Multimodal Image Guidance in Subthalamic Deep Brain Stimulation for Parkinson's Disease.

Code ↔ Paper

7 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 7 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted

[1] § Materials and Methods › DBS Electrode Localization and Electric Field Calculation ↔ ea_normalize_ants.m, the whole file · a weak match · score 0.75 · ANTs SyN, Advanced Normalization Tools, template space, subcortical, MNI, reconstructed
[2] § Materials and Methods › DBS Electrode Localization and Electric Field Calculation ↔ ea_normalize_schoenecker.m, the whole file · a weak match · score 0.72 · ANTs SyN, co registered, template space, CT, subcortical, post
[3] § Materials and Methods › DBS Electrode Localization and Electric Field Calculation ↔ explorers/unifiedmapping_explorer/ea_unifiedmapping.m, lines 125–236 · score 0.64 · electric field, native space, OSS DBS, voxels, Stimulation, modeling
[4] § Results › Optimal DBS Target Sites Across Multiple Modalities ↔ explorers/unifiedmapping_explorer/ea_unifiedmapping.m, lines 1–116 · score 0.63 · fold CV, structural connectivity, CVs, mirroring, peaked, thresholding
[5] § Results › Optimal DBS Target Sites Across Multiple Modalities ↔ explorers/fiberfiltering_explorer/ea_disctract.m, lines 1–113 · score 0.62 · fold CV, structural connectivity, CVs, mirroring, overlapped, peaked
[6] § Results › Optimal DBS Target Sites Across Multiple Modalities ↔ explorers/networkmapping_explorer/ea_networkmapping.m, lines 1–74 · score 0.59 · cross validation, DBS Network, network maps, fold, peaked, CV
[7] § Materials and Methods › Statistical Analysis › Validation of the Models and Calculation of Surrogate Values ↔ explorers/networkmapping_explorer/ea_networkmapping.m, lines 1–74 · score 0.55 · fold CV, cross validation, networks, correlated, variables, patient

