A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction.

Code ↔ Paper

4 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 4 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted

[1] § Methods › Cognitive outcome-prediction model training ↔ run_modeling_gui.m, lines 1–103 · score 0.63 · misclassification cost, hyper parameter, cross validation, MATLAB, multivariate, stratified
[2] § Methods › Cognitive outcome-prediction model training ↔ run_modeling_gui.m, lines 1–100 · score 0.63 · misclassification cost, hyper parameter, cross validation, MATLAB, multivariate, stratified
[3] § Results › Cognitive outcome predictions ↔ plot_cognitive_outcome_models.ipynb, the whole file · a weak match · score 0.52 · fLNM, sLNM, unthresholded, impaired, class, map
[4] § Methods › Cognitive outcome-prediction model evaluation ↔ model_code/ensemble/run_censemble_modeling.m, the whole file · a weak match · score 0.50 · tuned classification, score thresholds, ROC, AUC, optimized, predicted

Paper

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The authors' code

MATLAB · 1,699 lines · 86 KB · GPL-3.0 · 1 match

classdef run_modeling_gui < matlab.apps.AppBase
% Properties that correspond to app components
properties (Access = public)
UIFigure matlab.ui.Figure
IdPathFlag matlab.ui.control.CheckBox
RankCorCheckBox matlab.ui.control.CheckBox
RegressLesionVolumefromXandYCheckBox matlab.ui.control.CheckBox
CatYCheckBox matlab.ui.control.CheckBox
NuisanceRegressorCheckBox matlab.ui.control.CheckBox
RegressLesionVolumefromYCheckBox matlab.ui.control.CheckBox
SelectNuisanceRegressorsListBox matlab.ui.control.ListBox
SelectNuisanceRegressorsListBoxLabel matlab.ui.control.Label
SelectOutcomeVariableYListBox matlab.ui.control.ListBox
SelectOutcomeVariableYListBoxLabel matlab.ui.control.Label
MisclassificationCostsPanel matlab.ui.container.Panel
MisClasCostG1 matlab.ui.control.NumericEditField
Group1EditField_3Label matlab.ui.control.Label
MisClasCostG2 matlab.ui.control.NumericEditField
Group1EditField_4Label matlab.ui.control.Label
StatisticalThresholdsPanel matlab.ui.container.Panel
FDRLabel matlab.ui.control.Label
FDRThresh matlab.ui.control.NumericEditField
UncPThresh matlab.ui.control.NumericEditField
UncEditFieldLabel matlab.ui.control.Label
FweThresh matlab.ui.control.NumericEditField
FWEEditFieldLabel matlab.ui.control.Label
PermutationTestingPanel matlab.ui.container.Panel
RegistryFlag matlab.ui.control.CheckBox
OutDirText matlab.ui.control.TextArea
SelectOutputDirectoryButton matlab.ui.control.Button
RunAnalysisButton matlab.ui.control.Button
MultivariateModelingToolAnalysisConfigurationLabel matlab.ui.control.Label
UseParallelProcessingRecommendedCheckBox matlab.ui.control.CheckBox
FitExplanatoryModelCheckBox matlab.ui.control.CheckBox
ResultOptionsPanel matlab.ui.container.Panel
WritePermutationImagesCheckBox matlab.ui.control.CheckBox
WriteBootstrapImagesCheckBox matlab.ui.control.CheckBox
SelectModelDropDown matlab.ui.control.DropDown
SelectModelLabel matlab.ui.control.Label
BrainMaskText matlab.ui.control.TextArea
ImDirText matlab.ui.control.TextArea
CSVTtext matlab.ui.control.TextArea
PredictorTypeButtonGroup matlab.ui.container.ButtonGroup
OtherPredButton matlab.ui.control.RadioButton
LesionButton matlab.ui.control.RadioButton
MinimumDataThresholdsPanel matlab.ui.container.Panel
MinObs matlab.ui.control.NumericEditField
MinimumObservationLabel matlab.ui.control.Label
MinFreq matlab.ui.control.NumericEditField
MinimumFrequencyLabel matlab.ui.control.Label
SelectCSVfileButton matlab.ui.control.Button
SelectDataDirectoryButton matlab.ui.control.Button
HyperParameterTuningPanel matlab.ui.container.Panel
OptimizeHyperparametersCheckBox matlab.ui.control.CheckBox
HpOptRepeats matlab.ui.control.NumericEditField
HpOptFolds matlab.ui.control.NumericEditField
HpOptCvType matlab.ui.control.DropDown
HpOptNumberofRepeatsLabel matlab.ui.control.Label
HpOptNumberofFoldsLabel matlab.ui.control.Label
HpOptCVStrategyLabel matlab.ui.control.Label
SelectBrainMaskButton matlab.ui.control.Button
PredictorModalityButtonGroup matlab.ui.container.ButtonGroup
ArrayButton matlab.ui.control.RadioButton
MatrixButton matlab.ui.control.RadioButton
NIFTIButton matlab.ui.control.RadioButton
CrossValidationOptionsNestedifHyperParameterTuningEnabledPanel matlab.ui.container.Panel
EnableModelStackingCheckBox matlab.ui.control.CheckBox
CVPermIter matlab.ui.control.NumericEditField
PermutationsEditField_2Label matlab.ui.control.Label
CVPermPanel matlab.ui.container.Panel
CVPermCheckBox matlab.ui.control.CheckBox
RunCrossValidationCheckBox matlab.ui.control.CheckBox
CrossValidationSettingsPanel matlab.ui.container.Panel
OuterRepeats matlab.ui.control.NumericEditField
OuterFolds matlab.ui.control.NumericEditField
CVType matlab.ui.control.DropDown
NumberofRepeatsLabel_2 matlab.ui.control.Label
NumberofOuterFoldsLabel matlab.ui.control.Label
CVStrategyLabel_2 matlab.ui.control.Label
GeneralModelingOptionsPanel matlab.ui.container.Panel
BootIter matlab.ui.control.NumericEditField
BootAnalysesPanel matlab.ui.container.Panel
BootstrapLabel matlab.ui.control.Label
PermutationsEditFieldLabel matlab.ui.control.Label
BootCheckBox matlab.ui.control.CheckBox
PermCheckBox matlab.ui.control.CheckBox
StratifyTrainTestSplitsonYCheckBox matlab.ui.control.CheckBox
ConfIntWidth matlab.ui.control.NumericEditField
PermIter matlab.ui.control.NumericEditField
ConfIntLabel matlab.ui.control.Label
BootCIsCheckBox matlab.ui.control.CheckBox
PermVoxPValsCheckBox matlab.ui.control.CheckBox
PermApplycFWECheckBox matlab.ui.control.CheckBox
ApplyDTLVCCheckBox matlab.ui.control.CheckBox
StandardizeXCheckBox matlab.ui.control.CheckBox
StandardizeYCheckBox matlab.ui.control.CheckBox
end
% Callbacks that handle component events
methods (Access = private)
% Code that executes after component creation
function startupFcn(app)
model_dir = fileparts(which('run_modeling_gui.mlapp'));
addpath(model_dir);
end
% Callback function: CSVTtext, SelectCSVfileButton
function SelectCSVfileButtonPushed(app, event)
model_dir = fileparts(which('run_modeling_gui.mlapp'));
if isfile(fullfile(model_dir, 'default_paths.mat'))
load(fullfile(model_dir, 'default_paths.mat'), 'csv_dir');
[csv_file, csv_path] = uigetfile({'*.csv'; '*xls'; '*xlsx'; '*.'},...
'Select CSV file containing behavioral data', csv_dir);
else
[csv_file, csv_path] = uigetfile({'*.csv'; '*xls'; '*xlsx'; '*.'},...
'Select CSV file containing behavioral data');
end
if csv_file == 0
error('No file selected; please try again');
else
app.CSVTtext.Value = fullfile(csv_path, csv_file);
end
my_csv = readtable(fullfile(csv_path, csv_file),'NumHeaderLines', 0, 'Delimiter', ',');
my_vars = my_csv.Properties.VariableNames;
my_vars = setdiff(my_vars, "study_id");
app.SelectOutcomeVariableYListBox.Items = my_vars;
if app.NuisanceRegressorCheckBox.Value == 1
app.SelectNuisanceRegressorsListBox.Items = my_vars;
end
end
% Button pushed function: SelectDataDirectoryButton
function SelectDataDirectoryButtonPushed(app, event)
model_dir = fileparts(which('run_modeling_gui.mlapp'));
if isfile(fullfile(model_dir, 'default_paths.mat'))
load(fullfile(model_dir, 'default_paths.mat'), 'data_dir');
cfg.img_dir = uigetdir(data_dir, 'Select directory containing imaging data');
else
cfg.img_dir = uigetdir(pwd, 'Select directory containing imaging data');
end
if cfg.img_dir == 0
error('No directory selected; please try again');
else
app.ImDirText.Value = cfg.img_dir;
end
end
% Button pushed function: SelectBrainMaskButton
function SelectBrainMaskButtonPushed(app, event)
model_dir = fileparts(which('run_modeling_gui.mlapp'));
if app.NIFTIButton.Value == 1
if isfile(fullfile(model_dir, 'default_paths.mat'))
load(fullfile(model_dir, 'default_paths.mat'), 'mask_file');
[mask_file, mask_path] = uigetfile({'*.nii.gz'; '*.nii'; '*.'}, 'Select brain mask file',...
mask_file);
else
[mask_file, mask_path] = uigetfile({'*.nii.gz'; '*.nii'; '*.'}, 'Select brain mask file', pwd);
end
if mask_file == 0
error('No file selected; please try again');
else
app.BrainMaskText.Value = fullfile(mask_path, mask_file);
end
elseif app.MatrixButton.Value == 1
[parcel_file, parcel_path] = uigetfile({'*.mat'}, 'Select parcellation_table',...
pwd);
if parcel_file == 0
error('No file selected; please try again');
else
app.BrainMaskText.Value = fullfile(parcel_path, parcel_file);
end
end
end
% Button pushed function: RunAnalysisButton
function RunAnalysisButtonPushed(app, event)
% Get model directory
model_dir = fileparts(which('run_modeling_gui.mlapp'));
cfg.model_dir = fullfile(model_dir, 'model_code');
% Add core functions to path
addpath(fullfile(cfg.model_dir));
addpath(genpath(fullfile(cfg.model_dir, 'common')));
% Set analysis modality
if app.LesionButton.Value == 1
cfg.modality = 'lesion';
else
cfg.modality = 'other';
end
% Set imaging flag
if app.NIFTIButton.Value == 1
cfg.write_nifti_images = 1;
cfg.mask_path = string(app.BrainMaskText.Value);
elseif app.MatrixButton.Value == 1
cfg.write_nifti_images = 0;
cfg.parcel_table = string(app.BrainMaskText.Value);
elseif app.ArrayButton.Value == 1
cfg.write_nifti_images = 0;
end
% Set up basic cfg file
cfg.model_spec = string(app.SelectModelDropDown.Value);
cfg.fit_explanatory_model = app.FitExplanatoryModelCheckBox.Value;
cfg.bootstrap = app.BootCheckBox.Value;
cfg.jackknife = 0;
cfg.permutation = app.PermCheckBox.Value;
cfg.optimize_hyperparams = app.OptimizeHyperparametersCheckBox.Value;
cfg.cross_validation = app.RunCrossValidationCheckBox.Value;
cfg.out_dir = string(app.OutDirText.Value);
cfg.stacked_model = app.EnableModelStackingCheckBox.Value;
% Get default cfg
cfg = get_default_model_cfg(cfg);
%%%%%% Update defaults with user choices
%%%% General analysis params
% Data thresholds
cfg.min_obs = app.MinObs.Value;
cfg.freq_thresh = app.MinFreq.Value;
% Standardization
if app.StandardizeXCheckBox.Value == 1 && app.StandardizeYCheckBox.Value == 1
cfg.standardize = 3;
elseif app.StandardizeYCheckBox.Value == 1 && app.StandardizeXCheckBox.Value == 0
cfg.standardize = 2;
elseif app.StandardizeXCheckBox.Value == 1 && app.StandardizeYCheckBox.Value == 0
cfg.standardize = 1;
else
cfg.standardize = 0;
end
% DTLVC
cfg.dtlvc = app.ApplyDTLVCCheckBox.Value;
% Hyperparameter optimization
cfg.hp_opt.cv_type = string(app.HpOptCvType.Value);
cfg.hp_opt.folds = app.HpOptFolds.Value;
cfg.hp_opt.repeats = app.HpOptRepeats.Value;
% Statistical testing
cfg.boot.n_boot = app.BootIter.Value;
cfg.perm.n_perm = app.PermIter.Value;
if app.UncPThresh.Value ~= 0
cfg.unc_thresh = app.UncPThresh.Value;
else
cfg.unc_thresh = 0.001;
end
if app.FDRThresh.Value ~= 0
cfg.fdr_thresh = app.FDRThresh.Value;
else
cfg.fdr_thresh = 0.05;
end
if app.FweThresh.Value ~= 0
cfg.fwe_thresh = app.FweThresh.Value;
else
cfg.fwe_thresh = 0.05;
end
cfg.perm.coeff_p = app.PermVoxPValsCheckBox.Value;
if cfg.perm.coeff_p == 1
cfg.perm.coeff_fdr = 1;
cfg.perm.coeff_fwe = 1;
end
cfg.perm.coeff_cfwe = app.PermApplycFWECheckBox.Value;
% Cross-validation
if app.EnableModelStackingCheckBox == 1
cfg.cv.stacked_model = 1;
else
cfg.cv.stacked_model = 0;
end
cfg.cv.cv_type = char(app.CVType.Value);
cfg.cv.folds = app.OuterFolds.Value;
cfg.cv.repeats = app.OuterRepeats.Value;
cfg.cv.permutation = app.CVPermCheckBox.Value;
cfg.cv.n_perm = app.CVPermIter.Value;
% Parallel processing
cfg.parallel = app.UseParallelProcessingRecommendedCheckBox.Value;
% Image output
cfg.perm.write_perm_images = app.WritePermutationImagesCheckBox.Value;
cfg.boot.write_boot_images = app.WriteBootstrapImagesCheckBox.Value;
cfg.jack.write_jack_images = 0;
% Format and load behavioral and image data
beh_csv = readtable(string(app.CSVTtext.Value), 'NumHeaderLines', 0, 'Delimiter', ',');
beh_col = app.SelectOutcomeVariableYListBox.Value;
id_col = "study_id";
% Get data and put into cfg as long as predictors are NIFTI lesions
if app.NIFTIButton.Value == 1
data_dir = app.ImDirText.Value;
if app.LesionButton.Value == 1
if app.IdPathFlag.Value == 0
cfg = get_and_format_lesion_data(beh_csv, id_col, beh_col, app.RegistryFlag.Value, data_dir, cfg);
else
cfg = get_and_format_lesion_data2(beh_csv, id_col, beh_col, cfg);
end
else
if app.IdPathFlag.Value == 0
cfg = get_and_format_nifti_data(beh_csv, id_col, beh_col, app.RegistryFlag.Value, data_dir, cfg);
else
cfg = get_and_format_nifti_data2(beh_csv, id_col, beh_col, cfg);
end
end
elseif app.MatrixButton.Value == 1
data_dir = app.ImDirText.Value;
if app.IdPathFlag.Value == 0
cfg = get_and_format_matrix_data(beh_csv, id_col, beh_col, app.RegistryFlag.Value, data_dir, cfg);
else
cfg = get_and_format_matrix_data2(beh_csv, id_col, beh_col, cfg);
end
elseif app.ArrayButton.Value == 1
data_dir = app.ImDirText.Value;
if app.IdPathFlag.Value == 0
cfg = get_and_format_array_data(beh_csv, id_col, beh_col, app.RegistryFlag.Value, data_dir, cfg);
else
cfg = get_and_format_array_data2(beh_csv, id_col, beh_col, cfg);
end
end
% Set up confounds for nuisance regression if indicated
disp('Nuisance Regressors: ');
cfg.confounds = [];
for i = 1:length(app.SelectNuisanceRegressorsListBox.Value)
