Convergent functional networks of intrinsic activity alterations in temporal lobe epilepsy and their molecular correlates.

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The authors' code

MATLAB · 1,154 lines · 51 KB · no license

% App designed with MATLAB App Designer
classdef JuSpace < matlab.apps.AppBase
% Components declaration
properties (Access = public)
UIFigure matlab.ui.Figure
TabGroup matlab.ui.container.TabGroup
Tab1 matlab.ui.container.Tab
Tab2 matlab.ui.container.Tab
Tab3 matlab.ui.container.Tab
LogoImage matlab.ui.control.Image
% Tab 1 components
AtlasLabel matlab.ui.control.Label
AtlasDropDown matlab.ui.control.DropDown
SelectAtlasButton matlab.ui.control.Button
AnalysisLabel matlab.ui.control.Label
AnalysisDropDown matlab.ui.control.DropDown
FirstSetButton matlab.ui.control.Button
SecondSetButton matlab.ui.control.Button
SelectSaveDirButton matlab.ui.control.Button
SaveDirLabel matlab.ui.control.Label
FirstSetListBox matlab.ui.control.ListBox
SecondSetListBox matlab.ui.control.ListBox
FirstSetLabel matlab.ui.control.Label
SecondSetLabel matlab.ui.control.Label
AnalysisTooltipLabel matlab.ui.control.Label
NameSaveField matlab.ui.control.EditField
NameSaveFieldLabel matlab.ui.control.Label
% Tab 2 components
NeuroTemplateLabel matlab.ui.control.Label
NeuroTemplateListBox matlab.ui.control.ListBox
CellularMarkerLabel matlab.ui.control.Label
CellularMarkerListBox matlab.ui.control.ListBox
MetricsLabel matlab.ui.control.Label
MetricsListBox matlab.ui.control.ListBox
RunAnalysisButton matlab.ui.control.Button
PermutationsField matlab.ui.control.EditField
PermutationsFieldLabel matlab.ui.control.Label
T1CheckBox matlab.ui.control.CheckBox
InstructionsLabel matlab.ui.control.Label
% Tab 3 components
PlotSelected matlab.ui.control.Button
FigureNeuro matlab.ui.control.UIAxes
ResultsTable matlab.ui.control.Table
SwitchScatterLabel1 matlab.ui.control.Label
SwitchScatterLabel2 matlab.ui.control.Label
SwitchScatter matlab.ui.control.Switch
SaveFigureButton matlab.ui.control.Button
RankPlotCheckBox matlab.ui.control.CheckBox
ResultsLabel matlab.ui.control.Label
xaxisListLabel matlab.ui.control.Label
xaxisList matlab.ui.control.ListBox
yaxisList matlab.ui.control.ListBox
FDRCheckBox matlab.ui.control.CheckBox
% Navigation buttons
NextTabButton matlab.ui.control.Button
PrevTabButton matlab.ui.control.Button
end
properties (Access = private)
list_PET;
list_cell;
files_set1;
files_set2;
atlas;
Results;
Tab3Enabled = false;
dir_tool;
end
methods (Access = private)
function tabChanged(app, event)
selectedTab = event.NewValue;
if selectedTab == app.Tab3 && ~app.Tab3Enabled
% Block access: force back to Tab1
uialert(app.UIFigure, 'Please set-up and run the analysis first.', 'Tab Locked');
app.TabGroup.SelectedTab = app.Tab1; % revert selection
end
check_inputs(app)
end
% Navigate tabs
function nextTab(app, ~) % <-- added 'event' here
idx = find(app.TabGroup.Children == app.TabGroup.SelectedTab);
if idx < 3
app.TabGroup.SelectedTab = app.TabGroup.Children(idx + 1);
end
drawnow;
check_inputs(app)
end
function prevTab(app, ~)
idx = find(app.TabGroup.Children == app.TabGroup.SelectedTab);
if idx > 1
app.TabGroup.SelectedTab = app.TabGroup.Children(idx - 1);
end
check_inputs(app)
end
% Select custom Atlas
function selectAtlas(app, ~, ~)
[file, path] = uigetfile('*.nii;*.img', 'Select Atlas Image');
atlas_all = app.atlas;
atlas_all(end+1).name = file;
atlas_all(end).folder = path;
app.atlas = atlas_all;
if isequal(file,0)
app.AtlasDropDown.Value = 'm_labels_Neuromorphometrics.nii';
else
app.AtlasDropDown.Items = [app.AtlasDropDown.Items, {file}];
app.AtlasDropDown.Value = file;
end
check_inputs(app)
end
% Study design options
function updateAnalysisVisibility(app)
% update visibility of set 2
study_design_opt = find(ismember(app.AnalysisDropDown.Items,app.AnalysisDropDown.Value))-1;
set2_opt = [1,2,5,6];
if ismember(study_design_opt, set2_opt)
app.SecondSetButton.Enable = 'on';
else
app.SecondSetButton.Enable = 'off';
app.SecondSetListBox.Items = {''};
app.files_set2 = {''};
app.SecondSetButton.BackgroundColor = [0.5 0.7 0.9];
end
% Update tooltip based on dropdown selection
switch study_design_opt
% opt_comp = 1 --> es between
% opt_comp = 2 --> es within
% opt_comp = 3 --> mean list 1
% opt_comp = 4 --> list 1 each
% opt_comp = 5 --> ind z-score list 1 to list 2
% opt_comp = 6 --> pair-wise difference list 1 to list 2
% opt_comp = 7 --> ind z-scores from list 1
% opt_comp = 8 --> list 1 each compares against null distribution of
% correlation coefficients
case 1
app.AnalysisTooltipLabel.Text = sprintf('Computes and uses Cohen''s d effect size per region for set 1 versus set 2');
case 2
app.AnalysisTooltipLabel.Text = sprintf('Computes pre-post Cohen''s d effect size \nper region for set 1 relative to set 2. \nNumber of selected images must be the same for set 1 and 2');
case 3
app.AnalysisTooltipLabel.Text = sprintf('Uses mean per region from set 1');
case 4
app.AnalysisTooltipLabel.Text = sprintf('Tests each image from set 1 against a null distribution');
case 5
app.AnalysisTooltipLabel.Text = sprintf('Converts each image in set 1 to z-scores relative to images in set 2');
case 6
app.AnalysisTooltipLabel.Text = sprintf('Computes pair-wise differences for set 1 versus set 2 \nand tests these difference maps against null distribution. \nNumber of selected images must be the same for set 1 and 2');
case 7
app.AnalysisTooltipLabel.Text = sprintf('Converts each image in set 1 to z-scores relative to \nall other images in set 1');
case 8
app.AnalysisTooltipLabel.Text = sprintf('Tests the distribution of spatial correlation of all \nimages from set 1 against a null distribution');
otherwise
app.AnalysisTooltipLabel.Text = sprintf('Please select an analysis option');
end
check_inputs(app)
end
% Select First Image Set
function selectFirstSet(app, ~, ~)
app.files_set1 = cellstr(spm_select(Inf,'image','Select files for set 1'));
for i = 1:length(app.files_set1)
[~,file] = fileparts(app.files_set1{i});
set1{i} = file;
end
app.UIFigure.Visible = 'off';
app.UIFigure.Visible = 'on'; % keeps figure in the foreground
if isequal(set1,0)
app.FirstSetListBox.Items = {''};
else
if ischar(set1)
