You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: icatb_run_analysis_settings.m
    path of file: ./repos/bspm/thirdparty/MIALAB/GroupICATv4.0a/icatb/icatb_analysis_functions
    
    the code of the file until where you have to start completion: function run_analysis_settings = icatb_run_analysis_settings(sesInfo)
%% Run
    
function helpCallback(hObject, event_data, handles)
%% Open help dialog box
%

msgString = str2mat('I. Select Analysis Step/Steps - There are 8 options available: ', ...
    '', ...
    '  a. All*** - Run all steps including Parameter Initialization, Data Reduction, Calculate ICA, Back Reconstruction, Calibrate Components and Group Stats.', ...
    '', ...
    '  b. Resume - Resume an incomplete analysis.', ...
    '', ...
    '  c. Parameter Initialization - Parameters initialization step parses user input and does error check before running group ICA.', ...
    '', ...
    '  d. Data Reduction - Subjects data is reduced to a few no. of components and passed to group data reduction step for multi-subject analysis.', ...
    '', ...
    '  e. Calculate ICA - ICA is run on the final reduced data from the data reduction step. ', ...
    '', ...
    '  f. Back Reconstruction - Individual subject components are reconstructed using the ICA information', ...
    '', ...
    '  g. Calibrate Components - Component maps and time courses could be scaled to either percent signal change or z-scores.', ...
    '', ...
    '  h. Group Stats - Group statistics are done on the individual subject components ', ...
    '', ...
    '', ...
    'II. Group PCA Performance Settings - There are three options available. The best match for each option is dependent on the MAX_AVAILABLE_MEM variable in icatb_defaults.', ...
    '', ...
    '  a. Maximize Performance - Data from subjects are blindly stacked. Covariance based PCA or expectation maximization is used.', ...
    '', ...
    '  b. Less Memory Usage - Typically data from subjects will not be stacked. For large data-sets covariance matrix size might be larger than stacked data-sets. In such cases expectation maximization is used.', ...
    '', ...
    '  c. User Specified settings - User settings will be selected.');

h = icatb_dialogBox('title', 'Run Analysis Settings', 'textBody', msgString, 'textType', 'large');

try
    waitfor(h);
catch
end