 11
Changing sign of component 12
Changing sign of component 14
Changing sign of component 15
Changing sign of component 16
Changing sign of component 17
Changing sign of component 18
Changing sign of component 19
Changing sign of component 20
Changing sign of component 24
Changing sign of component 25
DONE CALCULATING GROUP ICA
STARTING BACK RECONSTRUCTION STEP
Using GICA Back Reconstruction Approach ...
Back reconstructing set 1
-done back reconstructing set 1
-saving back reconstructed ica data for set 1 -> test1_ica_br1.mat
Back reconstructing set 2
-done back reconstructing set 2
-saving back reconstructed ica data for set 2 -> test1_ica_br2.mat
Back reconstructing set 3
-done back reconstructing set 3
-saving back reconstructed ica data for set 3 -> test1_ica_br3.mat
Back reconstructing set 4
-done back reconstructing set 4
-saving back reconstructed ica data for set 4 -> test1_ica_br4.mat
Back reconstructing set 5
-done back reconstructing set 5
-saving back reconstructed ica data for set 5 -> test1_ica_br5.mat
Back reconstructing set 6
-done back reconstructing set 6
-saving back reconstructed ica data for set 6 -> test1_ica_br6.mat
Back reconstructing set 7
-done back reconstructing set 7
-saving back reconstructed ica data for set 7 -> test1_ica_br7.mat
Back reconstructing set 8
-done back reconstructing set 8
-saving back reconstructed ica data for set 8 -> test1_ica_br8.mat
Back reconstructing set 9
-done back reconstructing set 9
-saving back reconstructed ica data for set 9 -> test1_ica_br9.mat
Back reconstructing set 10
-done back reconstructing set 10
-saving back reconstructed ica data for set 10 -> test1_ica_br10.mat
Back reconstructing set 11
-done back reconstructing set 11
-saving back reconstructed ica data for set 11 -> test1_ica_br11.mat
DONE WITH BACK RECONSTRUCTION STEP
STARTING TO SCALE COMPONENT SETS
Computing offset using the mean component maps which will be subtracted to the subject component maps ...
Done
--Subject 1 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 1 session 1 in nifti format and as matlab file
--Subject 2 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 2 session 1 in nifti format and as matlab file
--Subject 3 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 3 session 1 in nifti format and as matlab file
--Subject 4 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 4 session 1 in nifti format and as matlab file
--Subject 5 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 5 session 1 in nifti format and as matlab file
--Subject 6 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 6 session 1 in nifti format and as matlab file
--Subject 7 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 7 session 1 in nifti format and as matlab file
--Subject 8 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 8 session 1 in nifti format and as matlab file
--Subject 9 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 9 session 1 in nifti format and as matlab file
--Subject 10 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 10 session 1 in nifti format and as matlab file
--Subject 11 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 11 session 1 in nifti format and as matlab file
DONE SCALING COMPONENTS
STARTING GROUP STATS STEP
--calculating mean ica component and timecourse
done calculating mean for session 1
done calculating mean for different sessions
--calculating variance and standard deviation of components
done calculating variance and standard deviation
--calculating tmaps
done calculating tmaps
...saving group stats data...
Comparing mean image with the aggregate ...
Value shows how much the mean component is close w.r.t aggregate component
The comparison value is found to be 0.97484
Computing spectra and FNC correlations of all subjects and sessions components ...
Timecourses will be despiked when computing FNC correlations...
Timecourses will be filtered when computing FNC correlations using HF cutoff of 0.15 Hz ...
......................................
Group ICA Error Information:
Undefined function 'abcdchk' for input arguments of type 'double'.
Error in ==> icatb_butter at 0
Error in ==> icatb_butter at 0
Error in ==> filt_data at 25
Error in ==> icatb_filt_data at 14
Error in ==> icatb_postprocess_timecourses at 194
Error in ==> icatb_groupStats at 503
Error in ==> icatb_runAnalysis at 444
Error in ==> runAnalysis_Callback at 95
Error in ==> gui_mainfcn at 95
Error in ==> gift at 30
......................................
Error using icatb_displayErrorMsg (line 23)
Error in icatb_runAnalysis (line 550)
icatb_displayErrorMsg;
Error in gift>runAnalysis_Callback (line 95)
icatb_runAnalysis;
Error in gui_mainfcn (line 95)
feval(varargin{:});
Error in gift (line 30)
gui_mainfcn(gui_State, varargin{:});
Error while evaluating UIControl Callback
Reading data from source directory C:\Users\gehan\Documents\MRI\analysis\FunImgARCW ...
The selected data folders are in the following order:
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_001
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_0010
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_0011
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_002
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_003
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_004
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_005
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_006
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_007
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_008
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_009
Please see the text file C:\Users\gehan\Documents\MRI\gift\test1\test1SelectedDataFolders.txt for the selected data folders in order
Creating Mask
Default mask includes voxels >= mean. Using first file of each subject to create default mask ...
Done Creating Mask
Parameters are saved in C:\Users\gehan\Documents\MRI\gift\test1\test1_ica_parameter_info.mat
Please run the analysis using the same parameter file
GETTING DATA REDUCTION PARAMETERS----------------------
Reduction step 1 starts with 11 groups and gets reduced to 11 groups
-New Group #1: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #2: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #3: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #4: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #5: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #6: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #7: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #8: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #9: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #10: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #11: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
Reduction step 2 starts with 11 groups and gets reduced to 1 groups
-New Group #1: 11 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 30 principal components
The new group will have a total of 330 stacked principal components
This group will then be reduced to 20 principal components
END GETTING DATA REDUCTION PARAMETERS----------------------
Checking to make sure parameters are correct...
Checking mask
Checking principal component parameters
Done with parameter error check
STARTING DATA REDUCTION (PRINCIPAL COMPONENTS ANALYSIS)
--Extracting principal components for data reduction( time #1 )
--Doing pca on Subject #1 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
92.0733% of (non-zero) eigenvalues retained.
--Doing pca on Subject #2 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
96.2107% of (non-zero) eigenvalues retained.
--Doing pca on Subject #3 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
92.0875% of (non-zero) eigenvalues retained.
--Doing pca on Subject #4 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
93.6451% of (non-zero) eigenvalues retained.
--Doing pca on Subject #5 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
96.4671% of (non-zero) eigenvalues retained.
--Doing pca on Subject #6 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
94.9076% of (non-zero) eigenvalues retained.
--Doing pca on Subject #7 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
94.9058% of (non-zero) eigenvalues retained.
--Doing pca on Subject #8 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
98.9318% of (non-zero) eigenvalues retained.
--Doing pca on Subject #9 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
96.3886% of (non-zero) eigenvalues retained.
--Doing pca on Subject #10 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
97.9203% of (non-zero) eigenvalues retained.
--Doing pca on Subject #11 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
92.0558% of (non-zero) eigenvalues retained.
--Extracting principal components for data reduction( time #2 )
Loading data-set 1 ...
Loading data-set 2 ...
Loading data-set 3 ...
Loading data-set 4 ...
Loading data-set 5 ...
Loading data-set 6 ...
Loading data-set 7 ...
Loading data-set 8 ...
Loading data-set 9 ...
Loading data-set 10 ...
Loading data-set 11 ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
46.8382% of (non-zero) eigenvalues retained.
Done with data reduction( time # 2)
ENDING DATA REDUCTION (PRINCIPAL COMPONENTS ANALYSIS)
Using skewness of the distribution to determine the sign of the components ...
Changing sign of component 2
Changing sign of component 3
Changing sign of component 5
Changing sign of component 6
Changing sign of component 7
Changing sign of component 8
Changing sign of component 13
Changing sign of component 14
Changing sign of component 19
Changing sign of component 20
DONE CALCULATING GROUP ICA
STARTING BACK RECONSTRUCTION STEP
Using GICA Back Reconstruction Approach ...
Back reconstructing set 1
-done back reconstructing set 1
-saving back reconstructed ica data for set 1 -> test1_ica_br1.mat
Back reconstructing set 2
-done back reconstructing set 2
-saving back reconstructed ica data for set 2 -> test1_ica_br2.mat
Back reconstructing set 3
-done back reconstructing set 3
-saving back reconstructed ica data for set 3 -> test1_ica_br3.mat
Back reconstructing set 4
-done back reconstructing set 4
-saving back reconstructed ica data for set 4 -> test1_ica_br4.mat
Back reconstructing set 5
-done back reconstructing set 5
-saving back reconstructed ica data for set 5 -> test1_ica_br5.mat
Back reconstructing set 6
-done back reconstructing set 6
-saving back reconstructed ica data for set 6 -> test1_ica_br6.mat
Back reconstructing set 7
-done back reconstructing set 7
-saving back reconstructed ica data for set 7 -> test1_ica_br7.mat
Back reconstructing set 8
-done back reconstructing set 8
-saving back reconstructed ica data for set 8 -> test1_ica_br8.mat
Back reconstructing set 9
-done back reconstructing set 9
-saving back reconstructed ica data for set 9 -> test1_ica_br9.mat
Back reconstructing set 10
-done back reconstructing set 10
-saving back reconstructed ica data for set 10 -> test1_ica_br10.mat
Back reconstructing set 11
-done back reconstructing set 11
-saving back reconstructed ica data for set 11 -> test1_ica_br11.mat
DONE WITH BACK RECONSTRUCTION STEP
STARTING TO SCALE COMPONENT SETS
Computing offset using the mean component maps which will be subtracted to the subject component maps ...