set1 = {set1};
end
app.FirstSetListBox.Items = set1;
app.FirstSetListBox.Visible = 'on';
app.FirstSetLabel.Visible = 'on';
app.FirstSetButton.BackgroundColor = [0.4 0.8 0.6];
end
check_inputs(app)
end
% Select Second Image Set (optional)
function selectSecondSet(app, ~, ~)
ana_opt = find(ismember(app.AnalysisDropDown.Items,app.AnalysisDropDown.Value))-1;
app.files_set2 = cellstr(spm_select(Inf,'image','Select files for set 1'));
if ismember(ana_opt,[2 6]) && length(app.files_set1) ~= length(app.files_set2)
uialert(app.UIFigure, 'For analysis options 2 and 6 the number of images in set 2 must match the number selected in set 1', 'Check selection');
else
for i = 1:length(app.files_set2)
[~,file] = fileparts(app.files_set2{i});
set2{i} = file;
end
app.UIFigure.Visible = 'off';
app.UIFigure.Visible = 'on';
if isequal(set2,0)
app.SecondSetListBox.Visible = 'off';
app.SecondSetLabel.Visible = 'off';
else
if ischar(set2)
set2 = {set2};
end
app.SecondSetListBox.Items = set2;
app.SecondSetListBox.Visible = 'on';
app.SecondSetLabel.Visible = 'on';
app.SecondSetButton.BackgroundColor = [0.4 0.8 0.6];
end
check_inputs(app)
end
end
% Select save directory
function selectSaveDir(app, ~, ~)
saveDir = uigetdir;
if saveDir ~= 0
app.SaveDirLabel.Text = saveDir;
app.SelectSaveDirButton.BackgroundColor = [0.4 0.8 0.6];
end
app.UIFigure.Visible = 'off';
app.UIFigure.Visible = 'on';
drawnow;
check_inputs(app);
end
function run_analysis(app,~)
d = uiprogressdlg(app.UIFigure, 'Title', 'Please Wait', 'Message', 'Running analysis...', 'Indeterminate', 'on', 'Cancelable', 'off');
try
% get selections from tab 1 (Inputs)
try
d.Message = 'Loading all settings ...';
atlases = app.atlas;
atlas_sel = app.AtlasDropDown.Value;
atlas_all = app.AtlasDropDown.Items;
[~,ind_atlas] = ismember(atlas_sel, atlas_all);
atlas = fullfile(atlases(ind_atlas).folder,atlases(ind_atlas).name);
ana_opt = find(ismember(app.AnalysisDropDown.Items,app.AnalysisDropDown.Value))-1;
list1 = app.files_set1;
list2 = app.files_set2;
dir_save = app.SaveDirLabel.Text;
name_save = app.NameSaveField.Value;
app.SwitchScatter.Value = {'Bar plot'};
app.xaxisList.Items = {''};
app.yaxisList.Items = {''};
% get selections from tab 2 (Templates and Metrics)
[ind_PET,ind_cell,ind_ana] = SelectTemplates(app);
files_PET = app.list_PET(ind_PET);
files_cell = app.list_cell(ind_cell);
files_PET = [files_PET;files_cell];
opt_perm = 0;
opt_spat_perm = 0;
Nperm = str2num(app.PermutationsField.Value);
switch ana_opt
case {1,2,5,6}
opt_perm = 1;
case {3,4,7,8}
opt_spat_perm = 1;
end
image_save = fullfile(dir_save, [name_save '.nii']);
opt_T1 = app.T1CheckBox.Value;
options = [ana_opt ind_ana opt_perm opt_T1 opt_spat_perm];
catch ME
uialert(app.UIFigure, getReport(ME), 'Analysis Error');
fid = fopen('error_log.txt', 'a');
fprintf(fid, '[%s] ERROR: %s\n', char(datetime), ME.message);
fprintf(fid, '%s\n\n', getReport(ME, 'extended'));
fclose(fid);
end
d.Message = 'Computing spatial correlations...';
try
Results = compute_DomainGauges(list1,list2,files_PET,atlas, options,image_save);
catch ME
fid = fopen('error_log.txt', 'a');
fprintf(fid, '[%s] ERROR: %s\n', char(datetime), ME.message);
fprintf(fid, '%s\n\n', getReport(ME, 'extended'));
fclose(fid);
end
opt_for_perm = [1,2,5,6];
opt_for_spat_perm = [3, 4, 7, 8];
try
if options(3)==1 && ismember(options(1),opt_for_perm)% && options(2)~=3
d.Message = 'Computing exact p-value...';
disp('Computing exact p-value');
[p_exact,~] = compute_exact_pvalue(Results.data_set1,Results.data_set2,Results.data_PET,Results.res,Nperm,options,Results.T1,Results.stats);
Results.Resh(:,end+1) = [{'p_exact'}; num2cell_my(p_exact')];
end
if options(5)==1 && ismember(options(1),opt_for_spat_perm)
d.Message = 'Computing exact spatial p-value. This option may take quite a while if the null maps are not yet precomputed';
disp('Computing exact spatial p-value')
[p_exact,~,~] = compute_exact_spatial_pvalue(Results.data_set1,Results.data_PET,atlas,Results.res,Nperm,options,files_PET, Results.T1,Results.stats,d);
Results.Resh(:,end+1) = [{'p_exact_spatial'}; num2cell_my(p_exact')];
end
[h_sig, crit_p, adj_p_fdrBH] = fdr_bh(p_exact, 0.05);
Results.Resh(:,end+1) = [{'p_fdr_BH'}; num2cell_my(adj_p_fdrBH)];
catch ME
fid = fopen('error_log.txt', 'a');
fprintf(fid, '[%s] ERROR: %s\n', char(datetime), ME.message);
fprintf(fid, '%s\n\n', getReport(ME, 'extended'));
fclose(fid);
end
d.Message = 'Saving and visualizing results...';
if options(2)<3
Results.Resh = Results.Resh(:,[1 6 7 2 3 4 5]);
else
Results.Resh = Results.Resh(:,[1 5 6 2 3 4]);
end
Results.p_exact = p_exact;
Results.p_exact_fdr_BH = adj_p_fdrBH;
Results.dir_save = dir_save;
Results.Nperm = Nperm;
Results.options = options;
Results.atlas = atlas;
Results.filesPET = files_PET;
Results.set1_images = list1;
Results.set2_images = list2;
app.Results = Results;
app.ResultsTable.ColumnName = Results.Resh(1,:);
data = Results.Resh(2:end,:);
app.ResultsTable.Data = data;
save_results(Results,app,name_save);
try
bar_plot(app,h_sig);
app.TabGroup.SelectedTab = app.TabGroup.Children(3);
app.Tab3Enabled = true;
catch ME
fid = fopen('error_log.txt', 'a');
fprintf(fid, '[%s] ERROR: %s\n', char(datetime), ME.message);
fprintf(fid, '%s\n\n', getReport(ME, 'extended'));
fclose(fid);
end
catch ME2
uialert(app.UIFigure, getReport(ME2), 'Analysis Error');
end
close(d);
end
function listBoxItemClicked(app, event)
check_inputs(app);
end
function switchscatterbox(app)
switch_plot = app.SwitchScatter.Value;
if strcmp(switch_plot,'Scatter plot')
cla(app.FigureNeuro, 'reset');
app.xaxisList.Enable = 'on';
app.yaxisList.Enable = 'on';
app.RankPlotCheckBox.Enable = 'on';
app.FDRCheckBox.Enable = 'off';
app.PlotSelected.Enable = 'on';
all = app.Results.filesPET;
for i = 1:length(all)
[~,name] = fileparts(all{i});
xlist_all{i,1} = name;
end
app.xaxisList.Items = xlist_all;
if size(app.Results.data,1) > 1
app.yaxisList.Items = app.files_set1;
else
app.yaxisList.Items = {'data modality'};
end
else
app.FDRCheckBox.Enable = 'on';
app.FDRCheckBox.Value = false;
cla(app.FigureNeuro, 'reset');
app.xaxisList.Enable = 'off';
app.yaxisList.Enable = 'off';
app.RankPlotCheckBox.Enable = 'off';
app.PlotSelected.Enable = 'off';