disp(app.SelectNuisanceRegressorsListBox.Value{i});
cfg.confounds(:,i) = beh_csv.(app.SelectNuisanceRegressorsListBox.Value{i});
cfg.confound_names{i} = app.SelectNuisanceRegressorsListBox.Value{i};
end
if ~isempty(cfg.confounds)
cfg.confounds = cfg.confounds(cfg.include_inds,:);
if ~isempty(find(isnan(cfg.confounds)))
disp('Checking nuisance regressors...')
disp('Some subjects have missing data for nuisance regressors; removing subjects with missing data...');
[cfg.confounds, cfg.missing_inds] = rmmissing(cfg.confounds, 1);
cfg.X = cfg.X(cfg.missing_inds == 0, :);
cfg.Y = cfg.Y(cfg.missing_inds == 0, :);
if isfield(cfg, 'lvol')
cfg.lvol = cfg.lvol(cfg.missing_inds == 0);
end
disp([num2str(numel(find(cfg.missing_inds==1))) ' subjects were removed due to missing data for nuisance regressors.']);
disp('Removed subjects will have values of 1 in model_results.cfg.missing_inds');
end
end
% Set up lesion volume confound if indicated
if app.LesionButton.Value == 1
if app.RegressLesionVolumefromYCheckBox.Value == 1 || app.RegressLesionVolumefromXandYCheckBox.Value == 1
disp('Lesion volume')
cfg.confounds = [cfg.confounds, zscore(cfg.lvol)];
if app.RegressLesionVolumefromXandYCheckBox.Value == 1
cfg.confound_opt = 'regress_both';
end
end
else
cfg.confounds = cfg.confounds;
end
% Do stratification on outcome if indicated
if app.StratifyTrainTestSplitsonYCheckBox.Value == 1
cfg.strat_var = cfg.Y;
else
cfg.strat_var = [];
end
% Set misclassification costs if relevant
if cfg.cat_Y==1
cfg.cost(1,2) = app.MisClasCostG1.Value;
cfg.cost(2,1) = app.MisClasCostG2.Value;
end
% Set grouping flag for T-test
if strcmp(cfg.model_spec, 'ttest')
if app.CatYCheckBox.Value == 1
cfg.group_var = 'Y';
cfg.cat_Y = 1;
else
cfg.group_var = 'X';
cfg.cat_Y = 0;
end
end
% Train and evaluate model
model_results = fit_and_evaluate_model(cfg);
% Write out images if indicated
if app.NIFTIButton.Value == 1
write_model_weight_maps(model_results);
elseif app.MatrixButton.Value == 1
write_model_weights_connectome(model_results);
end
% Print main results to command window and save to text file
cd(cfg.out_dir);
% Print results for regression models
if ~contains(cfg.model_spec, ["municorr", "bmunz", "ttest", "munilr", "muniolsr", "munimnr", "prop_sub"])
if cfg.fit_explanatory_model == 1 && cfg.cat_Y == 0
% Print model R-squared
diary('explanatory_model_results.txt')
disp('--------Model Result Summary-------');
disp('-----Inferential Model Results-----');
disp('-----------------------------------');
disp('Warning - This R-squared statistic is from the model fit to the full dataset and may reflect overfitting.');
disp('Interpret this result with extreme caution. It is recommended to use cross-validtion statistics for interpreting model performance.');
disp(['Model R-squared: ' num2str(model_results.r2)]);
% Print additional statistics depending on analysis settings
if cfg.bootstrap == 1 && cfg.boot.get_cis == 1
disp([num2str(100-(10*cfg.boot.ci_alpha_thresh_model)) '% Bootstrap CIs on R-squared:']);
disp(['Lower: ' num2str(model_results.boot.r2_ci(1))]);
disp(['Upper: ' num2str(model_results.boot.r2_ci(2))]);
end
if cfg.permutation == 1
disp('----- Permutation Test Results -----')
disp(['Permutation Test: p=' num2str(model_results.perm.model_pval)]);
end
if cfg.cross_validation == 1
load(fullfile(cfg.out_dir, 'cv_results.mat'));
disp('------ Cross-validation Test Results -------');
disp(['Cross-validation Correlation: r=' num2str(cv_results.all.corr) ', p=' num2str(cv_results.all.corr_pval)]);
if isfield(cv_results, 'perm_pval')
disp('-----Predictive Model Results-----');
disp(['Cross-Validation Permutation Test: p= ' num2str(cv_results.perm_pval)]);
disp(['Mean cross-validation R-squared: ' num2str(mean(cv_results.avg.r2_ss(:)))]);
end
end
diary('off');
elseif cfg.fit_explanatory_model == 0 && cfg.cat_Y == 0 && cfg.cross_validation == 1
load(fullfile(cfg.out_dir, 'cv_results.mat'));
if isfield(cv_results, 'perm_pval')
disp('-----Predictive Model Results-----');
disp(['Cross-Validation Permutation Test: p= ' num2str(cv_results.perm_pval)]);
disp(['Mean cross-validation R-squared: ' num2str(mean(cv_results.avg.r2_ss(:)))]);
else
disp('-----Predictive Model Results-----');
disp(['Mean cross-validation R-squared: ' num2str(mean(cv_results.avg.r2_ss(:)))]);
end
end
% Print results for classification models
if cfg.fit_explanatory_model == 1 && cfg.cat_Y == 1
% Print model R-squared
diary('explanatory_model_results.txt')
disp('--------Model Result Summary-------');
disp('-----Inferential Model Results-----');
disp('-----------------------------------');
disp('Warning - These accuracy and ROC statistics are from the model fit to the full dataset and may reflect overfitting.');
disp('Interpret these results with extreme caution. It is recommended to use cross-validtion statistics for interpreting model performance.');
disp(['Model Accuracy (Group -1): ' num2str(model_results.classrate(1))]);
disp(['Model Accuracy (Group 1): ' num2str(model_results.classrate(2))]);
disp(['Model Accuracy (Overall): ' num2str(model_results.classrate(3))]);
disp(['Model Area Under ROC Curve: ' num2str(model_results.roc_auc)]);
% Print additional statistics depending on analysis settings
if cfg.bootstrap == 1 && cfg.boot.get_cis == 1
disp([num2str(100-(10*cfg.boot.ci_alpha_thresh_model)) '% Bootstrap CIs on Group -1 Classification Rate:']);
disp(['Lower: ' num2str(model_results.boot.classrate_ci(1,1))]);
disp(['Upper: ' num2str(model_results.boot.classrate_ci(2,1))]);
disp([num2str(100-(10*cfg.boot.ci_alpha_thresh_model)) '% Bootstrap CIs on Group 1 Classification Rate:']);
disp(['Lower: ' num2str(model_results.boot.classrate_ci(1,2))]);
disp(['Upper: ' num2str(model_results.boot.classrate_ci(2,2))]);
disp([num2str(100-(10*cfg.boot.ci_alpha_thresh_model)) '% Bootstrap CIs on Overall Classification Rate:']);
disp(['Lower: ' num2str(model_results.boot.classrate_ci(1,3))]);
disp(['Upper: ' num2str(model_results.boot.classrate_ci(2,3))]);
disp([num2str(100-(10*cfg.boot.ci_alpha_thresh_model)) '% Bootstrap CIs on ROC Area Under Curve:']);
disp(['Lower: ' num2str(model_results.boot.roc_auc_ci(1,1))]);
disp(['Upper: ' num2str(model_results.boot.roc_auc_ci(2,1))]);
end
if cfg.permutation == 1
disp ('------ Permutation Test Results --------')
disp(['Permutation Test (AUC): p=' num2str(model_results.perm.model_pval)]);
end
if cfg.cross_validation == 1
disp('------ Cross-Validation Test Results -------')
load(fullfile(cfg.out_dir, 'cv_results.mat'));
disp(['Cross-validation Fisher Exact Test: stat=' num2str(cv_results.all.fisher_stat.OddsRatio) ', p=' num2str(cv_results.all.fisher_pval)]);
if isfield(cv_results, 'perm_pval')
disp('-----Predictive Model Results-----');
disp(['Cross-Validation Permutation Test: p= ' num2str(cv_results.perm_pval)]);
disp(['Mean Cross-Validation Area Under ROC Curve: ' num2str(mean(cv_results.avg.roc_auc(:)))]);
else
disp('-----Predictive Model Results-----');
disp(['Mean Cross-Validation Area Under ROC Curve: ' num2str(mean(cv_results.avg.roc_auc(:)))]);
end
end
diary('off');
elseif cfg.fit_explanatory_model == 0 && cfg.cat_Y == 1 && cfg.cross_validation == 1
load(fullfile(cfg.out_dir, 'cv_results.mat'));
if isfield(cv_results, 'perm_pval')
disp('-----Predictive Model Results-----');
disp(['Cross-Validation Permutation Test: p= ' num2str(cv_results.perm_pval)]);
disp(['Mean Cross-Validation Area Under ROC Curve: ' num2str(mean(cv_results.avg.roc_auc(:)))]);
else
disp('-----Predictive Model Results-----');
disp(['Mean Cross-Validation Area Under ROC Curve: ' num2str(mean(cv_results.avg.roc_auc(:)))]);
end
end
end
end
% Button pushed function: SelectOutputDirectoryButton
function SelectOutputDirectoryButtonPushed(app, event)
[out_dir] = uigetdir(pwd, 'Select directory to save results');
if out_dir == 0
error('No directory selected; please try again');
else
app.OutDirText.Value = out_dir;
end
end
% Value changed function: HpOptCvType
function HpOptCvTypeValueChanged(app, event)
value = app.HpOptCvType.Value;
if strcmp(string(value), 'LOOCV')
app.HpOptFolds.Enable = 'off';
app.HpOptFolds.Value = 'N/A';
app.HpOptRepeats.Value = 1;
else
app.HpOptFolds.Enable = 'on';
app.HpOptFolds.Value = 5;
app.HpOptRepeats.Value = 5;
end
end
% Value changed function: StratifyTrainTestSplitsonYCheckBox
function StratifyTrainTestSplitsonYCheckBoxValueChanged(app, event)
end
% Value changed function: PermCheckBox
function PermCheckBoxValueChanged(app, event)
value = app.PermCheckBox.Value;
if value == 0
app.PermIter.Enable = 'off';
app.PermIter.Value = 0;
app.PermApplycFWECheckBox.Enable = 'off';
app.PermApplycFWECheckBox.Value = 0;
app.WritePermutationImagesCheckBox.Enable = 'off';
app.WritePermutationImagesCheckBox.Value = 0;
if app.BootCheckBox.Value == 0
app.FweThresh.Value = 0.05;
app.FweThresh.Enable = 'off';
app.FDRThresh.Value = 0.05;
app.FDRThresh.Enable = 'off';
app.UncPThresh.Value = 0.001;
app.UncPThresh.Enable = 'off';
else
app.FweThresh.Value = 0.05;
app.FweThresh.Enable = 'on';
app.FDRThresh.Value = 0.05;
app.FDRThresh.Enable = 'on';
app.UncPThresh.Value = 0.001;
app.UncPThresh.Enable = 'on';
end
else
app.PermIter.Enable = 'on';
app.PermIter.Value = 1000;
app.PermApplycFWECheckBox.Enable = 'on';
app.PermApplycFWECheckBox.Value = 1;
app.FweThresh.Value = 0.05;
app.FweThresh.Enable = 'on';
app.FDRThresh.Value = 0.05;
app.FDRThresh.Enable = 'on';
app.UncPThresh.Value = 0.001;
app.UncPThresh.Enable = 'on';
app.WritePermutationImagesCheckBox.Enable = 'on';
app.WritePermutationImagesCheckBox.Value = 1;
end
end
% Value changed function: BootCheckBox
function BootCheckBoxValueChanged(app, event)
value = app.BootCheckBox.Value;
if value == 0
app.BootIter.Enable = 'off';
app.BootIter.Value = 0;
app.BootCIsCheckBox.Enable = "off";
app.BootCIsCheckBox.Value = 0;
app.ConfIntWidth.Value = 0;
app.ConfIntWidth.Enable = 'off';
app.WriteBootstrapImagesCheckBox.Value = 0;
app.WriteBootstrapImagesCheckBox.Enable = 0;
if app.PermCheckBox.Value == 0
app.WritePermutationImagesCheckBox.Value = 0;
app.WritePermutationImagesCheckBox.Enable = 0;
app.FweThresh.Value = 0.05;
app.FweThresh.Enable = 'off';
app.FDRThresh.Value = 0.05;
app.FDRThresh.Enable = 'off';
app.UncPThresh.Value = 0.001;
app.UncPThresh.Enable = 'off';
else
app.WritePermutationImagesCheckBox.Value = 1;
app.WritePermutationImagesCheckBox.Enable = 1;
app.FweThresh.Value = 0.05;
app.FweThresh.Enable = 'on';
app.FDRThresh.Value = 0.05;
app.FDRThresh.Enable = 'on';
app.UncPThresh.Value = 0.001;
app.UncPThresh.Enable = 'on';
end
else
app.WriteBootstrapImagesCheckBox.Value = 1;
app.WriteBootstrapImagesCheckBox.Enable = 1;
app.BootIter.Enable = 'on';
app.BootIter.Value = 1000;
app.BootCIsCheckBox.Enable = 'on';
app.BootCIsCheckBox.Value = 1;
app.ConfIntWidth.Value = 95;
app.ConfIntWidth.Enable = 'on';
app.FweThresh.Value = 0.05;
app.FweThresh.Enable = 'on';
app.FDRThresh.Value = 0.05;
app.FDRThresh.Enable = 'on';
app.UncPThresh.Value = 0.001;
app.UncPThresh.Enable = 'on';
end
end
% Selection changed function: PredictorTypeButtonGroup
function PredictorTypeButtonGroupSelectionChanged(app, event)
if app.LesionButton.Value == 1 && ~contains(app.SelectModelDropDown.Value, ["ttest", "bmunz", "municorr", "munilr", "munimnr", "muniolsr", "prop_sub"])
app.ApplyDTLVCCheckBox.Enable = "on";
app.ApplyDTLVCCheckBox.Value = 0;
app.RegressLesionVolumefromYCheckBox.Enable = "on";
app.RegressLesionVolumefromYCheckBox.Value = 0;
app.RegressLesionVolumefromXandYCheckBox.Enable = "on";
app.RegressLesionVolumefromXandYCheckBox.Value = 0;
app.MinFreq.Value = 4;
app.MinObs.Value = 1;
elseif app.LesionButton.Value == 1 && contains(app.SelectModelDropDown.Value, ["ttest", "bmunz", "municorr", "munilr", "munimnr", "muniolsr"])
app.ApplyDTLVCCheckBox.Enable = "on";
app.ApplyDTLVCCheckBox.Value = 0;
app.RegressLesionVolumefromYCheckBox.Enable = "on";
app.RegressLesionVolumefromYCheckBox.Value = 1;
app.RegressLesionVolumefromXandYCheckBox.Enable = "off";
app.RegressLesionVolumefromXandYCheckBox.Value = 0;
app.MinFreq.Value = 4;
app.MinObs.Value = 1;
else
app.ApplyDTLVCCheckBox.Value = 0;
app.ApplyDTLVCCheckBox.Enable = 'off';
app.RegressLesionVolumefromYCheckBox.Enable = "off";
app.RegressLesionVolumefromYCheckBox.Value = 0;
app.RegressLesionVolumefromXandYCheckBox.Enable = "on";
app.RegressLesionVolumefromXandYCheckBox.Value = 0;
app.MinFreq.Value = 1;
app.MinObs.Value = 0;
end
end
% Value changed function: RunCrossValidationCheckBox
function RunCrossValidationCheckBoxValueChanged(app, event)
value = app.RunCrossValidationCheckBox.Value;
if value == 1
app.CVType.Enable = "on";
app.CVType.Value = "KFold";
app.CVPermIter.Enable = "on";
app.CVPermIter.Value = 20;
app.OuterFolds.Enable = "on";
app.OuterFolds.Value = 5;
app.OuterRepeats.Enable = "on";
app.OuterRepeats.Value = 5;
app.CVPermCheckBox.Enable = "on";
app.CVPermIter.Enable = "on";
else
app.CVType.Enable = "off";
app.CVType.Value = "KFold";
app.CVPermIter.Enable = "off";
app.CVPermIter.Value = 0;
app.OuterFolds.Enable = "off";
app.OuterFolds.Value = 0;
app.OuterRepeats.Enable = "off";
