Getting Started

Running with example data

  1. First, open the NLA software (as described in Setup). Select Pearson's r as the edge-level test from the edge-level test dropdown.

  2. Click Select to choose a network atlas, navigating to the support_files folder withing your NetworkLevelAnalysis installation and selecting Wheelock_2020_CerebralCortex_15nets_288ROI_on_MNI.mat. This file is used to parcellate the data.

    Network Atlas Select Button ‣ support files ‣ Wheelock_2020_CerebralCortex_15nets_288ROI_on_MNI.mat

  3. Then, select the functional connectivity, located in the examples/fc_and_behavior folder under the name sample_func_conn.mat. Click ‘Yes’ to Fisher z-transform the data. Take a moment to visualize the functional connectivity (FC) average by clicking View. Note that the FC appears to match the parcellation, (effects generally line up with network boundaries) - this can be a useful diagnostic tool if you are having issues with parcellations not matching data.

    Functional Connectivity Select Button ‣ examples ‣ fc_and_behavior ‣ sample_func_conn.mat

  4. Finally, load the behavior sample_behavior.mat from the examples/fc_and_behavior folder (The ‘file type’ drop-down will need to be changed from Text to MATLAB table in the file browser). Set the behavioral variable to ‘Flanker_AgeAdj’ by clicking on that column in the table and then the Set Behavior button.

    Behavior Select Button ‣ examples ‣ fc_and_behavior ‣ sample_behavior.mat

  5. Having finished our edge-level inputs, we now move over to the network-level panel on the right side. Select all the tests by left clicking the top one, and then shift + clicking the bottom one.

Run the tests using the Run button on the bottom-right. The number of permutations can be changed with the input field to the left of the Run button. After pushing the Run button, a result window will open. The edge-level test will run and the results can be visualized by pressing View in the upper-left of the result window. To run the network-level tests, push the Run button in the results window. This will take longer and a progress window will show up displaying the progress. To visualize the results, expand the lists in the reloaded (automatically) panel, and highlight a test. Multiple tests can be selected by left-clicking the first and then left-click + Shift the last. Press the View figures button. Other visualization options, such as chord plots and convergence maps, can also be shown by selecting those options from the figure GUI. The results can be saved using the File menu in the top-left of the figure GUI. These results can be loaded into MATLAB as a variable, or opened in the NLA GUI using the File menu on the mainwindow.

Running with example pre-calculated data

Similarly to the previous example, open the NLA window and load the Wheelock_2020_CerebralCortex_15nets_288ROI_on_MNI.mat parcellation. This time, select the Precalculated data edge-level test. Load the four input matrices in the examples/precalculated folder.

  • Observed coefficients: SIM_obs_coeff.mat

  • Observed, thresholded p-values: SIM_obs_p.mat

  • Permuted coefficients: SIM_perm_coeff.mat

  • Permuted, thresholded p-values: SIM_perm_p.mat

Set the lower and upper coefficient bounds to the range of the coefficients. For this case, the range is [-2, 2]. These bounds can be checked with the View button for the edge-level results button. In the bottom right corner, set the perm_count to the desired number of permutations. The example data provided has a maximum of 600 permutations. Run the tests using the procedure described in the previous section.