bar_plot(app);
end
end
function addSignficanttoBoxPlot(app)
plot_sig = app.FDRCheckBox.Value;
if plot_sig
try
h_sig = app.Results.p_exact_fdr_BH<0.05;
bar_plot(app,h_sig);
catch
uialert(app.UIFigure, 'FDR significance not computed','Information');
end
else
bar_plot(app);
end
end
function plotSelected(app)
scatter_plot(app);
end
function exportWithPrint(app)
% Create a new figure and axes
plot_opt = app.SwitchScatter.Value;
if strcmp(plot_opt,'Scatter plot')
f = figure('Position',[400 400 800 600],'Visible', 'off');
else
if length(app.Results.filesPET)<3
f = figure('Position',[400 400 500 500],'Visible', 'off');
elseif length(app.Results.filesPET)<10
f = figure('Position',[400 200 length(app.Results.filesPET).*150 700],'Visible', 'off');
else
f = figure('Position',[400 200 length(app.Results.filesPET).*100 700],'Visible', 'off');
end
end
ax = axes(f);
% Copy content from App Designer UIAxes
copyobj(allchild(app.FigureNeuro), ax);
% Copy axis labels and limits
ax.XLim = app.FigureNeuro.XLim;
ax.YLim = app.FigureNeuro.YLim;
xlabel(ax, app.FigureNeuro.XLabel.String);
ylabel(ax, app.FigureNeuro.YLabel.String);
title(ax, app.FigureNeuro.Title.String);
set(ax, 'XTick', app.FigureNeuro.XTick);
set(ax, 'XTickLabel', app.FigureNeuro.XTickLabel);
set(ax, 'FontSize', app.FigureNeuro.FontSize);
set(ax, 'XTickLabelRotation', app.FigureNeuro.XTickLabelRotation);
if strcmp(plot_opt,'Scatter plot')
grid on
end
try
legend(ax, app.FigureNeuro.Legend.String, 'Location', 'northeastoutside');
catch
end
grid on
set(gcf,'color','w');
time_now = datestr(datetime('now'),'ddmmmyyyy_HHMMSS');
if strcmp(plot_opt,'Scatter plot')
print(f,fullfile(app.Results.dir_save,['Scatter_' app.NameSaveField.Value '_' time_now '.png']),'-dpng','-r300');
f.Visible='on';
savefig(f,fullfile(app.Results.dir_save,['Scatter_' app.NameSaveField.Value '_' time_now '.fig']));
f.Visible='off';
else
print(f,fullfile(app.Results.dir_save,['Bar_' app.NameSaveField.Value '_' time_now '.png']),'-dpng','-r300');
f.Visible='on';
saveas(f,fullfile(app.Results.dir_save,['Bar_' app.NameSaveField.Value '_' time_now '.fig']),'fig');
f.Visible='off';
end
close(f);
end
function loadAnalysis(app)
d = uiprogressdlg(app.UIFigure, 'Title', 'Please Wait', 'Message', 'Loading analysis...', 'Indeterminate', 'on', 'Cancelable', 'off');
[file, path] = uigetfile('*.mat', 'Load Analysis');
if isequal(file, 0)
return;
end
loaded = load(fullfile(path, file));
if ~isfield(loaded, 'app_save')
uialert(app.UIFigure, 'Invalid file format.', 'Error');
return;
end
analysisData = loaded.app_save;
% Restore Tab 1
app.AtlasDropDown.Items = analysisData.AtlasDropDown.Items;
app.AtlasDropDown.Value = analysisData.AtlasDropDown.Value;
app.AnalysisDropDown.Items = analysisData.AnalysisDropDown.Items;
app.AnalysisDropDown.Value = analysisData.AnalysisDropDown.Value;
app.FirstSetListBox.Items = analysisData.FirstSetListBox.Items;
app.FirstSetListBox.Value = analysisData.FirstSetListBox.Value;
app.SecondSetListBox.Items = analysisData.SecondSetListBox.Items;
app.SecondSetListBox.Value = analysisData.SecondSetListBox.Value;
app.AnalysisTooltipLabel.Text = analysisData.AnalysisTooltipLabel.Text;
app.NameSaveField.Value = analysisData.NameSaveField.Value;
app.SaveDirLabel.Text = analysisData.SaveDirLabel.Text;
app.files_set1 = analysisData.files_set1;
app.files_set2 = analysisData.files_set2;
app.list_PET = analysisData.list_PET;
app.list_cell = analysisData.list_cell;
app.atlas = analysisData.atlas;
% Restore Tab 2
app.NeuroTemplateListBox.Items = analysisData.NeuroTemplateListBox.Items;
app.NeuroTemplateListBox.Value = analysisData.NeuroTemplateListBox.Value;
app.CellularMarkerListBox.Items = analysisData.CellularMarkerListBox.Items;
app.CellularMarkerListBox.Value = analysisData.CellularMarkerListBox.Value;
app.MetricsListBox.Items = analysisData.MetricsListBox.Items;
app.MetricsListBox.Value = analysisData.MetricsListBox.Value;
app.PermutationsField.Value = analysisData.PermutationsField.Value;
app.T1CheckBox.Value = analysisData.T1CheckBox.Value;
% Restore Tab 3
if ~isempty(analysisData.ResultsTable.Data)
cla(app.FigureNeuro, 'reset');
app.TabGroup.SelectedTab = app.TabGroup.Children(3);
app.Tab3Enabled = true;
app.ResultsTable.ColumnName = analysisData.ResultsTable.ColumnName;
app.ResultsTable.Data = analysisData.ResultsTable.Data;
app.SwitchScatter.Value = analysisData.SwitchScatter.Value;
app.RankPlotCheckBox.Value = analysisData.RankPlotCheckBox.Value;
app.xaxisList.Items = analysisData.xaxisList.Items;
app.xaxisList.Value = analysisData.xaxisList.Value;
app.yaxisList.Items = analysisData.yaxisList.Items;
app.yaxisList.Value = analysisData.yaxisList.Value;
app.Results = loaded.Results;
app.xaxisList.Enable = 'off';
app.yaxisList.Enable = 'off';
app.RankPlotCheckBox.Enable = 'off';
app.PlotSelected.Enable = 'off';
app.TabGroup.SelectedTab = app.TabGroup.Children(3);
bar_plot(app);
else
app.TabGroup.SelectedTab = app.TabGroup.Children(1);
app.Tab3Enabled = false;
end
close(d);
drawnow;
check_inputs(app);
uialert(app.UIFigure, 'Analysis loaded.', 'Loaded');
end
function saveAnalysis(app)
Results = app.Results;
% Results.JuSpace_version = 'v2.1';
% file_save = fullfile(path,file);
% save(file_save,'Results','app_save');
save_results(Results,app);
uialert(app.UIFigure, 'Analysis saved.', 'Success');
end
end
methods (Access = private)
function createComponents(app)
warning off
if isdeployed
[~, ~] = system('path');
app.dir_tool = pwd;
else
app.dir_tool= fileparts(which('JuSpace'));
end
% Main UI figure
app.UIFigure = uifigure('Name', 'JuSpace 2.1', 'Position', [100, 100, 900, 600], 'Color', [0.96 0.96 0.98], 'AutoResizeChildren','on');
fileMenu = uimenu(app.UIFigure, 'Text', 'File');
uimenu(fileMenu, 'Text', 'Load Existing Analysis...','MenuSelectedFcn', @(src, event) loadAnalysis(app));
uimenu(fileMenu, 'Text', 'Save Analysis Settings...', 'MenuSelectedFcn', @(src, event) saveAnalysis(app));
app.LogoImage = uiimage(app.UIFigure, 'ImageSource', fullfile(app.dir_tool,'splash.png'),'Position', [420, 1, 60, 60]);
% Tab Group
app.TabGroup = uitabgroup(app.UIFigure, 'Position', [10 60 880 520],'SelectionChangedFcn', @(src,event) tabChanged(app, event));
% Tab 1