Paper

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The authors' code

MATLAB · 1,712 lines · 85 KB · GPL-3.0 · 2 matches

classdef ea_unifiedmapping < handle
% Discriminative fiber class to handle visualizations of discriminative fibers in lead dbs resultfig / 3D Matlab figures
% A. Horn
properties (SetObservable)
tool = 1; %option of 1 = sweetspotmapping, 2 = fiberfiltering, 3 = networkmapping
fileformatversion; % 1.2 is current format
M % content of lead group project
resultfig % figure handle to plot results
ID % name / ID of unified analysis object
posvisible = 1 % pos tract/vox visible
negvisible = 0 % neg tract/vox visible
roivisible = 0 % show ROI (usually VTAs)
connfibvisible = 0 % show all connected tracts in white
showposamount = [25 25] % two entries for right and left
shownegamount = [25 25] % two entries for right and left
statmetric = nan;
statsettings = struct;
calcsettings = struct; %calcthreshold, connectivity_type
threshstrategy = 'Percentage Relative to Peak'; % can be 'Relative to Amount' or 'Fixed Amount'
multi_pathways = 0 % if structural connectome is devided into pathways (multiple .mat in dMRI_MultiTract)
map_list % list that contains global indices of the first fibers in each pathway (relevant when multi_pathways = 1)
pathway_list % list that contains names of pathways (relevant when multi_pathways = 1)
connFiberInd % legacy
switch_connectivity = 0 % flag if connectivity type was changed in the GUI
nestedLOO = false % if true, will conducted LOO in the training set
corrtype = 'Spearman' % correlation strategy in case of statmetric == Correlations / E-fields (Irmen 2020). In case of one-sample tests used for 'T-Tests' vs 'Wicoxon Signed Rank Tests'.
SigmoidTransform = 0;
multitractmode = 'Single Tract Analysis' % multi mode now coded by this value %should we use abreviation?
numpcs = 4; % standard value of how many PCs to compute in case of PCA mode
doactualprediction = 0; % set up nested CVs to carry out actual predictions of response variables
predictionmodel = 'Linear'; % type of glm used to fit fiber values to actual scores
showsignificantonly = 0
alphalevel = 0.05
multcompstrategy = 'FDR'; % could be 'Bonferroni'
subscore
explorerdrawn
results = struct
customRoi = struct % struct used only for pseudoM case (customRoi.isbinary and customRoi.minmax)
activateby={}; % entry to use to show fiber activations
cvlivevisualize = 0; % if set to 1 shows crossvalidation results during processing.
basepredictionon = 'Mean of Scores';
fiberdrawn % struct contains fibercell and vals drawn in the resultfig
spotdrawn % struct contains nifti of the sweetspot
networkdrawn %struct contains nifti of the networkmap
vizmode
smooth_fp = 0; %for networkmapping, smooth fingerprints
normalize_fp = 0; %for networkmapping, normalize fingerprints
cvmask = 'Gray Matter';
model='Smoothed'; %for networkmapping
drawobject = struct % actual streamtube handle
drawvals % weights of the fibers drawn
connfiberdrawn % struct contains white connected fibers
conndrawobject % actial streamtube handle for the latter
roidrawobject % actual patch handle for ROI/VTAs
roidata % data used for ROIs (nifti file cell usually w/ Efields
roiprotocol % protocol for drawing rois used to check if need to be redrawn.
patientselection % selected patients to include. Note that connected fibers are always sampled from all (& mirrored) VTAs of the lead group file
setlabels={};
setselections={};
customselection % selected patients in the custom test list
allpatients % list of all patients (as from M.patient.list)
mirrorsides = 0 % flag to mirror VTAs / Efields to contralateral sides using ea_flip_lr_nonlinear()
responsevar % response variable
responsevarlabel % label of response variable
covars = {} % covariates
covarlabels = {} % covariate labels
analysispath % where to store results
leadgroup % redundancy protocol only, path to original lead group project
useExternalModel = false
ExternalModelFile = 'None'
% NM visualization
NMviz=struct;
% NMviz.vizmode='Regions'; % way to plot results
% NMviz.model='Smoothed'; % in case of surface above, on which surface to plot.
% NMviz.modelLH=1; % show left hemisphere
% NMviz.modelRH=1; % show right hemisphere
% stats: (how many fibers available and shown etc for GUI)
modelNormalization = 'None';
numBins=15;
stats
% additional settings:
rngseed = 'default';
Nperm = 1000 % how many permutations in leave-nothing-out permtest strategy
kfold = 5 % divide into k sets when doing k-fold CV
Nsets = 5 % divide into N sets when doing Custom (random) set test
adjustforgroups = 1 % adjust correlations for group effects
kIter = 1;
roiintersectdata = {}; %roi, usually efield with which you can calculate fiber intersection
roithresh = 200; % threshold above which efield metrics are considered
drawTool = 'sweetspotmapping'; % active draw tool: sweetspotmapping, fiberfiltering, networkmapping
activated = struct; % activated.fiberfiltering, activated.sweetspotmapping, activated.networkmapping % for activation status mapping.
% misc
runwhite = 0; % flag to calculate connected tracts instead of stat tracts
e_field_metric = 'Magnitude'; % 'Magnitude' or 'Projection'
%color
colorbar % colorbar information
posBaseColor = [1,1,1] % positive main color
poscolor = [0.9176,0.2000,0.1373] % positive peak color
negBaseColor = [1,1,1] % negative main color
negcolor = [0.2824,0.6157,0.9725] % negative peak color
hasResults = false; % results check
end
properties (Access = private)
switchedFromSpace=3 % if switching space, this will protocol where from
end
methods
function obj=ea_unifiedmapping(analysispath) % class constructor
if exist('analysispath', 'var') && ~isempty(analysispath)
obj.analysispath = analysispath;
[~, ID] = fileparts(obj.analysispath);
obj.ID = ID;
end
end
function initialize(obj,datapath,resultfig)
% statsettings
% initial hard threshold to impose on (absolute) nifti files only when calculating the data
obj.statsettings.doVoxels = 1;
obj.statsettings.doFibers = 1;
obj.statsettings.outcometype = 'gradual';
obj.statsettings.stimulationmodel = 'Electric Field';
obj.statsettings.efieldmetric = 'Peak'; % if statmetric == ;Correlations / E-fields (Irmen 2020)’, efieldmetric can calculate sum, mean or peak along tracts
obj.statsettings.efieldthreshold = 200;
obj.statsettings.nanthreshold = 0; % set values below this number to nan on the fly when calculating sweetspot statistics
obj.statsettings.sweetspotresolution = 0.5; % resolution of sweetspot in mm
obj.statsettings.connthreshold = 20;
obj.statsettings.statfamily = 'Correlations'; % the
obj.statsettings.stattest = 'Spearman';
obj.statsettings.H0 = 'Average';
obj.calcsettings.selectedTool = 1; %1 = sweetspotmapping, 2 = fiberfiltering, 3 = networkmapping;
obj.calcsettings.calcthreshold = 100;
obj.calcsettings.switch_connectivity = 1;
obj.calcsettings.connectivity_type = 1; %1 = vta, 2 = PAM
obj.calcsettings.functionalresolution = '2 mm';
obj.calcsettings.structuralresolution = '2 mm';
obj.calcsettings.calcmethod = 1; %1 = e-field based method, 2 = fiber based method
obj.calcsettings.calcspace = 1; %0 = native space, 1 = MNI space
obj.calcsettings.netmap_connectome = '';
obj.calcsettings.fibfilt_connectome = '';
if ~isfield(obj.NMviz,'modelRH')
obj.NMviz.modelRH=1;
end
if ~isfield(obj.NMviz,'modelLH')
obj.NMviz.modelLH=1;
end
obj.subscore.vars = {};
obj.subscore.labels = {};
obj.subscore.pcavars = {};
obj.subscore.weights = [];
obj.subscore.colors{1,1} = ea_color_wes('all');
obj.subscore.colors{1,2} = flip(ea_color_wes('all'));
obj.subscore.vis.showposamount = repmat([25,25],10,1); %total of 10 subscores - will delete when we know the total number of subscores
obj.subscore.vis.shownegamount = repmat([25,25],10,1);
obj.subscore.vis.pos_shown = repmat([25,25],10,1);
obj.subscore.vis.neg_shown = repmat([25,25],10,1);
obj.subscore.negvisible = zeros(10,1);
obj.subscore.posvisible = ones(10,1);
obj.subscore.splitbysubscore = 0;
obj.subscore.special_case = 0;
obj.covarlabels={};
obj.fiberdrawn = struct();
obj.spotdrawn = struct();
obj.networkdrawn = struct();
obj.vizmode = 'Regions';
obj.model = 'Smoothed';
obj.smooth_fp = 0;
obj.normalize_fp = 0;
obj.cvmask = 'Gray Matter';
obj.fileformatversion=1.2; % new current version with settings to harmonize stats.
datapath = GetFullPath(datapath);
U = load(datapath, '-mat');
if isfield(U, 'M') % Lead Group analysis path loaded
obj.M = U.M;
obj.leadgroup = datapath;
testID = obj.M.guid;
ea_mkdir([fileparts(obj.leadgroup),filesep,'UnifiedMappingExplorer',filesep]);
id = 1;
while exist([fileparts(obj.leadgroup),filesep,'UnifiedMappingExplorer',filesep,testID,'.explorer'],'file')
testID = [obj.M.guid, '_', num2str(id)];
id = id + 1;
end
obj.ID = testID;
obj.resultfig = resultfig;
if isfield(obj.M,'pseudoM')
obj.allpatients = obj.M.ROI.list;
obj.patientselection = 1:length(obj.M.ROI.list);
obj.M.root = [fileparts(datapath),filesep];
obj.M.patient.list = cell(size(obj.M.ROI.list,1), 1);
for i = 1:size(obj.M.ROI.list,1)
obj.M.patient.list{i,1} = obj.M.ROI.list{i,1};
end
obj.M.patient.group=obj.M.ROI.group; % copies
else
obj.allpatients = obj.M.patient.list;
obj.patientselection = obj.M.ui.listselect;
end
obj.responsevar = obj.M.clinical.vars{1};
obj.responsevarlabel = obj.M.clinical.labels{1};
elseif isfield(U, explorer) % Saved explorer class loaded
props = properties(U.explorer);
for p = 1:length(props) %copy all public properties
if ~(strcmp(props{p}, 'analysispath') && ~isempty(obj.analysispath) ...
|| strcmp(props{p}, 'ID') && ~isempty(obj.ID))
obj.(props{p}) = U.explorer.(props{p});
end
end
clear U
else
ea_error('You have opened a file of unknown type.')
return
end
obj.compat_statmetric; % check and resolve for old statmetric code (which used to be integers)
addlistener(obj,'activateby','PostSet',@activatebychange);
% added a check here otherwise errors out for files w/o vatmodels
if ~isfield(obj.M,'pseudoM')
if ~isempty(obj.M.vatmodel) && contains(obj.M.vatmodel, 'OSS-DBS (Butenko 2020)')
obj.statmetric = 3;
end
end
end
function compat_statmetric(obj)
if ~ischar(obj.statmetric) % old language used:
switch obj.statmetric % 3 was never used
case 1
obj.statmetric='Two-Sample T-Tests / VTAs (Baldermann 2019) / PAM (OSS-DBS)';
case 2
obj.statmetric='Correlations / E-fields (Irmen 2020)';
case 4
obj.statmetric='Proportion Test (Chi-Square) / VTAs (binary vars)';
case 5
obj.statmetric='Binomial Tests / VTAs (binary vars)';
case 6
obj.statmetric='Reverse T-Tests / E-Fields (binary vars)';
case 7
obj.statmetric='Plain Connections';
case 8
obj.statmetric='Odds Ratios / EF-Sigmoid (Jergas 2023)';
case 9
obj.statmetric='Weighted Linear Regression / EF-Sigmoid (Dembek 2023)';
end
end
ea_unifiedmapping_compat_statmetrics2statsettings(obj);
end
function calculate(obj)
% check that this has not been calculated before:
%first store the rois for automatic calculations
if ~isfield(obj.results,'roi')
if isfield(obj.M,'pseudoM')
vatlist = obj.M.ROI.list;
else
vatlist = ea_sweetspotmapping_getvats(obj);
end
for vat=1:length(vatlist)
for side = 1:2
vta_nii = ea_load_nii(vatlist{vat,side});
obj.results.roi{vat,side} = vta_nii;
end
end
end
if obj.calcsettings.selectedTool == 1 % sweetspotmapping
% in case of the sweetspot explorer, calculate rather means to
% gather all E-Fields. To keep consistency of the logic with
% discfiberexplorer and networkmappingexplorer, we will keep
% the same name (calculate) for the function, nonetheless.
% check that results aren't already there
if isfield(obj.M,'pseudoM')
vatlist = obj.M.ROI.list;
else
vatlist = ea_sweetspotmapping_getvats(obj);
end
[AllX,space] = ea_unifiedmapping_exportefieldmap(vatlist,obj);
% Apply threshold: set all values below nanthreshold to
% NaN, like in fiberfiltering
for i = 1:numel(AllX)
if ~isempty(AllX{i})
AllX{i}(AllX{i} < obj.calcsettings.calcthreshold) = nan;
end
end
obj.results.sweetspotmapping.efield = AllX;
obj.results.sweetspotmapping.space = space;
if ~isfield(obj.M,'pseudoM')
try
% get active coordinates, as well
for pt=1:length(obj.M.patient.list)
for side=1:2
obj.results.sweetspotmapping.activecnt{side}(pt,:)=...
mean(obj.M.elstruct(pt).coords_mm{side}(find(obj.M.S(pt).activecontacts{side}),:),1); %#ok<FNDSB> % find is necessary here
end
end
for side=1:2
obj.results.sweetspotmapping.activecnt{side}=[obj.results.sweetspotmapping.activecnt{side};ea_flip_lr_nonlinear(obj.results.sweetspotmapping.activecnt{side})];
end
end
end
elseif obj.calcsettings.selectedTool == 2 % Fiber Filtering
% if multi_pathways = 1, assemble cfile from multiple
% pathway.dat files in dMRI_MultiTract/Connectome_name/
% stores the result in the LeadGroup folder
% also merges fiberActivation_.._.mat and stores them in
% stimulation folders
if ~isempty(obj.results) % something has been calculated
if isfield(obj.results,'fiberfiltering')
fname = ea_unifiedmapping_conn2connid(obj.calcsettings.fibfilt_connectome);
if isfield(obj.results.fiberfiltering, fname)
connField = obj.results.fiberfiltering.(fname);
% now check the specific subfields safely
if (isfield(connField,'PAM_Ttest') && obj.calcsettings.connectivity_type==2) || ...
(isfield(connField,'efield_mean') && obj.calcsettings.connectivity_type==1)
answ = questdlg('This has already been calculated. Are you sure you want to re-calculate everything?', ...
'Recalculate Results','No','Yes','No');
if ~strcmp(answ,'Yes')
return
end
end
end
end
end
if obj.calcsettings.multi_pathways == 1
[cfile, obj.map_list, obj.pathway_list] = ea_unifiedmapping_mergePathways(obj);
else
cfile = [ea_getconnectomebase('dMRI'), obj.calcsettings.fibfilt_connectome, filesep, 'data.mat'];
end
%check if files exist
FilesExist = check_stimvols(obj);
if isfield(obj.M,'pseudoM') % failsave - this should not be necessary but still making sure things are set correctly for the pseudoM case.
obj.calcsettings.connectivity_type=1;
obj.calcsettings.calcmethod=1;
end
switch obj.calcsettings.connectivity_type
case 2 % if PAM, then just extracts activation states from fiberActivation.mat
fprintf("Calculating using the PAM method. Using dMRI connectome: %s",obj.calcsettings.fibfilt_connectome);
if all(FilesExist)
calculate_on_pam(obj,cfile)
end
otherwise % check fiber recruitment via intersection with VTA
if obj.calcsettings.calcmethod == 1 %'E-field/Voxel Based Method'
fprintf("Calculating using the traditional E-field based method. Using dMRI connectome: %s",obj.calcsettings.fibfilt_connectome);
if all(FilesExist)
calculate_on_efield(obj,cfile)
end
elseif obj.calcsettings.calcmethod == 2 %'Fiber Based Method'
% check whether to use new (calc_on_fibers) or old method:
fprintf("Calculating using the Fiber based method. Using dMRI connectome: %s",obj.calcsettings.fibfilt_connectome);
if all(FilesExist)%why can this not be psuedo M?
calculate_on_fibers(obj,cfile)
end
end
end
elseif obj.calcsettings.selectedTool == 3 % network mapping
if ~isempty(obj.results) % something has been calculated
if isfield(obj.results,'networkmapping')
if isfield(obj.results.networkmapping,ea_unifiedmapping_conn2connid(obj.calcsettings.netmap_connectome))
answ=questdlg('This has already been calculated. Are you sure you want to re-calculate everything?','Recalculate Results','No','Yes','No');
if ~strcmp(answ,'Yes')
return
end
end
end
end
if isfield(obj.M,'pseudoM')
vatlist = obj.M.ROI.list;
else
%TODO:I have removed this from the networkmapping explorer folder and added it to the unified mapping explorer. Please adjust based on the future of the tool. I refrained from making a copy since the name of this script makes sense and would be redundant to change the name
vatlist = ea_unified_nm_getvats(obj);
end
%TODO:I have removed this from the networkmapping explorer folder and added it to the unified mapping explorer. Please adjust based on the future of the tool. I refrained from making a copy since the name of this script makes sense and would be redundant to change the name
[AllX] = ea_unified_nm_calcvals(vatlist, obj.calcsettings.netmap_connectome);
obj.results.networkmapping.(ea_unifiedmapping_conn2connid(obj.calcsettings.netmap_connectome)).connval = AllX;
% Functional connectome, add spacedef to results
if contains(obj.calcsettings.netmap_connectome, ' > ')
connid = (ea_unifiedmapping_conn2connid(obj.calcsettings.netmap_connectome));
connName = regexprep(obj.calcsettings.netmap_connectome, ' > .*$', '');
load([ea_getconnectomebase('fmri'), connName, filesep, 'dataset_volsurf.mat'], 'vol');
obj.results.networkmapping.(connid).space = vol.space;
if isfield(vol,'cifti')
obj.results.networkmapping.(connid).space.cifti=vol.cifti; % add cifti space as well (hidden in nii space)
obj.results.networkmapping.(connid).space.outidx=vol.outidx;
obj.results.networkmapping.(connid).space.inidx=vol.inidx;
end
end
end
obj.hasResults = true;
end
function FilesExist = check_stimvols(obj)
switch obj.calcsettings.connectivity_type
case 2
[~,FilesExist] = ea_unifiedmapping_getpams(obj);
if ~all(FilesExist)
answ=questdlg('It seems like PAM has not been (completely) run. We can initiate the process now, but this will take some time. Proceed?','PAM not run','yes','no','yes');
switch answ
case 'yes'
options=ea_defaultoptions;
options.prefs.machine.vatsettings.butenko_calcPAM=1;
options.prefs.machine.vatsettings.butenko_calcVAT=0;
options.prefs.machine.vatsettings.butenko_connectome=obj.calcsettings.fibfilt_connectome;
options.groupdir=fileparts(obj.leadgroup);
obj.M.vatmodel='OSS-DBS (Butenko 2020)';
if isfield(obj.M.ui, 'stimSetMode') && obj.M.ui.stimSetMode
options.stimSetMode = 1;
else
options.stimSetMode = 0;
end
filesToCalc = find(sum(FilesExist(1:length(obj.M.patient.list),:),2)<2)';
calc_biophysical(obj,options,filesToCalc);
case 'no'
return
end
end
%recheck
[~,FilesExist] = ea_unifiedmapping_getpams(obj);
otherwise
if obj.calcsettings.calcmethod == 2 %Fiber based method
[~,FilesExist] = ea_unifiedmapping_getlattice(obj);
else
if isfield(obj.M,'pseudoM')
for entry=1:length(obj.M.ROI.list)
FilesExist(entry)=exist(obj.M.ROI.list{entry},'file');
end
else
[~,FilesExist] = ea_unifiedmapping_getvats(obj);
end
end
while ~all(FilesExist(:))
answ=questdlg('It seems like not all stimulation volumes have been calculated. We can initiate the process now, but this will take some time. Proceed?','Stimvolumes not calculated','yes','no','yes');
switch answ
case 'yes'
if obj.calcsettings.calcmethod == 2 %Fiber based method
obj.M.vatmodel='OSS-DBS (Butenko 2020)';
switch obj.calcsettings.calcspace
case 0
space = 'native';
case 1
space = 'MNI';
end
end
options=ea_defaultoptions;
options.prefs.machine.vatsettings.butenko_calcPAM=0;
options.prefs.machine.vatsettings.butenko_calcVAT=1;
options.groupdir=fileparts(obj.leadgroup);
if isfield(obj.M.ui, 'stimSetMode') && obj.M.ui.stimSetMode
options.stimSetMode = 1;
else
options.stimSetMode = 0;
end
filesToCalc = find(sum(FilesExist(1:length(obj.M.patient.list),:),2)<2)';
calc_biophysical(obj,options,filesToCalc);
[~,FilesExist] = ea_unifiedmapping_getvats(obj);
case 'no'
return
end
end
%recheck
end
return
end
function calculate_on_pam(obj,cfile)
connid = (ea_unifiedmapping_conn2connid(obj.calcsettings.fibfilt_connectome));
[pamlist,~] = ea_unifiedmapping_getpams(obj);
[fibsvalBin, fibsvalprob,~, ~, ~, fibcell_pam, connFiberInd, totalFibers] = ea_unifiedmapping_calcvals_pam_prob(pamlist, obj, cfile);
obj.results.fiberfiltering.(connid).('PAM_probA').fibsval = fibsvalprob;
obj.results.fiberfiltering.(connid).connFiberInd_PAM = connFiberInd;
obj.results.fiberfiltering.(connid).totalFibers = totalFibers; % total number of fibers in the connectome to work with global indices
obj.results.fiberfiltering.(connid).('pam_fibers').fibcell= fibcell_pam;
% temp. duplicate fibcell, will be fixed in the new explorer
obj.results.fiberfiltering.(ea_unifiedmapping_conn2connid(obj.calcsettings.fibfilt_connectome)).fibcell = obj.results.fiberfiltering.(ea_unifiedmapping_conn2connid(obj.calcsettings.fibfilt_connectome)).('pam_fibers').fibcell;
%add a provision for the results
obj.results.fiberfiltering.(ea_unifiedmapping_conn2connid(obj.calcsettings.fibfilt_connectome)).calculationMethod = obj.calcsettings.calcmethod;
end
function calculate_on_efield(obj,cfile)
connid = ea_unifiedmapping_conn2connid(obj.calcsettings.fibfilt_connectome);
if isfield(obj.M,'pseudoM')
vatlist=obj.M.ROI.list;
[obj.customRoi.isbinary,obj.customRoi.minmax]=ea_unifiedmapping_checkcustomNii(vatlist);
if obj.customRoi.isbinary
obj.statsettings.stimulationmodel='VTA';
end
else
[vatlist,~] = ea_unifiedmapping_getvats(obj);
end
[fibsvalBin, fibsvalSum, fibsvalMean, fibsvalPeak, fibsval5Peak, fibcell_efield, connFiberInd, totalFibers] = ea_fiberfiltering_calcvals(vatlist, cfile, obj.calcsettings.calcthreshold);
obj.results.fiberfiltering.(connid).('VAT_Ttest').fibsval = fibsvalBin;
obj.results.fiberfiltering.(connid).connFiberInd_VAT = connFiberInd; % old fiberfiltering files do not have these data and will fail when using pathway atlases
obj.results.fiberfiltering.(connid).totalFibers = totalFibers; % total number of fibers in the connectome to work with global indices
% only for e-fields
obj.results.fiberfiltering.(connid).('efield_sum').fibsval = fibsvalSum;
obj.results.fiberfiltering.(connid).('efield_mean').fibsval = fibsvalMean;
obj.results.fiberfiltering.(connid).('efield_peak').fibsval = fibsvalPeak;
obj.results.fiberfiltering.(connid).('efield_5peak').fibsval = fibsval5Peak;
obj.results.fiberfiltering.(connid).('plainconn').fibsval = fibsvalBin;
obj.results.fiberfiltering.(connid).('efield_fibers').fibcell= fibcell_efield;
% temp. duplicate fibcell, will be fixed in the new explorer
obj.results.fiberfiltering.(connid).fibcell = obj.results.fiberfiltering.(ea_unifiedmapping_conn2connid(obj.calcsettings.fibfilt_connectome)).('efield_fibers').fibcell;
%add a provision for results
obj.results.fiberfiltering.(connid).calculationMethod = obj.calcsettings.calcmethod;
end
function calculate_on_fibers(obj,cfile)
connid = (ea_unifiedmapping_conn2connid(obj.calcsettings.fibfilt_connectome));
% OSS-DBS E-field should be computed (not just warped!) in this space
% get VAT list
if isfield(obj.M,'pseudoM')
vatlist = obj.M.ROI.list;
else
[vatlist,~] = ea_unifiedmapping_getlattice(obj);
end
% warp connectome to native space and compute E-field metrics
ea_unified_get_Eproj(obj,vatlist)
% define space again
switch obj.calcsettings.calcspace
case 0
space = 'native';
case 1
space = 'MNI';
end
% load e-field projection metrics
[fibsvalBin_proj, fibsvalSum_proj, fibsvalMean_proj, fibsvalPeak_proj, fibsval5Peak_proj, fibcell_proj, connFiberInd_proj,fibsvalBin_magn, fibsvalSum_magn, fibsvalMean_magn, fibsvalPeak_magn, fibsval5Peak_magn, fibcell_magn, connFiberInd_magn, totalFibers] = ea_unifiedmapping_native_calcvals(vatlist, cfile, space, obj);
obj.results.fiberfiltering.(connid).totalFibers = totalFibers; % total number of fibers in the connectome to work with global indices
obj.results.fiberfiltering.(connid).('VAT_Ttest').fibsval = fibsvalBin_magn;
obj.results.fiberfiltering.(connid).('efield_sum').fibsval = fibsvalSum_magn;
obj.results.fiberfiltering.(connid).('efield_mean').fibsval = fibsvalMean_magn;
obj.results.fiberfiltering.(connid).('efield_peak').fibsval = fibsvalPeak_magn;
obj.results.fiberfiltering.(connid).('efield_5peak').fibsval = fibsval5Peak_magn;
obj.results.fiberfiltering.(connid).('plainconn').fibsval = fibsvalBin_magn;
obj.results.fiberfiltering.(connid).('efield_fibers').fibcell = fibcell_magn;
obj.results.fiberfiltering.(connid).('efield_fibers').connFiberInd_VAT = connFiberInd_magn; % old fiberfiltering files do not have these data and will fail when using pathway atlases
obj.results.fiberfiltering.(connid).('VAT_Ttest_proj').fibsval = fibsvalBin_proj;
obj.results.fiberfiltering.(connid).('efield_proj_sum').fibsval = fibsvalSum_proj;
obj.results.fiberfiltering.(connid).('efield_proj_mean').fibsval = fibsvalMean_proj;
obj.results.fiberfiltering.(connid).('efield_proj_peak').fibsval = fibsvalPeak_proj;
obj.results.fiberfiltering.(connid).('efield_proj_5peak').fibsval = fibsval5Peak_proj;
obj.results.fiberfiltering.(connid).('plainconn_proj').fibsval = fibsvalBin_proj;
obj.results.fiberfiltering.(connid).('efield_proj').fibcell = fibcell_proj;
obj.results.fiberfiltering.(connid).('efield_proj').connFiberInd_VAT = connFiberInd_proj; % old fiberfiltering files do not have these data and will fail when using pathway atlases
obj.results.fiberfiltering.(connid).calculationMethod = 'Fiber Based Method';
if strcmp(obj.e_field_metric,'Magnitude')
obj.results.fiberfiltering.(connid).fibcell = obj.results.fiberfiltering.(ea_unifiedmapping_conn2connid(obj.calcsettings.fibfilt_connectome)).('efield_fibers').fibcell;
obj.results.fiberfiltering.(connid).connFiberInd_VAT = obj.results.fiberfiltering.(ea_unifiedmapping_conn2connid(obj.calcsettings.fibfilt_connectome)).('efield_fibers').connFiberInd_VAT;
else
obj.results.fiberfiltering.(connid).fibcell = obj.results.fiberfiltering.(ea_unifiedmapping_conn2connid(obj.calcsettings.fibfilt_connectome)).('efield_proj').fibcell;
obj.results.fiberfiltering.(connid).connFiberInd_VAT = obj.results.fiberfiltering.(ea_unifiedmapping_conn2connid(obj.calcsettings.fibfilt_connectome)).('efield_proj').connFiberInd_VAT;
end
end
function recalculate_fiberfiltering_threshold(obj)
% Recomputes fiber connectivity with current obj.calcsettings.calcthreshold.
% Call this after changing the E-field threshold so results and drawing use
% the new threshold. Applies when Fiber Filtering is selected and E-field
% (voxel-based) or Fiber-based method is used.
% After calling, the GUI should refresh stats and redraw (e.g. obj.draw()).
if obj.calcsettings.selectedTool ~= 2
ea_cprintf('CmdWinWarnings', 'recalculate_fiberfiltering_threshold: Fiber Filtering is not selected. No action.\n');
return;
end
connid = ea_unifiedmapping_conn2connid(obj.calcsettings.fibfilt_connectome);
if ~isfield(obj.results,'fiberfiltering') || ~isfield(obj.results.fiberfiltering, connid)
ea_cprintf('CmdWinWarnings', 'No prior fiberfiltering results for current connectome. Run Calculate first.\n');
return;
end
if obj.calcsettings.multi_pathways == 1
[cfile, obj.map_list, obj.pathway_list] = ea_unifiedmapping_mergePathways(obj);
else
cfile = [ea_getconnectomebase('dMRI'), obj.calcsettings.fibfilt_connectome, filesep, 'data.mat'];
end
FilesExist = check_stimvols(obj);
if ~all(FilesExist(:))
ea_cprintf('CmdWinWarnings', 'Not all stimulation volumes exist. Recalculation aborted.\n');
return;
end
switch obj.calcsettings.calcmethod
case 1
fprintf('Recalculating fiber connectivity with E-field threshold %g ...\n', obj.calcsettings.calcthreshold);
calculate_on_efield(obj, cfile);
case 2
fprintf('Recalculating fiber connectivity (fiber-based) with E-field threshold %g ...\n', obj.calcsettings.calcthreshold);
calculate_on_fibers(obj, cfile);
otherwise
ea_cprintf('CmdWinWarnings', 'recalculate_fiberfiltering_threshold: unsupported calcmethod. No action.\n');
end
end
function results = calc_biophysical(obj,options,filesToCalc)
for pt = filesToCalc
[options.root, options.patientname] = fileparts(obj.M.patient.list{pt});
options.root = [options.root, filesep];
options = ea_getptopts(fullfile(options.root, options.patientname), options);
fprintf('\nProcessing %s...\n\n', options.patientname);
if ~isfield(obj.M,'S')
ea_error(['Stimulation parameters for ', options.subj.subjId, ' are not set.']);
end
vfs = ea_regexpdir(ea_getearoot, 'ea_genvat_.*\.m$', 0);
vfs = regexp(vfs, '(ea_genvat_.*)(?=\.m)', 'match', 'once');
[vfnames,~,~] = cellfun(@(x) eval([x, '(''prompt'');']), vfs, 'Uni', 0);
[~,ix]=ismember(obj.M.vatmodel,vfnames);
try
ea_genvat=eval(['@',vfs{ix}]);
catch
keyboard
end
if ~isfield(options.subj, 'norm')
ea_cprintf('CmdWinWarnings', 'Running in Miniset mode: %s...\n', options.subj.subjId);
volumespresent=0;
elseif isempty(dir([options.subj.norm.transform.inverseBaseName, '*']))
ea_cprintf('CmdWinWarnings', 'Tranformation not found for %s...\n', options.subj.subjId);
volumespresent=0;
else
volumespresent=1;
end
options.orignative=options.native; % backup
options.native=~ea_getprefs('vatsettings.estimateInTemplate'); % see whether VTAs should be directly estimated in template space or not
if options.native && ~volumespresent
ea_cprintf('CmdWinWarnings', 'Calculating VTA in template space since patient folder %s is incomplete.\n', options.subj.subjId);
options.native=0;
end
if options.native % Reload native space coordinates
coords = ea_load_reconstruction(options);
else
coords = obj.M.elstruct(pt).coords_mm;
end
if strcmp(obj.M.vatmodel, 'OSS-DBS (Butenko 2020)')
if options.prefs.machine.vatsettings.butenko_calcAxonActivation
feval(ea_genvat,obj.M.S(pt),options);
ea_cprintf('CmdWinWarnings', 'OSS-DBS axon activation mode detect, skipping calc stats for %s!\n', options.patientname);
continue;
else
[vatCalcPassed, ~] = feval(ea_genvat,obj.M.S(pt),options);
end
else
for side=1:2
try
[vtafv,vtavolume] = feval(ea_genvat,coords,obj.M.S(pt),side,options,['gs_',obj.M.guid]);
vatCalcPassed(side) = 1;
catch
vatCalcPassed(side) = 0;
end
if ~vatCalcPassed(side)
ea_cprintf('CmdWinWarnings', 'VTA calculation failed for %s!\n', options.patientname);
end
end
end
options.native=options.orignative; % restore
end
end
function Amps = getstimamp(obj)
Amps=zeros(length(obj.M.patient.list),2);
for pt=1:length(obj.M.patient.list)
for side=1:2
thisamp=obj.M.stats(pt).ea_stats.stimulation.vat(side).amp;
thisamp(thisamp==0)=nan;
Amps(pt,side)=ea_nanmean(thisamp');
end
end
end
function VTAvolumes = getvtavolumes(obj)
if ~isfield(obj.M.stats(1).ea_stats.stimulation.vat(1),'volume')
VTAvolumes = obj.getstimamp;
warning('No VTA volumes found. Using stimulation amplitudes instead. Re-run stats in Lead-group to obtain volumes.');
return
end
VTAvolumes=zeros(length(obj.M.patient.list),2);
for pt=1:length(obj.M.patient.list)
for side=1:2
VTAvolumes(pt,side)=obj.M.stats(pt).ea_stats.stimulation.vat(side).volume;
end
end
end
function Efieldmags = getefieldmagnitudes(obj)
if ~isfield(obj.M.stats(1).ea_stats.stimulation.efield(1),'volume')
Efieldmags = obj.getstimamp;
warning('No Efield magnitude sums found. Using stimulation amplitudes instead. Re-run stats in Lead-group to obtain values.');