Done
--Subject 1 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 1 session 1 in nifti format and as matlab file
--Subject 2 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 2 session 1 in nifti format and as matlab file
--Subject 3 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 3 session 1 in nifti format and as matlab file
--Subject 4 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 4 session 1 in nifti format and as matlab file
--Subject 5 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 5 session 1 in nifti format and as matlab file
--Subject 6 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 6 session 1 in nifti format and as matlab file
--Subject 7 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 7 session 1 in nifti format and as matlab file
--Subject 8 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 8 session 1 in nifti format and as matlab file
--Subject 9 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 9 session 1 in nifti format and as matlab file
--Subject 10 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 10 session 1 in nifti format and as matlab file
--Subject 11 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 11 session 1 in nifti format and as matlab file
DONE SCALING COMPONENTS
STARTING GROUP STATS STEP
--calculating mean ica component and timecourse
done calculating mean for session 1
done calculating mean for different sessions
--calculating variance and standard deviation of components
done calculating variance and standard deviation
--calculating tmaps
done calculating tmaps
...saving group stats data...
Comparing mean image with the aggregate ...
Value shows how much the mean component is close w.r.t aggregate component
The comparison value is found to be 0.9857
Computing spectra and FNC correlations of all subjects and sessions components ...
Timecourses will be despiked when computing FNC correlations...
Timecourses will be filtered when computing FNC correlations using HF cutoff of 0.15 Hz ...
......................................
Group ICA Error Information:
Undefined function 'abcdchk' for input arguments of type 'double'.
Error in ==> icatb_butter at 0
Error in ==> icatb_butter at 0
Error in ==> filt_data at 25
Error in ==> icatb_filt_data at 14
Error in ==> icatb_postprocess_timecourses at 194
Error in ==> icatb_groupStats at 503
Error in ==> icatb_runAnalysis at 444
Error in ==> runAnalysis_Callback at 95
Error in ==> gui_mainfcn at 95
Error in ==> gift at 30
......................................
Error using icatb_displayErrorMsg (line 23)
Error in icatb_runAnalysis (line 550)
icatb_displayErrorMsg;
Error in gift>runAnalysis_Callback (line 95)
icatb_runAnalysis;
Error in gui_mainfcn (line 95)
feval(varargin{:});
Error in gift (line 30)
gui_mainfcn(gui_State, varargin{:});
Error while evaluating UIControl Callback
gift
gift
Reading data from source directory C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW ...
The selected data folders are in the following order:
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_001
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_0010
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_0011
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_002
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_003
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_004
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_005
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_006
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_007
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_008
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_009
Please see the text file C:\Users\gehan\Documents\MRI\New folder (9)\test1SelectedDataFolders.txt for the selected data folders in order
Creating Mask
Default mask includes voxels >= mean. Using first file of each subject to create default mask ...
Done Creating Mask
Parameters are saved in C:\Users\gehan\Documents\MRI\New folder (9)\test1_ica_parameter_info.mat
Please run the analysis using the same parameter file
GETTING DATA REDUCTION PARAMETERS----------------------
Reduction step 1 starts with 11 groups and gets reduced to 11 groups
-New Group #1: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #2: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #3: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #4: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #5: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #6: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #7: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #8: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #9: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #10: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #11: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
Reduction step 2 starts with 11 groups and gets reduced to 1 groups
-New Group #1: 11 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 30 principal components
The new group will have a total of 330 stacked principal components
This group will then be reduced to 20 principal components
END GETTING DATA REDUCTION PARAMETERS----------------------
Checking to make sure parameters are correct...
Checking mask
Checking principal component parameters
Done with parameter error check
STARTING DATA REDUCTION (PRINCIPAL COMPONENTS ANALYSIS)
--Extracting principal components for data reduction( time #1 )
--Doing pca on Subject #1 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
93.9941% of (non-zero) eigenvalues retained.
--Doing pca on Subject #2 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
96.924% of (non-zero) eigenvalues retained.
--Doing pca on Subject #3 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
92.6803% of (non-zero) eigenvalues retained.
--Doing pca on Subject #4 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
93.774% of (non-zero) eigenvalues retained.
--Doing pca on Subject #5 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
96.1738% of (non-zero) eigenvalues retained.
--Doing pca on Subject #6 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
94.2407% of (non-zero) eigenvalues retained.
--Doing pca on Subject #7 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
95.9145% of (non-zero) eigenvalues retained.
--Doing pca on Subject #8 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
99.0648% of (non-zero) eigenvalues retained.
--Doing pca on Subject #9 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
96.2079% of (non-zero) eigenvalues retained.
--Doing pca on Subject #10 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
97.6711% of (non-zero) eigenvalues retained.
--Doing pca on Subject #11 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
94.3467% of (non-zero) eigenvalues retained.
--Extracting principal components for data reduction( time #2 )
Loading data-set 1 ...
Loading data-set 2 ...
Loading data-set 3 ...
Loading data-set 4 ...
Loading data-set 5 ...
Loading data-set 6 ...
Loading data-set 7 ...
Loading data-set 8 ...
Loading data-set 9 ...
Loading data-set 10 ...
Loading data-set 11 ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
50.7147% of (non-zero) eigenvalues retained.
Done with data reduction( time # 2)
ENDING DATA REDUCTION (PRINCIPAL COMPONENTS ANALYSIS)
Using skewness of the distribution to determine the sign of the components ...
Changing sign of component 1
Changing sign of component 2
Changing sign of component 7
Changing sign of component 10
Changing sign of component 11
Changing sign of component 13
Changing sign of component 14
Changing sign of component 18
Changing sign of component 19
Changing sign of component 20
DONE CALCULATING GROUP ICA
STARTING BACK RECONSTRUCTION STEP
Using GICA Back Reconstruction Approach ...
Back reconstructing set 1
-done back reconstructing set 1
-saving back reconstructed ica data for set 1 -> test1_ica_br1.mat
Back reconstructing set 2
-done back reconstructing set 2
-saving back reconstructed ica data for set 2 -> test1_ica_br2.mat
Back reconstructing set 3
-done back reconstructing set 3
-saving back reconstructed ica data for set 3 -> test1_ica_br3.mat
Back reconstructing set 4
-done back reconstructing set 4
-saving back reconstructed ica data for set 4 -> test1_ica_br4.mat
Back reconstructing set 5
-done back reconstructing set 5
-saving back reconstructed ica data for set 5 -> test1_ica_br5.mat
Back reconstructing set 6
-done back reconstructing set 6
-saving back reconstructed ica data for set 6 -> test1_ica_br6.mat
Back reconstructing set 7
-done back reconstructing set 7
-saving back reconstructed ica data for set 7 -> test1_ica_br7.mat
Back reconstructing set 8
-done back reconstructing set 8
-saving back reconstructed ica data for set 8 -> test1_ica_br8.mat
Back reconstructing set 9
-done back reconstructing set 9
-saving back reconstructed ica data for set 9 -> test1_ica_br9.mat
Back reconstructing set 10
-done back reconstructing set 10
-saving back reconstructed ica data for set 10 -> test1_ica_br10.mat
Back reconstructing set 11
-done back reconstructing set 11
-saving back reconstructed ica data for set 11 -> test1_ica_br11.mat
DONE WITH BACK RECONSTRUCTION STEP
STARTING TO SCALE COMPONENT SETS
Computing offset using the mean component maps which will be subtracted to the subject component maps ...
Done
--Subject 1 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 1 session 1 in nifti format and as matlab file
--Subject 2 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 2 session 1 in nifti format and as matlab file
--Subject 3 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 3 session 1 in nifti format and as matlab file
--Subject 4 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 4 session 1 in nifti format and as matlab file
--Subject 5 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 5 session 1 in nifti format and as matlab file
--Subject 6 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 6 session 1 in nifti format and as matlab file
--Subject 7 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 7 session 1 in nifti format and as matlab file
--Subject 8 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 8 session 1 in nifti format and as matlab file
--Subject 9 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 9 session 1 in nifti format and as matlab file
--Subject 10 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 10 session 1 in nifti format and as matlab file
--Subject 11 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 11 session 1 in nifti format and as matlab file
DONE SCALING COMPONENTS
STARTING GROUP STATS STEP
--calculating mean ica component and timecourse
done calculating mean for session 1
done calculating mean for different sessions
--calculating variance and standard deviation of components
done calculating variance and standard deviation
--calculating tmaps
done calculating tmaps
...saving group stats data...
Comparing mean image with the aggregate ...
Value shows how much the mean component is close w.r.t aggregate component
The comparison value is found to be 0.99509
Computing spectra and FNC correlations of all subjects and sessions components ...
Timecourses will be despiked when computing FNC correlations...
Timecourses will be filtered when computing FNC correlations using HF cutoff of 0.15 Hz ...
......................................
Group ICA Error Information:
Undefined function 'abcdchk' for input arguments of type 'double'.