app.Tab1 = uitab(app.TabGroup, 'Title', 'Inputs');
% Update AtlasDropDown items, keeping existing entries
% Atlas options
app.AtlasLabel = uilabel(app.Tab1, 'Text', 'Select Atlas:', 'Position', [20, 440, 100, 22], 'FontWeight', 'bold');
app.AtlasDropDown = uidropdown(app.Tab1, 'Items', {'Atlas1'}, 'Position', [130, 440, 250, 22]);
app.SelectAtlasButton = uibutton(app.Tab1, 'push', 'Text', 'Custom Atlas', 'Position', [400, 440, 120, 22], 'BackgroundColor',[0.5 0.7 0.9],'FontColor','white','ButtonPushedFcn', @app.selectAtlas);
%Study design options
study_designs = {'Select an option', '1) Effect size between groups', '2) Effect size within group','3) Mean from set 1', '4) Set 1 each image', '5) Individual z-scores for set 1 relative to set 2', '6) pair-wise difference set 1 relative to set 2','7) Leave-one-out from set 1', '8) Set 1 each compares against null distribution'};
app.AnalysisTooltipLabel = uilabel(app.Tab1, 'Text', 'Description of the selected spatial correlation approach', 'Position', [400, 390, 400, 44],'FontAngle', 'italic', 'FontColor', [0.3 0.3 0.3]);
app.AnalysisLabel = uilabel(app.Tab1, 'Text', 'Study Design:', 'Position', [20, 400, 100, 22], 'FontWeight', 'bold');
app.AnalysisDropDown = uidropdown(app.Tab1, 'Items', study_designs, 'Position', [130, 400, 250, 22],'ValueChangedFcn', @(src, event) updateAnalysisVisibility(app));
app.SelectSaveDirButton = uibutton(app.Tab1, 'push', 'Text', 'Select Save Directory', 'Position', [20, 350 360, 22],'BackgroundColor',[0.8 0.8 0.5],'FontColor','white' ,'ButtonPushedFcn', @app.selectSaveDir);
app.SaveDirLabel = uilabel(app.Tab1, 'Text', '', 'Position', [20, 320, 600, 22]);
% Label for editable field
app.NameSaveFieldLabel = uilabel(app.Tab1, 'Text', 'Name save', 'Position', [400, 370, 100, 22], 'FontWeight', 'bold');
app.NameSaveField = uieditfield(app.Tab1, 'text', 'Position', [400, 350 200, 22], 'Value', ''); % initial value
app.FirstSetButton = uibutton(app.Tab1, 'push', 'Text', 'Select First Set', 'Position', [20, 290, 120, 22],'BackgroundColor',[0.5 0.7 0.9],'FontColor','white','ButtonPushedFcn', @app.selectFirstSet);
app.FirstSetLabel = uilabel(app.Tab1, 'Text', 'First Set Images:', 'Position', [20, 260, 340, 20],'FontWeight', 'bold');
app.FirstSetListBox = uilistbox(app.Tab1,'Items',{''}, 'Position', [20, 30, 380, 220]);
app.SecondSetButton = uibutton(app.Tab1, 'push', 'Text', 'Select Second Set', 'Position', [450, 290, 120, 22],'BackgroundColor',[0.5 0.7 0.9],'FontColor','white', 'ButtonPushedFcn', @app.selectSecondSet);
app.SecondSetLabel = uilabel(app.Tab1, 'Text', 'Second Set Images:', 'Position', [450, 260, 340, 20],'FontWeight', 'bold');
app.SecondSetListBox = uilistbox(app.Tab1,'Items',{''}, 'Position', [450, 30, 380, 220]);
app.SecondSetButton.Enable = 'off';
% Components in Tab 2
% Tab 2
app.Tab2 = uitab(app.TabGroup, 'Title', 'Templates & Metrics');
dir_PET = fullfile(app.dir_tool,'PETatlas');
files_PET = select_con_maps_forfMRI_my(dir_PET,'PETatlas','.*.nii');
for i = 1:length(files_PET)
[~,file] = fileparts(files_PET{i});
list_PET{i} = file;
end
app.list_PET = files_PET;
app.NeuroTemplateLabel = uilabel(app.Tab2,'Text','PET/Neurotransmitter Templates:','Position',[20,470,200,22],'FontWeight','bold');
app.NeuroTemplateListBox = uilistbox(app.Tab2,'Items',list_PET,'Position',[20,110,260,360],'Multiselect','on','ValueChangedFcn', @(src,event) listBoxItemClicked(app, event));
app.NeuroTemplateListBox.Value = cell(0,0);
dir_cell = fullfile(app.dir_tool,'CELLatlas');
files_cell = select_con_maps_forfMRI_my(dir_cell,'CELLatlas','.*.nii');
for i = 1:length(files_cell)
[path,file] = fileparts(files_cell{i});
list_cell{i} = file;
end
app.list_cell = files_cell;
app.CellularMarkerLabel = uilabel(app.Tab2,'Text','Cellular Markers:','Position',[310,470,300,22],'FontWeight','bold');
app.CellularMarkerListBox = uilistbox(app.Tab2,'Items',list_cell,'Position',[310,110,260,360],'Multiselect','on','ValueChangedFcn', @(src,event) listBoxItemClicked(app, event));
app.CellularMarkerListBox.Value = cell(0,0);
app.MetricsLabel = uilabel(app.Tab2,'Text','Select Analysis Type:','Position',[600,470,200,22],'FontWeight','bold');
app.MetricsListBox = uilistbox(app.Tab2,'Items',{'Spearman','Pearson','Multiple Linear Regression'},'Position',[600,110,260,360],'Multiselect','off','ValueChangedFcn', @(src,event) listBoxItemClicked(app, event));
app.MetricsListBox.Value = cell(0,0);
app.PermutationsFieldLabel = uilabel(app.Tab2, 'Text', 'Number of permutations', 'Position', [20, 90, 200, 22], 'FontWeight', 'bold');
app.PermutationsField = uieditfield(app.Tab2, 'text', 'Position', [20, 70, 150, 22], 'Value', '1000'); % initial value
app.T1CheckBox = uicheckbox(app.Tab2, 'Text', 'Partial volume effect correction using T1 probabilities', 'Position', [200, 60, 350, 30]);
app.RunAnalysisButton = uibutton(app.Tab2,'push','Text','Run Analysis','Position',[20,20,840,40],'BackgroundColor',[0.2 0.6 0.8],'FontColor','white', 'ButtonPushedFcn', @(src, event) run_analysis(app));
app.RunAnalysisButton.Enable = 'off';
app.InstructionsLabel = uilabel(app.Tab2, 'Text', sprintf('Please cite the reference for each image as described in \nsources_template_release.txt. \nTo (un)select multiple images keep CTRL key pressed'), 'Position', [550, 60, 330, 50]);
% Tab 3
app.Tab3 = uitab(app.TabGroup, 'Title', 'Results');
app.FigureNeuro = uiaxes(app.Tab3,'Position', [50, 60, 430, 430]);
title(app.FigureNeuro, 'Results');
app.ResultsLabel = uilabel(app.Tab3, 'Text', 'Results table', 'Position', [650, 470, 100, 22],'FontWeight','bold');
app.ResultsTable = uitable(app.Tab3,'Position', [650, 20, 200, 450], 'Data', {},'ColumnName', {'Column 1', 'Column 2'},'RowName', {});
% plot options
app.SwitchScatter = uiswitch(app.Tab3, 'slider','Position', [60, 10, 45, 20],'Items', {'Bar plot', 'Scatter plot'}, 'ValueChangedFcn', @(src,event) switchscatterbox(app));
app.RankPlotCheckBox = uicheckbox(app.Tab3, 'Text', 'Rank Plot', 'Position', [200, 8, 90, 20]);
app.FDRCheckBox = uicheckbox(app.Tab3, 'Text', 'FDR sign.', 'Position', [290, 8, 100, 20],'ValueChangedFcn', @(src,event) addSignficanttoBoxPlot(app));