return
end
Efieldmags=zeros(length(obj.M.patient.list),2);
for pt=1:length(obj.M.patient.list)
for side=1:2
try
if isempty(obj.M.stats(pt).ea_stats.stimulation.efield(side).volume)
val=0;
else
val=obj.M.stats(pt).ea_stats.stimulation.efield(side).volume;
end
Efieldmags(pt,side)=val;
catch % could be efield(side) is not defined.
Efieldmags(pt,side)=0;
end
end
end
end
function refreshlg(obj)
if ~exist(obj.leadgroup,'file')
msgbox('Groupan alysis file has vanished. Please select file.');
[fn,pth]=uigetfile();
obj.leadgroup=fullfile(pth,fn);
end
U = load(obj.leadgroup);
obj.M = U.M;
obj.allpatients=obj.M.patient.list;
end
function coh = getcohortregressor(obj)
coh=ea_cohortregressor(obj.M.patient.group(obj.patientselection));
end
function [I, Ihat] = loocv(obj,silent)
if ~exist('silent','var')
silent=0;
end
rng(obj.rngseed);
cvp = cvpartition(length(obj.patientselection), 'LeaveOut');
[I, Ihat] = crossval(obj, cvp,[],0,silent);
end
function [I, Ihat] = lococv(obj,silent)
if length(unique(obj.M.patient.group(obj.patientselection))) == 1
ea_error(sprintf(['Only one cohort in the analysis.\n', ...
'Leave-One-Cohort-Out-validation not possible.']));
end
[I, Ihat] = crossval(obj, obj.M.patient.group(obj.patientselection),[],0,silent);
end
function [I, Ihat, val_struct] = kfoldcv(obj,silent)
if ~exist('silent','var')
silent=0;
end
I_iter = {};
Ihat_iter = {};
rng(obj.rngseed);
iter = obj.kIter;
if iter == 1
cvp = cvpartition(length(obj.patientselection),'KFold',obj.kfold);
[I,Ihat, val_struct] = crossval(obj,cvp,[],0,silent);
else
% plot some statistics over shuffles
r_over_iter = zeros(iter,1);
p_over_iter = zeros(iter,1);
for i=1:iter
cvp = cvpartition(length(obj.patientselection), 'KFold', obj.kfold);
if ~silent
fprintf("Iterating fold set: %d",i)
end
[I_iter{i}, Ihat_iter{i},val_struct] = crossval(obj, cvp, [], 1,silent);
if ~silent
switch obj.multitractmode
case 'Split & Color By PCA'
disp("Fold Agreement is not evaluated for PCA")
otherwise
inx_nnan = find(isnan(I_iter{i}) ~= 1);
[r_over_iter(i),p_over_iter(i)]=ea_permcorr(I_iter{i}(inx_nnan),Ihat_iter{i}(inx_nnan),'spearman');
end
end
end
% check model agreement over shuffles using Sequential Rank Agreement
% disabled for PCA
switch obj.multitractmode
case 'Split & Color By PCA'
if ~silent
disp("Fold Agreement is not evaluated for PCA")
end
otherwise
if ~silent
r_Ihat = zeros(size(Ihat_iter,2));
for i = 1:size(r_Ihat,1)
for j = 1:size(r_Ihat,1)
[r_Ihat(i,j),~]=ea_permcorr(Ihat_iter{i},Ihat_iter{j},'spearman');
end
end
% plot correlation matrix
figure('Name','Patient scores'' correlations','Color','w','NumberTitle','off')
imagesc(triu(r_Ihat));
title('Patient scores'' correlations over K-fold shuffles', 'FontSize', 16); % set title
colormap('bone');
cb = colorbar;
set(cb)
% plot r-vals over shuffles
p_above_05 = p_over_iter(find(p_over_iter>0.05),:);
p_above_01 = p_over_iter(find(p_over_iter>0.01),:);
h = figure('Name','Over-fold analysis','Color','w','NumberTitle','off');
g = ea_raincloud_plot(r_over_iter,'box_on',1);
a1=gca;
set(a1,'ytick',[])
a1.XLabel.String='Spearman''s R of model and clinical scores';
if min(r_over_iter) >= -0.9
r_lower_lim = min(r_over_iter) - 0.1;
else
r_lower_lim = -1.0;
end
if max(r_over_iter) <= 0.9
r_upper_lim = max(r_over_iter) + 0.1;
else
r_upper_lim = 1.0;
end
a1.XLim=([r_lower_lim r_upper_lim]);
text(0.25,0.9,['N(p>0.05) = ',sprintf('%d',length(p_above_05))],'FontWeight','bold','FontSize',14,'HorizontalAlignment','right','Units','normalized');
text(0.25,0.83,['N(p>0.01) = ',sprintf('%d',length(p_above_01))],'FontWeight','bold','FontSize',14,'HorizontalAlignment','right','Units','normalized');
end
end
% we should think about this part
I_iter = cell2mat(I_iter);
Ihat_iter = cell2mat(Ihat_iter);
I = mean(I_iter,2,'omitnan');
Ihat = mean(Ihat_iter,2,'omitnan');
end
end
function [I, Ihat, val_struct] = lno(obj, Iperm, silent)
if ~exist('silent','var')
silent=0;
end
rng(obj.rngseed);
cvp = cvpartition(length(obj.patientselection), 'resubstitution');
if ~exist('Iperm', 'var') || isempty(Iperm)
[I, Ihat, val_struct] = crossval(obj, cvp, [], [], silent);
else
[I, Ihat, val_struct] = crossval(obj, cvp, Iperm, [], silent);
end
end
function [Improvement, Ihat, val_struct] = crossval(obj, cvp, Iperm, shuffle, silent)
if ~exist('silent','var')
silent=0;
end
if ~exist('shuffle','var') || isempty(shuffle)
shuffle=0;
end
if isnumeric(cvp) % cvp is crossvalind
cvIndices = cvp;
cvID = unique(cvIndices);
cvp = struct;
cvp.NumTestSets = length(cvID);
for i=1:cvp.NumTestSets
cvp.training{i} = cvIndices~=cvID(i);
cvp.test{i} = cvIndices==cvID(i);
end
end
% Check if patients are selected in the custom training/test list
if isempty(obj.customselection)
patientsel = obj.patientselection;
else
patientsel = obj.customselection;
end
switch obj.multitractmode
case 'Split & Color By PCA'
if ~exist('Iperm', 'var') || isempty(Iperm)
%Improvement = obj.subscore.vars;
for i=1:length(obj.subscore.vars)
Improvement{i} = obj.subscore.vars{i}(patientsel);
end
else
for i=1:length(obj.subscore.vars)
Improvement{i} = Iperm(patientsel,i);
end
end
otherwise
if ~exist('Iperm', 'var') || isempty(Iperm)
Improvement = obj.responsevar(patientsel,:);
else
Improvement = Iperm(patientsel,:);
end
end
% Ihat is the estimate of improvements (not scaled to real improvements)
if strcmp(obj.multitractmode,'Single Tract Analysis')
Ihat = nan(length(patientsel),2);
Ihat_train_global = nan(cvp.NumTestSets,length(patientsel),2);
else
Ihat = nan(length(patientsel),2,length(obj.subscore.vars));
Ihat_train_global = nan(cvp.NumTestSets,length(patientsel),2,length(obj.subscore.vars));
end
if strcmp(obj.drawTool,'fiberfiltering')
if obj.useExternalModel == true && ~strcmp(obj.ExternalModelFile, 'None')
S = load(obj.ExternalModelFile);
if ~strcmp(ea_unifiedmapping_method2methodid(obj),S.fibsvalType)
waitfor(msgbox('Change the Model Setup! See terminal'));
disp('The loaded model uses: ')
disp(S.fibsvalType)
end
fibsval = full(obj.results.fiberfiltering.(ea_unifiedmapping_conn2connid(obj.calcsettings.fibfilt_connectome)).(S.fibsvalType).fibsval);
else
fibsval = full(obj.results.fiberfiltering.(ea_unifiedmapping_conn2connid(obj.calcsettings.fibfilt_connectome)).(ea_unifiedmapping_method2methodid(obj)).fibsval);
end
else
fibsval = {};
end
% for nested LOO, store some statistics
if obj.nestedLOO
Abs_pred_error = zeros(cvp.NumTestSets, 1);
Predicted_scores = zeros(length(patientsel), 1);
Slope = zeros(cvp.NumTestSets, 1);
Intercept = zeros(cvp.NumTestSets, 1);
end
for c=1:cvp.NumTestSets
if cvp.NumTestSets ~= 1
if ~silent
fprintf(['\nIterating set: %0',num2str(numel(num2str(cvp.NumTestSets))),'d/%d\n'], c, cvp.NumTestSets);
end
end
if isobject(cvp)
training = cvp.training(c);
test = cvp.test(c);
elseif isstruct(cvp)
training = cvp.training{c};
test = cvp.test{c};
end
% now do LOO within the training group
if obj.nestedLOO
% use all patients, but outer loop left-out is always 0
if strcmp(obj.multitractmode,'Single Tract Analysis')
Ihat_inner = nan(length(patientsel),2);
Ihat_train_global_inner = nan(cvp.NumTestSets,length(patientsel),2);
else
Ihat_inner = nan(length(patientsel),2,length(obj.subscore.vars));
Ihat_train_global_inner = nan(cvp.NumTestSets,length(patientsel),2,length(obj.subscore.vars));
end
for test_i = 1:length(training)
training_inner = training;
training_inner(test_i) = 0;
% check if inner and outer left-out match
if all(training_inner == training)
continue
end
test_inner = logical(zeros(length(training), 1));
test_inner(test_i) = logical(training(test_i));
% updates Ihat_inner(test_inner)
if ~exist('Iperm', 'var') || isempty(Iperm)
[Ihat_inner, ~, ~] = ea_compute_unified_model(c,obj, fibsval, Ihat_inner, Ihat_train_global_inner, patientsel, training_inner, test_inner);
else
[Ihat_inner, ~, ~] = ea_compute_unified_model(c, obj, fibsval, Ihat_inner, Ihat_train_global_inner, patientsel, training_inner, test_inner,Iperm);
end
end
% fit the linear model based on inner loop fibscores
predictor=squeeze(ea_nanmean(Ihat_inner,2));
% iterating over all test_inner gives us training
mdl=fitglm(predictor(training),Improvement(training),lower(obj.predictionmodel));
Intercept(c) = mdl.Coefficients.Estimate(1);
Slope(c) = mdl.Coefficients.Estimate(2);
end
% now compute Ihat for the true 'test' left out
% updates Ihat(test)
if ~exist('Iperm', 'var') || isempty(Iperm)
[Ihat, Ihat_train_global, val_struct{c}] = ea_compute_unified_model(c,obj, fibsval, Ihat, Ihat_train_global, patientsel, training, test);
else
[Ihat, Ihat_train_global, val_struct{c}] = ea_compute_unified_model(c,obj, fibsval, Ihat, Ihat_train_global, patientsel, training, test, Iperm);
end
% predict the improvement in the left-out patient (fold) of
% the outer loop
if obj.nestedLOO
predictor=squeeze(ea_nanmean(Ihat,2));
Ihat_voters_prediction = repmat(predict(mdl,predictor(test)),1,2);
%Abs_pred_error(c) = abs(Improvement(test) - Ihat_voters_prediction(test));
Predicted_scores(test) = Ihat_voters_prediction(1:end,1); % only one value here atm
end
end
% check if binary variable and not permutation test
if (~exist('Iperm', 'var') || isempty(Iperm)) && all(ismember(Improvement(:,1), [0,1])) && size(val_struct{c}.vals,1) == 1
% average across sides. This might be wrong for capsular response.
Ihat_av_sides = ea_nanmean(Ihat,2);
if isobject(cvp)
% In-sample
AUC = ea_logit_regression(0 ,Ihat_av_sides, Improvement, 1:size(Improvement,1), 1:size(Improvement,1));
elseif isstruct(cvp)
% actual training and test
Ihat_train_global_av_sides = ea_nanmean(Ihat_train_global,3); % in this case, dimens is (1, N, sides)
AUC = ea_logit_regression(Ihat_train_global_av_sides(training)', Ihat_av_sides, Improvement, training, test);
end
end
if ~silent
% plot patient score correlation matrix over folds
if (~exist('shuffle', 'var')) || shuffle == 0 || isempty(shuffle)
if cvp.NumTestSets ~= 1 && (strcmp(obj.multitractmode,'Single Tract Analysis') || strcmp(obj.multitractmode,'Single Tract Analysis Button'))
% put training and test scores together
Ihat_combined = cell(1,cvp.NumTestSets);
%Ihat_combined = Ihat_train_global;
for c=1:cvp.NumTestSets
if isobject(cvp)
training = cvp.training(c);
test = cvp.test(c);
elseif isstruct(cvp)
training = cvp.training{c};
test = cvp.test{c};
end
Ihat_combined{c}(training,1) = Ihat_train_global(c,training,1)';
Ihat_combined{c}(test,1) = Ihat(test,1);
end
r_Ihat = zeros(size(Ihat_combined,2));
for i = 1:size(r_Ihat,1)
for j = 1:size(r_Ihat,1)
[r_Ihat(i,j),~]=ea_permcorr(Ihat_combined{i},Ihat_combined{j},'spearman');
end
end
figure('Name','Patient scores'' correlations','Color','w','NumberTitle','off')
imagesc(triu(r_Ihat)); % Display correlation matrix as an image
title('Patient scores'' correlations over folds', 'FontSize', 16); % set title
colormap('bone');
cb = colorbar;
% set(cb)
end
end
end
if obj.nestedLOO
% cvs = 'L-O-O-O';
% h = ea_corrbox(Improvement,Predicted_dif_models,'permutation',{['Disc. Fiber prediction ',upper(cvs)],empiricallabel,fibscorelabel});
LM_values_slope = [num2str(mean(Slope)) ' ' char(177) ' ' num2str(std(Slope))];
LM_values_intercept = [num2str(mean(Intercept)) ' ' char(177) ' ' num2str(std(Intercept))];
disp('Mean and STD for slopes and intercepts of LMs')
disp(LM_values_slope)
disp(LM_values_intercept)
% visualize lms and CIs for 5-fold or less
if cvp.NumTestSets < 6
groups_nested = zeros(length(Predicted_scores),1);
for group_idx = 1:cvp.NumTestSets
groups_nested(cvp.test(group_idx)) = group_idx;
end
side = 1;
plotName = 'Fitting of linear models for K-folds using nested LOO';
empiricallabel = 'Empirical score';
pred_label = 'Predicted score';
h=ea_corrbox(Improvement,Predicted_scores,'permutation',{['Disc. Fiber prediction ',plotName],empiricallabel,pred_label, plotName, LM_values_slope, LM_values_intercept},groups_nested);
end
end
if obj.doactualprediction % repeat loops partly to fit to actual response variables:
Ihat_voters_prediction=nan(size(Ihat));
%add some warnings
switch obj.multitractmode
case 'Single Tract Analysis'
if obj.useExternalModel && size(val_struct{c}.vals,1) > 1
ea_error("You can only use the Fit-to-Score feature with a Single Tract Analysis analysis model");
end
otherwise
if obj.useExternalModel
ea_error("You can only use the Fit-to-Score feature with Single Tract Analysis");
end
end
numVoters = size(val_struct{c}.vals,1);
for c=1:cvp.NumTestSets
if isobject(cvp)
training = cvp.training(c);
test = cvp.test(c);
elseif isstruct(cvp)
training = cvp.training{c};
test = cvp.test{c};
end
for voter=1:numVoters
switch obj.multitractmode
case 'Split & Color By Subscore'
if ~exist('Iperm', 'var') || isempty(Iperm)
useI=obj.subscore.vars{voter}(patientsel);
else
% to be added by Nanditha
end
case 'Split & Color By PCA'
if ~exist('Iperm', 'var') || isempty(Iperm)
useI=obj.subscore.pcavars{voter}(patientsel);
else
PCscores = ea_nanzscore(Iperm(patientsel, : ))*obj.subscore.pcacoeff;
useI = PCscores(:, voter);
end
otherwise
if ~exist('Iperm', 'var') || isempty(Iperm)
useI=obj.responsevar(patientsel);
else
useI=Iperm(patientsel);
end
end
if size(useI,2)>1
ea_error('This has not been implemented for hemiscores.');
end
% these predictors are defined within the same fiberfiltering model
% of iteration 'c'
% do not get rid of the first dimension when it has size of 1
Ihat_train_global_av_sides = ea_nanmean(Ihat_train_global,3);
predictor_training = reshape(Ihat_train_global_av_sides, ...
size(Ihat_train_global_av_sides,1),...
size(Ihat_train_global_av_sides,2),...
size(Ihat_train_global_av_sides,4));
predictor_test = squeeze(ea_nanmean(Ihat,2));
%predictor=squeeze(ea_nanmean(Ihat_voters,2));
covariates=[];
for cv = 1:length(obj.covars)
covariates = [covariates,obj.covars{cv}(patientsel)];
end
if obj.useExternalModel == true %only use for single tract analysis
if ~strcmp(obj.multitractmode,'Single Tract Analysis')
ea_error("Sorry, you cannot use exported model and fit-to-scores for multi-tract model");
else
mdl = S.mdl;
end
else
if ~isempty(covariates)
mdl=fitglm([predictor_training(c,training,voter)',covariates(training,:)],useI(training),lower(obj.predictionmodel));
else
mdl=fitglm([predictor_training(c,training,voter)],useI(training),lower(obj.predictionmodel));
end
end
if size(useI,2) == 1 % global scores
if ~isempty(covariates)
Ihat_voters_prediction(test,:,voter)=repmat(predict(mdl,[predictor_test(test,voter),covariates(test,:)]),1,2); % fill both sides equally
else
Ihat_voters_prediction(test,:,voter)=repmat(predict(mdl,[predictor_test(test,voter)]),1,2); % fill both sides equally
end
elseif size(useI,2)==2 % bihemispheric scores
ea_error('Fitting to scores has not been implemented for bihemispheric scores.');
end
end
end
% quantify the prediction accuracy (if Train-Test)
if cvp.NumTestSets == 1 && voter == 1 && size(obj.responsevar,2) == 1 && (~exist('Iperm', 'var') || isempty(Iperm))
side = 1;
SS_tot = var(useI(test)) * (length(useI(test)) - 1); % just a trick to use one line
SS_res = sum((Ihat_voters_prediction(test,side,1) - useI(test)).^2);
R2 = 1 - SS_res/SS_tot;
RMS = sqrt(mean((Ihat_voters_prediction(test,side,1) - useI(test)).^2));
MAD = median(abs(Ihat_voters_prediction(test,side,1) - useI(test)));
MAE = mean(abs(Ihat_voters_prediction(test,side,1) - useI(test)));
plotName = 'TRAIN-TEST';
R2_label = ['R2 = ', sprintf('%.3f',R2)];
RMS_label = ['RMS = ', sprintf('%.3f',RMS)];
MAD_label = ['MAD = ', sprintf('%.3f',MAD)];
empiricallabel = 'Empirical score';
pred_label = 'Predicted score';
h = ea_corrbox(useI(test),Ihat_voters_prediction(test,side,1),'permutation',{['Disc. Fiber prediction ',plotName],empiricallabel,pred_label, plotName, R2_label, RMS_label, MAD_label});
% h2 = ea_corrbox(-1*useI(test),Ihat_voters_prediction(test,side,1),'permutation',{['Disc. Fiber prediction ',plotName],empiricallabel,pred_label, plotName, R2_label, RMS_label, MAD_label});
end
Ihat=Ihat_voters_prediction; % replace with actual response variables.
end
switch obj.multitractmode
case 'Split & Color By Subscore'
if ~obj.CleartuneOptim
% here we map back to the single response variable using a
% weightmatrix
if isempty(obj.customselection)
selected_pts = obj.patientselection;
else
selected_pts = obj.customselection;
end
weightmatrix=zeros(size(Ihat));
for voter=1:size(Ihat,3)
if ~isnan(obj.subscore.weights(voter)) % same weight for all subjects in that voter (slider was used)
weightmatrix(:,:,voter)=obj.subscore.weights(voter);
else % if the weight value is nan, this means we will need to derive a weight from the variable of choice
weightmatrix(:,:,voter)=repmat(ea_minmax(obj.subscore.weightvars{voter}(selected_pts)),1,size(weightmatrix,2)/size(obj.subscore.weightvars{voter}(selected_pts),2));
weightmatrix(:,:,voter)=weightmatrix(:,:,voter)./max(obj.subscore.weightvars{voter}(selected_pts)); % weight for unnormalized data across voters *
% *) e.g. in case one symptom - bradykinesia -
% has a max of 20, while a second - tremor -
% will have a max of 5, we want to equilize
% those. We use minmax() in the line above to
% get rid of negative values and use
% ./ea_nansum below to take the average.
end
end
for xx=1:size(Ihat,1) % make sure voter weights sum up to 1
for yy=1:size(Ihat,2)
% for xx=1:size(Ihat_voters,1) % make sure voter weights sum up to 1
% for yy=1:size(Ihat_voters,2)
weightmatrix(xx,yy,:)=weightmatrix(xx,yy,:)./ea_nansum(weightmatrix(xx,yy,:));
end
end
Ihat=ea_nansum(Ihat.*weightmatrix,3);
else
Ihat = Ihat(test,:,:);
Ihat = reshape(Ihat,2,length(obj.subscore.vars))';
Improvement = Improvement(test);
return;
end
case 'Split & Color By PCA'
Ihat=squeeze(ea_nanmean(Ihat,2));
%Ihat_voters=squeeze(ea_nanmean(Ihat_voters,2)); % need to assume global scores here for now.
% map back to PCA:
for i=1:length(obj.subscore.vars)
selected_subscores{i} = obj.subscore.vars{i}(patientsel);
end
subvars=ea_nanzscore(cell2mat(selected_subscores));
if size(subvars,2) <= 2
ea_warndlg("You may not have enough subscores & this might result in errors. Please consider selecting more subscores.")
end
% [coeff,score,latent,tsquared,explained,mu]=pca(subvars,'Rows','complete');
% use saved weights to ensure consistency
coeff = obj.subscore.pcacoeff;
if ~silent
% show predictions for PC scores
if ~exist('Iperm', 'var') || isempty(Iperm) % avoid plotting for each permutation if using permutations!
for pcc=1:obj.numpcs
if obj.subscore.posvisible(pcc)==1 || obj.subscore.negvisible(pcc)==1 % don't try to plot if not showing any fibers for this PC
ea_corrplot(obj.subscore.pcavars{pcc}(patientsel),Ihat(:,pcc), 'noperm', ...
{['Disc. Fiber prediction for PC ',num2str(pcc)],'PC score (Empirical)','PC score (Predicted)'},...
[], [], obj.subscore.pcacolors(pcc, :));
% sum(obj.subscore.pcavars{pcc}(obj.patientselection) - score(:,pcc)) % quick check
end
end
end
end
% data is zscored, such as mu is 0 (+ some computer rounding error)
% then adding mean is not required
% also, we want to take scores of the chosen PCs ONLY,
% and multiply by coeff of these PCs (= how they map to
% the variables) to get estimated clinical scores
Ihatout = Ihat(:,1:obj.numpcs)*coeff(:,1:obj.numpcs)';
%Ihatout = Ihat*coeff(:,1:obj.numpcs)' + repmat(mu,size(score,1),1);
%Ihatout = Ihat_voters*coeff(:,1:obj.numpcs)' + repmat(mu,size(score,1),1);
Ihat = mat2cell(Ihatout, size(Ihatout,1), ones(1,length(obj.subscore.vars)));
otherwise
Ihat=squeeze(Ihat);
%Ihat=squeeze(Ihat_voters);
end
if ~iscell(Ihat)
if cvp.NumTestSets == 1
Ihat = Ihat(test,:);
Improvement = Improvement(test);
end
if size(obj.responsevar,2)==2 % hemiscores
Ihat = Ihat(:); % compare hemiscores (electrode wise)
Improvement = Improvement(:);
else
Ihat = ea_nanmean(Ihat,2); % compare bodyscores (patient wise)
end
end
% restore original view in case of live drawing
if obj.cvlivevisualize
obj.draw;
end
end
function [Iperm, Ihat, R0, R1, pperm, Rp95, val_struct] = lnopb(obj, corrType, silent)
if ~exist('corrType', 'var')
corrType = 'Spearman';
end
if ~exist('silent','var')
silent=0;
end
numPerm = obj.Nperm;
if strcmp(obj.multitractmode,'Split & Color By PCA')
Iperm = ea_shuffle(cell2mat(obj.subscore.vars'), numPerm, obj.patientselection, obj.rngseed);
Iperm(2:numPerm+1,:,:) = Iperm;
Iperm(1,:,:) = cell2mat(obj.subscore.vars');
Ihat = cell(numPerm+1,1);
R = zeros(numPerm+1, length(obj.subscore.vars));
for perm=1:numPerm+1
if perm==1
if ~silent; fprintf('Calculating without permutation\n\n'); end
[~, Ihat{perm},thisval_struct] = lno(obj, [], silent);
else
if ~silent; fprintf('Calculating permutation: %d/%d\n\n', perm-1, numPerm); end
[~, Ihat{perm},thisval_struct] = lno(obj, squeeze(Iperm(perm,:,:)), silent);
end
val_struct{perm}=thisval_struct{1};
for subvar = 1:length(obj.subscore.vars)
R(perm,subvar) = corr(Iperm(perm, obj.patientselection, subvar)',...
Ihat{perm}{subvar},'type',corrType,'rows','pairwise');
end
end
R(isnan(R)) = 1e-5; % do not get rid of Nans
% generate null distribution
R1 = R(1,:);
for subvar = 1:length(obj.subscore.vars)
R0(:,subvar) = sort(R(2:end,subvar), 'descend');
Rp95(subvar) = R0(round(0.05*numPerm),subvar);
pperm(subvar) = mean(abs(R0(:,subvar))>=abs(R1(subvar)));
if ~silent; fprintf(['Permuted p for ' obj.subscore.labels{subvar} ' = ' num2str(pperm(subvar)) '.\n']); end
end
% Return only selected I
Iperm = Iperm(:,obj.patientselection,:);
else % any mode except PCA
Iperm = ea_shuffle(obj.responsevar, numPerm, obj.patientselection, obj.rngseed)';
Iperm = [obj.responsevar, Iperm];
Ihat = cell(numPerm+1, 1);
R = zeros(numPerm+1, 1);
for perm=1:numPerm+1
if perm==1
if ~silent; fprintf('Calculating without permutation\n\n'); end
[~, Ihat{perm},val_struct{perm}] = lno(obj, [], silent);
else
if ~silent; fprintf('Calculating permutation: %d/%d\n\n', perm-1, numPerm); end
[~, Ihat{perm},val_struct{perm}] = lno(obj, Iperm(:, perm), silent);
end
R(perm) = corr(Iperm(obj.patientselection,perm),Ihat{perm},'type',corrType,'rows','pairwise');
end
R(isnan(R)) = 1e-5;
% generate null distribution
R1 = R(1);
R0 = sort((R(2:end)),'descend');
Rp95 = R0(round(0.05*numPerm));
pperm = mean(abs(R0)>=abs(R1));
if ~silent; disp(['Permuted p = ',sprintf('%0.2f',pperm),'.']); end
% Return only selected I
Iperm = Iperm(obj.patientselection,:);
end
end
function save(obj)
% Create a temporary object with only the required fields
explorer = ea_unifiedmapping;
Incprops = {'results','calcsettings','leadgroup','ID','M'};
for i = 1:length(Incprops)
explorer.(Incprops{i}) = obj.(Incprops{i});
end
% Get all properties of the object
%This is necessary to match the settings file
%only save results in this
if isempty(obj.analysispath)
[pth,~,~] = fileparts(obj.leadgroup);
obj.analysispath=[pth,filesep,'UnifiedMappingExplorer',filesep,obj.ID,'.explorer'];
ea_mkdir([pth,filesep,'UnifiedMappingExplorer']);
end
rf=obj.resultfig; % need to stash fig handle for saving.
rd=obj.drawobject; % need to stash handle of drawing before saving.
try % could be figure is already closed.
setappdata(rf,['dt_',explorer.ID],rd); % store handle of tract to figure.
end
save(obj.analysispath,'explorer','-v7.3');
saveObjectToJson(obj);
obj.resultfig=rf;
obj.drawobject=rd;
end
function saveObjectToJson(obj)
% Convert object to a struct (including nested objects)
voxtractsettings = objectToStruct(obj);
% Convert struct to JSON
jsonStr = jsonencode(voxtractsettings, 'PrettyPrint', true);
%define filepaths
if isempty(obj.analysispath)
[DBSMappingfolder,~,~] = fileparts(obj.leadgroup);
else
[DBSMappingfolder,~,~] = fileparts(obj.analysispath);
end
conn_val = 'default';
switch obj.drawTool
case 'sweetspotmapping'
conn_val = 'default';
case 'fiberfiltering'
conn_val = ea_unifiedmapping_conn2connid(obj.calcsettings.fibfilt_connectome);
case 'networkmapping'
conn_val = ea_unifiedmapping_conn2connid(obj.calcsettings.netmap_connectome);
end
if ~isfolder(DBSMappingfolder)
ea_mkdir(DBSMappingfolder)
end
jsonPath=[DBSMappingfolder,filesep,'Settings-',obj.ID,'_conn-',conn_val,'.json'];
% Write JSON to a file
fileID = fopen(jsonPath, 'w');
if fileID == -1
error('Cannot open file for writing.');
end
fprintf(fileID, '%s', jsonStr);
fclose(fileID);
end
function s = objectToStruct(obj)
% Convert an object to a struct, handling nested objects
if nargin < 2
ignoreList = {'results','resultfig','drawobject','M'}; %M should be present in the explorer mat file. This is because there are some complicated structures in M files that are not well translated in struct (for json encoding). % Default: Do not ignore any properties unless specified
end
if isobject(obj)
props = properties(obj);
s = struct();
for i = 1:length(props)
propName = props{i};
propValue = obj.(props{i});
if ismember(propName, ignoreList) %skip some of the properties.
continue;
end
if isa(propValue, 'matlab.ui.Figure') || isa(propValue, 'handle') %also skip handles
continue;
end
if isobject(propValue) % Recursively convert nested objects
s.(props{i}) = objectToStruct(propValue);
else
s.(props{i}) = propValue;
end
end
else
s = obj; % If it's not an object, return as is (handles arrays, numbers, strings)
end
end
function draw(obj)
if ~isfield(obj.activated,'sweetspotmapping')
obj.activated.sweetspotmapping='Off';
end
if ~isfield(obj.activated,'fiberfiltering')
obj.activated.fiberfiltering='Off';
end
if ~isfield(obj.activated,'networkmapping')
obj.activated.networkmapping='Off';
end
% delete prior spots:
if isfield(obj.drawobject,'sweetspotmapping')
if ~isempty(obj.drawobject.sweetspotmapping)
for s=1:numel(obj.drawobject.sweetspotmapping)
for ins=1:numel(obj.drawobject.sweetspotmapping{s})
try delete(obj.drawobject.sweetspotmapping{s}{ins}.toggleH); end
try delete(obj.drawobject.sweetspotmapping{s}{ins}.patchH); end
try delete(obj.drawobject.sweetspotmapping{s}{ins}); end
end
end
end
end
% plot new spots
switch lower(obj.activated.sweetspotmapping)
case 'on'
obj.drawTool='sweetspotmapping';
ea_unified_draw(obj);
end
% delete prior tracts
if isfield(obj.drawobject,'fiberfiltering')
if ~isempty(obj.drawobject.fiberfiltering)
for s=1:numel(obj.drawobject.fiberfiltering)
surfArray=obj.drawobject.fiberfiltering{s};
for ins=1:numel(surfArray)
try delete(surfArray(ins).toggleH); end
try delete(surfArray(ins).patchH); end
try delete(surfArray(ins)); end
end
end
obj.drawobject.fiberfiltering{s} = [];
end
end
% plot new tracts
switch lower(obj.activated.fiberfiltering)
case 'on'
obj.drawTool='fiberfiltering';
ea_unified_draw(obj);
end
% delete prior nets
if isfield(obj.drawobject,'networkmapping')
if ~isempty(obj.drawobject.networkmapping)
for s=1:numel(obj.drawobject.networkmapping)
for ins=1:numel(obj.drawobject.networkmapping{s})
try delete(obj.drawobject.networkmapping{s}{ins}.toggleH); end
try delete(obj.drawobject.networkmapping{s}{ins}.patchH); end
try delete(obj.drawobject.networkmapping{s}{ins}); end
end
end
end
end
% plot new nets
switch lower(obj.activated.networkmapping)
case 'on'
obj.drawTool='networkmapping';
ea_unified_draw(obj);
end
end
end
methods (Static)
function changeevent(~,event)
update_trajectory(event.AffectedObject,event.Source.Name);
end
end
end
function activatebychange(~,event)
% activate_tractset();
end
function calculateIntersection(obj)
for nroi = 1:length(obj.roiintersectdata)
vat = ea_load_nii(obj.roiintersectdata{nroi}); %use only one, otherwise drawing doesn't make sense
thresh = obj.roithresh;
vatInd = find(abs(vat.img(:))>thresh);
[xvox, yvox, zvox] = ind2sub(size(vat.img), vatInd);
vatmm = ea_vox2mm([xvox, yvox, zvox], vat.mat);
for side = 1:2
for i=1:size(obj.drawobject,1)
vals = {};
valsPeak = {};
connected = [];
trimmedFiberInd = [];
resultFibers = obj.fiberdrawn.fibcell{i,side};
if isempty(resultFibers)
continue
end
fibers=ea_fibcell2fibmat(resultFibers);
filter = all(fibers(:,1:3)>=min(vatmm),2) & all(fibers(:,1:3)<=max(vatmm), 2);
if ~any(filter)
zeros_arr = zeros(size(obj.drawobject{i,side},1),1);
normwts = mat2cell(zeros_arr,ones(size(obj.drawobject{i,side},1),1));
[obj.drawobject{i,side}.FaceAlpha]=normwts{:};
continue
end
trimmedFiber = fibers(filter,:);
% Map mm connectome fibers into VAT voxel space
[trimmedFiberInd, ~, trimmedFiberID] = unique(trimmedFiber(:,4), 'stable');
fibVoxInd = splitapply(@(fib) {ea_mm2uniqueVoxInd(fib, vat)}, trimmedFiber(:,1:3), trimmedFiberID);
% Remove outliers
fibVoxInd(cellfun(@(x) any(isnan(x)), fibVoxInd)) = [];
trimmedFiberInd(cellfun(@(x) any(isnan(x)), fibVoxInd)) = [];
connected = cellfun(@(fib) any(ismember(fib, vatInd)), fibVoxInd);
vals = cellfun(@(fib) vat.img(intersect(fib, vatInd)), fibVoxInd(connected), 'Uni', 0);
valsPeak{1}(trimmedFiberInd(connected)) = cellfun(@mean, vals);
wts = cell2mat(valsPeak)';
if ~isempty(wts)
if length(wts) ~= size(obj.drawobject{i,side},1)
diff = length(wts) - size(obj.drawobject{i,side},1);
if diff < 0
wts = [wts;zeros(abs(diff),1)];
end
end
normwts = normalize(ea_contrast(wts,10,0),'range');
normwts = mat2cell(normwts,ones(size(normwts,1),1));
if ~isempty(normwts) && ~isempty(obj.drawobject{i,side})
try
[obj.drawobject{i,side}.FaceAlpha]=normwts{:};
disp(['Changed alpha of tract',num2str(i)]);
normwts = {};
end
end
else %if it is not connected then they should have zero alpha!!
zeros_arr = zeros(size(obj.drawobject{i,side},1),1);
normwts = mat2cell(zeros_arr,ones(size(obj.drawobject{i,side},1),1));
[obj.drawobject{i,side}.FaceAlpha]=normwts{:};
end
end
end
end
end
function check_and_update_visibility(obj, field, ~, condition, group)
if nargin < 5 % If group is not provided, operate on obj directly
group = [];
end
if eval(sprintf('obj.%s%s && all(values %s)', field, group_access(group), condition))
eval(sprintf('obj.%s%s = 0;', field, group_access(group)));
fprintf('\n')
warning('off', 'backtrace');
warning('No %s values found, %s is set to 0 now!', condition_description(condition), field);
warning('on', 'backtrace');
fprintf('\n')
end
end
function access = group_access(group)
if isempty(group)
access = '';
else
access = sprintf('(group)');
end
end
function desc = condition_description(condition)
if strcmp(condition, '<0')
desc = 'positive';
else
desc = 'negative';
end
end
function fibers=ea_fibcell2fibmat(fibers)
[idx,~]=cellfun(@size,fibers);
fibers=cell2mat(fibers);
idxv=zeros(size(fibers,1),1);
lid=1; cnt=1;
for id=idx'
idxv(lid:lid+id-1)=cnt;
lid=lid+id;
cnt=cnt+1;
end
fibers=[fibers,idxv];
end