Error in ==> icatb_butter at 0
Error in ==> icatb_butter at 0
Error in ==> filt_data at 25
Error in ==> icatb_filt_data at 14
Error in ==> icatb_postprocess_timecourses at 194
Error in ==> icatb_groupStats at 503
Error in ==> icatb_runAnalysis at 444
Error in ==> runAnalysis_Callback at 95
Error in ==> gui_mainfcn at 95
Error in ==> gift at 30
......................................
Error using icatb_displayErrorMsg (line 23)
Error in icatb_runAnalysis (line 550)
icatb_displayErrorMsg;
Error in gift>runAnalysis_Callback (line 95)
icatb_runAnalysis;
Error in gui_mainfcn (line 95)
feval(varargin{:});
Error in gift (line 30)
gui_mainfcn(gui_State, varargin{:});
Error while evaluating UIControl Callback
gift
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
dear experts,
i do resting state functional MRI using 1.5 T machine and i am beginner using GIFT and get the following error can you help me?
dear experts:
Error in icatb_enterParametersGUI (line 17)
icatb_setup_analysis;
Error in gift>groupAnalysis_Callback (line 87)
icatb_enterParametersGUI;
Error in gui_mainfcn (line 95)
feval(varargin{:});
Error in gift (line 30)
gui_mainfcn(gui_State, varargin{:});
Error while evaluating UIControl Callback
Figure window was quit
Figure window was quit
Error using icatb_select_data>listCallback (line 344)
Data for dataset 2 is not selected
Figure window was quit
Error using icatb_select_data>listCallback (line 344)
Data for dataset 2 is not selected
Figure window was quit
Error using icatb_select_data>listCallback (line 344)
Data for dataset 3 is not selected
Figure window was quit
Error using icatb_select_data>listCallback (line 344)
Data for dataset 3 is not selected
Figure window was quit
Error using icatb_select_data>listCallback (line 344)
Data for dataset 2 is not selected
Error using icatb_select_data>listCallback (line 344)
Data for dataset 2 is not selected
Figure window was quit
Error using icatb_select_data>listCallback (line 344)
Data for dataset 1 is not selected
Creating Mask
Default mask includes voxels >= mean. Using first file of each subject to create default mask ...
Done Creating Mask
Parameters are saved in C:\Users\gehan\Documents\MRI\gift\test1_ica_parameter_info.mat
Please run the analysis using the same parameter file
Parameters file succesfully loaded
Opening run analysis GUI. Please wait ...
GETTING DATA REDUCTION PARAMETERS----------------------
Reduction step 1 starts with 11 groups and gets reduced to 11 groups
-New Group #1: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #2: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #3: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #4: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #5: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #6: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #7: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #8: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #9: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #10: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
-New Group #11: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 38 principal components
Reduction step 2 starts with 11 groups and gets reduced to 1 groups
-New Group #1: 11 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 38 principal components
The new group will have a total of 418 stacked principal components
This group will then be reduced to 25 principal components
END GETTING DATA REDUCTION PARAMETERS----------------------
Checking to make sure parameters are correct...
Checking mask
Checking principal component parameters
Done with parameter error check
STARTING DATA REDUCTION (PRINCIPAL COMPONENTS ANALYSIS)
--Extracting principal components for data reduction( time #1 )
--Doing pca on Subject #1 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #2 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #3 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #4 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #5 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #6 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #7 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #8 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #9 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #10 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Doing pca on Subject #11 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
100% of (non-zero) eigenvalues retained.
--Extracting principal components for data reduction( time #2 )
Loading data-set 1 ...
Loading data-set 2 ...
Loading data-set 3 ...
Loading data-set 4 ...
Loading data-set 5 ...
Loading data-set 6 ...
Loading data-set 7 ...
Loading data-set 8 ...
Loading data-set 9 ...
Loading data-set 10 ...
Loading data-set 11 ...
Using Eigen Value Decomposition ...
Covariance matrix size is 71 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
79.9882% of (non-zero) eigenvalues retained.
Done with data reduction( time # 2)
ENDING DATA REDUCTION (PRINCIPAL COMPONENTS ANALYSIS)
STARTING GROUP ICA STEP
Using spatial ica ...
Number of times ICA will run is 1
Run 1 / 1
Input data size [25,71] = 25 channels, 71 frames.
Finding 25 ICA components using logistic ICA.
Initial learning rate will be 0.00466, block size 4.
Learning rate will be multiplied by 0.9 whenever angledelta >= 60 deg.
Training will end when wchange < 1e-06 or after 512 steps.
Online bias adjustment will be used.
Removing mean of each channel ...
Not removing mean of each channel!!!
Final training data range: -4.91186 to 3.94865
Computing the sphering matrix...
Starting weights are the identity matrix ...
Sphering the data ...
Beginning ICA training ...
step 1 - lrate 0.004660, wchange 0.502347
step 2 - lrate 0.004660, wchange 0.290478
step 3 - lrate 0.004660, wchange 0.169014, angledelta 57.3 deg
step 4 - lrate 0.004660, wchange 0.258932, angledelta 71.1 deg
step 5 - lrate 0.004194, wchange 0.211452, angledelta 79.0 deg
step 6 - lrate 0.003775, wchange 0.145158, angledelta 75.3 deg
step 7 - lrate 0.003397, wchange 0.147465, angledelta 63.7 deg
step 8 - lrate 0.003057, wchange 0.154712, angledelta 92.0 deg
step 9 - lrate 0.002752, wchange 0.073239, angledelta 77.4 deg
step 10 - lrate 0.002477, wchange 0.057438, angledelta 61.3 deg
step 11 - lrate 0.002229, wchange 0.030905, angledelta 54.9 deg
step 12 - lrate 0.002229, wchange 0.052305, angledelta 70.2 deg
step 13 - lrate 0.002006, wchange 0.029043, angledelta 65.8 deg
step 14 - lrate 0.001805, wchange 0.028002, angledelta 51.4 deg
step 15 - lrate 0.001805, wchange 0.025693, angledelta 44.1 deg
step 16 - lrate 0.001805, wchange 0.024127, angledelta 44.7 deg
step 17 - lrate 0.001805, wchange 0.038375, angledelta 54.1 deg
step 18 - lrate 0.001805, wchange 0.038327, angledelta 57.9 deg
step 19 - lrate 0.001805, wchange 0.030858, angledelta 47.1 deg
step 20 - lrate 0.001805, wchange 0.032243, angledelta 50.2 deg
step 21 - lrate 0.001805, wchange 0.030432, angledelta 50.7 deg
step 22 - lrate 0.001805, wchange 0.033305, angledelta 54.6 deg
step 23 - lrate 0.001805, wchange 0.032198, angledelta 62.6 deg
step 24 - lrate 0.001625, wchange 0.028622, angledelta 62.2 deg
step 25 - lrate 0.001462, wchange 0.030154, angledelta 65.9 deg
step 26 - lrate 0.001316, wchange 0.025722, angledelta 78.7 deg
step 27 - lrate 0.001185, wchange 0.023005, angledelta 82.5 deg
step 28 - lrate 0.001066, wchange 0.010949, angledelta 70.6 deg
step 29 - lrate 0.000959, wchange 0.006935, angledelta 51.6 deg
step 30 - lrate 0.000959, wchange 0.006351, angledelta 47.5 deg
step 31 - lrate 0.000959, wchange 0.010831, angledelta 56.4 deg
step 32 - lrate 0.000959, wchange 0.008047, angledelta 48.4 deg
step 33 - lrate 0.000959, wchange 0.008563, angledelta 55.4 deg
step 34 - lrate 0.000959, wchange 0.007829, angledelta 48.4 deg
step 35 - lrate 0.000959, wchange 0.010469, angledelta 64.1 deg
step 36 - lrate 0.000864, wchange 0.007972, angledelta 65.1 deg
step 37 - lrate 0.000777, wchange 0.005104, angledelta 53.8 deg
step 38 - lrate 0.000777, wchange 0.004924, angledelta 52.4 deg
step 39 - lrate 0.000777, wchange 0.008782, angledelta 63.1 deg
step 40 - lrate 0.000699, wchange 0.005566, angledelta 65.9 deg
step 41 - lrate 0.000629, wchange 0.002535, angledelta 57.4 deg
step 42 - lrate 0.000629, wchange 0.003493, angledelta 60.5 deg
step 43 - lrate 0.000567, wchange 0.002250, angledelta 55.6 deg
step 44 - lrate 0.000567, wchange 0.003391, angledelta 58.8 deg
step 45 - lrate 0.000567, wchange 0.002644, angledelta 34.3 deg
step 46 - lrate 0.000567, wchange 0.004001, angledelta 70.2 deg
step 47 - lrate 0.000510, wchange 0.003879, angledelta 81.1 deg
step 48 - lrate 0.000459, wchange 0.001259, angledelta 76.2 deg
step 49 - lrate 0.000413, wchange 0.001081, angledelta 39.9 deg