app.OuterRepeats.Value = 0;
app.CVPermCheckBox.Enable = "off";
app.CVPermIter.Enable = "off";
end
end
% Value changed function: SelectModelDropDown
function SelectModelDropDownValueChanged(app, event)
value = app.SelectModelDropDown.Value;
if ~contains(value, ["logistic", "pls_da", "svc", "censemble"])
app.MisClasCostG1.Enable = 0;
app.MisClasCostG2.Enable = 0;
else
app.MisClasCostG1.Enable = 1;
app.MisClasCostG2.Enable = 1;
end
if app.LesionButton.Value == 1
if contains(value, ["logistic", "pls_da", "svc", "censemble"])
app.RegressLesionVolumefromYCheckBox.Text = 'Regress Lesion Volume from X';
app.RegressLesionVolumefromXandYCheckBox.Visible = 0;
app.RegressLesionVolumefromXandYCheckBox.Value = 0;
app.RegressLesionVolumefromXandYCheckBox.Enable = 0;
elseif contains(value, ["muniolsr", "munilr", "munimnr", "municorr"])
app.RegressLesionVolumefromYCheckBox.Text = 'Include Lesion Volume as Covariate';
app.RegressLesionVolumefromXandYCheckBox.Visible = 0;
app.RegressLesionVolumefromXandYCheckBox.Value = 0;
app.RegressLesionVolumefromXandYCheckBox.Enable = 0;
else
app.RegressLesionVolumefromYCheckBox.Text = 'Regress Lesion Volume from Y';
app.RegressLesionVolumefromXandYCheckBox.Visible = 1;
app.RegressLesionVolumefromXandYCheckBox.Value = 0;
app.RegressLesionVolumefromXandYCheckBox.Enable = 1;
if ~contains(value, ["ttest", "bmunz", "prop_sub"])
app.RegressLesionVolumefromXandYCheckBox.Text = 'Regress Lesion Volume from X & Y';
app.RegressLesionVolumefromXandYCheckBox.Value = 0;
app.RegressLesionVolumefromXandYCheckBox.Enable = 1;
else
app.RegressLesionVolumefromXandYCheckBox.Visible = 0;
app.RegressLesionVolumefromXandYCheckBox.Value = 0;
app.RegressLesionVolumefromXandYCheckBox.Enable = 0;
end
end
else
app.RegressLesionVolumefromXandYCheckBox.Visible = 0;
app.RegressLesionVolumefromXandYCheckBox.Value = 0;
app.RegressLesionVolumefromXandYCheckBox.Enable = 0;
app.RegressLesionVolumefromYCheckBox.Value = 0;
app.RegressLesionVolumefromYCheckBox.Enable = 0;
app.RegressLesionVolumefromYCheckBox.Visible = 0;
end
if contains(value, ["municorr", "ttest", "bmunz", "munilr", "muniolsr", "munimnr", ...
'prop_sub'])
% Correlation options
if strcmp(value, 'municorr')
app.RankCorCheckBox.Visible = 1;
app.RankCorCheckBox.Enable = 1;
app.RankCorCheckBox.Value = 0;
else
app.RankCorCheckBox.Visible = 0;
app.RankCorCheckBox.Enable = 0;
app.RankCorCheckBox.Value = 0;
end
% Bootstrap options
app.BootCheckBox.Enable = 0;
app.BootCIsCheckBox.Enable = 0;
app.BootstrapLabel.Enable = 0;
app.BootCIsCheckBox.Enable = 0;
app.BootIter.Enable = 0;
app.BootIter.Value = 0;
app.BootCheckBox.Value = 0;
app.BootCIsCheckBox.Value = 0;
app.ConfIntWidth.Enable = 0;
app.WriteBootstrapImagesCheckBox.Enable = 0;
app.WriteBootstrapImagesCheckBox.Value = 0;
% Hyperparameter optimization options
app.HpOptRepeats.Value = 0;
app.HpOptFolds.Value = 0;
app.OptimizeHyperparametersCheckBox.Value = 0;
app.OptimizeHyperparametersCheckBox.Enable = 0;
app.HpOptCvType.Enable = 0;
app.HpOptFolds.Enable = 0;
app.HpOptRepeats.Enable = 0;
% Permutation testing options
app.PermCheckBox.Value = 1;
app.PermCheckBox.Enable = 1;
app.PermApplycFWECheckBox.Value = 1;
app.PermApplycFWECheckBox.Enable = 1;
app.PermIter.Value = 10000;
app.PermIter.Enable = 1;
app.FweThresh.Value = 0.05;
app.FweThresh.Enable = 1;
app.WritePermutationImagesCheckBox.Value = 1;
app.WritePermutationImagesCheckBox.Enable = 1;
if ~strcmp(value, 'prop_sub')
app.PermVoxPValsCheckBox.Value = 0;
app.PermVoxPValsCheckBox.Enable = 1;
app.StandardizeXCheckBox.Enable = 1;
app.StandardizeXCheckBox.Value = 0;
app.StandardizeYCheckBox.Enable = 1;
app.StandardizeYCheckBox.Value = 0;
app.FDRThresh.Value = 0.05;
app.FDRThresh.Enable = 1;
app.UncPThresh.Value = .001;
app.UncPThresh.Enable = 1;
app.ApplyDTLVCCheckBox.Enable = 1;
app.ApplyDTLVCCheckBox.Value = 0;
app.MinFreq.Value = 10;
app.RegressLesionVolumefromYCheckBox.Enable = 1;
app.RegressLesionVolumefromYCheckBox.Value = 0;
app.SelectNuisanceRegressorsListBox.Enable = 1;
app.NuisanceRegressorCheckBox.Enable = 1;
else
app.PermVoxPValsCheckBox.Value = 0;
app.PermVoxPValsCheckBox.Enable = 0;
app.StandardizeXCheckBox.Enable = 0;
app.StandardizeXCheckBox.Value = 0;
app.StandardizeYCheckBox.Enable = 0;
app.StandardizeYCheckBox.Value = 0;
app.FDRThresh.Value = 0;
app.FDRThresh.Enable = 0;
app.FweThresh.Value = 0;
app.FweThresh.Enable = 0;
app.UncPThresh.Value = 0;
app.UncPThresh.Enable = 0;
app.ApplyDTLVCCheckBox.Enable = 0;
app.ApplyDTLVCCheckBox.Value = 0;
app.MinFreq.Value = 1;
app.RegressLesionVolumefromYCheckBox.Enable = 0;
app.RegressLesionVolumefromYCheckBox.Value = 0;
app.SelectNuisanceRegressorsListBox.Enable = 0;
app.SelectNuisanceRegressorsListBox.Value = {};
app.NuisanceRegressorCheckBox.Value = 0;
app.NuisanceRegressorCheckBox.Enable = 0;
end
% Cross-validation options
app.CVType.Enable = 0;
app.CVPermPanel.Enable = 0;
app.EnableModelStackingCheckBox.Enable = 0;
app.StratifyTrainTestSplitsonYCheckBox.Enable = 0;
app.RunCrossValidationCheckBox.Enable = 0;
app.RunCrossValidationCheckBox.Value = 0;
app.CVPermIter.Value = 0;
app.CVPermIter.Enable = 0;
app.EnableModelStackingCheckBox.Value = 0;
app.OuterFolds.Value = 0;
app.OuterFolds.Enable = 0;
app.OuterRepeats.Value = 0;
app.OuterRepeats.Enable = 0;
% General options
app.ApplyDTLVCCheckBox.Value = 0;
% T-test options
if strcmp(value, 'ttest')
app.CatYCheckBox.Visible = 1;
app.CatYCheckBox.Enable = 1;
app.CatYCheckBox.Value = 1;
else
app.CatYCheckBox.Visible = 0;
app.CatYCheckBox.Enable = 0;
app.CatYCheckBox.Value = 0;
end
else
% Bootstrap options
app.BootCheckBox.Enable = 1;
app.BootCIsCheckBox.Enable = 1;
app.BootstrapLabel.Enable = 1;
app.BootCIsCheckBox.Enable = 1;
app.BootIter.Enable = 1;
app.BootIter.Value = 1000;
app.BootCheckBox.Value = 1;
app.BootCIsCheckBox.Value = 1;
app.ConfIntWidth.Enable = 1;
app.WriteBootstrapImagesCheckBox.Enable = 1;
app.WriteBootstrapImagesCheckBox.Value = 1;
% Hyperparameter optimization options
app.HpOptRepeats.Value = 5;
app.HpOptFolds.Value = 5;
app.OptimizeHyperparametersCheckBox.Value = 1;
app.OptimizeHyperparametersCheckBox.Enable = 1;
app.HpOptCvType.Enable = 1;
app.HpOptFolds.Enable = 1;
app.HpOptRepeats.Enable = 1;
% Permutation testing options
app.PermCheckBox.Value = 0;
app.PermCheckBox.Enable = 1;
app.PermApplycFWECheckBox.Value = 0;
app.PermApplycFWECheckBox.Enable = 1;
app.PermIter.Value = 0;
app.PermIter.Enable = 1;
app.PermVoxPValsCheckBox.Value = 0;
app.PermVoxPValsCheckBox.Enable = 1;
app.WritePermutationImagesCheckBox.Value = 0;
app.WritePermutationImagesCheckBox.Enable = 1;
% Cross-validation options
app.CVType.Enable = 1;
app.CVPermPanel.Enable = 1;
app.EnableModelStackingCheckBox.Enable = 1;
app.StratifyTrainTestSplitsonYCheckBox.Enable = 1;
app.RunCrossValidationCheckBox.Enable = 1;
app.RunCrossValidationCheckBox.Value = 1;
app.CVPermIter.Value = 50;
app.CVPermIter.Enable = 1;
app.OuterFolds.Enable = 1;
app.OuterFolds.Value = 5;
app.OuterRepeats.Enable = 1;
app.OuterRepeats.Value = 5;
app.EnableModelStackingCheckBox.Value = 1;
% T-test options
app.CatYCheckBox.Visible = 0;
app.CatYCheckBox.Enable = 0;
app.CatYCheckBox.Value = 0;
% Other
app.StandardizeXCheckBox.Enable = 1;
app.StandardizeXCheckBox.Value = 0;
app.StandardizeYCheckBox.Enable = 1;
app.StandardizeYCheckBox.Value = 0;
app.FDRThresh.Value = 0.05;
app.FDRThresh.Enable = 1;
app.FweThresh.Value = 0.05;
app.FweThresh.Enable = 1;
app.UncPThresh.Value = .001;
app.UncPThresh.Enable = 1;
app.ApplyDTLVCCheckBox.Enable = 1;
app.ApplyDTLVCCheckBox.Value = 0;
app.MinFreq.Value = 10;
app.RegressLesionVolumefromYCheckBox.Enable = 1;
app.SelectNuisanceRegressorsListBox.Enable = 1;
app.NuisanceRegressorCheckBox.Enable = 1;
app.RankCorCheckBox.Visible = 0;
app.RankCorCheckBox.Enable = 0;
app.RankCorCheckBox.Value = 0;
end
end
% Value changed function: OptimizeHyperparametersCheckBox
function OptimizeHyperparametersCheckBoxValueChanged(app, event)
value = app.OptimizeHyperparametersCheckBox.Value;
if value == 1
app.HpOptCvType.Enable = "on";
app.HpOptFolds.Enable = "on";
app.HpOptRepeats.Enable = "on";
elseif value == 0
app.HpOptCvType.Enable = "off";
app.HpOptFolds.Enable = "off";
app.HpOptRepeats.Enable = "off";
end
end
% Value changed function: EnableModelStackingCheckBox
function EnableModelStackingCheckBoxValueChanged(app, event)
end
% Button down function: PredictorModalityButtonGroup
function PredictorModalityButtonGroupButtonDown(app, event)
end
% Selection changed function: PredictorModalityButtonGroup
function PredictorModalityButtonGroupSelectionChanged(app, event)
if app.MatrixButton.Value == 1
app.BrainMaskText.Enable = 1;
app.BrainMaskText.Editable = 1;
app.BrainMaskText.Placeholder = 'Parcellation table not selected (required to write .node files)';
app.SelectBrainMaskButton.Text = 'Select Parcel Table';
app.SelectBrainMaskButton.Enable = 1;
elseif app.NIFTIButton.Value == 1
app.BrainMaskText.Enable = 1;
app.BrainMaskText.Editable = 1;
app.BrainMaskText.Placeholder = 'No mask selected';
app.SelectBrainMaskButton.Text = 'Select Brain Mask';
app.SelectBrainMaskButton.Enable = 1;
elseif app.ArrayButton.Value == 1
app.BrainMaskText.Enable = 0;
app.BrainMaskText.Editable = 0;
app.BrainMaskText.Placeholder = 'N/A';
app.SelectBrainMaskButton.Text = 'N/A';
app.SelectBrainMaskButton.Enable = 0;
elseif app.TableButton.Value == 1
app.BrainMaskText.Enable = 0;
app.BrainMaskText.Enable = 0;
app.BrainMaskText.Editable = 0;
app.BrainMaskText.Placeholder = 'N/A';
app.SelectBrainMaskButton.Text = 'N/A';
app.SelectBrainMaskButton.Enable = 0;
end
end
% Callback function
function PredictorTypeButtonGroupButtonDown(app, event)
end
% Value changed function: CVPermCheckBox
function CVPermCheckBoxValueChanged(app, event)
value = app.CVPermCheckBox.Value;
if value == 1
app.CVPermIter.Enable = 1;
app.CVPermIter.Value = 50;
else
app.CVPermIter.Enable = 0;
app.CVPermIter.Value = 0;
end
end
% Value changed function: CVType
function CVTypeValueChanged(app, event)
value = app.CVType.Value;
if value == 1
app.OuterFolds.Enable = 1;
app.OuterFolds.Value = 5;
app.HpOptRepeats.Enable = 1;
app.HpOptRepeats.Value = 20;
else
app.OuterFolds.Enable = 0;
app.OuterFolds.Value = 0;
app.HpOptRepeats.Enable = 0;
app.HpOptRepeats.Value = 0;
end
end
% Value changed function: NuisanceRegressorCheckBox
function NuisanceRegressorCheckBoxValueChanged(app, event)
value = app.NuisanceRegressorCheckBox.Value;
if value == 1
app.SelectNuisanceRegressorsListBox.Enable = 1;
app.SelectNuisanceRegressorsListBox.Items = setdiff(app.SelectOutcomeVariableYListBox.Items, app.SelectOutcomeVariableYListBox.Value);
else
app.SelectNuisanceRegressorsListBox.Enable = 0;
app.SelectNuisanceRegressorsListBox.Items = string();
app.SelectNuisanceRegressorsListBox.Value = {};
end
end
% Value changed function: CatYCheckBox
function CatYCheckBoxValueChanged(app, event)
value = app.CatYCheckBox.Value;
if value == 1
app.RegressLesionVolumefromYCheckBox.Text = 'Regress lesion volume from X';
else
app.RegressLesionVolumefromYCheckBox.Text = 'Regress lesion volume from Y';
end
end
% Value changed function: RegressLesionVolumefromYCheckBox
function RegressLesionVolumefromYCheckBoxValueChanged(app, event)
value = app.RegressLesionVolumefromYCheckBox.Value;
if value == 1
app.ApplyDTLVCCheckBox.Value = 0;
app.ApplyDTLVCCheckBox.Enable = 0;
else
app.ApplyDTLVCCheckBox.Enable = 1;
end
end
% Value changed function: RegressLesionVolumefromXandYCheckBox
function RegressLesionVolumefromXandYCheckBoxValueChanged(app, event)
value = app.RegressLesionVolumefromXandYCheckBox.Value;
if value == 1
app.ApplyDTLVCCheckBox.Value = 0;
app.ApplyDTLVCCheckBox.Enable = 0;
else
app.ApplyDTLVCCheckBox.Enable = 1;
end
end
% Value changed function: ApplyDTLVCCheckBox
function ApplyDTLVCCheckBoxValueChanged(app, event)
value = app.ApplyDTLVCCheckBox.Value;
if value == 1
app.RegressLesionVolumefromYCheckBox.Value = 0;
app.RegressLesionVolumefromYCheckBox.Enable = 0;
app.RegressLesionVolumefromXandYCheckBox.Value = 0;
app.RegressLesionVolumefromXandYCheckBox.Enable = 0;
else
app.RegressLesionVolumefromYCheckBox.Enable = 1;
app.RegressLesionVolumefromXandYCheckBox.Enable = 1;
end
end
% Value changed function: IdPathFlag
function IdPathFlagValueChanged(app, event)
value = app.IdPathFlag.Value;
if value == 1
app.SelectDataDirectoryButton.Enable = 0;
app.ImDirText.Enable = 0;
else
app.SelectDataDirectoryButton.Enable = 1;
app.ImDirText.Enable = 1;
end
end
end
% Component initialization
methods (Access = private)
% Create UIFigure and components
function createComponents(app)
% Create UIFigure and hide until all components are created
app.UIFigure = uifigure('Visible', 'off');
app.UIFigure.Position = [100 100 857 886];
app.UIFigure.Name = 'MATLAB App';
% Create GeneralModelingOptionsPanel
app.GeneralModelingOptionsPanel = uipanel(app.UIFigure);
app.GeneralModelingOptionsPanel.Title = 'General Modeling Options';
app.GeneralModelingOptionsPanel.BackgroundColor = [0.9412 0.9412 0.9412];
app.GeneralModelingOptionsPanel.FontWeight = 'bold';
app.GeneralModelingOptionsPanel.FontSize = 14;