ea_unifiedmapping.m at commit 5b1008d, under GPL-3.0 · at the source

Overview

Authors: Patricia Zvarova^1,2,3,4, Christina van der Linden⁵, Ningfei Li^1,4, Konstantin Butenko¹, Thea Berger⁵, Garance M Meyer³, Ilkem Aysu Sahin^1,2,4, Lukas L Goede^1,3, Bahne H Bahners^3,6,7, Barbara Hollunder^1,2,8, Till A Dembek⁵, Andrew R Pines^3,9, Martin Reich¹⁰, Jens Volkmann¹⁰, Vincent J J Odekerken¹¹, Rob M A de Bie¹¹, Xin Xu¹², Zhipei Ling¹³, Chen Yao¹⁴, Andrea A Kühn^1,2,8,15,16

and 8 other authors

Surjo R Soekadar^2,17, Kerstin Ritter^2,18,19, Michael T Barbe⁵, Veerle Visser‐Vandewalle²⁰, Michael D Fox³, Jan Niklas Petry‐Schmelzer⁵, Nanditha Rajamani^1,3, Andreas Horn^3,4,21

ORCID iDs: Patricia Zvarova, Christina van der Linden, Ningfei Li, Garance M Meyer, Ilkem Aysu Sahin, Lukas L Goede, Till A Dembek, Andrea A Kühn, Michael D Fox, Jan Niklas Petry‐Schmelzer, Andreas Horn

21 affiliations

Institutions: Brigham and Women's Hospital (United States); Harvard University (United States); Humboldt-Universität zu Berlin (Germany); University Hospital Cologne (Germany); Einstein Center for Neurosciences Berlin (Germany); Freie Universität Berlin (Germany); Charité - Universitätsmedizin Berlin (Germany); University of Cologne (Germany); Düsseldorf University Hospital (Germany); Heinrich Heine University Düsseldorf (Germany); University of Würzburg (Germany); Universitätsklinikum Würzburg (Germany); Amsterdam University Medical Centers (Netherlands); University of Amsterdam (Netherlands); Chinese PLA General Hospital (China); Shenzhen University (China); Shenzhen Second People's Hospital (China); Bernstein Center for Computational Neuroscience Berlin (Germany); Stiftung Charité; University of Tübingen (Germany); Massachusetts General Hospital (United States)

Journal: Annals of neurology, volume 100, issue 1, pages 22-35

Dates: received 1 September 2025; accepted 25 February 2026; published online 17 April 2026; in print July 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1002/ana.78206 · PMID 41992934 · PMCID PMC13327559 · OpenAlex W4414141573

Open access: hybrid, a free copy (OpenAlex)

Preprint: doi:10.1101/2025.09.03.25334924

Status: code verified

Categories: human (organism), Parkinson's (population), clinical / translational (subfield)

Methods: Machine learning, Statistics, Connectivity

MeSH: Deep Brain Stimulation*, Multimodal Imaging*, Parkinson Disease*, Subthalamic Nucleus*, Aged, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, Retrospective Studies, Treatment Outcome (* major topic)

Topic: Neurological disorders and treatments (Neurology, Medicine), according to OpenAlex

Funding: NIH HHS (1R01NS127892-01, R01MH130666, UM1NS132358, 2R01 MH113929); National Institutes of Health (R01MH130666, UM1NS132358, 1R01NS127892‐01, 2R01 MH113929); Koeln Fortune Scholarship; Einstein Stiftung Berlin (424778381); Deutsche Forschungsgemeinschaft (502436811); Einstein Center for Neurosciences Berlin

Citations: not cited yet (Europe PMC); 43 references in the paper

Abstract

Objective: Accurate electrode placement and individual stimulation parameters influence the outcomes of subthalamic deep brain stimulation in Parkinson's disease. Neuroimaging‐based models can help evaluate how electrode placement impacts improvement, aiming to reduce the burden of programming. However, most existing models have been developed to explain differences between patients rather than differences between contacts within the same patient, leaving the clinical relevance of image‐guided programming unclear.

Methods: We analyzed data from patients with Parkinson's disease treated with subthalamic deep brain stimulation to develop and validate a neuroimaging‐informed model of motor improvement measured by the Unified Parkinson's Disease Scale. Five approaches were tested: active contact coordinates, electric fields, tract activations, as well as structural and functional networks. All approaches were integrated into a combined ridge regression model and validated using 2 hold‐out datasets.

Results: The sample included 236 patients (604 stimulation sites), divided into a training cohort (N = 129), a retrospective validation cohort (N = 89), and a prospectively acquired validation cohort (N = 21 electrodes). Consistent with expectations, our model explained approximately 12% of the variance in unseen group‐level data (R 2 = 0.12, p = 0.001). At the individual level, the model identified the optimal clinical contact or its neighboring contact in all but one case (mixed‐effects R 2 = 0.31, p = 3.67 × 10−10).

Interpretation: An imaging‐informed model explained the expected variance at the group level and demonstrated potential for guiding stimulation programming, suggesting that image‐guided approaches may improve clinical decision making while reducing the need for lengthy postoperative testing. ANN NEUROL 2026;100:22–35

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

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

leaddbs/leaddbs

License: GPL-3.0

State: the link answers, verified on 29 September 2026

Evidence: files inventoried

Commit: 5b1008d705e97fe0c8f693dece14455607f4afac, 2 March 2026

Languages: MATLAB (2362), Python (101), C++ (30), C (29), Shell (10), C/C++ (4), Java (3)

Size: 6,260 files, 2,539 scripts

Software Heritage: archived

Found in: “Data availability”

Holds: README, license file, CITATION.cff, environment (ext_libs/PaCER/docs/requirements.txt), documentation

Not found: tests, continuous integration

Tools: SPM (121 files), Statistics and Machine Learning Toolbox (84 files), Tools for NIfTI and ANALYZE image (MATLAB) (38 files), Image Processing Toolbox (36 files), NumPy (35 files), FieldTrip (24 files), h5py (12 files), SciPy (8 files), FreeSurfer (7 files), cifti-matlab (6 files), Signal Processing Toolbox (6 files), Matplotlib (6 files), Parallel Computing Toolbox (5 files), pandas (5 files), TensorFlow (4 files), Keras (3 files), Optimization Toolbox (3 files), ANTs (2 files), export_fig (2 files), GIfTI library for MATLAB (2 files), NiBabel (2 files), Psychtoolbox (2 files), CAT12 (1 file), Curve Fitting Toolbox (1 file), pydicom (1 file), seaborn (1 file)

