Example using cortical thickness as brain features

In this example we'll run a BWAS analysis using cortical thickness as brain features. Resulting \( \beta\)-weights will be used to predict the corresponding brain-derived behavioral scores. This example runs the same way as the previous example using functional connectivity as input imaging data. The difference is the input data. Again, we assume you have succesfully downloaded the toolbox and associated data. Here we are using as brain features cortical thickness data obtained using human data that was parcellated using the Human Connectome Project ROI set with network assignment by Cole-Anticevic.

Considerations:

This is a toy example that uses a reduced sample to ilustrate the functionality of the toolbox.
The same dataset is used to calculate the models and to predict scores. You should not do this in a real project.
In this example we will assume that you have downloaded the data in the folder /home/example3/input_data/
Here is the input data you will need to run this example:

BWAS: Estimating beta-weights

The first part of the analysis is to calculate the \( \beta\)-weights. This section describes how to do it using Matlab and also using the container. See Detailed specs. for details about input arguments.

Runing the code using Matlab

Once you have opened Matlab and added to your session the path to the repo, define the input arguments:

                        path_imaging_reference = '/home/example3/input_data/cortical_thickness.mat';
             path_demographics_Table_reference = '/home/example3/input_data/demographics_Table.csv';
  path_dictionary_demographics_Table_reference = '/home/example3/input_data/Dictionary_for_demographics_Table.csv';
             path_group_Design_Table_reference = '/home/example3/input_data/Group_Design_Table.csv';
                            output_folder_BWAS = '/home/example3/BWAS';
                        path_parcellation_table= '/home/example3/input_data/HCP_ColeAnticevic.csv';
               path_Group_Color_Table_reference= '/home/example3/input_data/Group_Color_Table.csv';
                        model='var3 ~ brain_feature-1';

Now, you can call the function as follows:

run_BWAS (path_imaging_reference,...
    path_demographics_Table_reference,...
    path_dictionary_demographics_Table_reference,...
    path_group_Design_Table_reference,...
    'output_folder',output_folder_BWAS,...
    'model',model,...
    'path_parcellation_table',path_parcellation_table)

Once the run is completed, all the files will be saved in the folder you defined as output folder (in this example is /home/example3/BWAS). This folder will contain the following subfolders:

├── /home/example3  
    ├── BWAS
        └── tables
        ├── figures
            └── weights_explainedvariance
            ├── scatter_plots
               └── by_networks
            └── relativecontributions_plots
            └── pvalues_explainedvariance
            └── manhattan_plots
        └── ciftis

The section Exploring outputs describes all the outputs.

Runing the code using the container

Once you have a running version of Singularity, you need to open a terminal and define the following variables:

                        path_imaging_reference = '/home/example3/input_data/cortical_thickness.mat';
             path_demographics_Table_reference = '/home/example3/input_data/demographics_Table.csv';
  path_dictionary_demographics_Table_reference = '/home/example3/input_data/Dictionary_for_demographics_Table.csv';
             path_group_Design_Table_reference = '/home/example3/input_data/Group_Design_Table.csv';
                            output_folder_BWAS = '/home/example3/BWAS';
                        path_parcellation_table= '/home/example3/input_data/HCP_ColeAnticevic.csv';
               path_Group_Color_Table_reference= '/home/example3/input_data/Group_Color_Table.csv';
                        model='var3 ~ brain_feature-1';
                                    base_folder= '/home/'

Now, you can call the function as follows:

        singularity run -B $base_folder:$base_folder container_bwas.sif bwas \
        -path_imaging_reference path_imaging_reference \
        -path_demographics_table_reference $path_demographics_Table_reference \
        -path_dictionary_demographics_table_reference $path_dictionary_demographics_Table_reference \
        -path_group_design_table_reference $path_group_Design_Table_reference \
        -output_folder $output_folder_BWAS \
        -path_parcellation_table $path_parcellation_table \
        -model $model

Once the run is completed, all the files will be saved in the folder you defined as output folder (in this example is /home/example3/BWAS). This folder will contain the following subfolders:

├── /home/example3  
    ├── BWAS
        └── tables
        ├── figures
            └── weights_explainedvariance
            ├── scatter_plots
               └── by_networks
            └── relativecontributions_plots
            └── pvalues_explainedvariance
            └── manhattan_plots
        └── ciftis

The section Exploring outputs describes all the outputs.