app.GeneralModelingOptionsPanel.Position = [9 212 833 207];
% Create StandardizeYCheckBox
app.StandardizeYCheckBox = uicheckbox(app.GeneralModelingOptionsPanel);
app.StandardizeYCheckBox.Text = 'Standardize Y';
app.StandardizeYCheckBox.Position = [6 152 97 22];
% Create StandardizeXCheckBox
app.StandardizeXCheckBox = uicheckbox(app.GeneralModelingOptionsPanel);
app.StandardizeXCheckBox.Text = 'Standardize X';
app.StandardizeXCheckBox.Position = [108 152 97 22];
% Create ApplyDTLVCCheckBox
app.ApplyDTLVCCheckBox = uicheckbox(app.GeneralModelingOptionsPanel);
app.ApplyDTLVCCheckBox.ValueChangedFcn = createCallbackFcn(app, @ApplyDTLVCCheckBoxValueChanged, true);
app.ApplyDTLVCCheckBox.Text = 'Apply DTLVC';
app.ApplyDTLVCCheckBox.Position = [219 152 94 22];
% Create PermApplycFWECheckBox
app.PermApplycFWECheckBox = uicheckbox(app.GeneralModelingOptionsPanel);
app.PermApplycFWECheckBox.Text = 'Apply cFWE';
app.PermApplycFWECheckBox.Position = [413 47 88 22];
% Create PermVoxPValsCheckBox
app.PermVoxPValsCheckBox = uicheckbox(app.GeneralModelingOptionsPanel);
app.PermVoxPValsCheckBox.Enable = 'off';
app.PermVoxPValsCheckBox.Visible = 'off';
app.PermVoxPValsCheckBox.Text = 'Voxel p-values';
app.PermVoxPValsCheckBox.Position = [413 19 100 22];
% Create BootCIsCheckBox
app.BootCIsCheckBox = uicheckbox(app.GeneralModelingOptionsPanel);
app.BootCIsCheckBox.Text = 'Bootstrap CIs';
app.BootCIsCheckBox.Position = [550 47 95 22];
app.BootCIsCheckBox.Value = true;
% Create ConfIntLabel
app.ConfIntLabel = uilabel(app.GeneralModelingOptionsPanel);
app.ConfIntLabel.HorizontalAlignment = 'right';
app.ConfIntLabel.Position = [544 22 73 22];
app.ConfIntLabel.Text = 'CI Width (%)';
% Create PermIter
app.PermIter = uieditfield(app.GeneralModelingOptionsPanel, 'numeric');
app.PermIter.Limits = [0 Inf];
app.PermIter.Position = [492 71 46 22];
app.PermIter.Value = 1000;
% Create ConfIntWidth
app.ConfIntWidth = uieditfield(app.GeneralModelingOptionsPanel, 'numeric');
app.ConfIntWidth.Limits = [0 Inf];
app.ConfIntWidth.Position = [625 22 37 22];
app.ConfIntWidth.Value = 95;
% Create StratifyTrainTestSplitsonYCheckBox
app.StratifyTrainTestSplitsonYCheckBox = uicheckbox(app.GeneralModelingOptionsPanel);
app.StratifyTrainTestSplitsonYCheckBox.ValueChangedFcn = createCallbackFcn(app, @StratifyTrainTestSplitsonYCheckBoxValueChanged, true);
app.StratifyTrainTestSplitsonYCheckBox.Text = 'Stratify Train/Test Splits on Y';
app.StratifyTrainTestSplitsonYCheckBox.Position = [322 152 175 22];
% Create PermCheckBox
app.PermCheckBox = uicheckbox(app.GeneralModelingOptionsPanel);
app.PermCheckBox.ValueChangedFcn = createCallbackFcn(app, @PermCheckBoxValueChanged, true);
app.PermCheckBox.Text = 'Run Permutations';
app.PermCheckBox.Position = [414 94 118 22];
app.PermCheckBox.Value = true;
% Create BootCheckBox
app.BootCheckBox = uicheckbox(app.GeneralModelingOptionsPanel);
app.BootCheckBox.ValueChangedFcn = createCallbackFcn(app, @BootCheckBoxValueChanged, true);
app.BootCheckBox.Text = 'Run Bootstraps';
app.BootCheckBox.Position = [550 94 105 22];
app.BootCheckBox.Value = true;
% Create PermutationsEditFieldLabel
app.PermutationsEditFieldLabel = uilabel(app.GeneralModelingOptionsPanel);
app.PermutationsEditFieldLabel.HorizontalAlignment = 'right';
app.PermutationsEditFieldLabel.Position = [408 71 76 22];
app.PermutationsEditFieldLabel.Text = 'Permutations';
% Create BootstrapLabel
app.BootstrapLabel = uilabel(app.GeneralModelingOptionsPanel);
app.BootstrapLabel.HorizontalAlignment = 'right';
app.BootstrapLabel.Position = [545 71 62 22];
app.BootstrapLabel.Text = 'Bootstraps';
% Create BootAnalysesPanel
app.BootAnalysesPanel = uipanel(app.GeneralModelingOptionsPanel);
app.BootAnalysesPanel.BorderType = 'none';
app.BootAnalysesPanel.Title = 'Bootstrap Analyses';
app.BootAnalysesPanel.FontWeight = 'bold';
app.BootAnalysesPanel.Position = [548 113 135 30];
% Create BootIter
app.BootIter = uieditfield(app.GeneralModelingOptionsPanel, 'numeric');
app.BootIter.Limits = [0 Inf];
app.BootIter.Position = [616 71 46 22];
app.BootIter.Value = 1000;
% Create CrossValidationOptionsNestedifHyperParameterTuningEnabledPanel
app.CrossValidationOptionsNestedifHyperParameterTuningEnabledPanel = uipanel(app.UIFigure);
app.CrossValidationOptionsNestedifHyperParameterTuningEnabledPanel.Title = 'Cross-Validation Options (Nested if Hyper-Parameter Tuning Enabled)';
app.CrossValidationOptionsNestedifHyperParameterTuningEnabledPanel.FontWeight = 'bold';
app.CrossValidationOptionsNestedifHyperParameterTuningEnabledPanel.FontSize = 14;
app.CrossValidationOptionsNestedifHyperParameterTuningEnabledPanel.Position = [10 9 544 174];
% Create CrossValidationSettingsPanel
app.CrossValidationSettingsPanel = uipanel(app.CrossValidationOptionsNestedifHyperParameterTuningEnabledPanel);
app.CrossValidationSettingsPanel.BorderType = 'none';
app.CrossValidationSettingsPanel.Title = 'Cross-Validation Settings';
app.CrossValidationSettingsPanel.FontWeight = 'bold';
app.CrossValidationSettingsPanel.Position = [182 28 205 120];
% Create CVStrategyLabel_2
app.CVStrategyLabel_2 = uilabel(app.CrossValidationSettingsPanel);
app.CVStrategyLabel_2.Position = [11 70 70 22];
app.CVStrategyLabel_2.Text = 'CV Strategy';
% Create NumberofOuterFoldsLabel
app.NumberofOuterFoldsLabel = uilabel(app.CrossValidationSettingsPanel);
app.NumberofOuterFoldsLabel.Position = [11 41 127 22];
app.NumberofOuterFoldsLabel.Text = 'Number of Outer Folds';
% Create NumberofRepeatsLabel_2
app.NumberofRepeatsLabel_2 = uilabel(app.CrossValidationSettingsPanel);
app.NumberofRepeatsLabel_2.Position = [11 11 109 22];
app.NumberofRepeatsLabel_2.Text = 'Number of Repeats';
% Create CVType
app.CVType = uidropdown(app.CrossValidationSettingsPanel);
app.CVType.Items = {'KFold', 'LOOCV'};
app.CVType.Editable = 'on';
app.CVType.ValueChangedFcn = createCallbackFcn(app, @CVTypeValueChanged, true);
app.CVType.BackgroundColor = [1 1 1];
app.CVType.Position = [131 70 69 22];
app.CVType.Value = 'KFold';
% Create OuterFolds
app.OuterFolds = uieditfield(app.CrossValidationSettingsPanel, 'numeric');
app.OuterFolds.Position = [167 41 33 22];
app.OuterFolds.Value = 5;
% Create OuterRepeats
app.OuterRepeats = uieditfield(app.CrossValidationSettingsPanel, 'numeric');
app.OuterRepeats.Position = [167 10 33 22];
app.OuterRepeats.Value = 5;
% Create RunCrossValidationCheckBox
app.RunCrossValidationCheckBox = uicheckbox(app.CrossValidationOptionsNestedifHyperParameterTuningEnabledPanel);
app.RunCrossValidationCheckBox.ValueChangedFcn = createCallbackFcn(app, @RunCrossValidationCheckBoxValueChanged, true);
app.RunCrossValidationCheckBox.Text = 'Run Cross-Validation';
app.RunCrossValidationCheckBox.Position = [7 124 135 22];
app.RunCrossValidationCheckBox.Value = true;
% Create CVPermPanel
app.CVPermPanel = uipanel(app.CrossValidationOptionsNestedifHyperParameterTuningEnabledPanel);
app.CVPermPanel.BorderType = 'none';
app.CVPermPanel.Title = 'Permutation Testing';
app.CVPermPanel.FontWeight = 'bold';
app.CVPermPanel.Position = [395 89 143 59];
% Create CVPermCheckBox
app.CVPermCheckBox = uicheckbox(app.CVPermPanel);
app.CVPermCheckBox.ValueChangedFcn = createCallbackFcn(app, @CVPermCheckBoxValueChanged, true);
app.CVPermCheckBox.Text = 'Run Permutations';
app.CVPermCheckBox.Position = [9 9 118 22];
app.CVPermCheckBox.Value = true;
% Create PermutationsEditField_2Label
app.PermutationsEditField_2Label = uilabel(app.CrossValidationOptionsNestedifHyperParameterTuningEnabledPanel);
app.PermutationsEditField_2Label.HorizontalAlignment = 'right';
app.PermutationsEditField_2Label.Position = [397 68 76 22];
app.PermutationsEditField_2Label.Text = 'Permutations';
% Create CVPermIter
app.CVPermIter = uieditfield(app.CrossValidationOptionsNestedifHyperParameterTuningEnabledPanel, 'numeric');
app.CVPermIter.Position = [490 68 37 22];
app.CVPermIter.Value = 50;
% Create EnableModelStackingCheckBox
app.EnableModelStackingCheckBox = uicheckbox(app.CrossValidationOptionsNestedifHyperParameterTuningEnabledPanel);
app.EnableModelStackingCheckBox.ValueChangedFcn = createCallbackFcn(app, @EnableModelStackingCheckBoxValueChanged, true);
app.EnableModelStackingCheckBox.Text = 'Enable Model Stacking';
app.EnableModelStackingCheckBox.Position = [7 97 145 22];
app.EnableModelStackingCheckBox.Value = true;
% Create PredictorModalityButtonGroup
app.PredictorModalityButtonGroup = uibuttongroup(app.UIFigure);
app.PredictorModalityButtonGroup.SelectionChangedFcn = createCallbackFcn(app, @PredictorModalityButtonGroupSelectionChanged, true);
app.PredictorModalityButtonGroup.TitlePosition = 'centertop';
app.PredictorModalityButtonGroup.Title = 'Predictor Modality';
app.PredictorModalityButtonGroup.ButtonDownFcn = createCallbackFcn(app, @PredictorModalityButtonGroupButtonDown, true);
app.PredictorModalityButtonGroup.FontWeight = 'bold';
app.PredictorModalityButtonGroup.Position = [10 766 125 69];
% Create NIFTIButton
app.NIFTIButton = uiradiobutton(app.PredictorModalityButtonGroup);
app.NIFTIButton.Text = 'NIFTI';
app.NIFTIButton.Position = [11 23 52 22];
app.NIFTIButton.Value = true;
% Create MatrixButton
app.MatrixButton = uiradiobutton(app.PredictorModalityButtonGroup);
app.MatrixButton.Text = 'Matrix';
app.MatrixButton.Position = [65 22 65 22];
% Create ArrayButton
app.ArrayButton = uiradiobutton(app.PredictorModalityButtonGroup);
app.ArrayButton.Text = 'Array';
app.ArrayButton.Position = [11 1 65 22];
% Create SelectBrainMaskButton
app.SelectBrainMaskButton = uibutton(app.UIFigure, 'push');
app.SelectBrainMaskButton.ButtonPushedFcn = createCallbackFcn(app, @SelectBrainMaskButtonPushed, true);
app.SelectBrainMaskButton.BackgroundColor = [0.651 0.651 0.651];
app.SelectBrainMaskButton.Position = [163 687 135 23];
app.SelectBrainMaskButton.Text = 'Select Brain Mask';
% Create HyperParameterTuningPanel
app.HyperParameterTuningPanel = uipanel(app.UIFigure);
app.HyperParameterTuningPanel.BorderType = 'none';
app.HyperParameterTuningPanel.Title = 'Hyper-Parameter Tuning';
app.HyperParameterTuningPanel.FontWeight = 'bold';
app.HyperParameterTuningPanel.Position = [199 221 205 134];
% Create HpOptCVStrategyLabel
app.HpOptCVStrategyLabel = uilabel(app.HyperParameterTuningPanel);
app.HpOptCVStrategyLabel.Position = [11 65 70 22];
app.HpOptCVStrategyLabel.Text = 'CV Strategy';
% Create HpOptNumberofFoldsLabel
app.HpOptNumberofFoldsLabel = uilabel(app.HyperParameterTuningPanel);
app.HpOptNumberofFoldsLabel.Position = [11 36 94 22];
app.HpOptNumberofFoldsLabel.Text = 'Number of Folds';
% Create HpOptNumberofRepeatsLabel
app.HpOptNumberofRepeatsLabel = uilabel(app.HyperParameterTuningPanel);
app.HpOptNumberofRepeatsLabel.Position = [11 9 109 22];
app.HpOptNumberofRepeatsLabel.Text = 'Number of Repeats';
% Create HpOptCvType
app.HpOptCvType = uidropdown(app.HyperParameterTuningPanel);
app.HpOptCvType.Items = {'KFold', 'LOOCV'};
app.HpOptCvType.ValueChangedFcn = createCallbackFcn(app, @HpOptCvTypeValueChanged, true);
app.HpOptCvType.Position = [131 65 69 22];
app.HpOptCvType.Value = 'KFold';
% Create HpOptFolds
app.HpOptFolds = uieditfield(app.HyperParameterTuningPanel, 'numeric');
app.HpOptFolds.Position = [160 36 40 22];
app.HpOptFolds.Value = 5;
% Create HpOptRepeats
app.HpOptRepeats = uieditfield(app.HyperParameterTuningPanel, 'numeric');
app.HpOptRepeats.Position = [160 8 40 22];
app.HpOptRepeats.Value = 5;
% Create OptimizeHyperparametersCheckBox
app.OptimizeHyperparametersCheckBox = uicheckbox(app.HyperParameterTuningPanel);
app.OptimizeHyperparametersCheckBox.ValueChangedFcn = createCallbackFcn(app, @OptimizeHyperparametersCheckBoxValueChanged, true);
app.OptimizeHyperparametersCheckBox.Text = 'Optimize Hyper-parameters';
app.OptimizeHyperparametersCheckBox.Position = [11 89 169 22];
app.OptimizeHyperparametersCheckBox.Value = true;
% Create SelectDataDirectoryButton
app.SelectDataDirectoryButton = uibutton(app.UIFigure, 'push');
app.SelectDataDirectoryButton.ButtonPushedFcn = createCallbackFcn(app, @SelectDataDirectoryButtonPushed, true);
app.SelectDataDirectoryButton.BackgroundColor = [0.651 0.651 0.651];
app.SelectDataDirectoryButton.Position = [163 726 136 24];
app.SelectDataDirectoryButton.Text = 'Select Data Directory';
% Create SelectCSVfileButton
app.SelectCSVfileButton = uibutton(app.UIFigure, 'push');
app.SelectCSVfileButton.ButtonPushedFcn = createCallbackFcn(app, @SelectCSVfileButtonPushed, true);
app.SelectCSVfileButton.BackgroundColor = [0.651 0.651 0.651];
app.SelectCSVfileButton.Position = [164 811 134 23];
app.SelectCSVfileButton.Text = 'Select CSV file';
% Create MinimumDataThresholdsPanel
app.MinimumDataThresholdsPanel = uipanel(app.UIFigure);
app.MinimumDataThresholdsPanel.BorderType = 'none';
app.MinimumDataThresholdsPanel.Title = 'Minimum Data Thresholds';
app.MinimumDataThresholdsPanel.FontWeight = 'bold';
app.MinimumDataThresholdsPanel.Position = [12 266 180 89];
% Create MinimumFrequencyLabel
app.MinimumFrequencyLabel = uilabel(app.MinimumDataThresholdsPanel);
app.MinimumFrequencyLabel.Position = [10 10 114 22];
app.MinimumFrequencyLabel.Text = 'Minimum Frequency';