step 50 - lrate 0.000413, wchange 0.002138, angledelta 57.9 deg
step 51 - lrate 0.000413, wchange 0.001730, angledelta 49.2 deg
step 52 - lrate 0.000413, wchange 0.000991, angledelta 42.5 deg
step 53 - lrate 0.000413, wchange 0.002593, angledelta 59.1 deg
step 54 - lrate 0.000413, wchange 0.001667, angledelta 63.3 deg
step 55 - lrate 0.000372, wchange 0.000871, angledelta 67.8 deg
step 56 - lrate 0.000335, wchange 0.000708, angledelta 50.3 deg
step 57 - lrate 0.000335, wchange 0.002180, angledelta 78.3 deg
step 58 - lrate 0.000301, wchange 0.000539, angledelta 83.7 deg
step 59 - lrate 0.000271, wchange 0.000943, angledelta 67.0 deg
step 60 - lrate 0.000244, wchange 0.000277, angledelta 66.7 deg
step 61 - lrate 0.000219, wchange 0.000605, angledelta 69.1 deg
step 62 - lrate 0.000198, wchange 0.000472, angledelta 78.8 deg
step 63 - lrate 0.000178, wchange 0.000356, angledelta 71.1 deg
step 64 - lrate 0.000160, wchange 0.000159, angledelta 54.4 deg
step 65 - lrate 0.000160, wchange 0.000196, angledelta 56.5 deg
step 66 - lrate 0.000160, wchange 0.000199, angledelta 63.2 deg
step 67 - lrate 0.000144, wchange 0.000295, angledelta 76.0 deg
step 68 - lrate 0.000130, wchange 0.000081, angledelta 74.0 deg
step 69 - lrate 0.000117, wchange 0.000203, angledelta 72.9 deg
step 70 - lrate 0.000105, wchange 0.000093, angledelta 78.2 deg
step 71 - lrate 0.000094, wchange 0.000138, angledelta 73.0 deg
step 72 - lrate 0.000085, wchange 0.000036, angledelta 66.5 deg
step 73 - lrate 0.000077, wchange 0.000068, angledelta 63.0 deg
step 74 - lrate 0.000069, wchange 0.000024, angledelta 62.7 deg
step 75 - lrate 0.000062, wchange 0.000051, angledelta 66.8 deg
step 76 - lrate 0.000056, wchange 0.000020, angledelta 72.7 deg
step 77 - lrate 0.000050, wchange 0.000030, angledelta 70.7 deg
step 78 - lrate 0.000045, wchange 0.000023, angledelta 30.5 deg
step 79 - lrate 0.000045, wchange 0.000010, angledelta 68.2 deg
step 80 - lrate 0.000041, wchange 0.000014, angledelta 58.6 deg
step 81 - lrate 0.000041, wchange 0.000009, angledelta 48.3 deg
step 82 - lrate 0.000041, wchange 0.000010, angledelta 51.3 deg
step 83 - lrate 0.000041, wchange 0.000019, angledelta 57.3 deg
step 84 - lrate 0.000041, wchange 0.000018, angledelta 57.3 deg
step 85 - lrate 0.000041, wchange 0.000016, angledelta 59.7 deg
step 86 - lrate 0.000041, wchange 0.000012, angledelta 55.9 deg
step 87 - lrate 0.000041, wchange 0.000010, angledelta 53.5 deg
step 88 - lrate 0.000041, wchange 0.000013, angledelta 61.6 deg
step 89 - lrate 0.000037, wchange 0.000012, angledelta 71.1 deg
step 90 - lrate 0.000033, wchange 0.000008, angledelta 51.2 deg
step 91 - lrate 0.000033, wchange 0.000006, angledelta 67.1 deg
step 92 - lrate 0.000030, wchange 0.000005, angledelta 56.0 deg
step 93 - lrate 0.000030, wchange 0.000005, angledelta 53.3 deg
step 94 - lrate 0.000030, wchange 0.000006, angledelta 55.6 deg
step 95 - lrate 0.000030, wchange 0.000012, angledelta 69.8 deg
step 96 - lrate 0.000027, wchange 0.000004, angledelta 72.1 deg
step 97 - lrate 0.000024, wchange 0.000005, angledelta 62.4 deg
step 98 - lrate 0.000022, wchange 0.000007, angledelta 74.4 deg
step 99 - lrate 0.000019, wchange 0.000002, angledelta 67.8 deg
step 100 - lrate 0.000018, wchange 0.000004, angledelta 62.8 deg
step 101 - lrate 0.000016, wchange 0.000002, angledelta 69.7 deg
step 102 - lrate 0.000014, wchange 0.000001, angledelta 64.1 deg
step 103 - lrate 0.000013, wchange 0.000001, angledelta 52.0 deg
Sorting components in descending order of mean projected variance ...
Components not ordered by variance.
Using skewness of the distribution to determine the sign of the components ...
Changing sign of component 1
Changing sign of component 3
Changing sign of component 5
Changing sign of component 7
Changing sign of component 8
Changing sign of component 9
Changing sign of component 10
Changing sign of component 11
Changing sign of component 12
Changing sign of component 14
Changing sign of component 15
Changing sign of component 16
Changing sign of component 17
Changing sign of component 18
Changing sign of component 19
Changing sign of component 20
Changing sign of component 24
Changing sign of component 25
DONE CALCULATING GROUP ICA
STARTING BACK RECONSTRUCTION STEP
Using GICA Back Reconstruction Approach ...
Back reconstructing set 1
-done back reconstructing set 1
-saving back reconstructed ica data for set 1 -> test1_ica_br1.mat
Back reconstructing set 2
-done back reconstructing set 2
-saving back reconstructed ica data for set 2 -> test1_ica_br2.mat
Back reconstructing set 3
-done back reconstructing set 3
-saving back reconstructed ica data for set 3 -> test1_ica_br3.mat
Back reconstructing set 4
-done back reconstructing set 4
-saving back reconstructed ica data for set 4 -> test1_ica_br4.mat
Back reconstructing set 5
-done back reconstructing set 5
-saving back reconstructed ica data for set 5 -> test1_ica_br5.mat
Back reconstructing set 6
-done back reconstructing set 6
-saving back reconstructed ica data for set 6 -> test1_ica_br6.mat
Back reconstructing set 7
-done back reconstructing set 7
-saving back reconstructed ica data for set 7 -> test1_ica_br7.mat
Back reconstructing set 8
-done back reconstructing set 8
-saving back reconstructed ica data for set 8 -> test1_ica_br8.mat
Back reconstructing set 9
-done back reconstructing set 9
-saving back reconstructed ica data for set 9 -> test1_ica_br9.mat
Back reconstructing set 10
-done back reconstructing set 10
-saving back reconstructed ica data for set 10 -> test1_ica_br10.mat
Back reconstructing set 11
-done back reconstructing set 11
-saving back reconstructed ica data for set 11 -> test1_ica_br11.mat
DONE WITH BACK RECONSTRUCTION STEP
STARTING TO SCALE COMPONENT SETS
Computing offset using the mean component maps which will be subtracted to the subject component maps ...
Done
--Subject 1 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 1 session 1 in nifti format and as matlab file
--Subject 2 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 2 session 1 in nifti format and as matlab file
--Subject 3 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 3 session 1 in nifti format and as matlab file
--Subject 4 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 4 session 1 in nifti format and as matlab file
--Subject 5 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 5 session 1 in nifti format and as matlab file
--Subject 6 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 6 session 1 in nifti format and as matlab file
--Subject 7 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 7 session 1 in nifti format and as matlab file
--Subject 8 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 8 session 1 in nifti format and as matlab file
--Subject 9 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 9 session 1 in nifti format and as matlab file
--Subject 10 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 10 session 1 in nifti format and as matlab file
--Subject 11 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 11 session 1 in nifti format and as matlab file
DONE SCALING COMPONENTS
STARTING GROUP STATS STEP
--calculating mean ica component and timecourse
done calculating mean for session 1
done calculating mean for different sessions
--calculating variance and standard deviation of components
done calculating variance and standard deviation
--calculating tmaps
done calculating tmaps
...saving group stats data...
Comparing mean image with the aggregate ...
Value shows how much the mean component is close w.r.t aggregate component
The comparison value is found to be 0.97484
Computing spectra and FNC correlations of all subjects and sessions components ...
Timecourses will be despiked when computing FNC correlations...
Timecourses will be filtered when computing FNC correlations using HF cutoff of 0.15 Hz ...
......................................
Group ICA Error Information:
Undefined function 'abcdchk' for input arguments of type 'double'.
Error in ==> icatb_butter at 0
Error in ==> icatb_butter at 0
Error in ==> filt_data at 25
Error in ==> icatb_filt_data at 14
Error in ==> icatb_postprocess_timecourses at 194
Error in ==> icatb_groupStats at 503
Error in ==> icatb_runAnalysis at 444
Error in ==> runAnalysis_Callback at 95
Error in ==> gui_mainfcn at 95
Error in ==> gift at 30
......................................
Error using icatb_displayErrorMsg (line 23)
Error in icatb_runAnalysis (line 550)
icatb_displayErrorMsg;
Error in gift>runAnalysis_Callback (line 95)
icatb_runAnalysis;
Error in gui_mainfcn (line 95)
feval(varargin{:});
Error in gift (line 30)
gui_mainfcn(gui_State, varargin{:});
Error while evaluating UIControl Callback
Reading data from source directory C:\Users\gehan\Documents\MRI\analysis\FunImgARCW ...