Availability: 1 check, the latest on 29 September 2026: the link answers

29 September 2026: the link answers

2,000 files

classes/conda_utils/ea_conda.m, MATLAB, 172 lines
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classes/ea_roi/ea_loadatlas.m, MATLAB, 32 lines
classes/ea_roi/ea_roi.m, MATLAB, 369 lines
classes/ea_roi/ea_roi2struct.m, MATLAB, 13 lines
classes/ea_roi/ea_roicontrol.m, MATLAB, 282 lines
classes/ea_roi/ea_saveatlas.m, MATLAB, 12 lines
classes/ea_roi/ea_struct2roi.m, MATLAB, 11 lines
classes/ea_slicer_for_lead.m, MATLAB, 113 lines
classes/ea_sweetspot/ea_sweetspot_blob.m, MATLAB, 314 lines
classes/ea_sweetspot/ea_sweetspot_hemi.m, MATLAB, 314 lines
classes/ea_sweetspot/ea_sweetspotcontrol.m, MATLAB, 277 lines
classes/ea_sweetspot/ea_sweetspotcontrol_hemi.m, MATLAB, 309 lines
classes/ea_symmat.m, MATLAB, 103 lines
classes/ea_trajectory/ea_synctrajectoryhandles.m, MATLAB, 128 lines
classes/ea_trajectory/ea_trajectory.m, MATLAB, 605 lines
classes/ea_trajectory/ea_trajectorycontrol.m, MATLAB, 1,141 lines
classes/leadgroup/ea_calc_biophysical_lg.m, MATLAB, 165 lines
cluster/ea_checkfilefolders.m, MATLAB, 9 lines
cluster/ea_checknoerrorfolders.m, MATLAB, 23 lines
cluster/ea_checknofilefolders.m, MATLAB, 9 lines
cluster/ea_run_HMS_Orchestra.m, MATLAB, 19 lines
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cluster/ea_run_HMS_Orchestra_max.m, MATLAB, 19 lines
cluster/ea_run_HMS_Orchestra_medium.m, MATLAB, 19 lines
cluster/ea_run_Martinos_Launchpad.m, MATLAB, 19 lines
cluster/ea_run_Martinos_Launchpad_compile.m, MATLAB, 28 lines
common/ea_prefs_default.m, MATLAB, 284 lines
connectomics/DWI_calculation.m, MATLAB, 241 lines
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connectomics/ea_export_CM_png.m, MATLAB, 29 lines
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connectomics/ea_extract_timecourses.m, MATLAB, 470 lines
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connectomics/mapper/connectome.sh, Shell, 176 lines
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connectomics/mapper/cs_fmri_conseed_matrix.m, MATLAB, 423 lines
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connectomics/mapper/cs_fmri_conseed_nifti.m, MATLAB, 443 lines
connectomics/mapper/cs_fmri_conseed_pmap.m, MATLAB, 362 lines
connectomics/mapper/cs_fmri_conseed_pseed.m, MATLAB, 587 lines
connectomics/mapper/cs_fmri_conseed_seed_matrix.m, MATLAB, 296 lines
connectomics/mapper/cs_fmri_conseed_seed_tc.m, MATLAB, 749 lines
connectomics/mapper/ea_conformseedtofmri.m, MATLAB, 53 lines
connectomics/mapper/ea_getoutputfolder.m, MATLAB, 12 lines
connectomics/mapper/ea_getrois.m, MATLAB, 2 lines
connectomics/mapper/ea_handleseeds.m, MATLAB, 39 lines
connectomics/mapper/ea_lcm.m, MATLAB, 368 lines
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connectomics/mapper/ea_lcm_struc.m, MATLAB, 21 lines
connectomics/mapper/ea_updatemodpopups.m, MATLAB, 12 lines
connectomics/mapper/lead_mapper.m, MATLAB, 496 lines
connectomics/mapper/makeconseed.m, MATLAB, 5 lines
connectomics/mapper/makeroi2roicorrel.m, MATLAB, 1 line
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connectomics/mapper/roi2roicorrel.sh, Shell, 66 lines
dbshub/ea_exportscore.m, MATLAB, 18 lines
dependency/ea_findpeaks.m, MATLAB, 14 lines
dependency/ea_getrawct2preniimat.m, MATLAB, 75 lines
dependency/ea_newline.m, MATLAB, 5 lines
dependency/ea_pdist.m, MATLAB, 7 lines
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dependency/ea_sgolayfilt.m, MATLAB, 65 lines
dependency/ea_sqdist.m, MATLAB, 8 lines
dependency/ea_vecnorm.m, MATLAB, 16 lines
dependency/flag_implicit_skip_nan.m, MATLAB, 65 lines
dependency/octave_pdist.m, MATLAB, 223 lines
dependency/sumskipnan.m, MATLAB, 195 lines
dependency/sumsq.m, MATLAB, 50 lines
dev/craddock_2011_parcellations/crad2lead.m, MATLAB, 22 lines
dev/ea_atlaseditor.m, MATLAB, 186 lines
dev/ea_plan_revision.m, MATLAB, 54 lines
dev/ea_predict_module.m, MATLAB, 94 lines
dev/ea_upload_export.m, MATLAB, 70 lines
dev/genprobmaps/ea_generate_probmaps.m, MATLAB, 311 lines
dev/genprobmaps/ea_generate_probmaps_weighting.m, MATLAB, 309 lines
dev/genprobmaps/ea_normsubcorticalsegm.m, MATLAB, 52 lines
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dev/lead_md/leadMD.m, MATLAB, 1,518 lines
dev/lead_or.m, MATLAB, 1,566 lines
dev/predict/ea_getlookup_improvement.m, MATLAB, 19 lines
dev/seed2map/doseed2map_weighted.m, MATLAB, 51 lines
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dev/significance_works/maximize_mean_proximity.m, MATLAB, 36 lines
dev/standalone/dispctfroot.m, MATLAB, 3 lines
dev/standalone/ea_make_standalone.m, MATLAB, 206 lines
dev/standalone/run_dispctfroot.sh, Shell, 32 lines
dev/voxelwise_labeling_template/gen_indices.m, MATLAB, 51 lines
dev/voxelwise_labeling_template/gen_indices_4mm.m, MATLAB, 71 lines
dev/voxelwise_labeling_template/gen_indices_5mm.m, MATLAB, 71 lines
dev/voxelwise_labeling_template/gen_indices_6mm.m, MATLAB, 71 lines
ea_absor.m, MATLAB, 344 lines
ea_activecontacts.m, MATLAB, 24 lines
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ea_add_overlay_singlefile.m, MATLAB, 241 lines
ea_addobj.m, MATLAB, 804 lines
ea_amendtoolboxoptions.m, MATLAB, 12 lines
ea_anatomycontrol.m, MATLAB, 542 lines
ea_anatomyslices.m, MATLAB, 203 lines
ea_anatpreprocess.m, MATLAB, 9 lines
ea_apply_coregistration.m, MATLAB, 167 lines
ea_apply_normalization.m, MATLAB, 17 lines
ea_apply_normalization_tofile.m, MATLAB, 77 lines
ea_applyscrfmat.m, MATLAB, 26 lines
ea_assignbackdrop.m, MATLAB, 255 lines
ea_atlasselect.m, MATLAB, 688 lines
ea_autocoord.m, MATLAB, 502 lines
ea_build_DTD.m, MATLAB, 206 lines
ea_busyaction.m, MATLAB, 202 lines
ea_calc_distance.m, MATLAB, 35 lines
ea_calc_vatstats.m, MATLAB, 302 lines
ea_centralcomponent.m, MATLAB, 40 lines
ea_centrality_significance.m, MATLAB, 340 lines
ea_centroid.m, MATLAB, 7 lines
ea_checkfiles.m, MATLAB, 38 lines
ea_checkinstall.m, MATLAB, 452 lines
ea_checkmacaque.m, MATLAB, 36 lines
ea_checkreg.m, MATLAB, 923 lines
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ea_checkstructures.m, MATLAB, 849 lines
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ea_concatfv.m, MATLAB, 90 lines
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ea_conncomp.m, MATLAB, 180 lines
ea_connectomics_run_struct.m, MATLAB, 114 lines
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ea_coregpostopct_brainsfit.m, MATLAB, 30 lines
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ea_spm_dicom_import.m, MATLAB, 82 lines
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ea_subcorticalrefine.m, MATLAB, 290 lines
ea_tdhandles2options.m, MATLAB, 39 lines
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ea_updatecortex.m, MATLAB, 56 lines
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ea_viz2brainbrowser.m, MATLAB, 49 lines
ea_whichnormmethod.m, MATLAB, 50 lines
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ea_writeplanes.m, MATLAB, 542 lines
explorers/fiberfiltering_explorer/ea_add_discfiber.m, MATLAB, 26 lines
explorers/fiberfiltering_explorer/ea_apply_fiber_gaussmooth.m, MATLAB, 28 lines
explorers/fiberfiltering_explorer/ea_compute_fibscore_model.m, MATLAB, 626 lines
explorers/fiberfiltering_explorer/ea_conn2connid.m, MATLAB, 5 lines
explorers/fiberfiltering_explorer/ea_connectome_from_pathways.m, MATLAB, 81 lines
explorers/fiberfiltering_explorer/ea_corrsignan.m, MATLAB, 32 lines
explorers/fiberfiltering_explorer/ea_discfibers2nifti.m, MATLAB, 122 lines
explorers/fiberfiltering_explorer/ea_discfibers2raw.m, MATLAB, 71 lines
explorers/fiberfiltering_explorer/ea_discfibers2trk.m, MATLAB, 78 lines
explorers/fiberfiltering_explorer/ea_discfibers_TwoSample_weightedLinearRegression.m, MATLAB, 33 lines
explorers/fiberfiltering_explorer/ea_discfibers_addjitter.m, MATLAB, 6 lines
explorers/fiberfiltering_explorer/ea_discfibers_calcstats.m, MATLAB, 422 lines
explorers/fiberfiltering_explorer/ea_discfibers_calcvals.m, MATLAB, 169 lines
explorers/fiberfiltering_explorer/ea_discfibers_calcvals_cleartune.m, MATLAB, 94 lines
explorers/fiberfiltering_explorer/ea_discfibers_calcvals_pam_prob.m, MATLAB, 182 lines
explorers/fiberfiltering_explorer/ea_discfibers_checkcustomNii.m, MATLAB, 19 lines
explorers/fiberfiltering_explorer/ea_discfibers_compat_statmetrics2statsettings.m, MATLAB, 101 lines
explorers/fiberfiltering_explorer/ea_discfibers_getlattice.m, MATLAB, 44 lines
explorers/fiberfiltering_explorer/ea_discfibers_getpams.m, MATLAB, 107 lines
explorers/fiberfiltering_explorer/ea_discfibers_getpeak.m, MATLAB, 46 lines
explorers/fiberfiltering_explorer/ea_discfibers_getvats.m, MATLAB, 94 lines
explorers/fiberfiltering_explorer/ea_discfibers_loadModel_calcstats.m, MATLAB, 149 lines
explorers/fiberfiltering_explorer/ea_discfibers_merge_pathways.m, MATLAB, 229 lines
explorers/fiberfiltering_explorer/ea_discfibers_odds_ratios.m, MATLAB, 35 lines
explorers/fiberfiltering_explorer/ea_discfibers_optimize.m, MATLAB, 895 lines
explorers/fiberfiltering_explorer/ea_discfibers_predict.m, MATLAB, 58 lines
explorers/fiberfiltering_explorer/ea_discfibers_roi_collect.m, MATLAB, 57 lines
explorers/fiberfiltering_explorer/ea_discfibers_roi_nimage_sel.m, MATLAB, 54 lines
explorers/fiberfiltering_explorer/ea_discfibers_showroi.m, MATLAB, 64 lines
explorers/fiberfiltering_explorer/ea_discfibers_ui_enablerules.m, MATLAB, 499 lines
explorers/fiberfiltering_explorer/ea_discfibers_vtascore.m, MATLAB, 142 lines
explorers/fiberfiltering_explorer/ea_discfibers_weightedLinearRegression.m, MATLAB, 36 lines
explorers/fiberfiltering_explorer/ea_disctract.m, MATLAB, 2,143 lines, 1 match
explorers/fiberfiltering_explorer/ea_disctract_crossval.m, MATLAB, 227 lines
explorers/fiberfiltering_explorer/ea_disctract_crossval_visualize.m, MATLAB, 87 lines
explorers/fiberfiltering_explorer/ea_export_symptoms_tracts.m, MATLAB, 434 lines
explorers/fiberfiltering_explorer/ea_get_PCA_graphs.m, MATLAB, 127 lines
explorers/fiberfiltering_explorer/ea_get_fiber_spatial_corr.m, MATLAB, 64 lines
explorers/fiberfiltering_explorer/ea_method2methodid.m, MATLAB, 64 lines
explorers/fiberfiltering_explorer/ea_save_fibscore_model.m, MATLAB, 125 lines
explorers/fiberfiltering_explorer/ea_save_settings.m, MATLAB, 65 lines
explorers/fiberfiltering_explorer/ea_update_settings.m, MATLAB, 32 lines
explorers/fiberfiltering_explorer/native_Eproj/ea_discfibers_native_calcvals.m, MATLAB, 228 lines
explorers/fiberfiltering_explorer/native_Eproj/ea_get_4Dfield_from_csv.m, MATLAB, 54 lines
explorers/fiberfiltering_explorer/native_Eproj/ea_get_E_field_along_fibers.m, MATLAB, 211 lines
explorers/fiberfiltering_explorer/native_Eproj/ea_get_Eproj.m, MATLAB, 84 lines
explorers/fiberfiltering_explorer/native_Eproj/ea_get_native_Eproj.m, MATLAB, 83 lines
explorers/fiberfiltering_explorer/native_Eproj/ea_warp_fibers_MNI2native.m, MATLAB, 49 lines
explorers/fiberfiltering_explorer/native_Eproj/till_save_nii.m, MATLAB, 57 lines
explorers/navigator/ea_convertolddisctractobj.m, MATLAB, 21 lines
explorers/navigator/ea_explorer_applythresholding.m, MATLAB, 37 lines
explorers/navigator/ea_explorer_calcfibervals.m, MATLAB, 75 lines
explorers/navigator/ea_explorer_calcstats.m, MATLAB, 103 lines
explorers/navigator/ea_explorer_calcvoxelvals.m, MATLAB, 63 lines
explorers/navigator/ea_explorer_cleanfigure.m, MATLAB, 42 lines
explorers/navigator/ea_explorer_corrsignan.m, MATLAB, 32 lines
explorers/navigator/ea_explorer_createcolormap.m, MATLAB, 21 lines
explorers/navigator/ea_explorer_crossval.m, MATLAB, 227 lines
explorers/navigator/ea_explorer_efoverlapfiber.m, MATLAB, 34 lines
explorers/navigator/ea_explorer_isocolors.m, MATLAB, 35 lines
explorers/navigator/ea_explorer_method2methodid.m, MATLAB, 22 lines
explorers/navigator/ea_explorer_roi.m, MATLAB, 383 lines
explorers/navigator/ea_explorer_showroi.m, MATLAB, 64 lines
explorers/navigator/ea_explorer_value2color.m, MATLAB, 12 lines
explorers/navigator/ea_explorer_visualizefibers.m, MATLAB, 155 lines
explorers/navigator/ea_explorer_visualizevoxels.m, MATLAB, 149 lines
explorers/navigator/ea_explorerclass.m, MATLAB, 904 lines
explorers/navigator/launchmyapp.m, MATLAB, 5 lines
explorers/networkmapping_explorer/ea_add_networkmapping.m, MATLAB, 26 lines
explorers/networkmapping_explorer/ea_getobjmask.m, MATLAB, 72 lines
explorers/networkmapping_explorer/ea_heatmap2surface.m, MATLAB, 104 lines
explorers/networkmapping_explorer/ea_ihat_databaselookup_netmap.m, MATLAB, 156 lines
explorers/networkmapping_explorer/ea_networkmapping.m, MATLAB, 837 lines, 2 matches
explorers/networkmapping_explorer/ea_networkmapping_calcstats.m, MATLAB, 199 lines
explorers/networkmapping_explorer/ea_networkmapping_calcvals.m, MATLAB, 201 lines
explorers/networkmapping_explorer/ea_networkmapping_getvats.m, MATLAB, 218 lines
explorers/networkmapping_explorer/ea_networkmapping_recalcvals_sk.m, MATLAB, 24 lines
explorers/networkmapping_explorer/ea_parcellation2multisurface.m, MATLAB, 80 lines
explorers/networkmapping_explorer/ea_parcellation2surface.m, MATLAB, 132 lines
explorers/networkmapping_explorer/ea_readMz3.m, MATLAB, 94 lines
explorers/networkmapping_explorer/ea_readObj.m, MATLAB, 57 lines
explorers/networkmapping_explorer/ea_save_networkmapping_model.m, MATLAB, 59 lines
explorers/stattests/ea_explorer_statlist.m, MATLAB, 60 lines
explorers/stattests/ea_explorer_stats_1samplettest.m, MATLAB, 54 lines
explorers/stattests/ea_explorer_stats_1sampleweightedlinreg.m, MATLAB, 109 lines
explorers/stattests/ea_explorer_stats_2samplettest.m, MATLAB, 48 lines
explorers/stattests/ea_explorer_stats_2sampleweightedlinreg.m, MATLAB, 96 lines
explorers/stattests/ea_explorer_stats_bend.m, MATLAB, 30 lines
explorers/stattests/ea_explorer_stats_meanmap.m, MATLAB, 35 lines
explorers/stattests/ea_explorer_stats_nmap.m, MATLAB, 30 lines
explorers/stattests/ea_explorer_stats_pearson.m, MATLAB, 30 lines
explorers/stattests/ea_explorer_stats_proportiontest.m, MATLAB, 50 lines
explorers/stattests/ea_explorer_stats_ranksumtest.m, MATLAB, 39 lines
explorers/stattests/ea_explorer_stats_reverse_2samplettest.m, MATLAB, 39 lines
explorers/stattests/ea_explorer_stats_signedranktest.m, MATLAB, 48 lines
explorers/stattests/ea_explorer_stats_spearman.m, MATLAB, 31 lines
explorers/sweetspot_explorer/ea_add_sweetspot.m, MATLAB, 26 lines
explorers/sweetspot_explorer/ea_createTemplateSpace.m, MATLAB, 52 lines
explorers/sweetspot_explorer/ea_exportefieldmapping.m, MATLAB, 54 lines
explorers/sweetspot_explorer/ea_exportefieldmapping_till.m, MATLAB, 73 lines
explorers/sweetspot_explorer/ea_save_sweetspot_model.m, MATLAB, 54 lines
explorers/sweetspot_explorer/ea_sweetspot.m, MATLAB, 671 lines
explorers/sweetspot_explorer/ea_sweetspot_calcstats.m, MATLAB, 384 lines
explorers/sweetspot_explorer/ea_sweetspot_calcvals.m, MATLAB, 89 lines
explorers/sweetspot_explorer/ea_sweetspot_getvats.m, MATLAB, 40 lines
explorers/sweetspot_explorer/ea_sweetspot_importedModel2Efields.m, MATLAB, 60 lines
explorers/sweetspot_explorer/ea_sweetspot_predict.m, MATLAB, 58 lines
explorers/sweetspot_explorer/ea_sweetspot_vtascore.m, MATLAB, 142 lines
explorers/unifiedmapping_explorer/ea_add_unifiedmapping.m, MATLAB, 26 lines
explorers/unifiedmapping_explorer/ea_compute_unified_model.m, MATLAB, 411 lines
explorers/unifiedmapping_explorer/ea_export_as_atlas.m, MATLAB, 47 lines
explorers/unifiedmapping_explorer/ea_fiberfiltering_calcvals.m, MATLAB, 98 lines
explorers/unifiedmapping_explorer/ea_sweetspotmapping_getvats.m, MATLAB, 40 lines
explorers/unifiedmapping_explorer/ea_unified_discfibers_calcstats.m, MATLAB, 413 lines
explorers/unifiedmapping_explorer/ea_unified_discfibers_compat_statmetrics2statsettings.m, MATLAB, 96 lines
explorers/unifiedmapping_explorer/ea_unified_draw.m, MATLAB, 320 lines
explorers/unifiedmapping_explorer/ea_unified_draw_tracts.m, MATLAB, 24 lines
explorers/unifiedmapping_explorer/ea_unified_draw_volumetric.m, MATLAB, 176 lines
explorers/unifiedmapping_explorer/ea_unified_getobjmask.m, MATLAB, 65 lines
explorers/unifiedmapping_explorer/ea_unified_networkmapping_recalcvals_sk.m, MATLAB, 24 lines
explorers/unifiedmapping_explorer/ea_unified_nm_calcvals.m, MATLAB, 201 lines
explorers/unifiedmapping_explorer/ea_unified_nm_getvats.m, MATLAB, 218 lines
explorers/unifiedmapping_explorer/ea_unified_save_model.m, MATLAB, 91 lines
explorers/unifiedmapping_explorer/ea_unified_ss_getvats.m, MATLAB, 40 lines
explorers/unifiedmapping_explorer/ea_unified_sweetspot_calcstats.m, MATLAB, 379 lines
explorers/unifiedmapping_explorer/ea_unifiedmapping.m, MATLAB, 1,712 lines, 2 matches
explorers/unifiedmapping_explorer/ea_unifiedmapping_addjitter.m, MATLAB, 6 lines
explorers/unifiedmapping_explorer/ea_unifiedmapping_calcstats.m, MATLAB, 383 lines
explorers/unifiedmapping_explorer/ea_unifiedmapping_calcvals_pam_prob.m, MATLAB, 182 lines
explorers/unifiedmapping_explorer/ea_unifiedmapping_checkcustomNii.m, MATLAB, 19 lines
explorers/unifiedmapping_explorer/ea_unifiedmapping_compat_statmetrics2statsettings.m, MATLAB, 97 lines
explorers/unifiedmapping_explorer/ea_unifiedmapping_conn2connid.m, MATLAB, 5 lines
explorers/unifiedmapping_explorer/ea_unifiedmapping_crossval.m, MATLAB, 253 lines
explorers/unifiedmapping_explorer/ea_unifiedmapping_crossval_visualize.m, MATLAB, 87 lines
explorers/unifiedmapping_explorer/ea_unifiedmapping_exportefieldmap.m, MATLAB, 51 lines
explorers/unifiedmapping_explorer/ea_unifiedmapping_getlattice.m, MATLAB, 44 lines
explorers/unifiedmapping_explorer/ea_unifiedmapping_getpams.m, MATLAB, 64 lines
explorers/unifiedmapping_explorer/ea_unifiedmapping_getvats.m, MATLAB, 94 lines
explorers/unifiedmapping_explorer/ea_unifiedmapping_mergePathways.m, MATLAB, 196 lines
explorers/unifiedmapping_explorer/ea_unifiedmapping_method2methodid.m, MATLAB, 64 lines