% Create MinFreq
app.MinFreq = uieditfield(app.MinimumDataThresholdsPanel, 'numeric');
app.MinFreq.Position = [142 10 39 22];
app.MinFreq.Value = 10;
% Create MinimumObservationLabel
app.MinimumObservationLabel = uilabel(app.MinimumDataThresholdsPanel);
app.MinimumObservationLabel.Position = [10 41 122 22];
app.MinimumObservationLabel.Text = 'Minimum Observation';
% Create MinObs
app.MinObs = uieditfield(app.MinimumDataThresholdsPanel, 'numeric');
app.MinObs.Position = [142 41 39 22];
app.MinObs.Value = 1;
% Create PredictorTypeButtonGroup
app.PredictorTypeButtonGroup = uibuttongroup(app.UIFigure);
app.PredictorTypeButtonGroup.SelectionChangedFcn = createCallbackFcn(app, @PredictorTypeButtonGroupSelectionChanged, true);
app.PredictorTypeButtonGroup.TitlePosition = 'centertop';
app.PredictorTypeButtonGroup.Title = 'Predictor Type';
app.PredictorTypeButtonGroup.FontWeight = 'bold';
app.PredictorTypeButtonGroup.Position = [10 704 124 51];
% Create LesionButton
app.LesionButton = uiradiobutton(app.PredictorTypeButtonGroup);
app.LesionButton.Text = 'Lesion';
app.LesionButton.Position = [11 5 58 22];
app.LesionButton.Value = true;
% Create OtherPredButton
app.OtherPredButton = uiradiobutton(app.PredictorTypeButtonGroup);
app.OtherPredButton.Text = 'Other';
app.OtherPredButton.Position = [69 5 65 22];
% Create CSVTtext
app.CSVTtext = uitextarea(app.UIFigure);
app.CSVTtext.ValueChangedFcn = createCallbackFcn(app, @SelectCSVfileButtonPushed, true);
app.CSVTtext.Placeholder = 'No file selected';
app.CSVTtext.Position = [308 811 533 24];
% Create ImDirText
app.ImDirText = uitextarea(app.UIFigure);
app.ImDirText.Placeholder = 'No directory selected';
app.ImDirText.Position = [308 726 533 24];
% Create BrainMaskText
app.BrainMaskText = uitextarea(app.UIFigure);
app.BrainMaskText.Placeholder = 'No mask selected (NIFTI Modality Only)';
app.BrainMaskText.Position = [308 686 533 25];
% Create SelectModelLabel
app.SelectModelLabel = uilabel(app.UIFigure);
app.SelectModelLabel.BackgroundColor = [0.9412 0.9412 0.9412];
app.SelectModelLabel.HorizontalAlignment = 'right';
app.SelectModelLabel.FontSize = 14;
app.SelectModelLabel.Position = [9 446 90 22];
app.SelectModelLabel.Text = 'Select Model:';
% Create SelectModelDropDown
app.SelectModelDropDown = uidropdown(app.UIFigure);
app.SelectModelDropDown.Items = {'plsr', 'pls_da', 'ridge', 'logistic_ridge', 'linsvr', 'linsvc', 'kernsvr', 'kernsvc', 'rensemble', 'censemble', 'municorr', 'ttest', 'bmunz', 'munilr', 'munimnr', 'muniolsr', 'prop_sub'};
app.SelectModelDropDown.ValueChangedFcn = createCallbackFcn(app, @SelectModelDropDownValueChanged, true);
app.SelectModelDropDown.FontSize = 14;
app.SelectModelDropDown.BackgroundColor = [0.651 0.651 0.651];
app.SelectModelDropDown.Position = [103 442 100 30];
app.SelectModelDropDown.Value = 'plsr';
% Create ResultOptionsPanel
app.ResultOptionsPanel = uipanel(app.UIFigure);
app.ResultOptionsPanel.Title = 'Result Options';
app.ResultOptionsPanel.FontWeight = 'bold';
app.ResultOptionsPanel.FontSize = 14;
app.ResultOptionsPanel.Position = [595 80 204 77];
% Create WriteBootstrapImagesCheckBox
app.WriteBootstrapImagesCheckBox = uicheckbox(app.ResultOptionsPanel);
app.WriteBootstrapImagesCheckBox.Text = 'Write Bootstrap Images';
app.WriteBootstrapImagesCheckBox.Position = [6 3 147 22];
app.WriteBootstrapImagesCheckBox.Value = true;
% Create WritePermutationImagesCheckBox
app.WritePermutationImagesCheckBox = uicheckbox(app.ResultOptionsPanel);
app.WritePermutationImagesCheckBox.Text = 'Write Permutation Images';
app.WritePermutationImagesCheckBox.Position = [6 27 161 22];
app.WritePermutationImagesCheckBox.Value = true;
% Create FitExplanatoryModelCheckBox
app.FitExplanatoryModelCheckBox = uicheckbox(app.UIFigure);
app.FitExplanatoryModelCheckBox.Text = 'Fit Explanatory Model';
app.FitExplanatoryModelCheckBox.FontSize = 14;
app.FitExplanatoryModelCheckBox.Position = [218 444 157 22];
app.FitExplanatoryModelCheckBox.Value = true;
% Create UseParallelProcessingRecommendedCheckBox
app.UseParallelProcessingRecommendedCheckBox = uicheckbox(app.UIFigure);
app.UseParallelProcessingRecommendedCheckBox.Text = 'Use Parallel Processing (Recommended)';
app.UseParallelProcessingRecommendedCheckBox.FontSize = 14;
app.UseParallelProcessingRecommendedCheckBox.Position = [386 444 282 22];
app.UseParallelProcessingRecommendedCheckBox.Value = true;
% Create MultivariateModelingToolAnalysisConfigurationLabel
app.MultivariateModelingToolAnalysisConfigurationLabel = uilabel(app.UIFigure);
app.MultivariateModelingToolAnalysisConfigurationLabel.FontSize = 16;
app.MultivariateModelingToolAnalysisConfigurationLabel.FontWeight = 'bold';
app.MultivariateModelingToolAnalysisConfigurationLabel.Position = [11 860 503 22];
app.MultivariateModelingToolAnalysisConfigurationLabel.Text = 'Multivariate Modeling Tool Analysis Configuration ';
% Create RunAnalysisButton
app.RunAnalysisButton = uibutton(app.UIFigure, 'push');
app.RunAnalysisButton.ButtonPushedFcn = createCallbackFcn(app, @RunAnalysisButtonPushed, true);
app.RunAnalysisButton.BackgroundColor = [0.651 0.651 0.651];
app.RunAnalysisButton.FontSize = 18;
app.RunAnalysisButton.FontWeight = 'bold';
app.RunAnalysisButton.Position = [588 10 224 48];
app.RunAnalysisButton.Text = 'Run Analysis';
% Create SelectOutputDirectoryButton
app.SelectOutputDirectoryButton = uibutton(app.UIFigure, 'push');
app.SelectOutputDirectoryButton.ButtonPushedFcn = createCallbackFcn(app, @SelectOutputDirectoryButtonPushed, true);
app.SelectOutputDirectoryButton.BackgroundColor = [0.651 0.651 0.651];
app.SelectOutputDirectoryButton.Position = [163 643 135 24];
app.SelectOutputDirectoryButton.Text = 'Select Output Directory';
% Create OutDirText
app.OutDirText = uitextarea(app.UIFigure);
app.OutDirText.Placeholder = 'No directory selected';
app.OutDirText.Position = [308 644 533 23];
% Create RegistryFlag
app.RegistryFlag = uicheckbox(app.UIFigure);
app.RegistryFlag.Text = 'Registry Patients (select if subject IDs are provided as 4-digit numeric codes in CSV file)';
app.RegistryFlag.FontSize = 14;
app.RegistryFlag.Position = [167 782 580 22];
% Create PermutationTestingPanel
app.PermutationTestingPanel = uipanel(app.UIFigure);
app.PermutationTestingPanel.BorderType = 'none';
app.PermutationTestingPanel.Title = 'Permutation Testing';
app.PermutationTestingPanel.FontWeight = 'bold';
app.PermutationTestingPanel.Position = [413 325 135 30];
% Create StatisticalThresholdsPanel
app.StatisticalThresholdsPanel = uipanel(app.UIFigure);
app.StatisticalThresholdsPanel.BorderType = 'none';
app.StatisticalThresholdsPanel.Title = 'Statistical Thresholds';
app.StatisticalThresholdsPanel.FontWeight = 'bold';
app.StatisticalThresholdsPanel.Position = [699 231 135 124];
% Create FWEEditFieldLabel
app.FWEEditFieldLabel = uilabel(app.StatisticalThresholdsPanel);
app.FWEEditFieldLabel.HorizontalAlignment = 'right';
app.FWEEditFieldLabel.Position = [1 39 32 22];
app.FWEEditFieldLabel.Text = 'FWE';
% Create FweThresh
app.FweThresh = uieditfield(app.StatisticalThresholdsPanel, 'numeric');
app.FweThresh.Limits = [0 1];
app.FweThresh.Position = [39 39 44 22];
app.FweThresh.Value = 0.05;
% Create UncEditFieldLabel
app.UncEditFieldLabel = uilabel(app.StatisticalThresholdsPanel);
app.UncEditFieldLabel.HorizontalAlignment = 'right';
app.UncEditFieldLabel.Position = [3 74 30 22];
app.UncEditFieldLabel.Text = 'Unc.';
% Create UncPThresh
app.UncPThresh = uieditfield(app.StatisticalThresholdsPanel, 'numeric');
app.UncPThresh.Limits = [0 1];
app.UncPThresh.Position = [39 74 44 22];
app.UncPThresh.Value = 0.001;
% Create FDRThresh
app.FDRThresh = uieditfield(app.StatisticalThresholdsPanel, 'numeric');
app.FDRThresh.Limits = [0 1];
app.FDRThresh.Position = [39 6 44 22];
app.FDRThresh.Value = 0.05;
% Create FDRLabel
app.FDRLabel = uilabel(app.StatisticalThresholdsPanel);
app.FDRLabel.HorizontalAlignment = 'right';
app.FDRLabel.Position = [1 8 30 22];
app.FDRLabel.Text = 'FDR';
% Create MisclassificationCostsPanel
app.MisclassificationCostsPanel = uipanel(app.UIFigure);
app.MisclassificationCostsPanel.BorderType = 'none';
app.MisclassificationCostsPanel.Title = 'Misclassification Costs';
app.MisclassificationCostsPanel.FontWeight = 'bold';
app.MisclassificationCostsPanel.Position = [15 10 168 90];
% Create Group1EditField_4Label
app.Group1EditField_4Label = uilabel(app.MisclassificationCostsPanel);
app.Group1EditField_4Label.HorizontalAlignment = 'right';
app.Group1EditField_4Label.Position = [15 13 52 22];
app.Group1EditField_4Label.Text = 'Group -1';
% Create MisClasCostG2
app.MisClasCostG2 = uieditfield(app.MisclassificationCostsPanel, 'numeric');
app.MisClasCostG2.Limits = [0 100];
app.MisClasCostG2.Enable = 'off';
app.MisClasCostG2.Position = [74 13 22 22];
app.MisClasCostG2.Value = 1;
% Create Group1EditField_3Label
app.Group1EditField_3Label = uilabel(app.MisclassificationCostsPanel);
app.Group1EditField_3Label.HorizontalAlignment = 'right';
app.Group1EditField_3Label.Position = [13 40 55 22];
app.Group1EditField_3Label.Text = 'Group +1';
% Create MisClasCostG1
app.MisClasCostG1 = uieditfield(app.MisclassificationCostsPanel, 'numeric');
app.MisClasCostG1.Limits = [0 1000];
app.MisClasCostG1.Enable = 'off';
app.MisClasCostG1.Position = [74 40 22 22];
app.MisClasCostG1.Value = 1;
% Create SelectOutcomeVariableYListBoxLabel
app.SelectOutcomeVariableYListBoxLabel = uilabel(app.UIFigure);
app.SelectOutcomeVariableYListBoxLabel.HorizontalAlignment = 'right';
app.SelectOutcomeVariableYListBoxLabel.Position = [50 590 156 22];
app.SelectOutcomeVariableYListBoxLabel.Text = 'Select Outcome Variable (Y)';
% Create SelectOutcomeVariableYListBox
app.SelectOutcomeVariableYListBox = uilistbox(app.UIFigure);
app.SelectOutcomeVariableYListBox.Items = {};
app.SelectOutcomeVariableYListBox.Position = [221 572 620 56];
app.SelectOutcomeVariableYListBox.Value = {};
% Create SelectNuisanceRegressorsListBoxLabel
app.SelectNuisanceRegressorsListBoxLabel = uilabel(app.UIFigure);
app.SelectNuisanceRegressorsListBoxLabel.HorizontalAlignment = 'right';
app.SelectNuisanceRegressorsListBoxLabel.Position = [34 520 156 22];
app.SelectNuisanceRegressorsListBoxLabel.Text = 'Select Nuisance Regressors';
% Create SelectNuisanceRegressorsListBox
app.SelectNuisanceRegressorsListBox = uilistbox(app.UIFigure);
app.SelectNuisanceRegressorsListBox.Items = {};
app.SelectNuisanceRegressorsListBox.Multiselect = 'on';
app.SelectNuisanceRegressorsListBox.Position = [221 501 620 56];
app.SelectNuisanceRegressorsListBox.Value = {};
% Create RegressLesionVolumefromYCheckBox
app.RegressLesionVolumefromYCheckBox = uicheckbox(app.UIFigure);
app.RegressLesionVolumefromYCheckBox.ValueChangedFcn = createCallbackFcn(app, @RegressLesionVolumefromYCheckBoxValueChanged, true);
app.RegressLesionVolumefromYCheckBox.Text = 'Regress Lesion Volume from Y';
app.RegressLesionVolumefromYCheckBox.Position = [221 476 243 22];
% Create NuisanceRegressorCheckBox
app.NuisanceRegressorCheckBox = uicheckbox(app.UIFigure);
app.NuisanceRegressorCheckBox.ValueChangedFcn = createCallbackFcn(app, @NuisanceRegressorCheckBoxValueChanged, true);
app.NuisanceRegressorCheckBox.Text = '';
app.NuisanceRegressorCheckBox.Position = [196 519 25 22];
% Create CatYCheckBox
app.CatYCheckBox = uicheckbox(app.UIFigure);
app.CatYCheckBox.ValueChangedFcn = createCallbackFcn(app, @CatYCheckBoxValueChanged, true);
app.CatYCheckBox.Enable = 'off';
app.CatYCheckBox.Visible = 'off';
app.CatYCheckBox.Text = 'Y is Grouping Variable';
app.CatYCheckBox.Position = [217 421 141 22];
% Create RegressLesionVolumefromXandYCheckBox
app.RegressLesionVolumefromXandYCheckBox = uicheckbox(app.UIFigure);
app.RegressLesionVolumefromXandYCheckBox.ValueChangedFcn = createCallbackFcn(app, @RegressLesionVolumefromXandYCheckBoxValueChanged, true);
app.RegressLesionVolumefromXandYCheckBox.Text = 'Regress Lesion Volume from X and Y';
app.RegressLesionVolumefromXandYCheckBox.Position = [466 477 301 22];
% Create RankCorCheckBox
app.RankCorCheckBox = uicheckbox(app.UIFigure);
app.RankCorCheckBox.Enable = 'off';
app.RankCorCheckBox.Visible = 'off';
app.RankCorCheckBox.Text = 'Spearman Rank Correlation';
app.RankCorCheckBox.Position = [217 421 228 22];
% Create IdPathFlag
app.IdPathFlag = uicheckbox(app.UIFigure);
app.IdPathFlag.ValueChangedFcn = createCallbackFcn(app, @IdPathFlagValueChanged, true);
app.IdPathFlag.Text = 'Subject IDs are full filepaths to imaging data';
app.IdPathFlag.FontSize = 14;
app.IdPathFlag.Position = [167 759 299 22];
% Show the figure after all components are created
app.UIFigure.Visible = 'on';
end
end
% App creation and deletion
methods (Access = public)
% Construct app
function app = run_modeling_gui
% Create UIFigure and components
createComponents(app)
% Register the app with App Designer
registerApp(app, app.UIFigure)
% Execute the startup function
runStartupFcn(app, @startupFcn)
if nargout == 0
clear app
end
end
% Code that executes before app deletion
function delete(app)
% Delete UIFigure when app is deleted
delete(app.UIFigure)
end
end
end