The selected data folders are in the following order:
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_001
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_0010
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_0011
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_002
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_003
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_004
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_005
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_006
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_007
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_008
C:\Users\gehan\Documents\MRI\analysis\FunImgARCW\Sub_009
Please see the text file C:\Users\gehan\Documents\MRI\gift\test1\test1SelectedDataFolders.txt for the selected data folders in order
Creating Mask
Default mask includes voxels >= mean. Using first file of each subject to create default mask ...
Done Creating Mask
Parameters are saved in C:\Users\gehan\Documents\MRI\gift\test1\test1_ica_parameter_info.mat
Please run the analysis using the same parameter file
Parameters file succesfully loaded
Opening run analysis GUI. Please wait ...
GETTING DATA REDUCTION PARAMETERS----------------------
Reduction step 1 starts with 11 groups and gets reduced to 11 groups
-New Group #1: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #2: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #3: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #4: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #5: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #6: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #7: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #8: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #9: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #10: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #11: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
Reduction step 2 starts with 11 groups and gets reduced to 1 groups
-New Group #1: 11 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 30 principal components
The new group will have a total of 330 stacked principal components
This group will then be reduced to 20 principal components
END GETTING DATA REDUCTION PARAMETERS----------------------
Checking to make sure parameters are correct...
Checking mask
Checking principal component parameters
Done with parameter error check
STARTING DATA REDUCTION (PRINCIPAL COMPONENTS ANALYSIS)
--Extracting principal components for data reduction( time #1 )
--Doing pca on Subject #1 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
92.0733% of (non-zero) eigenvalues retained.
--Doing pca on Subject #2 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
96.2107% of (non-zero) eigenvalues retained.
--Doing pca on Subject #3 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
92.0875% of (non-zero) eigenvalues retained.
--Doing pca on Subject #4 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
93.6451% of (non-zero) eigenvalues retained.
--Doing pca on Subject #5 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
96.4671% of (non-zero) eigenvalues retained.
--Doing pca on Subject #6 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
94.9076% of (non-zero) eigenvalues retained.
--Doing pca on Subject #7 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
94.9058% of (non-zero) eigenvalues retained.
--Doing pca on Subject #8 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
98.9318% of (non-zero) eigenvalues retained.
--Doing pca on Subject #9 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
96.3886% of (non-zero) eigenvalues retained.
--Doing pca on Subject #10 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
97.9203% of (non-zero) eigenvalues retained.
--Doing pca on Subject #11 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
92.0558% of (non-zero) eigenvalues retained.
--Extracting principal components for data reduction( time #2 )
Loading data-set 1 ...
Loading data-set 2 ...
Loading data-set 3 ...
Loading data-set 4 ...
Loading data-set 5 ...
Loading data-set 6 ...
Loading data-set 7 ...
Loading data-set 8 ...
Loading data-set 9 ...
Loading data-set 10 ...
Loading data-set 11 ...
Using Eigen Value Decomposition ...
Covariance matrix size is 74 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
46.8382% of (non-zero) eigenvalues retained.
Done with data reduction( time # 2)
ENDING DATA REDUCTION (PRINCIPAL COMPONENTS ANALYSIS)
STARTING GROUP ICA STEP
Using spatial ica ...
Number of times ICA will run is 1
Run 1 / 1
Input data size [20,74] = 20 channels, 74 frames.
Finding 20 ICA components using logistic ICA.
Initial learning rate will be 0.0050071, block size 4.
Learning rate will be multiplied by 0.9 whenever angledelta >= 60 deg.
Training will end when wchange < 1e-06 or after 512 steps.
Online bias adjustment will be used.
Removing mean of each channel ...
Not removing mean of each channel!!!
Final training data range: -5.35704 to 4.70659
Computing the sphering matrix...
Starting weights are the identity matrix ...
Sphering the data ...
Beginning ICA training ...
step 1 - lrate 0.005007, wchange 0.465591
step 2 - lrate 0.005007, wchange 0.318599
step 3 - lrate 0.005007, wchange 0.310854, angledelta 62.4 deg
step 4 - lrate 0.004506, wchange 0.188774, angledelta 58.4 deg
step 5 - lrate 0.004506, wchange 0.187884, angledelta 69.8 deg
step 6 - lrate 0.004056, wchange 0.255182, angledelta 77.4 deg
step 7 - lrate 0.003650, wchange 0.204472, angledelta 90.1 deg
step 8 - lrate 0.003285, wchange 0.143009, angledelta 81.6 deg
step 9 - lrate 0.002957, wchange 0.085531, angledelta 76.5 deg
step 10 - lrate 0.002661, wchange 0.077383, angledelta 52.9 deg
step 11 - lrate 0.002661, wchange 0.066396, angledelta 61.5 deg
step 12 - lrate 0.002395, wchange 0.052098, angledelta 58.3 deg
step 13 - lrate 0.002395, wchange 0.041586, angledelta 48.4 deg
step 14 - lrate 0.002395, wchange 0.065207, angledelta 49.6 deg
step 15 - lrate 0.002395, wchange 0.067768, angledelta 65.8 deg
step 16 - lrate 0.002155, wchange 0.059213, angledelta 88.1 deg
step 17 - lrate 0.001940, wchange 0.034297, angledelta 69.9 deg
step 18 - lrate 0.001746, wchange 0.023098, angledelta 62.7 deg
step 19 - lrate 0.001571, wchange 0.018538, angledelta 49.6 deg
step 20 - lrate 0.001571, wchange 0.018024, angledelta 58.0 deg
step 21 - lrate 0.001571, wchange 0.020089, angledelta 55.9 deg
step 22 - lrate 0.001571, wchange 0.027389, angledelta 57.8 deg
step 23 - lrate 0.001571, wchange 0.030231, angledelta 46.2 deg
step 24 - lrate 0.001571, wchange 0.022619, angledelta 65.1 deg
step 25 - lrate 0.001414, wchange 0.016864, angledelta 64.7 deg
step 26 - lrate 0.001273, wchange 0.013428, angledelta 65.5 deg
step 27 - lrate 0.001145, wchange 0.008705, angledelta 65.3 deg
step 28 - lrate 0.001031, wchange 0.006065, angledelta 47.0 deg
step 29 - lrate 0.001031, wchange 0.013791, angledelta 49.9 deg
step 30 - lrate 0.001031, wchange 0.021689, angledelta 63.1 deg
step 31 - lrate 0.000928, wchange 0.011382, angledelta 103.2 deg
step 32 - lrate 0.000835, wchange 0.003659, angledelta 68.0 deg
step 33 - lrate 0.000752, wchange 0.003688, angledelta 39.2 deg
step 34 - lrate 0.000752, wchange 0.005216, angledelta 66.1 deg
step 35 - lrate 0.000676, wchange 0.003652, angledelta 72.1 deg
step 36 - lrate 0.000609, wchange 0.005302, angledelta 58.4 deg
step 37 - lrate 0.000609, wchange 0.004631, angledelta 66.1 deg
step 38 - lrate 0.000548, wchange 0.001616, angledelta 53.2 deg
step 39 - lrate 0.000548, wchange 0.002522, angledelta 67.4 deg
step 40 - lrate 0.000493, wchange 0.001233, angledelta 53.6 deg
step 41 - lrate 0.000493, wchange 0.001303, angledelta 52.8 deg
step 42 - lrate 0.000493, wchange 0.003648, angledelta 70.5 deg
step 43 - lrate 0.000444, wchange 0.001904, angledelta 74.2 deg
step 44 - lrate 0.000399, wchange 0.001552, angledelta 61.6 deg
step 45 - lrate 0.000359, wchange 0.001732, angledelta 67.3 deg
step 46 - lrate 0.000324, wchange 0.000654, angledelta 65.0 deg
step 47 - lrate 0.000291, wchange 0.000608, angledelta 45.2 deg
step 48 - lrate 0.000291, wchange 0.000521, angledelta 38.6 deg
step 49 - lrate 0.000291, wchange 0.000410, angledelta 35.5 deg
step 50 - lrate 0.000291, wchange 0.000384, angledelta 34.1 deg
step 51 - lrate 0.000291, wchange 0.000468, angledelta 39.1 deg
step 52 - lrate 0.000291, wchange 0.000396, angledelta 33.6 deg
step 53 - lrate 0.000291, wchange 0.000537, angledelta 52.5 deg
step 54 - lrate 0.000291, wchange 0.000381, angledelta 35.1 deg
step 55 - lrate 0.000291, wchange 0.000636, angledelta 41.2 deg
step 56 - lrate 0.000291, wchange 0.001262, angledelta 63.0 deg
step 57 - lrate 0.000262, wchange 0.000470, angledelta 73.7 deg
step 58 - lrate 0.000236, wchange 0.000209, angledelta 44.9 deg