explorers/unifiedmapping_explorer/ea_unifiedmapping_save_settings.m, MATLAB, 65 lines
explorers/unifiedmapping_explorer/ea_unifiedmapping_update_settings.m, MATLAB, 32 lines
explorers/unifiedmapping_explorer/native_Eproj/ea_unified_get_Eproj.m, MATLAB, 84 lines
explorers/unifiedmapping_explorer/native_Eproj/ea_unifiedmapping_native_calcvals.m, MATLAB, 228 lines
ext_libs/@gramm/axe_property.m, MATLAB, 17 lines
ext_libs/@gramm/coord_flip.m, MATLAB, 18 lines
ext_libs/@gramm/draw.m, MATLAB, 1,280 lines
ext_libs/@gramm/export.m, MATLAB, 111 lines
ext_libs/@gramm/facet_grid.m, MATLAB, 68 lines
ext_libs/@gramm/facet_wrap.m, MATLAB, 50 lines
ext_libs/@gramm/fig.m, MATLAB, 13 lines
ext_libs/@gramm/geom_abline.m, MATLAB, 22 lines
ext_libs/@gramm/geom_bar.m, MATLAB, 147 lines
ext_libs/@gramm/geom_count.m, MATLAB, 47 lines
ext_libs/@gramm/geom_funline.m, MATLAB, 18 lines
ext_libs/@gramm/geom_hline.m, MATLAB, 17 lines
ext_libs/@gramm/geom_interval.m, MATLAB, 47 lines
ext_libs/@gramm/geom_jitter.m, MATLAB, 51 lines
ext_libs/@gramm/geom_label.m, MATLAB, 75 lines
ext_libs/@gramm/geom_line.m, MATLAB, 99 lines
ext_libs/@gramm/geom_point.m, MATLAB, 84 lines
ext_libs/@gramm/geom_polygon.m, MATLAB, 128 lines
ext_libs/@gramm/geom_raster.m, MATLAB, 59 lines
ext_libs/@gramm/geom_vline.m, MATLAB, 17 lines
ext_libs/@gramm/gramm.m, MATLAB, 276 lines
ext_libs/@gramm/no_legend.m, MATLAB, 9 lines
ext_libs/@gramm/private/comb.m, MATLAB, 15 lines
ext_libs/@gramm/private/combnan.m, MATLAB, 27 lines
ext_libs/@gramm/private/dodge_comp.m, MATLAB, 102 lines
ext_libs/@gramm/private/dodger.m, MATLAB, 18 lines
ext_libs/@gramm/private/draw_polar_axes.m, MATLAB, 144 lines
ext_libs/@gramm/private/draw_polygons.m, MATLAB, 55 lines
ext_libs/@gramm/private/fill_abline.m, MATLAB, 28 lines
ext_libs/@gramm/private/fill_gradient_legend.m, MATLAB, 56 lines
ext_libs/@gramm/private/fill_legend.m, MATLAB, 84 lines
ext_libs/@gramm/private/get_colormap.m, MATLAB, 234 lines
ext_libs/@gramm/private/multi_sel.m, MATLAB, 10 lines
ext_libs/@gramm/private/my_addParameter.m, MATLAB, 13 lines
ext_libs/@gramm/private/my_errorbar.m, MATLAB, 12 lines
ext_libs/@gramm/private/my_histcounts.m, MATLAB, 25 lines
ext_libs/@gramm/private/my_histplot.m, MATLAB, 193 lines
ext_libs/@gramm/private/my_tightplot.m, MATLAB, 43 lines
ext_libs/@gramm/private/pa_LCH2RGB.m, MATLAB, 128 lines
ext_libs/@gramm/private/padded_cell2mat.m, MATLAB, 25 lines
ext_libs/@gramm/private/parse_aes.m, MATLAB, 39 lines
ext_libs/@gramm/private/parse_fill.m, MATLAB, 25 lines
ext_libs/@gramm/private/plotci.m, MATLAB, 163 lines
ext_libs/@gramm/private/point_patch.m, MATLAB, 31 lines
ext_libs/@gramm/private/pval_to_star.m, MATLAB, 11 lines
ext_libs/@gramm/private/select_aes.m, MATLAB, 14 lines
ext_libs/@gramm/private/set_alpha.m, MATLAB, 32 lines
ext_libs/@gramm/private/to_polar.m, MATLAB, 32 lines
ext_libs/@gramm/private/unique_and_sort.m, MATLAB, 66 lines
ext_libs/@gramm/private/unique_no_nan.m, MATLAB, 22 lines
ext_libs/@gramm/private/validate_aes.m, MATLAB, 104 lines
ext_libs/@gramm/redraw.m, MATLAB, 521 lines
ext_libs/@gramm/set_color_options.m, MATLAB, 77 lines
ext_libs/@gramm/set_continuous_color.m, MATLAB, 313 lines
ext_libs/@gramm/set_datetick.m, MATLAB, 13 lines
ext_libs/@gramm/set_layout_options.m, MATLAB, 54 lines
ext_libs/@gramm/set_limit_extra.m, MATLAB, 31 lines
ext_libs/@gramm/set_line_options.m, MATLAB, 29 lines
ext_libs/@gramm/set_names.m, MATLAB, 33 lines
ext_libs/@gramm/set_order_options.m, MATLAB, 47 lines
ext_libs/@gramm/set_point_options.m, MATLAB, 29 lines
ext_libs/@gramm/set_polar.m, MATLAB, 25 lines
ext_libs/@gramm/set_stat_options.m, MATLAB, 13 lines
ext_libs/@gramm/set_text_options.m, MATLAB, 33 lines
ext_libs/@gramm/set_title.m, MATLAB, 29 lines
ext_libs/@gramm/stat_bin.m, MATLAB, 36 lines
ext_libs/@gramm/stat_bin2d.m, MATLAB, 136 lines
ext_libs/@gramm/stat_boxplot.m, MATLAB, 150 lines
ext_libs/@gramm/stat_cornerhist.m, MATLAB, 289 lines
ext_libs/@gramm/stat_density.m, MATLAB, 69 lines
ext_libs/@gramm/stat_ellipse.m, MATLAB, 144 lines
ext_libs/@gramm/stat_fit.m, MATLAB, 100 lines
ext_libs/@gramm/stat_glm.m, MATLAB, 77 lines
ext_libs/@gramm/stat_qq.m, MATLAB, 67 lines
ext_libs/@gramm/stat_smooth.m, MATLAB, 357 lines
ext_libs/@gramm/stat_summary.m, MATLAB, 434 lines
ext_libs/@gramm/stat_violin.m, MATLAB, 171 lines
ext_libs/@gramm/update.m, MATLAB, 70 lines
ext_libs/ANTs/ea_ants_apply_transforms.m, MATLAB, 170 lines
ext_libs/ANTs/ea_ants_apply_transforms_to_points.m, MATLAB, 84 lines
ext_libs/ANTs/ea_ants_linear.m, MATLAB, 236 lines
ext_libs/ANTs/ea_ants_nonlinear.m, MATLAB, 270 lines
ext_libs/ANTs/ea_ants_nonlinear_coreg.m, MATLAB, 103 lines
ext_libs/ANTs/ea_ants_run.m, MATLAB, 168 lines
ext_libs/ANTs/ea_ants_schoenecker.m, MATLAB, 268 lines
ext_libs/ANTs/ea_antspy_apply_transforms_to_points.m, MATLAB, 71 lines
ext_libs/ANTs/ea_bias_field_correction.m, MATLAB, 21 lines
ext_libs/ANTs/ea_getantsnormfuns.m, MATLAB, 5 lines
ext_libs/ANTs/ea_resample_image_by_spacing.m, MATLAB, 60 lines
ext_libs/ANTs/presets/ea_antspreset_ants_default_synquick.m, MATLAB, 27 lines
ext_libs/ANTs/presets/ea_antspreset_ants_wiki.m, MATLAB, 28 lines
ext_libs/ANTs/presets/ea_antspreset_effective_highvar.m, MATLAB, 63 lines
ext_libs/ANTs/presets/ea_antspreset_effective_lowtomidvar.m, MATLAB, 72 lines
ext_libs/ANTs/presets/ea_antspreset_effective_lowvar_default.m, MATLAB, 72 lines
ext_libs/ANTs/presets/ea_antspreset_effective_lowvar_default_rat.m, MATLAB, 72 lines
ext_libs/ANTs/presets/ea_antspreset_effective_lowvar_highsmooth.m, MATLAB, 63 lines
ext_libs/ANTs/presets/ea_antspreset_effective_lowvar_vhighsmooth.m, MATLAB, 63 lines
ext_libs/ANTs/presets/ea_antspreset_effective_midvar.m, MATLAB, 62 lines
ext_libs/ANTs/presets/ea_antspreset_effective_ultrahighvar.m, MATLAB, 63 lines
ext_libs/ANTs/presets/ea_antspreset_legacy_default.m, MATLAB, 46 lines
ext_libs/ANTs/presets/ea_antspreset_legacy_fine.m, MATLAB, 46 lines
ext_libs/ANTs/presets/ea_antspreset_legacy_lowvar.m, MATLAB, 49 lines
ext_libs/ANTs/presets/ea_antspreset_legacy_preset1.m, MATLAB, 46 lines
ext_libs/ANTs/presets/ea_antspreset_legacy_preset2.m, MATLAB, 46 lines
ext_libs/ANTs/presets/ea_antspreset_legacy_quick.m, MATLAB, 46 lines
ext_libs/Approxon/approxonGui.m, MATLAB, 171 lines
ext_libs/Approxon/plotModelAndGeneralHeuristic.m, MATLAB, 61 lines
ext_libs/BRAINSTools/ea_brainsfit.m, MATLAB, 131 lines
ext_libs/BRAINSTools/ea_brainsresample.m, MATLAB, 58 lines
ext_libs/CaptureFigVid/ea_CaptureFigVid.m, MATLAB, 112 lines
ext_libs/EasyReg/ea_easyreg.m, MATLAB, 105 lines
ext_libs/Fibertools/Gibbstracker/AccumulateBilin.cpp, C++, 115 lines
ext_libs/Fibertools/Gibbstracker/AccumulateBilinWeighted.cpp, C++, 122 lines
ext_libs/Fibertools/Gibbstracker/BuildFibres.cpp, C++, 370 lines
ext_libs/Fibertools/Gibbstracker/CreateConnectivityMatrixROI.cpp, C++, 238 lines
ext_libs/Fibertools/Gibbstracker/EnergyComputerBase.cpp, C++, 405 lines
ext_libs/Fibertools/Gibbstracker/EnergyComputer_connec.cpp, C++, 348 lines
ext_libs/Fibertools/Gibbstracker/FunkRadonTrans.m, MATLAB, 54 lines
ext_libs/Fibertools/Gibbstracker/MersenneTwister.h, C/C++, 598 lines
ext_libs/Fibertools/Gibbstracker/ParticleGrid.cpp, C++, 639 lines
ext_libs/Fibertools/Gibbstracker/RJMCMCBase.cpp, C++, 283 lines
ext_libs/Fibertools/Gibbstracker/RJMCMC_randshift.cpp, C++, 706 lines
ext_libs/Fibertools/Gibbstracker/SphereInterpolator.cpp, C++, 148 lines
ext_libs/Fibertools/Gibbstracker/anisoDiffusion.cpp, C++, 160 lines
ext_libs/Fibertools/Gibbstracker/anisoDiffusionHomogenous.cpp, C++, 160 lines
ext_libs/Fibertools/Gibbstracker/auxilary_classes.cpp, C++, 664 lines
ext_libs/Fibertools/Gibbstracker/chooseThreshold_stackview.m, MATLAB, 153 lines
ext_libs/Fibertools/Gibbstracker/computeFiberCorrelation.m, MATLAB, 30 lines
ext_libs/Fibertools/Gibbstracker/cpmSH.m, MATLAB, 29 lines
ext_libs/Fibertools/Gibbstracker/createCM_GT.m, MATLAB, 56 lines
ext_libs/Fibertools/Gibbstracker/createStopButton.m, MATLAB, 21 lines
ext_libs/Fibertools/Gibbstracker/estimateWMmask.m, MATLAB, 24 lines
ext_libs/Fibertools/Gibbstracker/fiberGT_tool.m, MATLAB, 1,971 lines
ext_libs/Fibertools/Gibbstracker/fileexists.m, MATLAB, 10 lines
ext_libs/Fibertools/Gibbstracker/ft_make.m, MATLAB, 16 lines
ext_libs/Fibertools/Gibbstracker/ftr2FDmaps.m, MATLAB, 66 lines
ext_libs/Fibertools/Gibbstracker/genFDfromFTR.m, MATLAB, 70 lines
ext_libs/Fibertools/Gibbstracker/pcRJMCMC.cpp, C++, 371 lines
ext_libs/Fibertools/Gibbstracker/rebuildFibers.m, MATLAB, 88 lines
ext_libs/Fibertools/Gibbstracker/reparametrize_arclen.cpp, C++, 149 lines
ext_libs/Fibertools/Gibbstracker/saveFTR.m, MATLAB, 94 lines
ext_libs/Fibertools/Gibbstracker/smoothHardi.m, MATLAB, 9 lines
ext_libs/Fibertools/Gibbstracker/smoothODF.m, MATLAB, 10 lines
ext_libs/Fibertools/Gibbstracker/stackview.m, MATLAB, 97 lines
ext_libs/Fibertools/Gibbstracker/stopButtonOnTop.m, MATLAB, 3 lines
ext_libs/Fibertools/Gibbstracker/stopButtonPressed.m, MATLAB, 2 lines
ext_libs/Fibertools/Gibbstracker/wintime.h, C/C++, 45 lines
ext_libs/Fibertools/common/cfg_mlbatch_appcfg.m, MATLAB, 5 lines
ext_libs/Fibertools/common/dtdstruct_init.m, MATLAB, 177 lines
ext_libs/Fibertools/common/dtdstruct_istype.m, MATLAB, 102 lines
ext_libs/Fibertools/common/dtdstruct_modify.m, MATLAB, 708 lines
ext_libs/Fibertools/common/dtdstruct_query.m, MATLAB, 1,121 lines
ext_libs/Fibertools/common/dtdstruct_read.m, MATLAB, 58 lines
ext_libs/Fibertools/common/dtdstruct_write.m, MATLAB, 61 lines
ext_libs/Fibertools/common/dti_cfg.m, MATLAB, 58 lines
ext_libs/Fibertools/common/dti_cfg_fodop.m, MATLAB, 814 lines
ext_libs/Fibertools/common/dti_cfg_ftrop.m, MATLAB, 487 lines
ext_libs/Fibertools/common/dti_cfg_logroi.m, MATLAB, 110 lines
ext_libs/Fibertools/common/dti_cfg_mapop.m, MATLAB, 128 lines
ext_libs/Fibertools/common/dti_cfg_read.m, MATLAB, 285 lines
ext_libs/Fibertools/common/dti_cfg_realigndef.m, MATLAB, 61 lines
ext_libs/Fibertools/common/dti_cfg_roiop.m, MATLAB, 191 lines
ext_libs/Fibertools/common/dti_cfg_tensor.m, MATLAB, 80 lines
ext_libs/Fibertools/common/dti_cfg_tracking.m, MATLAB, 889 lines
ext_libs/Fibertools/common/dti_cfg_unring.m, MATLAB, 45 lines
ext_libs/Fibertools/common/dti_tracking_global_ui.m, MATLAB, 117 lines
ext_libs/Fibertools/common/ftrstruct_init.m, MATLAB, 94 lines
ext_libs/Fibertools/common/ftrstruct_istype.m, MATLAB, 79 lines
ext_libs/Fibertools/common/ftrstruct_modify.m, MATLAB, 618 lines
ext_libs/Fibertools/common/ftrstruct_query.m, MATLAB, 1,596 lines
ext_libs/Fibertools/common/ftrstruct_read.m, MATLAB, 73 lines
ext_libs/Fibertools/common/ftrstruct_write.m, MATLAB, 65 lines
ext_libs/Fibertools/common/get_unique_str.m, MATLAB, 110 lines
ext_libs/Fibertools/common/maskstruct_init.m, MATLAB, 57 lines
ext_libs/Fibertools/common/maskstruct_istype.m, MATLAB, 93 lines
ext_libs/Fibertools/common/maskstruct_modify.m, MATLAB, 929 lines
ext_libs/Fibertools/common/maskstruct_query.m, MATLAB, 345 lines
ext_libs/Fibertools/common/maskstruct_read.m, MATLAB, 154 lines
ext_libs/Fibertools/common/maskstruct_write.m, MATLAB, 57 lines
ext_libs/Fibertools/common/morph_data.m, MATLAB, 142 lines
ext_libs/Fibertools/common/ringRemoval.m, MATLAB, 28 lines
ext_libs/Fibertools/imp_exp/batch_ftrstruct_Curves2dx.m, MATLAB, 130 lines
ext_libs/Fibertools/imp_exp/cfg_mlbatch_appcfg.m, MATLAB, 9 lines
ext_libs/Fibertools/imp_exp/impexp_cfg_NiftiMrStruct.m, MATLAB, 1,049 lines
ext_libs/Fibertools/imp_exp/impexp_cfg_ftrstruct_Curves2dx.m, MATLAB, 78 lines
ext_libs/Fibertools/imp_exp/impexp_run_bo2nifti.m, MATLAB, 183 lines
ext_libs/Fibertools/imp_exp/impexp_run_eigVal1_2_nifti.m, MATLAB, 183 lines
ext_libs/Fibertools/imp_exp/impexp_run_eigVal2_2_nifti.m, MATLAB, 183 lines
ext_libs/Fibertools/imp_exp/impexp_run_eigVal3_2_nifti.m, MATLAB, 183 lines
ext_libs/Fibertools/imp_exp/impexp_run_fa2nifti.m, MATLAB, 183 lines
ext_libs/Fibertools/imp_exp/impexp_run_ftrstruct_Curves2dx.m, MATLAB, 126 lines
ext_libs/Fibertools/imp_exp/impexp_run_maskstruct2nifti.m, MATLAB, 202 lines
ext_libs/Fibertools/imp_exp/impexp_run_mrstruct2nifti.m, MATLAB, 176 lines
ext_libs/Fibertools/imp_exp/impexp_run_msnames2ind.m, MATLAB, 142 lines
ext_libs/Fibertools/imp_exp/impexp_run_nifti2DTDstruct.m, MATLAB, 149 lines
ext_libs/Fibertools/imp_exp/impexp_run_nifti2HARDIstruct.m, MATLAB, 166 lines
ext_libs/Fibertools/imp_exp/impexp_run_nifti2maskstruct.m, MATLAB, 191 lines
ext_libs/Fibertools/imp_exp/impexp_run_nifti2mrstruct.m, MATLAB, 153 lines
ext_libs/Fibertools/imp_exp/impexp_run_probstruct2nifti.m, MATLAB, 189 lines
ext_libs/Fibertools/imp_exp/impexp_run_trace2nifti.m, MATLAB, 183 lines
ext_libs/Fibertools/matlab/io/mrstruct_to_nifti.m, MATLAB, 217 lines
ext_libs/Fibertools/matlab/io/nifti_to_mrstruct.m, MATLAB, 267 lines
ext_libs/Fibertools/matlab/objects/mrstruct_checkin.m, MATLAB, 160 lines
ext_libs/Fibertools/matlab/objects/mrstruct_init.m, MATLAB, 207 lines
ext_libs/Fibertools/matlab/objects/mrstruct_istype.m, MATLAB, 256 lines
ext_libs/Fibertools/matlab/objects/mrstruct_query.m, MATLAB, 305 lines
ext_libs/Fibertools/matlab/objects/mrstruct_read.m, MATLAB, 118 lines
ext_libs/Fibertools/matlab/objects/mrstruct_write.m, MATLAB, 94 lines
ext_libs/Fibertools/matlab/utils/misc/reshape_index.m, MATLAB, 36 lines
ext_libs/Fibertools/matlab/utils/mn_isvector.m, MATLAB, 58 lines
ext_libs/GetFullPath/GetFullPath.m, MATLAB, 331 lines
ext_libs/GetMD5/GetMD5.c, C, 919 lines
ext_libs/GetMD5/GetMD5.m, MATLAB, 94 lines
ext_libs/GetMD5/GetMD5_helper.m, MATLAB, 110 lines
ext_libs/LeGUI/LeG_alignElecs.m, MATLAB, 22 lines
ext_libs/LeGUI/LeG_autoElecs.m, MATLAB, 256 lines
ext_libs/LeGUI/LeG_calcGrayWhite.m, MATLAB, 46 lines
ext_libs/LeGUI/LeG_determine_griddim.m, MATLAB, 140 lines
ext_libs/LeGUI/LeG_dialogCellstrHelper.m, MATLAB, 22 lines
ext_libs/LeGUI/LeG_findSubFiles.m, MATLAB, 33 lines
ext_libs/LeGUI/LeG_genRotMat.m, MATLAB, 17 lines
ext_libs/LeGUI/LeG_genSurfaces.m, MATLAB, 74 lines
ext_libs/LeGUI/LeG_getnicedialoglocation.m, MATLAB, 33 lines
ext_libs/LeGUI/LeG_intriangulation.m, MATLAB, 373 lines
ext_libs/LeGUI/LeG_lastDir.m, MATLAB, 20 lines
ext_libs/LeGUI/LeG_lastPath.m, MATLAB, 29 lines
ext_libs/LeGUI/LeG_match_str.m, MATLAB, 108 lines
ext_libs/LeGUI/LeG_msgbox.m, MATLAB, 485 lines
ext_libs/LeGUI/LeG_parfor_wait.m, MATLAB, 123 lines
ext_libs/LeGUI/LeG_removeSubSurf.m, MATLAB, 46 lines
ext_libs/LeGUI/LeG_rotateImg2Standard.m, MATLAB, 125 lines
ext_libs/LeGUI/LeG_setdefaultbutton.m, MATLAB, 71 lines
ext_libs/LeGUI/LeG_vox2atlaslabel.m, MATLAB, 687 lines
ext_libs/LeGUI/LeG_warp_hermes2010.m, MATLAB, 122 lines
ext_libs/Mesh2Tetra/Mesh2Tetra.m, MATLAB, 148 lines
ext_libs/Mesh2Tetra/compile_c_files.m, MATLAB, 22 lines
ext_libs/Mesh2Tetra/functions/BoundaryCollapse3D.m, MATLAB, 102 lines
ext_libs/Mesh2Tetra/functions/DelaunayInside3D.m, MATLAB, 94 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/BarycentricCoordinatesTetrahedron.c, C, 26 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/BarycentricCoordinatesTriangle.c, C, 24 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/CheckInsideFace.c, C, 33 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/CheckPointOutInside.c, C, 48 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/CheckVolumeFaceMesh.c, C, 56 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/CheckVolumeTetraMesh.c, C, 61 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/LineLineIntersect.c, C, 44 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/LineTriangleIntersection.c, C, 61 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/SphereFrom4Points.c, C, 27 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/TriangleTriangleIntersection.c, C, 36 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/func_BarycentricCoordinatesTetrahedron.c, C, 48 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/func_BarycentricCoordinatesTriangle.c, C, 27 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/func_CheckInsideFace.c, C, 24 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/func_Determinant.c, C, 25 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/func_LineLineIntersect.c, C, 63 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/func_LineTriangleIntersection.c, C, 84 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/func_SphereFrom4Points.c, C, 53 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/func_TriangleTriangleIntersection.c, C, 41 lines
ext_libs/Mesh2Tetra/functions/mexfunctions/func_cross.c, C, 3 lines
ext_libs/Mesh2Tetra/functions/subfunctions/BarycentricCoordinatesTetrahedron.m, MATLAB, 28 lines
ext_libs/Mesh2Tetra/functions/subfunctions/BarycentricCoordinatesTriangle.m, MATLAB, 19 lines
ext_libs/Mesh2Tetra/functions/subfunctions/CheckFaceOrientations.m, MATLAB, 12 lines
ext_libs/Mesh2Tetra/functions/subfunctions/CheckInputMesh.m, MATLAB, 36 lines
ext_libs/Mesh2Tetra/functions/subfunctions/CheckInsideFace.m, MATLAB, 8 lines
ext_libs/Mesh2Tetra/functions/subfunctions/CheckInsideTetrahedron.m, MATLAB, 7 lines
ext_libs/Mesh2Tetra/functions/subfunctions/CheckMeshInterSections.m, MATLAB, 34 lines
ext_libs/Mesh2Tetra/functions/subfunctions/CheckMoveInside3D.m, MATLAB, 23 lines
ext_libs/Mesh2Tetra/functions/subfunctions/CheckPointOutInside3D.m, MATLAB, 29 lines
ext_libs/Mesh2Tetra/functions/subfunctions/CheckPointsInsideTetrahedron.m, MATLAB, 19 lines
ext_libs/Mesh2Tetra/functions/subfunctions/CheckVisiblePoint3D.m, MATLAB, 31 lines
ext_libs/Mesh2Tetra/functions/subfunctions/CheckVolumeFaceMesh.m, MATLAB, 18 lines
ext_libs/Mesh2Tetra/functions/subfunctions/CheckVolumeTetraMesh.m, MATLAB, 26 lines
ext_libs/Mesh2Tetra/functions/subfunctions/GetRemainingFaces.m, MATLAB, 24 lines
ext_libs/Mesh2Tetra/functions/subfunctions/InsidePoints3D.m, MATLAB, 19 lines
ext_libs/Mesh2Tetra/functions/subfunctions/LineTriangleIntersection.m, MATLAB, 32 lines