run_modeling_gui.m at commit 389ea3e, under GPL-3.0 · at the source

Overview

Authors: Michal Brzus^1,2, Joseph Griffis², Cavan J Riley¹, Joel Bruss^2,3, Carrie Shea², Hans J Johnson^1,4, Aaron D Boes^2,3,4,5,6

ORCID iDs: Hans J Johnson

Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA USA
Department of Pediatrics, Carver College of Medicine, The University of Iowa, Iowa City, IA USA
Department of Neurology, Carver College of Medicine, The University of Iowa, Iowa City, IA USA
Department of Biomedical Engineering, The University of Iowa, Iowa City, IA USA
Department of Psychiatry, Carver College of Medicine, The University of Iowa, Iowa City, IA USA
Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA USA

Institutions: University of Iowa (United States)

Journal: NPJ digital medicine, volume 9, issue 1, article 646

Dates: received 25 September 2025; accepted 18 May 2026; published online 27 May 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41746-026-02803-2 · PMID 42204350 · PMCID PMC13493867 · OpenAlex W4410275398

Open access: gold, a free copy (OpenAlex)

Preprint: doi:10.1101/2025.05.09.25327310

Status: code verified

Categories: human (organism), stroke (population), clinical / translational (subfield)

Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging

Keywords: Computational biology and bioinformatics, Medical research, Neurology, Neuroscience

Topic: Acute Ischemic Stroke Management (Epidemiology, Medicine), according to OpenAlex

Funding: Roy J. Carver Charitable Trust; NINDS NIH HHS (1 R01 NS114405-02, R01 NS114405, R01 NS142391); National Institute of Neurological Disorders and Stroke (1 R01 NS114405-02); NIH HHS (S10 OD025025)

Citations: not cited yet (Europe PMC); 75 references in the paper

Abstract

Accurately predicting long-term outcomes after stroke remains a key challenge in personalized medicine. Here, we present a neuroimaging platform that forecasts individualized cognitive outcomes in patients with ischemic stroke using deep learning-based lesion segmentation and location-/network-based features. This novel, fully automated system is capable of processing raw DICOM MRI data from heterogeneous scanners and generating text-based, personalized outcome information. To demonstrate this pipeline, we trained cognitive outcome-prediction models using a large lesion cohort (N = 604) and applied them to an independent stroke cohort (N = 153). Multiple cognitive outcome predictions achieved reasonable accuracy, with 96% concordance with manual methods. A report generated by a large language model provides interpretable, patient-specific prognoses within ~3 min. This demonstrates the potential for imaging-informed prognostication to inform stroke care and guide rehabilitation strategies.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

Zenodo 15626633

License: CC-BY-4.0

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Languages: MATLAB (3), Jupyter (1)

Size: 4 files, 4 scripts

Software Heritage: not checked

Found in: “Data availability”

Holds: 4 notebooks

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NiBabel (1 file), Nilearn (1 file), NumPy (1 file), pandas (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)

28 September 2026: the link answers (HTTP 200)

4 files

plot_cognitive_outcome_models.ipynb, Jupyter, 51 lines, 1 match
plot_focus_outcomes.mlx, MATLAB, not shown here
plot_results_demog.mlx, MATLAB, not shown here
plot_results_full.mlx, MATLAB, not shown here

At the source: zenodo.org/records/15626633

Zenodo 15610144

License: GPL-3.0

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: “Data availability”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Statistics and Machine Learning Toolbox (62 files), Image Processing Toolbox (9 files), Tools for NIfTI and ANALYZE image (MATLAB) (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)