step 59 - lrate 0.000236, wchange 0.001148, angledelta 69.0 deg
step 60 - lrate 0.000212, wchange 0.000254, angledelta 71.8 deg
step 61 - lrate 0.000191, wchange 0.000120, angledelta 40.5 deg
step 62 - lrate 0.000191, wchange 0.000666, angledelta 71.6 deg
step 63 - lrate 0.000172, wchange 0.000578, angledelta 20.9 deg
step 64 - lrate 0.000172, wchange 0.000169, angledelta 86.8 deg
step 65 - lrate 0.000155, wchange 0.000093, angledelta 43.6 deg
step 66 - lrate 0.000155, wchange 0.000098, angledelta 46.0 deg
step 67 - lrate 0.000155, wchange 0.000124, angledelta 55.2 deg
step 68 - lrate 0.000155, wchange 0.000073, angledelta 39.2 deg
step 69 - lrate 0.000155, wchange 0.000086, angledelta 42.9 deg
step 70 - lrate 0.000155, wchange 0.000111, angledelta 49.6 deg
step 71 - lrate 0.000155, wchange 0.000164, angledelta 45.2 deg
step 72 - lrate 0.000155, wchange 0.000085, angledelta 47.1 deg
step 73 - lrate 0.000155, wchange 0.000141, angledelta 55.7 deg
step 74 - lrate 0.000155, wchange 0.000141, angledelta 59.3 deg
step 75 - lrate 0.000155, wchange 0.000069, angledelta 40.7 deg
step 76 - lrate 0.000155, wchange 0.000079, angledelta 41.7 deg
step 77 - lrate 0.000155, wchange 0.000095, angledelta 49.0 deg
step 78 - lrate 0.000155, wchange 0.000087, angledelta 49.2 deg
step 79 - lrate 0.000155, wchange 0.000070, angledelta 49.6 deg
step 80 - lrate 0.000155, wchange 0.000236, angledelta 65.5 deg
step 81 - lrate 0.000139, wchange 0.000070, angledelta 64.0 deg
step 82 - lrate 0.000125, wchange 0.000053, angledelta 45.3 deg
step 83 - lrate 0.000125, wchange 0.000176, angledelta 67.1 deg
step 84 - lrate 0.000113, wchange 0.000052, angledelta 70.1 deg
step 85 - lrate 0.000102, wchange 0.000105, angledelta 68.5 deg
step 86 - lrate 0.000091, wchange 0.000131, angledelta 79.2 deg
step 87 - lrate 0.000082, wchange 0.000055, angledelta 80.0 deg
step 88 - lrate 0.000074, wchange 0.000019, angledelta 65.9 deg
step 89 - lrate 0.000067, wchange 0.000015, angledelta 44.0 deg
step 90 - lrate 0.000067, wchange 0.000015, angledelta 41.7 deg
step 91 - lrate 0.000067, wchange 0.000020, angledelta 50.2 deg
step 92 - lrate 0.000067, wchange 0.000017, angledelta 48.5 deg
step 93 - lrate 0.000067, wchange 0.000016, angledelta 43.6 deg
step 94 - lrate 0.000067, wchange 0.000019, angledelta 51.4 deg
step 95 - lrate 0.000067, wchange 0.000021, angledelta 52.2 deg
step 96 - lrate 0.000067, wchange 0.000010, angledelta 32.4 deg
step 97 - lrate 0.000067, wchange 0.000013, angledelta 36.7 deg
step 98 - lrate 0.000067, wchange 0.000016, angledelta 44.9 deg
step 99 - lrate 0.000067, wchange 0.000047, angledelta 70.0 deg
step 100 - lrate 0.000060, wchange 0.000023, angledelta 76.6 deg
step 101 - lrate 0.000054, wchange 0.000010, angledelta 62.8 deg
step 102 - lrate 0.000049, wchange 0.000007, angledelta 45.1 deg
step 103 - lrate 0.000049, wchange 0.000017, angledelta 65.4 deg
step 104 - lrate 0.000044, wchange 0.000018, angledelta 76.8 deg
step 105 - lrate 0.000039, wchange 0.000005, angledelta 67.9 deg
step 106 - lrate 0.000035, wchange 0.000006, angledelta 57.9 deg
step 107 - lrate 0.000035, wchange 0.000006, angledelta 54.1 deg
step 108 - lrate 0.000035, wchange 0.000005, angledelta 47.8 deg
step 109 - lrate 0.000035, wchange 0.000007, angledelta 51.3 deg
step 110 - lrate 0.000035, wchange 0.000004, angledelta 45.2 deg
step 111 - lrate 0.000035, wchange 0.000008, angledelta 56.4 deg
step 112 - lrate 0.000035, wchange 0.000011, angledelta 67.0 deg
step 113 - lrate 0.000032, wchange 0.000007, angledelta 73.2 deg
step 114 - lrate 0.000029, wchange 0.000002, angledelta 56.9 deg
step 115 - lrate 0.000029, wchange 0.000003, angledelta 59.6 deg
step 116 - lrate 0.000029, wchange 0.000004, angledelta 68.1 deg
step 117 - lrate 0.000026, wchange 0.000002, angledelta 55.4 deg
step 118 - lrate 0.000026, wchange 0.000002, angledelta 55.7 deg
step 119 - lrate 0.000026, wchange 0.000004, angledelta 66.6 deg
step 120 - lrate 0.000023, wchange 0.000002, angledelta 56.7 deg
step 121 - lrate 0.000023, wchange 0.000002, angledelta 59.5 deg
step 122 - lrate 0.000023, wchange 0.000003, angledelta 64.8 deg
step 123 - lrate 0.000021, wchange 0.000002, angledelta 60.5 deg
step 124 - lrate 0.000019, wchange 0.000001, angledelta 51.3 deg
step 125 - lrate 0.000019, wchange 0.000001, angledelta 52.8 deg
step 126 - lrate 0.000019, wchange 0.000001, angledelta 56.4 deg
step 127 - lrate 0.000019, wchange 0.000002, angledelta 67.5 deg
step 128 - lrate 0.000017, wchange 0.000001, angledelta 69.1 deg
step 129 - lrate 0.000015, wchange 0.000001, angledelta 54.0 deg
Sorting components in descending order of mean projected variance ...
Components not ordered by variance.
Using skewness of the distribution to determine the sign of the components ...
Changing sign of component 2
Changing sign of component 3
Changing sign of component 5
Changing sign of component 6
Changing sign of component 7
Changing sign of component 8
Changing sign of component 13
Changing sign of component 14
Changing sign of component 19
Changing sign of component 20
DONE CALCULATING GROUP ICA
STARTING BACK RECONSTRUCTION STEP
Using GICA Back Reconstruction Approach ...
Back reconstructing set 1
-done back reconstructing set 1
-saving back reconstructed ica data for set 1 -> test1_ica_br1.mat
Back reconstructing set 2
-done back reconstructing set 2
-saving back reconstructed ica data for set 2 -> test1_ica_br2.mat
Back reconstructing set 3
-done back reconstructing set 3
-saving back reconstructed ica data for set 3 -> test1_ica_br3.mat
Back reconstructing set 4
-done back reconstructing set 4
-saving back reconstructed ica data for set 4 -> test1_ica_br4.mat
Back reconstructing set 5
-done back reconstructing set 5
-saving back reconstructed ica data for set 5 -> test1_ica_br5.mat
Back reconstructing set 6
-done back reconstructing set 6
-saving back reconstructed ica data for set 6 -> test1_ica_br6.mat
Back reconstructing set 7
-done back reconstructing set 7
-saving back reconstructed ica data for set 7 -> test1_ica_br7.mat
Back reconstructing set 8
-done back reconstructing set 8
-saving back reconstructed ica data for set 8 -> test1_ica_br8.mat
Back reconstructing set 9
-done back reconstructing set 9
-saving back reconstructed ica data for set 9 -> test1_ica_br9.mat
Back reconstructing set 10
-done back reconstructing set 10
-saving back reconstructed ica data for set 10 -> test1_ica_br10.mat
Back reconstructing set 11
-done back reconstructing set 11
-saving back reconstructed ica data for set 11 -> test1_ica_br11.mat
DONE WITH BACK RECONSTRUCTION STEP
STARTING TO SCALE COMPONENT SETS
Computing offset using the mean component maps which will be subtracted to the subject component maps ...
Done
--Subject 1 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 1 session 1 in nifti format and as matlab file
--Subject 2 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 2 session 1 in nifti format and as matlab file
--Subject 3 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 3 session 1 in nifti format and as matlab file
--Subject 4 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 4 session 1 in nifti format and as matlab file
--Subject 5 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 5 session 1 in nifti format and as matlab file
--Subject 6 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 6 session 1 in nifti format and as matlab file
--Subject 7 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 7 session 1 in nifti format and as matlab file
--Subject 8 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 8 session 1 in nifti format and as matlab file
--Subject 9 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 9 session 1 in nifti format and as matlab file
--Subject 10 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 10 session 1 in nifti format and as matlab file
--Subject 11 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 11 session 1 in nifti format and as matlab file
DONE SCALING COMPONENTS
STARTING GROUP STATS STEP
--calculating mean ica component and timecourse
done calculating mean for session 1
done calculating mean for different sessions
--calculating variance and standard deviation of components
done calculating variance and standard deviation
--calculating tmaps
done calculating tmaps
...saving group stats data...
Comparing mean image with the aggregate ...
Value shows how much the mean component is close w.r.t aggregate component
The comparison value is found to be 0.9857
Computing spectra and FNC correlations of all subjects and sessions components ...
Timecourses will be despiked when computing FNC correlations...
Timecourses will be filtered when computing FNC correlations using HF cutoff of 0.15 Hz ...
......................................