ext_libs/Mesh2Tetra/functions/subfunctions/PointToClosestPointOnLine.m, MATLAB, 13 lines
ext_libs/Mesh2Tetra/functions/subfunctions/PointToClosestPointOnPlane.m, MATLAB, 21 lines
ext_libs/Mesh2Tetra/functions/subfunctions/RemoveInvalidTetrahedrons.m, MATLAB, 35 lines
ext_libs/Mesh2Tetra/functions/subfunctions/ReturnSepparateFaceObjects.m, MATLAB, 33 lines
ext_libs/Mesh2Tetra/functions/subfunctions/TriangleTriangleIntersection.m, MATLAB, 19 lines
ext_libs/Mesh2Tetra/functions/subfunctions/VolumeCheck.m, MATLAB, 7 lines
ext_libs/Mesh2Tetra/functions/subfunctions/VolumeCheckNew.m, MATLAB, 6 lines
ext_libs/Mesh2Tetra/functions/subfunctions/collapse_edge.m, MATLAB, 62 lines
ext_libs/Mesh2Tetra/functions/subfunctions/make_left_vertice_list.m, MATLAB, 26 lines
ext_libs/Mesh2Tetra/functions/subfunctions/process.m, MATLAB, 30 lines
ext_libs/Mesh2Tetra/functions/subfunctions/retry_remove_tetrahedrons.m, MATLAB, 57 lines
ext_libs/Mesh2Tetra/functions/subfunctions/solveInterSections.m, MATLAB, 78 lines
ext_libs/Mesh2Tetra/functions/subfunctions/visibility_matrix_3D.m, MATLAB, 10 lines
ext_libs/NBS/ea_NBSfdr.m, MATLAB, 129 lines
ext_libs/NBS/ea_NBSglm.m, MATLAB, 221 lines
ext_libs/NBS/ea_NBSrun.m, MATLAB, 614 lines
ext_libs/NBS/ea_NBSstats.m, MATLAB, 247 lines
ext_libs/NBS/ea_readUI.m, MATLAB, 50 lines
ext_libs/NBS/private/get_components.m, MATLAB, 59 lines
ext_libs/OSS-DBS/Axon_Processing/OSS_DBS_Damaged2Activated.m, MATLAB, 38 lines
ext_libs/OSS-DBS/Axon_Processing/ea_get_fiberstate_from_axonstate.m, MATLAB, 188 lines
ext_libs/OSS-DBS/Axon_Processing/ea_get_probab_axon_state.m, MATLAB, 132 lines
ext_libs/OSS-DBS/Axon_Processing/ea_plot_Emetrics_on_fibers.m, MATLAB, 80 lines
ext_libs/OSS-DBS/Axon_Processing/ea_plot_fiber_state.m, MATLAB, 77 lines
ext_libs/OSS-DBS/Axon_Processing/ea_plot_prob_fiber_state.m, MATLAB, 68 lines
ext_libs/OSS-DBS/Axon_Processing/ea_till_creategridforelectrode.m, MATLAB, 94 lines
ext_libs/OSS-DBS/MRI_DTI_processing/Tensor_scaling.py, Python, 258 lines
ext_libs/OSS-DBS/MRI_DTI_processing/Tensor_scaling_one_thread.py, Python, 249 lines
ext_libs/OSS-DBS/MRI_DTI_processing/ea_convert_atlas2segmask.m, MATLAB, 25 lines
ext_libs/OSS-DBS/MRI_DTI_processing/ea_convert_synthSeg2segmask.m, MATLAB, 39 lines
ext_libs/OSS-DBS/ea_checkOSSDBSInstallv2.m, MATLAB, 73 lines
ext_libs/OSS-DBS/genvat_butenko/BndBoxDimensionsEdits.py, Python, 25 lines
ext_libs/OSS-DBS/genvat_butenko/atropos_segm.py, Python, 35 lines
ext_libs/OSS-DBS/genvat_butenko/ea_StimSets_VTAs.m, MATLAB, 38 lines
ext_libs/OSS-DBS/genvat_butenko/ea_atropos2segmask.m, MATLAB, 35 lines
ext_libs/OSS-DBS/genvat_butenko/ea_check_stimSources.m, MATLAB, 40 lines
ext_libs/OSS-DBS/genvat_butenko/ea_convert_ossdbs_StimSets_VTAs.m, MATLAB, 34 lines
ext_libs/OSS-DBS/genvat_butenko/ea_convert_ossdbs_VTAs.m, MATLAB, 79 lines
ext_libs/OSS-DBS/genvat_butenko/ea_convert_ossdbs_axons.m, MATLAB, 210 lines
ext_libs/OSS-DBS/genvat_butenko/ea_create_target_dict.m, MATLAB, 62 lines
ext_libs/OSS-DBS/genvat_butenko/ea_elshift_oss.m, MATLAB, 38 lines
ext_libs/OSS-DBS/genvat_butenko/ea_get_ANN_env.m, MATLAB, 11 lines
ext_libs/OSS-DBS/genvat_butenko/ea_get_oss_outputPaths.m, MATLAB, 19 lines
ext_libs/OSS-DBS/genvat_butenko/ea_get_oss_reco.m, MATLAB, 104 lines
ext_libs/OSS-DBS/genvat_butenko/ea_get_ossdbs_StimSets_VTA.m, MATLAB, 46 lines
ext_libs/OSS-DBS/genvat_butenko/ea_get_prob_fiber_states_for_multisource.m, MATLAB, 44 lines
ext_libs/OSS-DBS/genvat_butenko/ea_get_probab_axon_state_for_multisource.m, MATLAB, 87 lines
ext_libs/OSS-DBS/genvat_butenko/ea_get_stimProtocol.m, MATLAB, 137 lines
ext_libs/OSS-DBS/genvat_butenko/ea_load_prob_parameter.m, MATLAB, 27 lines
ext_libs/OSS-DBS/genvat_butenko/ea_postprocess_multisource.m, MATLAB, 64 lines
ext_libs/OSS-DBS/genvat_butenko/ea_postprocess_multisource_axonstates.m, MATLAB, 74 lines
ext_libs/OSS-DBS/genvat_butenko/ea_postprocess_multisource_pam.m, MATLAB, 79 lines
ext_libs/OSS-DBS/genvat_butenko/ea_prepare_DTI.m, MATLAB, 97 lines
ext_libs/OSS-DBS/genvat_butenko/ea_prepare_fibers.m, MATLAB, 295 lines
ext_libs/OSS-DBS/genvat_butenko/ea_prepare_ossdbs.m, MATLAB, 114 lines
ext_libs/OSS-DBS/genvat_butenko/ea_save_ossdbs_settings.m, MATLAB, 35 lines
ext_libs/OSS-DBS/genvat_butenko/ea_segmask_cleanup.m, MATLAB, 15 lines
ext_libs/OSS-DBS/genvat_butenko/ea_segment_MRI.m, MATLAB, 214 lines
ext_libs/OSS-DBS/genvat_butenko/ea_set_ANN_training.m, MATLAB, 60 lines
ext_libs/OSS-DBS/genvat_butenko/ea_set_optimizer.m, MATLAB, 57 lines
ext_libs/OSS-DBS/genvat_butenko/ea_sourceIndex4AxonStates.m, MATLAB, 34 lines
ext_libs/OSS-DBS/genvat_butenko/ea_svd_segmentation.m, MATLAB, 485 lines
ext_libs/OSS-DBS/genvat_butenko/ea_switch2VATgrid.m, MATLAB, 120 lines
ext_libs/OSS-DBS/genvat_butenko/ea_updatePAM_parameter.m, MATLAB, 54 lines
ext_libs/OSS-DBS/sEEG/DBS_RF_pipeline.m, MATLAB, 361 lines
ext_libs/OSS-DBS/sEEG/custom_dict.py, Python, 289 lines
ext_libs/OSS-DBS/sEEG/get_sEEG_field_from_csv.m, MATLAB, 84 lines
ext_libs/OSS-DBS/sEEG/get_sEEG_field_in_MNI_from_csv.m, MATLAB, 73 lines
ext_libs/OSS-DBS/sEEG/lead_settings_class.py, Python, 639 lines
ext_libs/OSS-DBS/sEEG/resliceNii2ROI.m, MATLAB, 81 lines
ext_libs/OSS-DBS/sEEG/run_OSS4DBS_RFs.py, Python, 473 lines
ext_libs/OSS-DBS/sEEG/run_OSS4SEEG.py, Python, 275 lines
ext_libs/OSS-DBS/sEEG/run_OSS4SEEG_Stim_no_shift.py, Python, 501 lines
ext_libs/OSS-DBS/sEEG/sEEG_RF_pipeline.m, MATLAB, 89 lines
ext_libs/PaCER/.artenolis/runtests.sh, Shell, 9 lines
ext_libs/PaCER/SETUP_PACER.m, MATLAB, 12 lines
ext_libs/PaCER/docs/source/conf.py, Python, 361 lines
ext_libs/PaCER/examples/EXAMPLE_1.m, MATLAB, 31 lines
ext_libs/PaCER/examples/EXAMPLE_1_1.m, MATLAB, 72 lines
ext_libs/PaCER/examples/advanced/EXAMPLE_1.m, MATLAB, 31 lines
ext_libs/PaCER/examples/advanced/EXAMPLE_1_1.m, MATLAB, 72 lines
ext_libs/PaCER/examples/advanced/EXAMPLE_1_2.m, MATLAB, 79 lines
ext_libs/PaCER/examples/advanced/EXAMPLE_2.m, MATLAB, 50 lines
ext_libs/PaCER/examples/advanced/EXAMPLE_2_1.m, MATLAB, 65 lines
ext_libs/PaCER/examples/advanced/EXAMPLE_3.m, MATLAB, 48 lines
ext_libs/PaCER/examples/advanced/EXAMPLE_4.m, MATLAB, 36 lines
ext_libs/PaCER/examples/advanced/EXAMPLE_5.m, MATLAB, 60 lines
ext_libs/PaCER/examples/advanced/EXAMPLE_5_1.m, MATLAB, 79 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uiextras/BoxPanel.m, MATLAB, 139 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uiextras/CardPanel.m, MATLAB, 93 lines
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ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uiextras/TabPanel.m, MATLAB, 224 lines
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ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uiextras/VButtonBox.m, MATLAB, 46 lines
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ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/+mixin/Container.m, MATLAB, 297 lines
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ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/+mixin/Panel.m, MATLAB, 188 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/Box.m, MATLAB, 45 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/BoxPanel.m, MATLAB, 551 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/ButtonBox.m, MATLAB, 96 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/CardPanel.m, MATLAB, 64 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/ChildEvent.m, MATLAB, 28 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/ChildObserver.m, MATLAB, 220 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/Container.m, MATLAB, 9 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/Divider.m, MATLAB, 344 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/Empty.m, MATLAB, 102 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/FigureData.m, MATLAB, 26 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/FigureObserver.m, MATLAB, 98 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/Grid.m, MATLAB, 321 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/GridFlex.m, MATLAB, 467 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/HBox.m, MATLAB, 194 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/HBoxFlex.m, MATLAB, 349 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/HButtonBox.m, MATLAB, 100 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/Node.m, MATLAB, 94 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/Panel.m, MATLAB, 63 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/PointerManager.m, MATLAB, 153 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/ScrollingPanel.m, MATLAB, 645 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/SelectionData.m, MATLAB, 31 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/TabPanel.m, MATLAB, 951 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/Text.m, MATLAB, 546 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/VBox.m, MATLAB, 194 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/VBoxFlex.m, MATLAB, 349 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/VButtonBox.m, MATLAB, 98 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/calcPixelSizes.m, MATLAB, 47 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/loadIcon.m, MATLAB, 102 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/setPosition.m, MATLAB, 29 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/+uix/tracking.m, MATLAB, 229 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/Contents.m, MATLAB, 29 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layout/layoutRoot.m, MATLAB, 18 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layoutdoc/Examples/axesexample.m, MATLAB, 41 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layoutdoc/Examples/callbackexample.m, MATLAB, 47 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layoutdoc/Examples/colorbarexample.m, MATLAB, 29 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layoutdoc/Examples/demoBrowser.m, MATLAB, 209 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layoutdoc/Examples/displayEndOfDemoMessage.m, MATLAB, 4 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layoutdoc/Examples/dockexample.m, MATLAB, 74 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layoutdoc/Examples/gridflexpositioning.m, MATLAB, 25 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layoutdoc/Examples/guideApp.m, MATLAB, 106 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layoutdoc/Examples/hierarchyexample.m, MATLAB, 33 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layoutdoc/Examples/minimizeexample.m, MATLAB, 54 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layoutdoc/Examples/paneltabexample.m, MATLAB, 59 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layoutdoc/Examples/visibleexample.m, MATLAB, 30 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layoutdoc/layoutDocRoot.m, MATLAB, 12 lines
ext_libs/PaCER/external/GUI Layout Toolbox 2.3.1/layoutdoc/termsOfUse.m, MATLAB, 8 lines
ext_libs/PaCER/external/NIfTI_20140122/affine.m, MATLAB, 554 lines
ext_libs/PaCER/external/NIfTI_20140122/bipolar.m, MATLAB, 94 lines
ext_libs/PaCER/external/NIfTI_20140122/bresenham_line3d.m, MATLAB, 189 lines
ext_libs/PaCER/external/NIfTI_20140122/clip_nii.m, MATLAB, 115 lines
ext_libs/PaCER/external/NIfTI_20140122/collapse_nii_scan.m, MATLAB, 260 lines
ext_libs/PaCER/external/NIfTI_20140122/expand_nii_scan.m, MATLAB, 48 lines
ext_libs/PaCER/external/NIfTI_20140122/extra_nii_hdr.m, MATLAB, 255 lines
ext_libs/PaCER/external/NIfTI_20140122/flip_lr.m, MATLAB, 84 lines
ext_libs/PaCER/external/NIfTI_20140122/get_nii_frame.m, MATLAB, 164 lines
ext_libs/PaCER/external/NIfTI_20140122/load_nii.m, MATLAB, 198 lines
ext_libs/PaCER/external/NIfTI_20140122/load_nii_ext.m, MATLAB, 207 lines
ext_libs/PaCER/external/NIfTI_20140122/load_nii_hdr.m, MATLAB, 280 lines
ext_libs/PaCER/external/NIfTI_20140122/load_nii_img.m, MATLAB, 392 lines
ext_libs/PaCER/external/NIfTI_20140122/load_untouch0_nii_hdr.m, MATLAB, 200 lines
ext_libs/PaCER/external/NIfTI_20140122/load_untouch_header_only.m, MATLAB, 187 lines
ext_libs/PaCER/external/NIfTI_20140122/load_untouch_nii.m, MATLAB, 191 lines
ext_libs/PaCER/external/NIfTI_20140122/load_untouch_nii_hdr.m, MATLAB, 217 lines
ext_libs/PaCER/external/NIfTI_20140122/load_untouch_nii_img.m, MATLAB, 468 lines
ext_libs/PaCER/external/NIfTI_20140122/make_ana.m, MATLAB, 210 lines
ext_libs/PaCER/external/NIfTI_20140122/make_nii.m, MATLAB, 256 lines
ext_libs/PaCER/external/NIfTI_20140122/mat_into_hdr.m, MATLAB, 83 lines
ext_libs/PaCER/external/NIfTI_20140122/pad_nii.m, MATLAB, 142 lines
ext_libs/PaCER/external/NIfTI_20140122/reslice_nii.m, MATLAB, 321 lines
ext_libs/PaCER/external/NIfTI_20140122/rri_file_menu.m, MATLAB, 179 lines
ext_libs/PaCER/external/NIfTI_20140122/rri_orient.m, MATLAB, 106 lines
ext_libs/PaCER/external/NIfTI_20140122/rri_orient_ui.m, MATLAB, 251 lines
ext_libs/PaCER/external/NIfTI_20140122/rri_select_file.m, MATLAB, 636 lines
ext_libs/PaCER/external/NIfTI_20140122/rri_xhair.m, MATLAB, 92 lines
ext_libs/PaCER/external/NIfTI_20140122/rri_zoom_menu.m, MATLAB, 33 lines
ext_libs/PaCER/external/NIfTI_20140122/save_nii.m, MATLAB, 286 lines
ext_libs/PaCER/external/NIfTI_20140122/save_nii_ext.m, MATLAB, 38 lines
ext_libs/PaCER/external/NIfTI_20140122/save_nii_hdr.m, MATLAB, 227 lines
ext_libs/PaCER/external/NIfTI_20140122/save_untouch0_nii_hdr.m, MATLAB, 219 lines
ext_libs/PaCER/external/NIfTI_20140122/save_untouch_header_only.m, MATLAB, 71 lines
ext_libs/PaCER/external/NIfTI_20140122/save_untouch_nii.m, MATLAB, 232 lines
ext_libs/PaCER/external/NIfTI_20140122/save_untouch_nii_hdr.m, MATLAB, 207 lines
ext_libs/PaCER/external/NIfTI_20140122/save_untouch_slice.m, MATLAB, 580 lines
ext_libs/PaCER/external/NIfTI_20140122/unxform_nii.m, MATLAB, 40 lines
ext_libs/PaCER/external/NIfTI_20140122/verify_nii_ext.m, MATLAB, 45 lines
ext_libs/PaCER/external/NIfTI_20140122/view_nii.m, MATLAB, 4,873 lines
ext_libs/PaCER/external/NIfTI_20140122/view_nii_menu.m, MATLAB, 480 lines
ext_libs/PaCER/external/NIfTI_20140122/xform_nii.m, MATLAB, 521 lines
ext_libs/PaCER/external/cylinder2P/cylinder2P.m, MATLAB, 63 lines
ext_libs/PaCER/external/inpolyhedron/inpolyhedron.m, MATLAB, 461 lines
ext_libs/PaCER/external/rgb/rgb.m, MATLAB, 279 lines
ext_libs/PaCER/lib/ANTSTransforms/applyANTSTransformToPoints.m, MATLAB, 61 lines
ext_libs/PaCER/lib/ANTSTransforms/applyANTSTransformToPolyCoeffs.m, MATLAB, 41 lines
ext_libs/PaCER/lib/FSLTransforms/applyFSLTransformToPoints.m, MATLAB, 24 lines
ext_libs/PaCER/lib/FSLTransforms/applyFSLTransformToPolyCoeffs.m, MATLAB, 26 lines
ext_libs/PaCER/lib/FSLTransforms/flirtmat2worldmatPaCER.m, MATLAB, 70 lines
ext_libs/PaCER/lib/Helpers/CTColormap.m, MATLAB, 34 lines
ext_libs/PaCER/lib/Helpers/CTColormapRGB.m, MATLAB, 43 lines
ext_libs/PaCER/lib/Helpers/Point3D.m, MATLAB, 49 lines
ext_libs/PaCER/lib/Helpers/convertMedtronicCoordToLPI.m, MATLAB, 16 lines
ext_libs/PaCER/lib/Helpers/createColorImage.m, MATLAB, 21 lines
ext_libs/PaCER/lib/Helpers/getFilenameFromWildcard.m, MATLAB, 15 lines
ext_libs/PaCER/lib/Helpers/getFilepathFromWildcard.m, MATLAB, 53 lines
ext_libs/PaCER/lib/Helpers/getIntensityForLabel.m, MATLAB, 9 lines
ext_libs/PaCER/lib/Helpers/plotSphere.m, MATLAB, 29 lines
ext_libs/PaCER/lib/Helpers/readLabels.m, MATLAB, 44 lines
ext_libs/PaCER/lib/Helpers/readMedtronicXMLTrajectory.m, MATLAB, 99 lines
ext_libs/PaCER/lib/Helpers/rowNorm.m, MATLAB, 9 lines
ext_libs/PaCER/lib/Helpers/scatterMatrix3.m, MATLAB, 38 lines
ext_libs/PaCER/lib/Helpers/scatterSpheres.m, MATLAB, 24 lines
ext_libs/PaCER/lib/Interfaces/MetaTrajectory.m, MATLAB, 14 lines
ext_libs/PaCER/lib/Interfaces/configurable.m, MATLAB, 16 lines
ext_libs/PaCER/lib/Interfaces/id.m, MATLAB, 31 lines
ext_libs/PaCER/lib/Interfaces/plotable3D.m, MATLAB, 49 lines
ext_libs/PaCER/lib/Interfaces/plotableImage.m, MATLAB, 90 lines
ext_libs/PaCER/lib/NiftiOOP/NiftiFilepath.m, MATLAB, 308 lines
ext_libs/PaCER/lib/NiftiOOP/NiftiMod.m, MATLAB, 253 lines
ext_libs/PaCER/lib/NiftiOOP/NiftiModSPM.m, MATLAB, 222 lines
ext_libs/PaCER/lib/NiftiOOP/NiftiSeg.m, MATLAB, 338 lines
ext_libs/PaCER/lib/NiftiOOP/applyNiiIntensityScaling.m, MATLAB, 61 lines
ext_libs/PaCER/lib/NiftiOOP/checkNiftiHdr.m, MATLAB, 30 lines
ext_libs/PaCER/lib/NiftiOOP/external/cbiQuaternionToHomogeneous.m, MATLAB, 93 lines
ext_libs/PaCER/src/Classes/PolynomialElectrodeModel.m, MATLAB, 267 lines
ext_libs/PaCER/src/Classes/SimpleFieldSim3D.m, MATLAB, 92 lines
ext_libs/PaCER/src/Classes/SimpleFieldSim3DViewElement.m, MATLAB, 89 lines
ext_libs/PaCER/src/Classes/TestElectrodes.m, MATLAB, 556 lines
ext_libs/PaCER/src/Classes/Trajectory.m, MATLAB, 74 lines
ext_libs/PaCER/src/Functions/createSimpleMPRWorldCoordinates.m, MATLAB, 15 lines
ext_libs/PaCER/src/Functions/determineElectrodeType.m, MATLAB, 36 lines
ext_libs/PaCER/src/Functions/electrodePointCloudModelEstimate.m, MATLAB, 87 lines
ext_libs/PaCER/src/Functions/extractBrainConvHull.m, MATLAB, 105 lines
ext_libs/PaCER/src/Functions/extractElectrodePointclouds.m, MATLAB, 169 lines
ext_libs/PaCER/src/Functions/fitParamPolyToSkeleton.m, MATLAB, 60 lines
ext_libs/PaCER/src/Functions/getIntensityPeaks.m, MATLAB, 49 lines
ext_libs/PaCER/src/Functions/invPolyArcLength3.m, MATLAB, 20 lines
ext_libs/PaCER/src/Functions/largestConnCompSliceWise.m, MATLAB, 18 lines
ext_libs/PaCER/src/Functions/oor.m, MATLAB, 79 lines
ext_libs/PaCER/src/Functions/plotIntensityProfileAndPeaks.m, MATLAB, 29 lines
ext_libs/PaCER/src/Functions/polyArcLength3.m, MATLAB, 37 lines
ext_libs/PaCER/src/Functions/polyval3.m, MATLAB, 13 lines
ext_libs/PaCER/src/Functions/refitElec.m, MATLAB, 219 lines
ext_libs/PaCER/src/PaCER.m, MATLAB, 64 lines
ext_libs/PathwayTune/ANN_module.py, Python, 394 lines
ext_libs/PathwayTune/ANN_predictor.py, Python, 33 lines