app.SaveFigureButton = uibutton(app.Tab3,'push','Text','Save figure','Position',[400,10,90,20], 'BackgroundColor',[0.2 0.6 0.8],'FontColor','white', 'ButtonPushedFcn', @(src, event) exportWithPrint(app));
app.xaxisListLabel = uilabel(app.Tab3, 'Text', 'Select plot items', 'Position', [530, 470, 100, 22],'FontWeight','bold');
app.xaxisList = uilistbox(app.Tab3,'Items',{'x axis'},'Position',[530,260,110,210],'Multiselect','on');
app.yaxisList = uilistbox(app.Tab3,'Items',{'y axis'},'Position',[530,40,110,210],'Multiselect','on');
app.PlotSelected = uibutton(app.Tab3,'push','Text','Plot selected','Position',[530,20,110,20], 'ButtonPushedFcn', @(src, event) plotSelected(app));
app.xaxisList.Enable = 'off';
app.RankPlotCheckBox.Enable = 'off';
app.PlotSelected.Enable = 'off';
app.yaxisList.Enable = 'off';
% Navigation Buttons
app.PrevTabButton = uibutton(app.UIFigure, 'push', 'Text', 'Previous', 'Position', [10, 20, 100, 30], 'ButtonPushedFcn', @(src,event) app.prevTab(event));
app.NextTabButton = uibutton(app.UIFigure, 'push', 'Text', 'Next', 'Position', [790, 20, 100, 30], 'ButtonPushedFcn', @(src,event) app.nextTab(event));
end
end
methods (Access = public)
function app = JuSpace
createComponents(app);
load_atlases(app);
end
end
end
function load_atlases(app) % checks for available atlases and sets the default
dir_juspace = app.dir_tool;
% dir_juspace = fileparts(which('JuSpace'));
dir_atlas = dir(fullfile(dir_juspace, 'atlas','*.nii')); % Adjust file extension if needed
app.atlas = dir_atlas;
atlases = {dir_atlas.name};
app.AtlasDropDown.Items = atlases;
app.AtlasDropDown.Value = 'm_labels_Neuromorphometrics.nii';
end
function [check_analysis] = check_inputs(app)
%check inputs
% tab 1
study_design = find(ismember(app.AnalysisDropDown.Items,app.AnalysisDropDown.Value))-1;
study_design_opt = study_design>0;
set2_opt = [1,2,5,6];
list1_opt = sum(~isemptycell(app.FirstSetListBox.Items))>0;
save_dir_opt = isdir(app.SaveDirLabel.Text);
check_all = [study_design_opt list1_opt save_dir_opt];
if ismember(study_design, set2_opt)
list2_opt = sum(~isemptycell(app.SecondSetListBox.Items))>0;
check_all = [check_all list2_opt];
end
[ind1,ind2,ind_ana] = SelectTemplates(app);
ind_check = ~isempty([ind1 ind2]);
ind_ana_check = ~isempty(ind_ana);
check_all = [check_all ind_check ind_ana_check];
if true(check_all)
app.RunAnalysisButton.Enable = 'on';
drawnow;
else
app.RunAnalysisButton.Enable = 'off';
drawnow;
end
end
function [ind_PET,ind_cell,ind_ana] = SelectTemplates(app)
selectedItems = app.NeuroTemplateListBox.Value;
allItems = app.NeuroTemplateListBox.Items;
[~, ind_PET] = ismember(selectedItems, allItems);
selectedItemsCell = app.CellularMarkerListBox.Value;
allItemsCell = app.CellularMarkerListBox.Items;
[~, ind_cell] = ismember(selectedItemsCell, allItemsCell);
selectedItems = app.MetricsListBox.Value;
allItems = app.MetricsListBox.Items;
[~, ind_ana] = ismember(selectedItems, allItems);
end
function save_results(Results,app,name_save)
Results.JuSpace_version = 'v2.1';
% Save Tab 1
app_save.AtlasDropDown.Items = app.AtlasDropDown.Items;
app_save.AtlasDropDown.Value = app.AtlasDropDown.Value;
app_save.AnalysisDropDown.Items = app.AnalysisDropDown.Items;
app_save.AnalysisDropDown.Value = app.AnalysisDropDown.Value;
app_save.FirstSetListBox.Items = app.FirstSetListBox.Items;
app_save.FirstSetListBox.Value = app.FirstSetListBox.Value;
app_save.SecondSetListBox.Items = app.SecondSetListBox.Items;
app_save.SecondSetListBox.Value = app.SecondSetListBox.Value;
app_save.AnalysisTooltipLabel.Text = app.AnalysisTooltipLabel.Text;
app_save.NameSaveField.Value = app.NameSaveField.Value;
app_save.SaveDirLabel.Text = app.SaveDirLabel.Text;
app_save.files_set1 = app.files_set1;
app_save.files_set2 = app.files_set2;
app_save.list_PET = app.list_PET;
app_save.list_cell = app.list_cell;
app_save.atlas = app.atlas;
% Save Tab 2
try
app_save.NeuroTemplateListBox.Items = app.NeuroTemplateListBox.Items;
app_save.NeuroTemplateListBox.Value = app.NeuroTemplateListBox.Value;
app_save.CellularMarkerListBox.Items = app.CellularMarkerListBox.Items;
app_save.CellularMarkerListBox.Value = app.CellularMarkerListBox.Value;
app_save.MetricsListBox.Items = app.MetricsListBox.Items;
app_save.MetricsListBox.Value = app.MetricsListBox.Value;
app_save.PermutationsField.Value = app.PermutationsField.Value;
app_save.T1CheckBox.Value = app.T1CheckBox.Value;
catch
end
time_now = datestr(datetime('now'),'ddmmmyyyy_HHMMSS');
% Save Tab 3
try
app_save.TabGroup.SelectedTab = app.TabGroup.Children(3);
app_save.Tab3Enabled = true;
app_save.ResultsTable.ColumnName = app.Results.Resh(1,:);
app_save.ResultsTable.Data = app.ResultsTable.Data;
app_save.SwitchScatter.Value = app.SwitchScatter.Value;
app_save.RankPlotCheckBox.Value = app.RankPlotCheckBox.Value;
app_save.xaxisList.Items = app.xaxisList.Items;
app_save.xaxisList.Value = app.xaxisList.Value;
app_save.yaxisList.Items = app.yaxisList.Items;
app_save.yaxisList.Value = app.yaxisList.Value;
app_save.Results = app.Results;
app_save.xaxisList.Enable = 'off';
app_save.yaxisList.Enable = 'off';
app_save.RankPlotCheckBox.Enable = 'off';
app_save.PlotSelected.Enable = 'off';
writecell(app.Results.Resh,fullfile(Results.dir_save,['ResultsTable_' name_save '_' time_now '.csv']),'Delimiter',';');
catch
end
if ~exist('name_save','var')
[file, path] = uiputfile('*.mat', 'Save Analysis to...');
if isequal(file, 0)
return; % User canceled
end
save(fullfile(path,file),'Results','app_save');
else
name_save = add_suffix(name_save,Results.options);
file_save = fullfile(Results.dir_save,[name_save '.mat']);
save(file_save,'Results','app_save');
end
end
function bar_plot(app,h_sig)
cla(app.FigureNeuro, 'reset');
Results = app.Results;
name_save = app.NameSaveField.Value;
res = Results.stats.res_ind;
ind_plot =[];
for i = 1:size(res,1)
for j = 1:size(res,2)
ind_plot(end+1,1) = j;
end
end
vals_plot= res; %cell2num_my(Resh(2:end,3));
for i = 1:length(Results.filesPET)
all_i = vals_plot(:,i);%vals_plot(ind_plot==i);
size_y_xi = sum(ind_plot==i);
y_dist = abs(vals_plot(ind_plot==i) - mean(vals_plot(ind_plot==i)));
y_dist_inv = abs(y_dist -max(y_dist))./max(abs(y_dist -max(y_dist)));
if isnan(y_dist_inv)
y_dist_inv = rand(size(y_dist_inv));
end