28 September 2026: the link answers (HTTP 200)

171 files

model_code/common/check_and_fill_cfg_fields.m, MATLAB, 56 lines
model_code/common/crossval/get_hp_opts.m, MATLAB, 86 lines
model_code/common/crossval/get_train_test_splits.m, MATLAB, 32 lines
model_code/common/crossval/perm_nested_cv.m, MATLAB, 66 lines
model_code/common/crossval/perm_stacked_cv.m, MATLAB, 69 lines
model_code/common/crossval/preallocate_cv_results.m, MATLAB, 112 lines
model_code/common/crossval/preallocate_cv_results_perm.m, MATLAB, 44 lines
model_code/common/crossval/predict_new.m, MATLAB, 61 lines
model_code/common/crossval/rescale_dataset.m, MATLAB, 37 lines
model_code/common/crossval/run_nested_cv.m, MATLAB, 106 lines
model_code/common/crossval/run_new_partition_cv.m, MATLAB, 117 lines
model_code/common/crossval/run_pre_partitioned_cv.m, MATLAB, 90 lines
model_code/common/crossval/summarize_cv_results.m, MATLAB, 42 lines
model_code/common/data_io/extract_upper_triangles.m, MATLAB, 12 lines
model_code/common/data_io/gen_node_file.m, MATLAB, 12 lines
model_code/common/data_io/get_and_format_array_data.m, MATLAB, 79 lines
model_code/common/data_io/get_and_format_lesion_data.m, MATLAB, 83 lines
model_code/common/data_io/get_and_format_matrix_data.m, MATLAB, 84 lines
model_code/common/data_io/get_and_format_nifti_data.m, MATLAB, 82 lines
model_code/common/data_io/get_and_format_table_data.m, MATLAB, 46 lines
model_code/common/data_io/get_behavior_data.m, MATLAB, 46 lines
model_code/common/data_io/get_lmwv.m, MATLAB, 77 lines
model_code/common/data_io/write_connectome_weights_mlsm.m, MATLAB, 154 lines
model_code/common/data_io/write_connectome_weights_ulsm.m, MATLAB, 172 lines
model_code/common/data_io/write_edge_file.m, MATLAB, 37 lines
model_code/common/data_io/write_mass_uni_weight_maps.m, MATLAB, 180 lines
model_code/common/data_io/write_mass_unicorr_weight_maps.m, MATLAB, 180 lines
model_code/common/data_io/write_mlsm_weight_maps.m, MATLAB, 211 lines
model_code/common/data_io/write_model_weight_maps.m, MATLAB, 27 lines
model_code/common/data_io/write_model_weight_maps_anat_prior.m, MATLAB, 173 lines
model_code/common/data_io/write_model_weights_connectome.m, MATLAB, 14 lines
model_code/common/explanatory/boot/get_boot_cis_cl.m, MATLAB, 29 lines
model_code/common/explanatory/boot/get_boot_cis_reg.m, MATLAB, 26 lines
model_code/common/explanatory/boot/get_boot_pvals_cl.m, MATLAB, 29 lines
model_code/common/explanatory/boot/get_boot_pvals_reg.m, MATLAB, 30 lines
model_code/common/explanatory/boot/get_boot_statistics.m, MATLAB, 39 lines
model_code/common/explanatory/boot/get_bootstrap_cis.m, MATLAB, 97 lines
model_code/common/explanatory/boot/get_ci_thresholded_betas.m, MATLAB, 12 lines
model_code/common/explanatory/boot/run_bootstrap_models.m, MATLAB, 119 lines
model_code/common/explanatory/fit_explanatory_model.m, MATLAB, 130 lines
model_code/common/explanatory/jack/run_jackknife_models.m, MATLAB, 98 lines
model_code/common/explanatory/perm/get_perm_beta_pvals.m, MATLAB, 18 lines
model_code/common/explanatory/perm/get_permutation_coefficient_pvalues.m, MATLAB, 14 lines
model_code/common/explanatory/perm/get_permutation_results.m, MATLAB, 30 lines
model_code/common/explanatory/perm/get_wholebrain_cfwe_thresh.m, MATLAB, 21 lines
model_code/common/fit_and_evaluate_model.m, MATLAB, 140 lines
model_code/common/gen_methods_text.m, MATLAB, 349 lines
model_code/common/gen_results_text.m, MATLAB, 99 lines
model_code/common/get_default_model_cfg.m, MATLAB, 371 lines
model_code/common/get_model_option_list.m, MATLAB, 34 lines
model_code/common/lsm/get_freq_map.m, MATLAB, 13 lines
model_code/common/lsm/get_lvol_map.m, MATLAB, 29 lines
model_code/common/model_eval/get_explained_variance.m, MATLAB, 12 lines
model_code/common/model_eval/get_model_r2.m, MATLAB, 9 lines
model_code/common/model_eval/get_model_r2_train.m, MATLAB, 9 lines
model_code/common/model_eval/get_mse.m, MATLAB, 8 lines
model_code/common/model_eval/get_optimal_threshold.m, MATLAB, 14 lines
model_code/common/preproc/apply_dtlvc.m, MATLAB, 15 lines
model_code/common/preproc/check_Y_data.m, MATLAB, 46 lines
model_code/common/preproc/check_for_missing_data.m, MATLAB, 37 lines
model_code/common/preproc/get_quartile_groups.m, MATLAB, 27 lines
model_code/common/preproc/regress_confounds_from_X.m, MATLAB, 12 lines
model_code/common/preproc/regress_confounds_from_Y.m, MATLAB, 16 lines
model_code/common/preproc/trim_X_data.m, MATLAB, 10 lines
model_code/common/preproc/trim_input_data.m, MATLAB, 52 lines
model_code/common/restore_model_path.m, MATLAB, 21 lines
model_code/common/stats/bonf_holm.m, MATLAB, 118 lines
model_code/ensemble/run_censemble_modeling.m, MATLAB, 97 lines
model_code/ensemble/run_censemble_modeling_cv.m, MATLAB, 90 lines
model_code/ensemble/run_rensemble_modeling.m, MATLAB, 77 lines
model_code/ensemble/run_rensemble_modeling_cv.m, MATLAB, 77 lines
model_code/mass_univariate/approxbrunnermunzel.m, MATLAB, 113 lines
model_code/mass_univariate/brunner_munzel.m, MATLAB, 163 lines
model_code/mass_univariate/get_bootstrapped_cis_municorr.m, MATLAB, 34 lines
model_code/mass_univariate/get_bootstrapped_cis_munilr.m, MATLAB, 52 lines
model_code/mass_univariate/run_brunner_munzel_test.m, MATLAB, 81 lines
model_code/mass_univariate/run_mass_univariate_corr.m, MATLAB, 22 lines
model_code/mass_univariate/run_mass_univariate_lr.m, MATLAB, 49 lines
model_code/mass_univariate/run_mass_univariate_mnr.m, MATLAB, 42 lines
model_code/mass_univariate/run_mass_univariate_olsr.m, MATLAB, 41 lines
model_code/mass_univariate/run_ols_modeling.m, MATLAB, 29 lines
model_code/mass_univariate/run_ols_modeling_cv.m, MATLAB, 46 lines
model_code/mass_univariate/run_perm_mass_uni_corr.m, MATLAB, 47 lines
model_code/mass_univariate/run_perm_mass_uni_lr.m, MATLAB, 71 lines
model_code/mass_univariate/run_perm_mass_uni_mnr.m, MATLAB, 62 lines
model_code/mass_univariate/run_perm_mass_uni_olsr.m, MATLAB, 69 lines
model_code/mass_univariate/run_proportional_subtraction.m, MATLAB, 59 lines
model_code/mass_univariate/run_ttests.m, MATLAB, 119 lines
model_code/nlinsvr/get_svr_hp_opts.m, MATLAB, 48 lines
model_code/nlinsvr/run_boot_svc.m, MATLAB, 67 lines
model_code/nlinsvr/run_boot_svr.m, MATLAB, 50 lines
model_code/nlinsvr/run_bootstrap_svc.m, MATLAB, 52 lines
model_code/nlinsvr/run_bootstrap_svr.m, MATLAB, 42 lines
model_code/nlinsvr/run_perm_svc.m, MATLAB, 121 lines
model_code/nlinsvr/run_perm_svr.m, MATLAB, 126 lines
model_code/nlinsvr/run_svc_modeling.m, MATLAB, 127 lines
model_code/nlinsvr/run_svc_modeling_cv.m, MATLAB, 118 lines
model_code/nlinsvr/run_svr_modeling.m, MATLAB, 105 lines
model_code/nlinsvr/run_svr_modeling_cv.m, MATLAB, 105 lines
model_code/plsr/fitplsda.m, MATLAB, 38 lines
model_code/plsr/fitplsrm.m, MATLAB, 20 lines
model_code/plsr/get_ci_thresholded_vips.m, MATLAB, 11 lines
model_code/plsr/run_boot_plsda.m, MATLAB, 41 lines
model_code/plsr/run_boot_plsr.m, MATLAB, 32 lines
model_code/plsr/run_bootstrap_plsda.m, MATLAB, 36 lines
model_code/plsr/run_bootstrap_plsr.m, MATLAB, 35 lines
model_code/plsr/run_kfold_plsr.m, MATLAB, 137 lines
model_code/plsr/run_loocv_plsr.m, MATLAB, 37 lines
model_code/plsr/run_perm_plsda.m, MATLAB, 90 lines
model_code/plsr/run_perm_plsr.m, MATLAB, 96 lines
model_code/plsr/run_plsda_modeling.m, MATLAB, 37 lines
model_code/plsr/run_plsda_modeling_cv.m, MATLAB, 59 lines
model_code/plsr/run_plsr_modeling.m, MATLAB, 46 lines
model_code/plsr/run_plsr_modeling_cv.m, MATLAB, 52 lines
model_code/reglm/get_regmdl_hp_opts.m, MATLAB, 118 lines
model_code/reglm/run_boot_classmdl.m, MATLAB, 63 lines
model_code/reglm/run_boot_regmdl.m, MATLAB, 45 lines
model_code/reglm/run_bootstrap_class_linear.m, MATLAB, 47 lines
model_code/reglm/run_bootstrap_reg_linear.m, MATLAB, 45 lines
model_code/reglm/run_class_linear_modeling.m, MATLAB, 164 lines
model_code/reglm/run_class_linear_modeling_cv.m, MATLAB, 153 lines
model_code/reglm/run_perm_classlm.m, MATLAB, 98 lines
model_code/reglm/run_perm_reglm.m, MATLAB, 111 lines
model_code/reglm/run_reg_linear_modeling.m, MATLAB, 143 lines
model_code/reglm/run_reg_linear_modeling_cv.m, MATLAB, 150 lines
model_code/stacked_models/create_stacked_models.m, MATLAB, 136 lines
model_code/stacked_models/create_stacked_reg_models.m, MATLAB, 155 lines
model_code/stacked_models/get_default_stack_cfg.m, MATLAB, 190 lines
model_code/stacked_models/get_mean_prediction.m, MATLAB, 17 lines
model_code/stacked_models/get_mean_score_prediction.m, MATLAB, 27 lines
model_code/stacked_models/get_mode_prediction.m, MATLAB, 27 lines
model_code/stacked_models/run_model_stacking.m, MATLAB, 31 lines
model_code/stacked_models/run_stacked_cv.m, MATLAB, 184 lines
model_code/stacked_models/stack_class_models_and_run_cv.m, MATLAB, 66 lines
model_code/stacked_models/stack_reg_models_and_run_cv.m, MATLAB, 75 lines
run_modeling_gui.m, MATLAB, 1,547 lines, 1 match
run_stacking_gui.m, MATLAB, 548 lines
tutorials/independent_predictions/logistic_ridge_prediction_tutorial.mlx, MATLAB, not shown here
tutorials/independent_predictions/plsda_prediction_tutorial.mlx, MATLAB, not shown here
tutorials/independent_predictions/plsr_prediction_tutorial.mlx, MATLAB, not shown here
tutorials/independent_predictions/ridge_prediction_tutorial.mlx, MATLAB, not shown here
tutorials/mass_univariate/mass_bmunz_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/mass_univariate/mass_correlation_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/mass_univariate/mass_logistic_regression_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/mass_univariate/mass_olsr_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/mass_univariate/mass_ttest_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/mass_univariate/prop_subtraction_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/model_stacking/model_stacking_tutorial_classification.mlx, MATLAB, not shown here
tutorials/model_stacking/model_stacking_tutorial_regression.mlx, MATLAB, not shown here
tutorials/multivariate_classification/class_ensemble_tutorial_parcel_damage.mlx, MATLAB, not shown here
tutorials/multivariate_classification/kernsvc_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/multivariate_classification/linsvc_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/multivariate_classification/logistic_lasso_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/multivariate_classification/logistic_ridge_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/multivariate_classification/plsda_discon_tutorial.mlx, MATLAB, not shown here
tutorials/multivariate_classification/plsda_flnm_tutorial.mlx, MATLAB, not shown here
tutorials/multivariate_classification/plsda_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/multivariate_classification/rlinsvc_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/multivariate_regression/lasso_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/multivariate_regression/linsvr_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/multivariate_regression/plsr_disconnectome_tutorial.mlx, MATLAB, not shown here
tutorials/multivariate_regression/plsr_flnm_tutorial.mlx, MATLAB, not shown here
tutorials/multivariate_regression/plsr_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/multivariate_regression/rbf_svr_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/multivariate_regression/reg_ensemble_tutorial_parcel_damage.mlx, MATLAB, not shown here
tutorials/multivariate_regression/reg_ensemble_tutorial_parcel_damage2.mlx, MATLAB, not shown here
tutorials/multivariate_regression/ridge_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/multivariate_regression/rlinsvr_lesion_tutorial.mlx, MATLAB, not shown here
LICENSE, License, 674 lines
README.md, Text, 12 lines
README.txt, Text, 23 lines

At the source: zenodo.org/records/15610144

jcgriffis/ibb_toolkit

License: GPL-3.0

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 389ea3e672a115ecc0532a212208ccdee0ad7ade, 31 August 2026

Languages: MATLAB (181)

Size: 193 files, 181 scripts

Software Heritage: not archived

Found in: the Zenodo archive record

Holds: README, license file, 31 notebooks

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Statistics and Machine Learning Toolbox (75 files), Image Processing Toolbox (11 files), Tools for NIfTI and ANALYZE image (MATLAB) (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