Group ICA Error Information:
Undefined function 'abcdchk' for input arguments of type 'double'.
Error in ==> icatb_butter at 0
Error in ==> icatb_butter at 0
Error in ==> filt_data at 25
Error in ==> icatb_filt_data at 14
Error in ==> icatb_postprocess_timecourses at 194
Error in ==> icatb_groupStats at 503
Error in ==> icatb_runAnalysis at 444
Error in ==> runAnalysis_Callback at 95
Error in ==> gui_mainfcn at 95
Error in ==> gift at 30
......................................
Error using icatb_displayErrorMsg (line 23)
Error in icatb_runAnalysis (line 550)
icatb_displayErrorMsg;
Error in gift>runAnalysis_Callback (line 95)
icatb_runAnalysis;
Error in gui_mainfcn (line 95)
feval(varargin{:});
Error in gift (line 30)
gui_mainfcn(gui_State, varargin{:});
Error while evaluating UIControl Callback
gift
Reading data from source directory C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW ...
The selected data folders are in the following order:
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_001
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_0010
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_0011
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_002
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_003
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_004
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_005
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_006
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_007
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_008
C:\Users\gehan\Documents\MRI\analysis\FunImgARglobalCW\Sub_009
Please see the text file C:\Users\gehan\Documents\MRI\New folder (9)\test1SelectedDataFolders.txt for the selected data folders in order
Creating Mask
Default mask includes voxels >= mean. Using first file of each subject to create default mask ...
Done Creating Mask
Parameters are saved in C:\Users\gehan\Documents\MRI\New folder (9)\test1_ica_parameter_info.mat
Please run the analysis using the same parameter file
Parameters file succesfully loaded
Opening run analysis GUI. Please wait ...
GETTING DATA REDUCTION PARAMETERS----------------------
Reduction step 1 starts with 11 groups and gets reduced to 11 groups
-New Group #1: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #2: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #3: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #4: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #5: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #6: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #7: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #8: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #9: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #10: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
-New Group #11: 1 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 90 BOLD timepoints
The new group will have a total of 90 stacked BOLD timepoints
This group will then be reduced to 30 principal components
Reduction step 2 starts with 11 groups and gets reduced to 1 groups
-New Group #1: 11 groups will be concatenated to form the new group.
Each of the to be concatenated groups is made up of 30 principal components
The new group will have a total of 330 stacked principal components
This group will then be reduced to 20 principal components
END GETTING DATA REDUCTION PARAMETERS----------------------
Checking to make sure parameters are correct...
Checking mask
Checking principal component parameters
Done with parameter error check
STARTING DATA REDUCTION (PRINCIPAL COMPONENTS ANALYSIS)
--Extracting principal components for data reduction( time #1 )
--Doing pca on Subject #1 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
93.9941% of (non-zero) eigenvalues retained.
--Doing pca on Subject #2 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
96.924% of (non-zero) eigenvalues retained.
--Doing pca on Subject #3 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
92.6803% of (non-zero) eigenvalues retained.
--Doing pca on Subject #4 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
93.774% of (non-zero) eigenvalues retained.
--Doing pca on Subject #5 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
96.1738% of (non-zero) eigenvalues retained.
--Doing pca on Subject #6 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
94.2407% of (non-zero) eigenvalues retained.
--Doing pca on Subject #7 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
95.9145% of (non-zero) eigenvalues retained.
--Doing pca on Subject #8 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
99.0648% of (non-zero) eigenvalues retained.
--Doing pca on Subject #9 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
96.2079% of (non-zero) eigenvalues retained.
--Doing pca on Subject #10 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
97.6711% of (non-zero) eigenvalues retained.
--Doing pca on Subject #11 Session #1
Removing mean per time point ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
94.3467% of (non-zero) eigenvalues retained.
--Extracting principal components for data reduction( time #2 )
Loading data-set 1 ...
Loading data-set 2 ...
Loading data-set 3 ...
Loading data-set 4 ...
Loading data-set 5 ...
Loading data-set 6 ...
Loading data-set 7 ...
Loading data-set 8 ...
Loading data-set 9 ...
Loading data-set 10 ...
Loading data-set 11 ...
Using Eigen Value Decomposition ...
Covariance matrix size is 69 ^2
Calculating eigendecomposition
Sorting eigenvalues
Selecting Desired Eigenvalues
50.7147% of (non-zero) eigenvalues retained.
Done with data reduction( time # 2)
ENDING DATA REDUCTION (PRINCIPAL COMPONENTS ANALYSIS)
STARTING GROUP ICA STEP
Using spatial ica ...
Number of times ICA will run is 1
Run 1 / 1
Input data size [20,69] = 20 channels, 69 frames.
Finding 20 ICA components using logistic ICA.
Initial learning rate will be 0.0050071, block size 4.
Learning rate will be multiplied by 0.9 whenever angledelta >= 60 deg.
Training will end when wchange < 1e-06 or after 512 steps.
Online bias adjustment will be used.
Removing mean of each channel ...
Not removing mean of each channel!!!
Final training data range: -7.8309 to 5.57776
Computing the sphering matrix...
Starting weights are the identity matrix ...
Sphering the data ...
Beginning ICA training ...
step 1 - lrate 0.005007, wchange 0.528499
step 2 - lrate 0.005007, wchange 0.346937
step 3 - lrate 0.005007, wchange 0.355923, angledelta 61.9 deg
step 4 - lrate 0.004506, wchange 0.256094, angledelta 61.9 deg
step 5 - lrate 0.004056, wchange 0.238540, angledelta 61.3 deg
step 6 - lrate 0.003650, wchange 0.230101, angledelta 76.1 deg
step 7 - lrate 0.003285, wchange 0.225969, angledelta 88.3 deg
step 8 - lrate 0.002957, wchange 0.322877, angledelta 110.6 deg
step 9 - lrate 0.002661, wchange 0.248124, angledelta 122.3 deg
step 10 - lrate 0.002395, wchange 0.144567, angledelta 111.8 deg
step 11 - lrate 0.002155, wchange 0.133593, angledelta 107.6 deg
step 12 - lrate 0.001940, wchange 0.085293, angledelta 104.2 deg
step 13 - lrate 0.001746, wchange 0.054512, angledelta 87.0 deg
step 14 - lrate 0.001571, wchange 0.035226, angledelta 66.2 deg
step 15 - lrate 0.001414, wchange 0.028894, angledelta 54.4 deg
step 16 - lrate 0.001414, wchange 0.025869, angledelta 45.0 deg
step 17 - lrate 0.001414, wchange 0.022151, angledelta 40.4 deg
step 18 - lrate 0.001414, wchange 0.021269, angledelta 46.5 deg
step 19 - lrate 0.001414, wchange 0.021815, angledelta 46.5 deg
step 20 - lrate 0.001414, wchange 0.018312, angledelta 60.1 deg
step 21 - lrate 0.001273, wchange 0.013876, angledelta 70.8 deg
step 22 - lrate 0.001145, wchange 0.009080, angledelta 54.5 deg
step 23 - lrate 0.001145, wchange 0.009849, angledelta 61.2 deg
step 24 - lrate 0.001031, wchange 0.012143, angledelta 58.5 deg
step 25 - lrate 0.001031, wchange 0.014253, angledelta 59.0 deg
step 26 - lrate 0.001031, wchange 0.008381, angledelta 49.8 deg
step 27 - lrate 0.001031, wchange 0.009972, angledelta 60.8 deg
step 28 - lrate 0.000928, wchange 0.005631, angledelta 75.0 deg
step 29 - lrate 0.000835, wchange 0.004418, angledelta 51.2 deg
step 30 - lrate 0.000835, wchange 0.004068, angledelta 51.2 deg
step 31 - lrate 0.000835, wchange 0.003151, angledelta 53.3 deg
step 32 - lrate 0.000835, wchange 0.003013, angledelta 52.2 deg
step 33 - lrate 0.000835, wchange 0.002685, angledelta 48.4 deg
step 34 - lrate 0.000835, wchange 0.006173, angledelta 62.8 deg
step 35 - lrate 0.000752, wchange 0.002909, angledelta 72.6 deg
step 36 - lrate 0.000676, wchange 0.001474, angledelta 54.9 deg
step 37 - lrate 0.000676, wchange 0.005734, angledelta 64.1 deg
step 38 - lrate 0.000609, wchange 0.001662, angledelta 73.1 deg
step 39 - lrate 0.000548, wchange 0.000830, angledelta 47.8 deg
step 40 - lrate 0.000548, wchange 0.000805, angledelta 55.8 deg
step 41 - lrate 0.000548, wchange 0.000721, angledelta 48.4 deg