x_n_n(ind_plot==i) = ind_plot(ind_plot==i) + 0.7.*(rand(size_y_xi,1)-0.5).*y_dist_inv;
all_ii = all_i(~isinf(all_i));
m_x(i) = mean(all_ii);
std_x(i,1) = 1.95996.*std(all_ii)./sqrt(length(all_ii));
end
% plot specs
n_files = length(Results.set1_images);
marker_color = [0 0.7 1];
opt_plot = 1;
if opt_plot == 1
bar_color = generate_colors_nice_my(length(Results.filesPET));
else
bar_color = generate_colors_blue_my(length(Results.filesPET));
end
opt_comp = Results.options(1);
if opt_comp<3 || size(Results.data,1)==1
h2 = bar(app.FigureNeuro,1:length(Results.filesPET),diag(m_x),0.9,'stacked','EdgeColor','none');
if min(m_x)>0
min_m_x = 0;
else
min_m_x = min(m_x);
end
if max(m_x)<0
max_m_x = 0;
else
max_m_x = max(m_x);
end
ylim(app.FigureNeuro,[min_m_x.*1.1 max_m_x.*1.1]);
else
h2 = boxplot(app.FigureNeuro, vals_plot,'widths',0.9,'outliersize',2);
set(app.FigureNeuro,'Visible','on');
% set(gca,'XTickLabel',Rec_list);
h3 = findobj(app.FigureNeuro,'Tag','Box');
flip_color = flip(bar_color);
for j=1:length(h3)
patch(app.FigureNeuro,get(h3(j),'XData'),get(h3(j),'YData'),flip_color(j,:),'EdgeColor','none');
end
hold(app.FigureNeuro, 'on');
h2 = boxplot(app.FigureNeuro, vals_plot,'widths',0.9,'outliersize',2,'Colors',zeros(3,3));
set(h2(7,:),'Visible','off');
lines = findobj(app.FigureNeuro, 'type', 'line', 'Tag', 'Median');
set(lines, 'Color', 'black');
grid(app.FigureNeuro,'on');
end
Rec_list = Results.Resh(2:end,1);
app.FigureNeuro.XTick = 1:length(Rec_list);
if length(Results.filesPET)>1
app.FigureNeuro.XTickLabel = Rec_list;
app.FigureNeuro.FontSize = 16;
app.FigureNeuro.XTickLabelRotation = 90;
else
app.FigureNeuro.XTickLabel = Rec_list;
app.FigureNeuro.FontSize = 16;
end
vv = vals_plot';
vv2 = vv(:);
hold(app.FigureNeuro, 'on');
for i = 1:length(Results.filesPET)
if size(Results.data,1)==1
set(h2(i),'facecolor',bar_color(i,:),'edgecolor','none');
end
if opt_comp>=4
if n_files>1
plot(app.FigureNeuro, x_n_n(ind_plot==i),vv2(ind_plot==i),'o','MarkerSize',8,'MarkerFaceColor',bar_color(i,:),'MarkerEdgeColor','black','LineWidth',1);
end
end
end
if opt_comp~=4 && opt_comp ~= 7
if exist('h_sig','var')
ind_sig = find(h_sig);
ind_sig = ind_sig(ind_sig<=max(app.FigureNeuro.XTick));
if ~exist('max_m_x','var')
max_m_x = max(vals_plot(:));
min_m_x = min(vals_plot(:));
end
% Add asterisks+
for i = 1:length(ind_sig)
text(app.FigureNeuro,ind_sig(i), max_m_x+0.04, '*', ...
'HorizontalAlignment', 'center',...
'FontSize', 40, 'FontWeight', 'bold');
end
ylim(app.FigureNeuro,[min_m_x.*1.1 max_m_x+0.20]);
end
end
app.FigureNeuro.YLimMode = 'manual';
opt_ana = Results.options(2);
switch opt_ana
case 1
ylabel(app.FigureNeuro,'Fisher''s z (Spearman rho)','fontweight','bold');
case 2
ylabel(app.FigureNeuro,'Fisher''s z (Pearson r)','fontweight','bold');
case 3
ylabel(app.FigureNeuro,'Standardized beta coeffiecient','fontweight','bold');
end
app.FigureNeuro.Color = 'white';
hold(app.FigureNeuro, 'off');
end
function scatter_plot(app)
cla(app.FigureNeuro, 'reset');
opt_ana = app.Results.options(1);
data_list = app.Results.set1_images;
data_all = app.Results.data;
All_PET = app.xaxisList.Items;
Selected_PET = app.xaxisList.Value;
[~, ind_sel] = ismember(Selected_PET, All_PET);
if size(data_all,1)>1
selected_data = app.yaxisList.Value;
ind_sel_data = find(ismember(data_list, selected_data));
else
ind_sel_data = 1;
end
Rec_list_all = app.Results.Resh(2:end,:);
Rec_list = Rec_list_all(ind_sel);
data_PET = app.Results.data_PET(ind_sel,:);
colors = generate_colors_nice_my(size(data_PET,1));
data = data_all(ind_sel_data,:);
opt_rank = app.RankPlotCheckBox.Value;
hold(app.FigureNeuro, 'on');
% plot first one only
for j = 1:size(data_PET,1)
xx = removenan_my([data_PET(j,:)' data(1,:)']);
x = xx(:,1);
y = xx(:,2);
if opt_rank == 1
x = tiedrank(x);
y = tiedrank(y);
end
plot(app.FigureNeuro,x,y,'o','MarkerSize',9,'MarkerFaceColor',colors(j,:),'MarkerEdgeColor','black');
end
% plot remaining
for j = 1:size(data_PET,1)
for i = 2:size(data,1)
xx = removenan_my([data_PET(j,:)' data(i,:)']);
x = xx(:,1);
y = xx(:,2);
if opt_rank == 1
x = tiedrank(x);
y = tiedrank(y);
end
plot(app.FigureNeuro,x,y,'o','MarkerSize',9,'MarkerFaceColor',colors(j,:),'MarkerEdgeColor','black');
end
end
for j = 1:size(data_PET,1)
for i = 1:size(data,1)
xx = removenan_my([data_PET(j,:)' data(i,:)']);
x = xx(:,1);
y = xx(:,2);
if opt_rank == 1
x = tiedrank(x);
y = tiedrank(y);
end
mx = min(x);
Mx = max(x);
my = min(y);
My = max(y);
limx = max([abs(mx) abs(Mx)]);
low_x = prctile(x,5);
high_x = prctile(y,95);
xfit = mx:0.1:Mx;
[p,dev,STATS] = glmfit(x, y);
[yfit,dlo,dhi] = glmval(p,xfit,'identity',STATS,0.95);
set(app.FigureNeuro,'fontsize',18);
DELTA_max = yfit + dlo;
DELTA_min = yfit - dhi;
y_area =[DELTA_min', fliplr(DELTA_max')];
x_area= [xfit, fliplr(xfit)];
faceAlpha = 0.2;
plot(app.FigureNeuro, xfit, yfit, 'r-', 'LineWidth', 2,'color',colors(j,:));
patch(app.FigureNeuro,x_area,y_area,1,'facecolor',colors(j,:),'edgecolor','none','facealpha',faceAlpha);
end
end
grid(app.FigureNeuro,'on');
if opt_rank == 1
xlabel(app.FigureNeuro,'Rank data PET)','FontSize',16);
ylabel(app.FigureNeuro,'Rank data modality','FontSize',16);
else
ylabel(app.FigureNeuro,'Data modality','FontSize',16);
xlabel(app.FigureNeuro,'Data PET','FontSize',16);
end
legend_plot = Rec_list;
legend(app.FigureNeuro, legend_plot,'AutoUpdate','off','Location','southeast');
end
function [name_save_new] = add_suffix(name_save,options)
file_part = '';
switch options(1)
case 1
file_part = [file_part '_ESb'];
case 2
file_part = [file_part '_ESw'];
case 3
file_part = [file_part '_mList1'];
case 4
file_part = [file_part '_List1Each'];
case 5
file_part = [file_part '_indZ'];
case 6
file_part = [file_part '_pwDiff'];
case 7
file_part = [file_part '_looList1'];
case 8
file_part = [file_part '_List1EachGroupTest'];
end
switch options(2)
case 1
file_part = [file_part '_Spearman'];
case 2
file_part = [file_part '_Pearson'];
case 3
file_part = [file_part '_multReg'];
end
if options(3) == 1
file_part = [file_part '_withExactP'];
end
if options(5) == 1
file_part = [file_part '_withExactSpatialP'];
end
time_now = datestr(datetime('now'),'ddmmmyyyy_HHMMSS');
name_save_new = [name_save file_part '_' time_now];
end