184 files

model_code/common/check_and_fill_cfg_fields.m, MATLAB, 56 lines
model_code/common/crossval/get_hp_opts.m, MATLAB, 86 lines
model_code/common/crossval/get_train_test_splits.m, MATLAB, 32 lines
model_code/common/crossval/perm_nested_cv.m, MATLAB, 66 lines
model_code/common/crossval/perm_stacked_cv.m, MATLAB, 69 lines
model_code/common/crossval/preallocate_cv_results.m, MATLAB, 112 lines
model_code/common/crossval/preallocate_cv_results_perm.m, MATLAB, 44 lines
model_code/common/crossval/predict_new.m, MATLAB, 61 lines
model_code/common/crossval/rescale_dataset.m, MATLAB, 37 lines
model_code/common/crossval/run_nested_cv.m, MATLAB, 110 lines
model_code/common/crossval/run_new_partition_cv.m, MATLAB, 143 lines
model_code/common/crossval/run_pre_partitioned_cv.m, MATLAB, 116 lines
model_code/common/crossval/summarize_cv_results.m, MATLAB, 42 lines
model_code/common/data_io/extract_upper_triangles.m, MATLAB, 12 lines
model_code/common/data_io/gen_node_file.m, MATLAB, 12 lines
model_code/common/data_io/get_and_format_array_data.m, MATLAB, 79 lines
model_code/common/data_io/get_and_format_array_data2.m, MATLAB, 53 lines
model_code/common/data_io/get_and_format_lesion_data.m, MATLAB, 87 lines
model_code/common/data_io/get_and_format_lesion_data2.m, MATLAB, 61 lines
model_code/common/data_io/get_and_format_matrix_data.m, MATLAB, 88 lines
model_code/common/data_io/get_and_format_matrix_data2.m, MATLAB, 58 lines
model_code/common/data_io/get_and_format_nifti_data.m, MATLAB, 85 lines
model_code/common/data_io/get_and_format_nifti_data2.m, MATLAB, 60 lines
model_code/common/data_io/get_and_format_table_data.m, MATLAB, 50 lines
model_code/common/data_io/get_behavior_data.m, MATLAB, 46 lines
model_code/common/data_io/get_lmwv.m, MATLAB, 77 lines
model_code/common/data_io/write_connectome_weights_mlsm.m, MATLAB, 154 lines
model_code/common/data_io/write_connectome_weights_ulsm.m, MATLAB, 172 lines
model_code/common/data_io/write_edge_file.m, MATLAB, 37 lines
model_code/common/data_io/write_mass_uni_weight_maps.m, MATLAB, 180 lines
model_code/common/data_io/write_mass_unicorr_weight_maps.m, MATLAB, 180 lines
model_code/common/data_io/write_mlsm_weight_maps.m, MATLAB, 211 lines
model_code/common/data_io/write_model_weight_maps.m, MATLAB, 27 lines
model_code/common/data_io/write_model_weight_maps_anat_prior.m, MATLAB, 173 lines
model_code/common/data_io/write_model_weights_connectome.m, MATLAB, 14 lines
model_code/common/explanatory/boot/get_boot_cis_cl.m, MATLAB, 29 lines
model_code/common/explanatory/boot/get_boot_cis_reg.m, MATLAB, 26 lines
model_code/common/explanatory/boot/get_boot_pvals_cl.m, MATLAB, 29 lines
model_code/common/explanatory/boot/get_boot_pvals_reg.m, MATLAB, 30 lines
model_code/common/explanatory/boot/get_boot_statistics.m, MATLAB, 39 lines
model_code/common/explanatory/boot/get_bootstrap_cis.m, MATLAB, 97 lines
model_code/common/explanatory/boot/get_ci_thresholded_betas.m, MATLAB, 12 lines
model_code/common/explanatory/boot/run_bootstrap_models.m, MATLAB, 101 lines
model_code/common/explanatory/fit_explanatory_model.m, MATLAB, 176 lines
model_code/common/explanatory/jack/run_jackknife_models.m, MATLAB, 98 lines
model_code/common/explanatory/perm/get_perm_beta_pvals.m, MATLAB, 18 lines
model_code/common/explanatory/perm/get_permutation_coefficient_pvalues.m, MATLAB, 14 lines
model_code/common/explanatory/perm/get_permutation_results.m, MATLAB, 30 lines
model_code/common/explanatory/perm/get_wholebrain_cfwe_thresh.m, MATLAB, 21 lines
model_code/common/fit_and_evaluate_model.m, MATLAB, 177 lines
model_code/common/gen_methods_text.m, MATLAB, 372 lines
model_code/common/gen_results_text.m, MATLAB, 99 lines
model_code/common/get_default_model_cfg.m, MATLAB, 357 lines
model_code/common/get_model_option_list.m, MATLAB, 34 lines
model_code/common/lsm/get_freq_map.m, MATLAB, 13 lines
model_code/common/lsm/get_lvol_map.m, MATLAB, 29 lines
model_code/common/model_eval/get_explained_variance.m, MATLAB, 12 lines
model_code/common/model_eval/get_model_r2.m, MATLAB, 9 lines
model_code/common/model_eval/get_model_r2_train.m, MATLAB, 9 lines
model_code/common/model_eval/get_mse.m, MATLAB, 8 lines
model_code/common/model_eval/get_optimal_threshold.m, MATLAB, 14 lines
model_code/common/preproc/apply_dtlvc.m, MATLAB, 15 lines
model_code/common/preproc/check_Y_data.m, MATLAB, 46 lines
model_code/common/preproc/check_for_missing_data.m, MATLAB, 44 lines
model_code/common/preproc/get_quartile_groups.m, MATLAB, 27 lines
model_code/common/preproc/regress_confounds_from_X.m, MATLAB, 12 lines
model_code/common/preproc/regress_confounds_from_X_cv.m, MATLAB, 12 lines
model_code/common/preproc/regress_confounds_from_Y.m, MATLAB, 16 lines
model_code/common/preproc/trim_X_data.m, MATLAB, 10 lines
model_code/common/preproc/trim_input_data.m, MATLAB, 52 lines
model_code/common/restore_model_path.m, MATLAB, 21 lines
model_code/common/stats/bonf_holm.m, MATLAB, 118 lines
model_code/ensemble/run_censemble_modeling.m, MATLAB, 97 lines, 1 match
model_code/ensemble/run_censemble_modeling_cv.m, MATLAB, 90 lines
model_code/ensemble/run_rensemble_modeling.m, MATLAB, 77 lines
model_code/ensemble/run_rensemble_modeling_cv.m, MATLAB, 77 lines
model_code/mass_univariate/approxbrunnermunzel.m, MATLAB, 113 lines
model_code/mass_univariate/brunner_munzel.m, MATLAB, 163 lines
model_code/mass_univariate/get_bootstrapped_cis_municorr.m, MATLAB, 34 lines
model_code/mass_univariate/get_bootstrapped_cis_munilr.m, MATLAB, 52 lines
model_code/mass_univariate/run_brunner_munzel_test.m, MATLAB, 81 lines
model_code/mass_univariate/run_mass_univariate_corr.m, MATLAB, 43 lines
model_code/mass_univariate/run_mass_univariate_corr_int.m, MATLAB, 68 lines
model_code/mass_univariate/run_mass_univariate_lr.m, MATLAB, 51 lines
model_code/mass_univariate/run_mass_univariate_lr_int.m, MATLAB, 50 lines
model_code/mass_univariate/run_mass_univariate_mnr.m, MATLAB, 72 lines
model_code/mass_univariate/run_mass_univariate_mnr_int.m, MATLAB, 73 lines
model_code/mass_univariate/run_mass_univariate_olsr.m, MATLAB, 41 lines
model_code/mass_univariate/run_mass_univariate_olsr_int.m, MATLAB, 42 lines
model_code/mass_univariate/run_ols_modeling.m, MATLAB, 29 lines
model_code/mass_univariate/run_ols_modeling_cv.m, MATLAB, 46 lines
model_code/mass_univariate/run_perm_mass_uni_corr.m, MATLAB, 83 lines
model_code/mass_univariate/run_perm_mass_uni_corr_int.m, MATLAB, 66 lines
model_code/mass_univariate/run_perm_mass_uni_lr.m, MATLAB, 77 lines
model_code/mass_univariate/run_perm_mass_uni_lr_int.m, MATLAB, 78 lines
model_code/mass_univariate/run_perm_mass_uni_mnr.m, MATLAB, 92 lines
model_code/mass_univariate/run_perm_mass_uni_mnr_int.m, MATLAB, 93 lines
model_code/mass_univariate/run_perm_mass_uni_olsr.m, MATLAB, 69 lines
model_code/mass_univariate/run_perm_mass_uni_olsr_int.m, MATLAB, 69 lines
model_code/mass_univariate/run_proportional_subtraction.m, MATLAB, 59 lines
model_code/mass_univariate/run_ttests.m, MATLAB, 119 lines
model_code/nlinsvr/get_svr_hp_opts.m, MATLAB, 48 lines
model_code/nlinsvr/run_boot_svc.m, MATLAB, 67 lines
model_code/nlinsvr/run_boot_svr.m, MATLAB, 51 lines
model_code/nlinsvr/run_bootstrap_svc.m, MATLAB, 52 lines
model_code/nlinsvr/run_bootstrap_svr.m, MATLAB, 42 lines
model_code/nlinsvr/run_perm_svc.m, MATLAB, 121 lines
model_code/nlinsvr/run_perm_svr.m, MATLAB, 126 lines
model_code/nlinsvr/run_svc_modeling.m, MATLAB, 139 lines
model_code/nlinsvr/run_svc_modeling_cv.m, MATLAB, 130 lines
model_code/nlinsvr/run_svr_modeling.m, MATLAB, 120 lines
model_code/nlinsvr/run_svr_modeling_cv.m, MATLAB, 120 lines
model_code/plsr/fitplsda.m, MATLAB, 38 lines
model_code/plsr/fitplsrm.m, MATLAB, 20 lines
model_code/plsr/get_ci_thresholded_vips.m, MATLAB, 11 lines
model_code/plsr/run_boot_plsda.m, MATLAB, 41 lines
model_code/plsr/run_boot_plsr.m, MATLAB, 32 lines
model_code/plsr/run_bootstrap_plsda.m, MATLAB, 36 lines
model_code/plsr/run_bootstrap_plsr.m, MATLAB, 35 lines
model_code/plsr/run_kfold_plsr.m, MATLAB, 135 lines
model_code/plsr/run_loocv_plsr.m, MATLAB, 37 lines
model_code/plsr/run_perm_plsda.m, MATLAB, 90 lines
model_code/plsr/run_perm_plsr.m, MATLAB, 96 lines
model_code/plsr/run_plsda_modeling.m, MATLAB, 46 lines
model_code/plsr/run_plsda_modeling_cv.m, MATLAB, 63 lines
model_code/plsr/run_plsr_modeling.m, MATLAB, 64 lines
model_code/plsr/run_plsr_modeling_cv.m, MATLAB, 65 lines
model_code/reglm/get_regmdl_hp_opts.m, MATLAB, 118 lines
model_code/reglm/run_boot_classmdl.m, MATLAB, 63 lines
model_code/reglm/run_boot_regmdl.m, MATLAB, 45 lines
model_code/reglm/run_bootstrap_class_linear.m, MATLAB, 47 lines
model_code/reglm/run_bootstrap_reg_linear.m, MATLAB, 45 lines
model_code/reglm/run_class_linear_modeling.m, MATLAB, 164 lines
model_code/reglm/run_class_linear_modeling_cv.m, MATLAB, 153 lines
model_code/reglm/run_perm_classlm.m, MATLAB, 98 lines
model_code/reglm/run_perm_reglm.m, MATLAB, 111 lines
model_code/reglm/run_reg_linear_modeling.m, MATLAB, 143 lines
model_code/reglm/run_reg_linear_modeling_cv.m, MATLAB, 150 lines
model_code/stacked_models/create_stacked_models.m, MATLAB, 136 lines
model_code/stacked_models/create_stacked_reg_models.m, MATLAB, 155 lines
model_code/stacked_models/get_default_stack_cfg.m, MATLAB, 225 lines
model_code/stacked_models/get_mean_prediction.m, MATLAB, 17 lines
model_code/stacked_models/get_mean_score_prediction.m, MATLAB, 27 lines
model_code/stacked_models/get_mode_prediction.m, MATLAB, 27 lines
model_code/stacked_models/run_model_stacking.m, MATLAB, 83 lines
model_code/stacked_models/run_stacked_cv.m, MATLAB, 172 lines
model_code/stacked_models/stack_class_models_and_run_cv.m, MATLAB, 70 lines
model_code/stacked_models/stack_reg_models_and_run_cv.m, MATLAB, 78 lines
run_modeling_gui.m, MATLAB, 1,699 lines, 1 match
run_stacking_gui.m, MATLAB, 548 lines
tutorials/independent_predictions/logistic_ridge_prediction_tutorial.mlx, MATLAB, not shown here
tutorials/independent_predictions/plsda_prediction_tutorial.mlx, MATLAB, not shown here
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tutorials/independent_predictions/ridge_prediction_tutorial.mlx, MATLAB, not shown here
tutorials/mass_univariate/mass_bmunz_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/mass_univariate/mass_correlation_lesion_tutorial.mlx, MATLAB, not shown here
tutorials/mass_univariate/mass_logistic_regression_lesion_tutorial.mlx, MATLAB, not shown here
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tutorials/mass_univariate/prop_subtraction_lesion_tutorial.mlx, MATLAB, not shown here
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tutorials/model_stacking/model_stacking_tutorial_regression.mlx, MATLAB, not shown here
tutorials/multivariate_classification/class_ensemble_tutorial_parcel_damage.mlx, MATLAB, not shown here
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tutorials/multivariate_regression/rlinsvr_lesion_tutorial.mlx, MATLAB, not shown here
LICENSE, License, 674 lines
README.md, Text, 12 lines
README.txt, Text, 23 lines

At the source: github.com/jcgriffis/ibb_toolkit/tree/v1.1.0-pipeline

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
353 scripts, each with its path and the digest of its content;
4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

The software used for lesion modeling can be downloaded at the provided URL. Deidentified data from this study may be made available upon reasonable request to the corresponding author, subject to a data use agreement ensuring compliance with human subjects ethical standards. Trained machine learning models for outcome prediction may be shared, subject to a proper software use agreement protecting intellectual property rights. The IBB toolkit version used for training and evaluating the cognitive outcome-prediction models is archived along with the notebooks used for generating the outcome-prediction figures here: https://zenodo.org/records/15626633. https://zenodo.org/records/15610144. The dcm-classifier package used for DICOM modality classification is available as PIP package at https://pypi.org/project/dcm-classifier/.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 4 keywords, 4 funders, 71 references.

Cite

This paper

Brzus, M., Griffis, J., Riley, C. J., Bruss, J., Shea, C., Johnson, H. J., & Boes, A. D. (2026). A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction. NPJ digital medicine, 9(1), 646. https://doi.org/10.1038/s41746-026-02803-2

BibTeX

@article{brzus2026clinical, author = {Brzus, Michal and Griffis, Joseph and Riley, Cavan J and Bruss, Joel and Shea, Carrie and Johnson, Hans J and Boes, Aaron D}, title = {{A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction}}, journal = {NPJ digital medicine}, year = {2026}, month = may, volume = {9}, number = {1}, pages = {646}, publisher = {Nature Publishing Group}, issn = {2398-6352}, doi = {10.1038/s41746-026-02803-2}, url = {https://doi.org/10.1038/s41746-026-02803-2}, pmid = {42204350}, pmcid = {PMC13493867} }

RIS

TY - JOUR AU - Brzus, Michal AU - Griffis, Joseph AU - Riley, Cavan J AU - Bruss, Joel AU - Shea, Carrie AU - Johnson, Hans J AU - Boes, Aaron D TI - A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction T2 - NPJ digital medicine J2 - NPJ Digit Med PY - 2026 DA - 2026/05/27 VL - 9 IS - 1 SP - 646 SN - 2398-6352 PB - Nature Publishing Group DO - 10.1038/s41746-026-02803-2 UR - https://doi.org/10.1038/s41746-026-02803-2 LA - en ER -

CSL-JSON

{ "id": "10.1038/s41746-026-02803-2", "type": "article-journal", "title": "A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction", "container-title": "NPJ digital medicine", "author": [ { "family": "Brzus", "given": "Michal" }, { "family": "Griffis", "given": "Joseph" }, { "family": "Riley", "given": "Cavan J" }, { "family": "Bruss", "given": "Joel" }, { "family": "Shea", "given": "Carrie" }, { "family": "Johnson", "given": "Hans J" }, { "family": "Boes", "given": "Aaron D" } ], "container-title-short": "NPJ Digit Med", "volume": "9", "issue": "1", "page": "646", "DOI": "10.1038/s41746-026-02803-2", "PMID": "42204350", "PMCID": "PMC13493867", "ISSN": "2398-6352", "publisher": "Nature Publishing Group", "URL": "https://doi.org/10.1038/s41746-026-02803-2", "language": "en", "issued": { "date-parts": [ [ 2026, 5, 27 ] ] } }

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A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction.

Code ↔ Paper

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Zenodo 15610144

jcgriffis/ibb_toolkit

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