PNRS: Estimating risk

Once the \( \beta\)-weights are calculated, you can use them to predict scores in an independent sample. Weights and explained variance files are saved in a subfolder named tables/ within the folder that contains the outputs of the BWAS analyses. See Detailed specs. for details about input arguments/

Runing the code using Matlab

First, you need to define input arguments:


                       path_imaging_target = '/home/example3/input_data/fconn.mat';
            path_demographics_Table_target = '/home/example3/input_data/demographcis_Table.csv';
 path_dictionary_demographics_Table_target = '/home/example3/input_data/Dictionary_for_demographics_Table.csv';

                          path_betaweights = '/home/example3/BWAS/tables/brain_feature.csv';
                             path_Rsquared = '/home/example3/BWAS/tables/Rsquared.csv';
          path_reference_table_by_networks = '/home/example3/BWAS/tables/correlations_by_networks.csv';

                         output_folder_PNS = '/home/example3/PNRS';
            path_group_Design_Table_target = '/home/example3/input_data/Group_Design_Table.csv';
             path_Group_Color_Table_target = '/home/example3/input_data/Group_Color_Table.csv';

Now, you can call the function as follows:

PBScores=run_PNRS(path_imaging_target,...
    path_betaweights,...
    path_Rsquared,...
    'output_folder',output_folder_PNS,...
    'path_demographics_Table',path_demographics_Table_target,...
    'path_dictionary_demographics_Table',path_dictionary_demographics_Table_target,...
    'path_group_Design_Table',path_group_Design_Table_target,...
    'path_Group_Color_Table',path_Group_Color_Table_target,...
    'path_parcellation_table',path_parcellation_table,...
    'path_reference_table_by_networks',path_reference_table_by_networks);

Once the run is completed, all the files will be saved in the folder you defined as output folder (in this example is /home/example3/PNRS). This folder will contain the following subfolders:

├── /home/example3/
    ├── PNRS
        └── tables
            └── weights_explainedvariance
        └── figures
            └── weights_explainedvariance
            └── scores
            └── scatter_plots
                └── by_top_connection
                └── by_networks
            └── pvalues_explainedvariance_2_samples
            └── pvalues_explainedvariance
            └── explainedvariance_and_null

The section Exploring outputs describes all the outputs.

Runing the code using the container

UPDATE ALL WHAT COMES AFTER THIS LINE

First, you need to define input arguments:

```Matlab SHOULD SAY CONTAINER

                   path_imaging_target = '/home/example3/input_data/fconn.mat';
        path_demographics_Table_target = '/home/example3/input_data/demographcis_Table.csv';

path_dictionary_demographics_Table_target = '/home/example3/input_data/Dictionary_for_demographics_Table.csv';

                      path_betaweights = '/home/example3/BWAS/tables/brain_feature.csv';
                         path_Rsquared = '/home/example3/BWAS/tables/Rsquared.csv';
      path_reference_table_by_networks = '/home/example3/BWAS/tables/correlations_by_networks.csv';

                     output_folder_PNS = '/home/example3/PNRS';
        path_group_Design_Table_target = '/home/example3/input_data/Group_Design_Table.csv';
         path_Group_Color_Table_target = '/home/example3/input_data/Group_Color_Table.csv';

Now, you can call the function as follows:

```Matlab update for container
PBScores=run_PNRS(path_imaging_target,...
    path_betaweights,...
    path_Rsquared,...
    'output_folder',output_folder_PNS,...
    'path_demographics_Table',path_demographics_Table_target,...
    'path_dictionary_demographics_Table',path_dictionary_demographics_Table_target,...
    'path_group_Design_Table',path_group_Design_Table_target,...
    'path_Group_Color_Table',path_Group_Color_Table_target,...
    'path_parcellation_table',path_parcellation_table,...
    'path_reference_table_by_networks',path_reference_table_by_networks);

Once the run is completed, all the files will be saved in the folder you defined as output folder (in this example is /home/example3/PNRS). This folder will contain the following subfolders:

├── /home/example3/
    ├── PNRS
        └── tables
            └── weights_explainedvariance
        └── figures
            └── weights_explainedvariance
            └── scores
            └── scatter_plots
                └── by_top_connection
                └── by_networks
            └── pvalues_explainedvariance_2_samples
            └── pvalues_explainedvariance
            └── explainedvariance_and_null

The section Exploring outputs describes all the outputs.