step 42 - lrate 0.000548, wchange 0.000744, angledelta 57.2 deg
step 43 - lrate 0.000548, wchange 0.001718, angledelta 68.3 deg
step 44 - lrate 0.000493, wchange 0.000718, angledelta 65.0 deg
step 45 - lrate 0.000444, wchange 0.000733, angledelta 56.8 deg
step 46 - lrate 0.000444, wchange 0.000848, angledelta 58.3 deg
step 47 - lrate 0.000444, wchange 0.000409, angledelta 45.9 deg
step 48 - lrate 0.000444, wchange 0.000824, angledelta 55.8 deg
step 49 - lrate 0.000444, wchange 0.000453, angledelta 53.8 deg
step 50 - lrate 0.000444, wchange 0.000397, angledelta 45.3 deg
step 51 - lrate 0.000444, wchange 0.000376, angledelta 46.3 deg
step 52 - lrate 0.000444, wchange 0.002386, angledelta 73.8 deg
step 53 - lrate 0.000399, wchange 0.000468, angledelta 78.5 deg
step 54 - lrate 0.000359, wchange 0.001200, angledelta 69.4 deg
step 55 - lrate 0.000324, wchange 0.000183, angledelta 70.3 deg
step 56 - lrate 0.000291, wchange 0.000138, angledelta 30.6 deg
step 57 - lrate 0.000291, wchange 0.000852, angledelta 70.0 deg
step 58 - lrate 0.000262, wchange 0.000113, angledelta 73.9 deg
step 59 - lrate 0.000236, wchange 0.000082, angledelta 25.7 deg
step 60 - lrate 0.000236, wchange 0.000252, angledelta 57.1 deg
step 61 - lrate 0.000236, wchange 0.000087, angledelta 29.5 deg
step 62 - lrate 0.000236, wchange 0.000506, angledelta 66.8 deg
step 63 - lrate 0.000212, wchange 0.000090, angledelta 74.9 deg
step 64 - lrate 0.000191, wchange 0.000059, angledelta 41.5 deg
step 65 - lrate 0.000191, wchange 0.000073, angledelta 46.5 deg
step 66 - lrate 0.000191, wchange 0.000049, angledelta 37.0 deg
step 67 - lrate 0.000191, wchange 0.000045, angledelta 37.7 deg
step 68 - lrate 0.000191, wchange 0.000123, angledelta 58.7 deg
step 69 - lrate 0.000191, wchange 0.000100, angledelta 49.7 deg
step 70 - lrate 0.000191, wchange 0.000197, angledelta 64.3 deg
step 71 - lrate 0.000172, wchange 0.000036, angledelta 61.4 deg
step 72 - lrate 0.000155, wchange 0.000118, angledelta 55.4 deg
step 73 - lrate 0.000155, wchange 0.000030, angledelta 20.8 deg
step 74 - lrate 0.000155, wchange 0.000028, angledelta 17.7 deg
step 75 - lrate 0.000155, wchange 0.000074, angledelta 50.2 deg
step 76 - lrate 0.000155, wchange 0.000030, angledelta 23.6 deg
step 77 - lrate 0.000155, wchange 0.000122, angledelta 60.5 deg
step 78 - lrate 0.000139, wchange 0.000040, angledelta 68.2 deg
step 79 - lrate 0.000125, wchange 0.000044, angledelta 55.4 deg
step 80 - lrate 0.000125, wchange 0.000034, angledelta 55.5 deg
step 81 - lrate 0.000125, wchange 0.000018, angledelta 43.1 deg
step 82 - lrate 0.000125, wchange 0.000047, angledelta 59.6 deg
step 83 - lrate 0.000125, wchange 0.000185, angledelta 72.6 deg
step 84 - lrate 0.000113, wchange 0.000033, angledelta 79.4 deg
step 85 - lrate 0.000102, wchange 0.000074, angledelta 70.3 deg
step 86 - lrate 0.000091, wchange 0.000023, angledelta 74.8 deg
step 87 - lrate 0.000082, wchange 0.000008, angledelta 50.9 deg
step 88 - lrate 0.000082, wchange 0.000013, angledelta 59.1 deg
step 89 - lrate 0.000082, wchange 0.000080, angledelta 78.3 deg
step 90 - lrate 0.000074, wchange 0.000049, angledelta 83.4 deg
step 91 - lrate 0.000067, wchange 0.000005, angledelta 71.3 deg
step 92 - lrate 0.000060, wchange 0.000018, angledelta 63.5 deg
step 93 - lrate 0.000054, wchange 0.000007, angledelta 74.6 deg
step 94 - lrate 0.000049, wchange 0.000009, angledelta 68.0 deg
step 95 - lrate 0.000044, wchange 0.000008, angledelta 72.9 deg
step 96 - lrate 0.000039, wchange 0.000002, angledelta 55.5 deg
step 97 - lrate 0.000039, wchange 0.000008, angledelta 73.4 deg
step 98 - lrate 0.000035, wchange 0.000001, angledelta 60.6 deg
step 99 - lrate 0.000032, wchange 0.000002, angledelta 39.1 deg
step 100 - lrate 0.000032, wchange 0.000005, angledelta 66.4 deg
step 101 - lrate 0.000029, wchange 0.000001, angledelta 66.6 deg
Sorting components in descending order of mean projected variance ...
Components not ordered by variance.
Using skewness of the distribution to determine the sign of the components ...
Changing sign of component 1
Changing sign of component 2
Changing sign of component 7
Changing sign of component 10
Changing sign of component 11
Changing sign of component 13
Changing sign of component 14
Changing sign of component 18
Changing sign of component 19
Changing sign of component 20
DONE CALCULATING GROUP ICA
STARTING BACK RECONSTRUCTION STEP
Using GICA Back Reconstruction Approach ...
Back reconstructing set 1
-done back reconstructing set 1
-saving back reconstructed ica data for set 1 -> test1_ica_br1.mat
Back reconstructing set 2
-done back reconstructing set 2
-saving back reconstructed ica data for set 2 -> test1_ica_br2.mat
Back reconstructing set 3
-done back reconstructing set 3
-saving back reconstructed ica data for set 3 -> test1_ica_br3.mat
Back reconstructing set 4
-done back reconstructing set 4
-saving back reconstructed ica data for set 4 -> test1_ica_br4.mat
Back reconstructing set 5
-done back reconstructing set 5
-saving back reconstructed ica data for set 5 -> test1_ica_br5.mat
Back reconstructing set 6
-done back reconstructing set 6
-saving back reconstructed ica data for set 6 -> test1_ica_br6.mat
Back reconstructing set 7
-done back reconstructing set 7
-saving back reconstructed ica data for set 7 -> test1_ica_br7.mat
Back reconstructing set 8
-done back reconstructing set 8
-saving back reconstructed ica data for set 8 -> test1_ica_br8.mat
Back reconstructing set 9
-done back reconstructing set 9
-saving back reconstructed ica data for set 9 -> test1_ica_br9.mat
Back reconstructing set 10
-done back reconstructing set 10
-saving back reconstructed ica data for set 10 -> test1_ica_br10.mat
Back reconstructing set 11
-done back reconstructing set 11
-saving back reconstructed ica data for set 11 -> test1_ica_br11.mat
DONE WITH BACK RECONSTRUCTION STEP
STARTING TO SCALE COMPONENT SETS
Computing offset using the mean component maps which will be subtracted to the subject component maps ...
Done
--Subject 1 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 1 session 1 in nifti format and as matlab file
--Subject 2 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 2 session 1 in nifti format and as matlab file
--Subject 3 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 3 session 1 in nifti format and as matlab file
--Subject 4 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 4 session 1 in nifti format and as matlab file
--Subject 5 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 5 session 1 in nifti format and as matlab file
--Subject 6 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 6 session 1 in nifti format and as matlab file
--Subject 7 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 7 session 1 in nifti format and as matlab file
--Subject 8 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 8 session 1 in nifti format and as matlab file
--Subject 9 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 9 session 1 in nifti format and as matlab file
--Subject 10 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 10 session 1 in nifti format and as matlab file
--Subject 11 Session 1's Component Set
Converting components to z-scores
...saving scaled ica data for subject 11 session 1 in nifti format and as matlab file
DONE SCALING COMPONENTS
STARTING GROUP STATS STEP
--calculating mean ica component and timecourse
done calculating mean for session 1
done calculating mean for different sessions
--calculating variance and standard deviation of components
done calculating variance and standard deviation
--calculating tmaps
done calculating tmaps
...saving group stats data...
Comparing mean image with the aggregate ...
Value shows how much the mean component is close w.r.t aggregate component
The comparison value is found to be 0.99509
Computing spectra and FNC correlations of all subjects and sessions components ...
Timecourses will be despiked when computing FNC correlations...
Timecourses will be filtered when computing FNC correlations using HF cutoff of 0.15 Hz ...
......................................
Group ICA Error Information:
Undefined function 'abcdchk' for input arguments of type 'double'.
Error in ==> icatb_butter at 0
Error in ==> icatb_butter at 0
Error in ==> filt_data at 25
Error in ==> icatb_filt_data at 14
Error in ==> icatb_postprocess_timecourses at 194
Error in ==> icatb_groupStats at 503
Error in ==> icatb_runAnalysis at 444
Error in ==> runAnalysis_Callback at 95
Error in ==> gui_mainfcn at 95
Error in ==> gift at 30
......................................
Error using icatb_displayErrorMsg (line 23)
Error in icatb_runAnalysis (line 550)
icatb_displayErrorMsg;
Error in gift>runAnalysis_Callback (line 95)
icatb_runAnalysis;
Error in gui_mainfcn (line 95)
feval(varargin{:});
Error in gift (line 30)
gui_mainfcn(gui_State, varargin{:});
Error while evaluating UIControl Callback