JuSpace.m at commit 99c08a8, no license · at the source

Overview

Authors: Liang-Liang Ma^1,2, Ling-Ling Yang³, Bao-Zhen Zhou⁴, Qian Gao^1,2, Bo-Chao Zheng^1,2, Hu-Cheng Yang⁵, Gen-Di Wang³, Ting-Ting Yang^1,2

Department of Pediatrics, Binhai County People’s Hospital, Yancheng, China
Department of Pediatrics, Binhai Clinical College, Yangzhou University Medical College, Yancheng, China
Department of Neurology, Yancheng Third People’s Hospital, Affiliated Hospital 6 of Nantong University, Yancheng, China
Department of Radiology, Yancheng Third People’s Hospital, Affiliated Hospital 6 of Nantong University, Yancheng, China
Department of Radiology, Binhai Maternal and Child Health Hospital, Yancheng, China

Institutions: Yangzhou University (China); Nantong University (China); Yancheng Third People's Hospital (China)

Journal: Frontiers in molecular neuroscience, volume 19, article 1909269

Dates: received 15 June 2026; accepted 16 July 2026; published online 10 August 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.3389/fnmol.2026.1909269 · PMID 42639001 · PMCID PMC13500546 · OpenAlex W7202074720

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: genetics / omics (modality), fMRI (modality), epilepsy (population), cellular / molecular (subfield)

Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Connectivity, fMRI & imaging

Keywords: brain network, functional connectivity network mapping, gene expression, neurotransmitter, temporal lobe epilepsy

Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Anhui Medical University

Citations: not cited yet (Europe PMC); 104 references in the paper

Abstract

Background: Temporal lobe epilepsy (TLE) is increasingly recognized as a network disorder, yet reported regional intrinsic neural activity alterations from resting-state fMRI studies remain spatially heterogeneous. This study aimed to determine whether these heterogeneous alterations converge onto a shared functional network and to characterize its normative transcriptomic and neurochemical correlates.

Methods: Using a coordinate-based network mapping approach (functional connectivity network mapping, FCNM), we delineated a common brain network functionally connected to regional intrinsic neural activity alterations reported across 20 published neuroimaging studies. The robustness of the resulting network was assessed in an independent cross-scanner validation connectome and across different seed sizes. We further characterized this TLE-related network by correlating its spatial topography with microscale gene expression data from the Allen Human Brain Atlas (AHBA) and with normative neurotransmitter receptor and transporter distributions derived from the JuSpace toolbox.

Results: Twenty studies comprising 345 foci of regional intrinsic neural activity alteration in TLE were included. The FCNM analysis revealed that heterogeneous regional alterations in TLE converged onto a common functional brain network. This network exhibited the greatest spatial overlap with the default mode network (DMN), while also showing substantial overlap with the limbic network (LN). Transcriptomic analysis revealed that the network’s topography was spatially correlated with gene expression profiles significantly enriched in adaptive immune response pathways, particularly antigen processing and presentation. Neurochemically, the TLE-related network exhibited a significant positive spatial correlation with the distribution of the 5-hydroxytryptamine receptor 1A (5-HT1A).

Conclusion: Our findings reconcile previously inconsistent reports of regional intrinsic neural activity alterations in TLE by demonstrating their convergence onto a shared brain network, primarily the DMN and LN. By linking this TLE-related network to specific normative transcriptomic and neurochemical signatures, we propose a multi-scale neurobiological framework for the disorder. These findings should be interpreted as spatial associations based on normative datasets rather than direct evidence of disease-specific molecular alterations. This framework reframes TLE from a collection of disparate regional changes toward a core network dysfunction with distinct molecular correlates, thereby opening new avenues for targeted, network-based interventions.

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

Repository

Its files are read in the Code ↔ Paper reader above.

juryxy/JuSpace

License: none: the authors keep all their rights

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: 99c08a889b292d1f28a89aa12df25663243f16c8, 8 June 2026

Languages: MATLAB (19), Shell (1)

Size: 182 files, 20 scripts

Software Heritage: not archived

Found in: the end of the paper

Holds: README

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

Tools: SPM (6 files), Statistics and Machine Learning Toolbox (4 files), fdr_bh (Benjamini-Hochberg FDR) (1 file)

Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

21 files

JuSpace_v2/JuSpace.m, MATLAB, 1,154 lines
JuSpace_v2/append_prefix_to_fileNames_my.m, MATLAB, 13 lines
JuSpace_v2/cell2num_my.m, MATLAB, 29 lines
JuSpace_v2/center_of_mass_my.m, MATLAB, 26 lines
JuSpace_v2/compute_DomainGauges.m, MATLAB, 315 lines
JuSpace_v2/compute_exact_pvalue.m, MATLAB, 163 lines
JuSpace_v2/compute_exact_spatial_pvalue.m, MATLAB, 227 lines
JuSpace_v2/fdr_bh.m, MATLAB, 58 lines
JuSpace_v2/fishers_r_to_z.m, MATLAB, 3 lines
JuSpace_v2/fishers_z_to_r.m, MATLAB, 3 lines
JuSpace_v2/generate_colors_blue_my.m, MATLAB, 9 lines
JuSpace_v2/generate_colors_nice_my.m, MATLAB, 54 lines
JuSpace_v2/generate_spatial_nullMaps.m, MATLAB, 250 lines
JuSpace_v2/isemptycell.m, MATLAB, 16 lines
JuSpace_v2/mean_time_course.m, MATLAB, 50 lines
JuSpace_v2/num2cell_my.m, MATLAB, 14 lines
JuSpace_v2/removenan_my.m, MATLAB, 28 lines
JuSpace_v2/resize_img_useTemp_imcalc.m, MATLAB, 17 lines
JuSpace_v2/run_JuSpace.sh, Shell, 36 lines
JuSpace_v2/select_con_maps_forfMRI_my.m, MATLAB, 39 lines
README.md, Text, 137 lines

At the source: github.com/juryxy/JuSpace

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:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
20 scripts, each with its path and the digest of its content;
no match between paragraphs and code yet;
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

Datasets cited

humanconnectome.org/study/hcp-young-adult, at Human Connectome Project; found in the text, “Rs-fMRI data acquisition and preprocessing”

Data availability statement

The original contributions presented in this study are included in this article/Supplementary material, further inquiries can be directed to the corresponding authors.

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 3, 28 September 2026

Funding: added Anhui Medical University

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 5 keywords, 104 references.

Cite

This paper

Ma, L.-L., Yang, L.-L., Zhou, B.-Z., Gao, Q., Zheng, B.-C., Yang, H.-C., Wang, G.-D., & Yang, T.-T. (2026). Convergent functional networks of intrinsic activity alterations in temporal lobe epilepsy and their molecular correlates. Frontiers in molecular neuroscience, 19, 1909269. https://doi.org/10.3389/fnmol.2026.1909269

BibTeX

@article{ma2026convergent, author = {Ma, Liang-Liang and Yang, Ling-Ling and Zhou, Bao-Zhen and Gao, Qian and Zheng, Bo-Chao and Yang, Hu-Cheng and Wang, Gen-Di and Yang, Ting-Ting}, title = {{Convergent functional networks of intrinsic activity alterations in temporal lobe epilepsy and their molecular correlates}}, journal = {Frontiers in molecular neuroscience}, year = {2026}, month = aug, volume = {19}, pages = {1909269}, publisher = {Frontiers Media SA}, issn = {1662-5099}, doi = {10.3389/fnmol.2026.1909269}, url = {https://doi.org/10.3389/fnmol.2026.1909269}, pmid = {42639001}, pmcid = {PMC13500546} }

RIS

TY - JOUR AU - Ma, Liang-Liang AU - Yang, Ling-Ling AU - Zhou, Bao-Zhen AU - Gao, Qian AU - Zheng, Bo-Chao AU - Yang, Hu-Cheng AU - Wang, Gen-Di AU - Yang, Ting-Ting TI - Convergent functional networks of intrinsic activity alterations in temporal lobe epilepsy and their molecular correlates T2 - Frontiers in molecular neuroscience J2 - Front Mol Neurosci PY - 2026 DA - 2026/08/10 VL - 19 SP - 1909269 SN - 1662-5099 PB - Frontiers Media SA DO - 10.3389/fnmol.2026.1909269 UR - https://doi.org/10.3389/fnmol.2026.1909269 LA - en ER -

CSL-JSON

{ "id": "10.3389/fnmol.2026.1909269", "type": "article-journal", "title": "Convergent functional networks of intrinsic activity alterations in temporal lobe epilepsy and their molecular correlates", "container-title": "Frontiers in molecular neuroscience", "author": [ { "family": "Ma", "given": "Liang-Liang" }, { "family": "Yang", "given": "Ling-Ling" }, { "family": "Zhou", "given": "Bao-Zhen" }, { "family": "Gao", "given": "Qian" }, { "family": "Zheng", "given": "Bo-Chao" }, { "family": "Yang", "given": "Hu-Cheng" }, { "family": "Wang", "given": "Gen-Di" }, { "family": "Yang", "given": "Ting-Ting" } ], "container-title-short": "Front Mol Neurosci", "volume": "19", "page": "1909269", "DOI": "10.3389/fnmol.2026.1909269", "PMID": "42639001", "PMCID": "PMC13500546", "ISSN": "1662-5099", "publisher": "Frontiers Media SA", "URL": "https://doi.org/10.3389/fnmol.2026.1909269", "language": "en", "issued": { "date-parts": [ [ 2026, 8, 10 ] ] } }

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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