ext_libs/PathwayTune/ANN_report.py, Python, 313 lines
ext_libs/PathwayTune/Bilateral_results.py, Python, 216 lines
ext_libs/PathwayTune/Improvement4Protocol.py, Python, 695 lines
ext_libs/PathwayTune/NB_outline.py, Python, 145 lines
ext_libs/PathwayTune/Optim_strategies.py, Python, 329 lines
ext_libs/PathwayTune/Pathways_Stats.py, Python, 102 lines
ext_libs/PathwayTune/TrainTest_Generator.py, Python, 338 lines
ext_libs/PathwayTune/ea_check_for_SSE_tracts.m, MATLAB, 14 lines
ext_libs/PathwayTune/ea_cleartune_generateSfile.m, MATLAB, 111 lines
ext_libs/PathwayTune/ea_displayOptimResults.m, MATLAB, 25 lines
ext_libs/PathwayTune/ea_get_N_pathways.m, MATLAB, 28 lines
ext_libs/PathwayTune/ea_get_OSS_DBS_options.m, MATLAB, 34 lines
ext_libs/PathwayTune/ea_get_currents_per_contact.m, MATLAB, 154 lines
ext_libs/PathwayTune/ea_get_default_ANN_settings.m, MATLAB, 25 lines
ext_libs/PathwayTune/ea_get_optimal_currents.m, MATLAB, 136 lines
ext_libs/PathwayTune/ea_get_patient_folder.m, MATLAB, 18 lines
ext_libs/PathwayTune/ea_get_reconstruction.m, MATLAB, 23 lines
ext_libs/PathwayTune/ea_merge_PathwayTune_dicts.m, MATLAB, 105 lines
ext_libs/PathwayTune/ea_oss2optimizePAM.m, MATLAB, 308 lines
ext_libs/PathwayTune/ea_save_fixed_weights.m, MATLAB, 71 lines
ext_libs/PathwayTune/misc/MARS_training.py, Python, 230 lines
ext_libs/PathwayTune/misc/VAT_pathway_recruitment.py, Python, 47 lines
ext_libs/PathwayTune/misc/ea_add_StimVector_to_S.m, MATLAB, 46 lines
ext_libs/PathwayTune/misc/ea_compute_pathways_recruitment.m, MATLAB, 130 lines
ext_libs/PathwayTune/pam_optimizer.py, Python, 277 lines
ext_libs/SlicerNetstim/ImportACPCAutodetect/ImportACPCAutodetect.py, Python, 182 lines
ext_libs/SlicerNetstim/ImportAtlas/ImportAtlas.py, Python, 559 lines
ext_libs/SlicerNetstim/LeadOR/LeadOR.py, Python, 928 lines
ext_libs/SlicerNetstim/LeadOR/LeadORLib/Widgets/__init__.py, Python, 1 line
ext_libs/SlicerNetstim/LeadOR/LeadORLib/Widgets/tables.py, Python, 156 lines
ext_libs/SlicerNetstim/LeadOR/LeadORLib/__init__.py, Python, 1 line
ext_libs/SlicerNetstim/LeadOR/LeadORLib/util.py, Python, 446 lines
ext_libs/SlicerNetstim/NetstimPreferences/NetstimPreferences.py, Python, 127 lines
ext_libs/SlicerNetstim/StereotacticPlan/StereotacticPlan.py, Python, 761 lines
ext_libs/SlicerNetstim/StereotacticPlan/StereotacticPlanLib/ImportFrom/Import_From_Brainlab.py, Python, 257 lines
ext_libs/SlicerNetstim/StereotacticPlan/StereotacticPlanLib/ImportFrom/Import_From_ROSA.py, Python, 98 lines
ext_libs/SlicerNetstim/StereotacticPlan/StereotacticPlanLib/ImportFrom/__init__.py, Python, 1 line
ext_libs/SlicerNetstim/StereotacticPlan/StereotacticPlanLib/ImportFrom/importerBase.py, Python, 53 lines
ext_libs/SlicerNetstim/StereotacticPlan/StereotacticPlanLib/Widgets/CustomWidgets.py, Python, 171 lines
ext_libs/SlicerNetstim/StereotacticPlan/StereotacticPlanLib/Widgets/__init__.py, Python, 1 line
ext_libs/SlicerNetstim/StereotacticPlan/StereotacticPlanLib/__init__.py, Python, 1 line
ext_libs/SlicerNetstim/WarpDrive/WarpDrive.py, Python, 807 lines
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Effects/CircleEffect.py, Python, 77 lines
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Effects/DrawEffect.py, Python, 75 lines
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Effects/Effect.py, Python, 117 lines
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Effects/PointToPointEffect.py, Python, 105 lines
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Effects/PointerEffect.py, Python, 106 lines
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Effects/ShrinkExpandEffect.py, Python, 97 lines
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Effects/__init__.py, Python, 1 line
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Helpers/GridNodeHelper.py, Python, 83 lines
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Helpers/LeadDBSCall.py, Python, 136 lines
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Helpers/__init__.py, Python, 1 line
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Tools/DrawTool.py, Python, 226 lines
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Tools/NoneTool.py, Python, 27 lines
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Tools/PointToPointTool.py, Python, 59 lines
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Tools/ShrinkExpandTool.py, Python, 88 lines
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Tools/SmudgeTool.py, Python, 147 lines
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Tools/__init__.py, Python, 1 line
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Widgets/Tables.py, Python, 801 lines
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Widgets/ToolWidget.py, Python, 52 lines
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Widgets/Toolbar.py, Python, 429 lines
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/Widgets/__init__.py, Python, 1 line
ext_libs/SlicerNetstim/WarpDrive/WarpDriveLib/__init__.py, Python, 1 line
ext_libs/SynthMorph/ea_synthmorph.m, MATLAB, 97 lines
ext_libs/SynthMorph/registration.py, Python, 313 lines
ext_libs/SynthMorph/utils.py, Python, 97 lines
ext_libs/SynthSR/ea_synthsr.m, MATLAB, 52 lines
ext_libs/SynthSeg/ea_synthseg.m, MATLAB, 31 lines
ext_libs/SynthStrip/ea_synthstrip.m, MATLAB, 40 lines
ext_libs/THOMAS/ea_thomas.m, MATLAB, 173 lines
ext_libs/THOMAS/ea_thomas_menu.m, MATLAB, 39 lines
ext_libs/UnbiasedNonLocalMeans/ea_denoise_mri.m, MATLAB, 26 lines
ext_libs/aacam/aacam.m, MATLAB, 212 lines
ext_libs/aacam/test_aacam.m, MATLAB, 13 lines
ext_libs/acpcdetect/ea_acpcdetect.m, MATLAB, 34 lines
ext_libs/arrow3ex.m, MATLAB, 824 lines
ext_libs/c3d/ea_c3d_reorient.m, MATLAB, 58 lines
ext_libs/cat/ea_cat_run.m, MATLAB, 818 lines
ext_libs/cat/ea_cat_seg.m, MATLAB, 50 lines
ext_libs/cifti/@xmltreemod/Contents.m, MATLAB, 50 lines
ext_libs/cifti/@xmltreemod/add.m, MATLAB, 90 lines
ext_libs/cifti/@xmltreemod/attributes.m, MATLAB, 111 lines
ext_libs/cifti/@xmltreemod/branch.m, MATLAB, 49 lines
ext_libs/cifti/@xmltreemod/char.m, MATLAB, 14 lines
ext_libs/cifti/@xmltreemod/children.m, MATLAB, 26 lines
ext_libs/cifti/@xmltreemod/convert.m, MATLAB, 129 lines
ext_libs/cifti/@xmltreemod/copy.m, MATLAB, 44 lines
ext_libs/cifti/@xmltreemod/delete.m, MATLAB, 31 lines
ext_libs/cifti/@xmltreemod/display.m, MATLAB, 19 lines
ext_libs/cifti/@xmltreemod/editor.m, MATLAB, 396 lines
ext_libs/cifti/@xmltreemod/find.m, MATLAB, 165 lines
ext_libs/cifti/@xmltreemod/flush.m, MATLAB, 37 lines
ext_libs/cifti/@xmltreemod/get.m, MATLAB, 37 lines
ext_libs/cifti/@xmltreemod/getfilename.m, MATLAB, 14 lines
ext_libs/cifti/@xmltreemod/isfield.m, MATLAB, 20 lines
ext_libs/cifti/@xmltreemod/length.m, MATLAB, 31 lines
ext_libs/cifti/@xmltreemod/move.m, MATLAB, 16 lines
ext_libs/cifti/@xmltreemod/parent.m, MATLAB, 14 lines
ext_libs/cifti/@xmltreemod/private/compile_mex.sh, Shell, 41 lines
ext_libs/cifti/@xmltreemod/private/struct2xml.m, MATLAB, 91 lines
ext_libs/cifti/@xmltreemod/private/xml_findstr.c, C, 111 lines
ext_libs/cifti/@xmltreemod/private/xml_findstr.m, MATLAB, 30 lines
ext_libs/cifti/@xmltreemod/private/xml_parser.m, MATLAB, 411 lines
ext_libs/cifti/@xmltreemod/root.m, MATLAB, 31 lines
ext_libs/cifti/@xmltreemod/save.m, MATLAB, 129 lines
ext_libs/cifti/@xmltreemod/save_string.m, MATLAB, 100 lines
ext_libs/cifti/@xmltreemod/set.m, MATLAB, 21 lines
ext_libs/cifti/@xmltreemod/setfilename.m, MATLAB, 13 lines
ext_libs/cifti/@xmltreemod/xmltreemod.m, MATLAB, 62 lines
ext_libs/cifti/cifti_diminfo_dense_get_structures.m, MATLAB, 21 lines
ext_libs/cifti/cifti_diminfo_dense_get_surface_info.m, MATLAB, 21 lines
ext_libs/cifti/cifti_diminfo_dense_get_volume_all_info.m, MATLAB, 52 lines
ext_libs/cifti/cifti_diminfo_dense_get_volume_structure_info.m, MATLAB, 44 lines
ext_libs/cifti/cifti_diminfo_make_scalars.m, MATLAB, 47 lines
ext_libs/cifti/cifti_diminfo_make_series.m, MATLAB, 30 lines
ext_libs/cifti/cifti_metadata_get.m, MATLAB, 14 lines
ext_libs/cifti/cifti_metadata_remove.m, MATLAB, 14 lines
ext_libs/cifti/cifti_metadata_set.m, MATLAB, 19 lines
ext_libs/cifti/cifti_read.m, MATLAB, 148 lines
ext_libs/cifti/cifti_struct_create_from_template.m, MATLAB, 110 lines
ext_libs/cifti/cifti_struct_create_sdseries.m, MATLAB, 25 lines
ext_libs/cifti/cifti_struct_dense_extract_surface_data.m, MATLAB, 40 lines
ext_libs/cifti/cifti_struct_dense_extract_volume_all_data.m, MATLAB, 59 lines
ext_libs/cifti/cifti_struct_dense_extract_volume_structure_data.m, MATLAB, 59 lines
ext_libs/cifti/cifti_struct_dense_replace_surface_data.m, MATLAB, 46 lines
ext_libs/cifti/cifti_struct_dense_replace_volume_all_data.m, MATLAB, 64 lines
ext_libs/cifti/cifti_struct_dense_replace_volume_structure_data.m, MATLAB, 64 lines
ext_libs/cifti/cifti_vox2ind.m, MATLAB, 17 lines
ext_libs/cifti/cifti_write.m, MATLAB, 193 lines
ext_libs/cifti/cifti_write_from_template.m, MATLAB, 38 lines
ext_libs/cifti/cifti_write_sdseries.m, MATLAB, 18 lines
ext_libs/cifti/ciftiopen.m, MATLAB, 11 lines
ext_libs/cifti/ciftisave.m, MATLAB, 33 lines
ext_libs/cifti/ciftisavereset.m, MATLAB, 28 lines
ext_libs/cifti/ft_cifti/@gifti/Contents.m, MATLAB, 39 lines
ext_libs/cifti/ft_cifti/@gifti/display.m, MATLAB, 25 lines
ext_libs/cifti/ft_cifti/@gifti/export.m, MATLAB, 53 lines
ext_libs/cifti/ft_cifti/@gifti/fieldnames.m, MATLAB, 16 lines
ext_libs/cifti/ft_cifti/@gifti/gifti.m, MATLAB, 111 lines
ext_libs/cifti/ft_cifti/@gifti/isfield.m, MATLAB, 13 lines
ext_libs/cifti/ft_cifti/@gifti/plot.m, MATLAB, 67 lines
ext_libs/cifti/ft_cifti/@gifti/private/base64decode.m, MATLAB, 81 lines
ext_libs/cifti/ft_cifti/@gifti/private/base64encode.m, MATLAB, 157 lines
ext_libs/cifti/ft_cifti/@gifti/private/getdict.m, MATLAB, 26 lines
ext_libs/cifti/ft_cifti/@gifti/private/isintent.m, MATLAB, 116 lines
ext_libs/cifti/ft_cifti/@gifti/private/miniz.c, C, 4,214 lines
ext_libs/cifti/ft_cifti/@gifti/private/mvtk_write.m, MATLAB, 564 lines
ext_libs/cifti/ft_cifti/@gifti/private/read_freesurfer_file.m, MATLAB, 25 lines
ext_libs/cifti/ft_cifti/@gifti/private/read_gifti_file_standalone.m, MATLAB, 236 lines
ext_libs/cifti/ft_cifti/@gifti/private/xml_parser.m, MATLAB, 429 lines
ext_libs/cifti/ft_cifti/@gifti/private/zstream.c, C, 77 lines
ext_libs/cifti/ft_cifti/@gifti/private/zstream.m, MATLAB, 49 lines
ext_libs/cifti/ft_cifti/@gifti/save.m, MATLAB, 253 lines
ext_libs/cifti/ft_cifti/@gifti/saveas.m, MATLAB, 365 lines
ext_libs/cifti/ft_cifti/@gifti/struct.m, MATLAB, 18 lines
ext_libs/cifti/ft_cifti/@gifti/subsasgn.m, MATLAB, 139 lines
ext_libs/cifti/ft_cifti/@gifti/subsref.m, MATLAB, 60 lines
ext_libs/cifti/ft_cifti/@xmltree/Contents.m, MATLAB, 54 lines
ext_libs/cifti/ft_cifti/@xmltree/add.m, MATLAB, 94 lines
ext_libs/cifti/ft_cifti/@xmltree/attributes.m, MATLAB, 117 lines
ext_libs/cifti/ft_cifti/@xmltree/branch.m, MATLAB, 55 lines
ext_libs/cifti/ft_cifti/@xmltree/char.m, MATLAB, 18 lines
ext_libs/cifti/ft_cifti/@xmltree/children.m, MATLAB, 31 lines
ext_libs/cifti/ft_cifti/@xmltree/convert.m, MATLAB, 149 lines
ext_libs/cifti/ft_cifti/@xmltree/copy.m, MATLAB, 50 lines
ext_libs/cifti/ft_cifti/@xmltree/delete.m, MATLAB, 36 lines
ext_libs/cifti/ft_cifti/@xmltree/display.m, MATLAB, 22 lines
ext_libs/cifti/ft_cifti/@xmltree/editor.m, MATLAB, 401 lines
ext_libs/cifti/ft_cifti/@xmltree/find.m, MATLAB, 174 lines
ext_libs/cifti/ft_cifti/@xmltree/flush.m, MATLAB, 43 lines
ext_libs/cifti/ft_cifti/@xmltree/get.m, MATLAB, 43 lines
ext_libs/cifti/ft_cifti/@xmltree/getfilename.m, MATLAB, 17 lines
ext_libs/cifti/ft_cifti/@xmltree/isfield.m, MATLAB, 26 lines
ext_libs/cifti/ft_cifti/@xmltree/length.m, MATLAB, 37 lines
ext_libs/cifti/ft_cifti/@xmltree/move.m, MATLAB, 22 lines
ext_libs/cifti/ft_cifti/@xmltree/parent.m, MATLAB, 17 lines
ext_libs/cifti/ft_cifti/@xmltree/private/compile_mex.sh, Shell, 41 lines
ext_libs/cifti/ft_cifti/@xmltree/private/xml_findstr.c, C, 110 lines
ext_libs/cifti/ft_cifti/@xmltree/private/xml_findstr.m, MATLAB, 54 lines
ext_libs/cifti/ft_cifti/@xmltree/private/xml_parser.m, MATLAB, 421 lines
ext_libs/cifti/ft_cifti/@xmltree/root.m, MATLAB, 36 lines
ext_libs/cifti/ft_cifti/@xmltree/save.m, MATLAB, 135 lines
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ext_libs/cifti/ft_cifti/ft_read_cifti.m, MATLAB, 1,016 lines
ext_libs/cifti/ft_cifti/ft_write_cifti.m, MATLAB, 849 lines
ext_libs/cifti/ft_cifti/package.sh, Shell, 126 lines
ext_libs/cifti/ft_cifti/private/copyfields.m, MATLAB, 52 lines
ext_libs/cifti/ft_cifti/private/fetch_url.m, MATLAB, 49 lines
ext_libs/cifti/ft_cifti/private/filetype_check_extension.m, MATLAB, 62 lines
ext_libs/cifti/ft_cifti/private/filetype_check_header.m, MATLAB, 99 lines
ext_libs/cifti/ft_cifti/private/filetype_check_uri.m, MATLAB, 263 lines
ext_libs/cifti/ft_cifti/private/find_outermost_boundary.m, MATLAB, 52 lines
ext_libs/cifti/ft_cifti/private/fixname.m, MATLAB, 51 lines
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ext_libs/cifti/ft_cifti/private/ft_convert_units.m, MATLAB, 267 lines
ext_libs/cifti/ft_cifti/private/ft_datatype.m, MATLAB, 292 lines
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ext_libs/cifti/ft_cifti/private/ft_estimate_units.m, MATLAB, 59 lines
ext_libs/cifti/ft_cifti/private/ft_filetype.m, MATLAB, 1,431 lines
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ext_libs/cifti/ft_cifti/private/ft_read_header.m, MATLAB, 2,352 lines
ext_libs/cifti/ft_cifti/private/ft_read_headshape.m, MATLAB, 1,010 lines
ext_libs/cifti/ft_cifti/private/ft_read_mri.m, MATLAB, 474 lines
ext_libs/cifti/ft_cifti/private/ft_read_sens.m, MATLAB, 378 lines
ext_libs/cifti/ft_cifti/private/ft_read_vol.m, MATLAB, 71 lines
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ext_libs/cifti/ft_cifti/private/ft_voltype.m, MATLAB, 138 lines
ext_libs/cifti/ft_cifti/private/ft_warning.m, MATLAB, 257 lines
ext_libs/cifti/ft_cifti/private/ft_warp_apply.m, MATLAB, 203 lines
ext_libs/cifti/ft_cifti/private/ft_write_headshape.m, MATLAB, 237 lines
ext_libs/cifti/ft_cifti/private/getdimord.m, MATLAB, 611 lines
ext_libs/cifti/ft_cifti/private/getdimsiz.m, MATLAB, 70 lines
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ext_libs/cifti/ft_cifti/private/individual2sn.m, MATLAB, 145 lines
ext_libs/cifti/ft_cifti/private/inflate_file.m, MATLAB, 84 lines
ext_libs/cifti/ft_cifti/private/istrue.m, MATLAB, 42 lines
ext_libs/cifti/ft_cifti/private/keepfields.m, MATLAB, 44 lines
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ext_libs/cifti/ft_cifti/private/pos2transform.m, MATLAB, 34 lines
ext_libs/cifti/ft_cifti/private/read_asa.m, MATLAB, 168 lines
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ext_libs/cifti/ft_cifti/private/read_caret_spec.m, MATLAB, 110 lines
ext_libs/cifti/ft_cifti/private/read_ctf_hc.m, MATLAB, 190 lines
ext_libs/cifti/ft_cifti/private/read_ctf_pos.m, MATLAB, 72 lines
ext_libs/cifti/ft_cifti/private/read_ctf_shape.m, MATLAB, 41 lines
ext_libs/cifti/ft_cifti/private/read_neuromag_hc.m, MATLAB, 165 lines
ext_libs/cifti/ft_cifti/private/read_nifti2_hdr.m, MATLAB, 110 lines
ext_libs/cifti/ft_cifti/private/read_off.m, MATLAB, 57 lines
ext_libs/cifti/ft_cifti/private/read_ply.m, MATLAB, 211 lines
ext_libs/cifti/ft_cifti/private/read_polhemus_fil.m, MATLAB, 101 lines
ext_libs/cifti/ft_cifti/private/read_stl.m, MATLAB, 136 lines
ext_libs/cifti/ft_cifti/private/read_vtk.m, MATLAB, 48 lines
ext_libs/cifti/ft_cifti/private/read_yokogawa_header.m, MATLAB, 223 lines
ext_libs/cifti/ft_cifti/private/read_yokogawa_header_new.m, MATLAB, 232 lines
ext_libs/cifti/ft_cifti/private/refine.m, MATLAB, 186 lines
ext_libs/cifti/ft_cifti/private/removefields.m, MATLAB, 44 lines
ext_libs/cifti/ft_cifti/private/renamefields.m, MATLAB, 56 lines
ext_libs/cifti/ft_cifti/private/sn2individual.m, MATLAB, 64 lines
ext_libs/cifti/ft_cifti/private/surf_to_tetgen.m, MATLAB, 65 lines
ext_libs/cifti/ft_cifti/private/tokenize.m, MATLAB, 83 lines
ext_libs/cifti/ft_cifti/private/write_nifti2_hdr.m, MATLAB, 82 lines
ext_libs/cifti/ft_cifti/private/write_off.m, MATLAB, 58 lines
ext_libs/cifti/ft_cifti/private/write_ply.m, MATLAB, 120 lines
ext_libs/cifti/ft_cifti/private/write_stl.m, MATLAB, 60 lines
ext_libs/cifti/ft_cifti/private/write_vtk.m, MATLAB, 54 lines
ext_libs/cifti/private/ambiguate_dims.m, MATLAB, 33 lines
ext_libs/cifti/private/child_match.m, MATLAB, 15 lines
ext_libs/cifti/private/cifti_diminfo_length.m, MATLAB, 19 lines
ext_libs/cifti/private/cifti_parse_xml.m, MATLAB, 613 lines
ext_libs/cifti/private/cifti_write_xml.m, MATLAB, 327 lines
ext_libs/cifti/private/fread_excepting.m, MATLAB, 8 lines
ext_libs/cifti/private/fwrite_excepting.m, MATLAB, 6 lines
ext_libs/cifti/private/make_nifti2_hdr.m, MATLAB, 41 lines
ext_libs/cifti/private/my_system.m, MATLAB, 36 lines
ext_libs/cifti/private/myargparse.m, MATLAB, 20 lines
ext_libs/cifti/private/myattrs.m, MATLAB, 10 lines
ext_libs/cifti/private/mydelete.m, MATLAB, 9 lines
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ext_libs/cifti/private/mygettext.m, MATLAB, 14 lines
ext_libs/cifti/private/read_nifti2_hdr.m, MATLAB, 183 lines
ext_libs/cifti/private/sanity_check_cdata.m, MATLAB, 29 lines
ext_libs/cifti/private/str2vec.m, MATLAB, 9 lines
ext_libs/cifti/private/write_nifti2_hdr.m, MATLAB, 95 lines
ext_libs/clearXattr.m, MATLAB, 13 lines
ext_libs/corr/ea_bendcorr.m, MATLAB, 134 lines
ext_libs/corr/ea_classSVD.m, MATLAB, 30 lines
ext_libs/corr/ea_distcorr.m, MATLAB, 43 lines
ext_libs/corr/ea_greatsort.m, MATLAB, 23 lines
ext_libs/corr/ea_idealf.m, MATLAB, 18 lines
ext_libs/corr/ea_mahalanobis.m, MATLAB, 96 lines
ext_libs/corr/ea_mahalanobis_cov.m, MATLAB, 8 lines
ext_libs/corr/ea_mcdcov.m, MATLAB, 1,445 lines
ext_libs/corr/ea_skipped_correlation.m, MATLAB, 149 lines
ext_libs/corr/ea_uniran.m, MATLAB, 12 lines
ext_libs/corr/ea_updatecov.m, MATLAB, 41 lines
ext_libs/corr/ea_weightmecov.m, MATLAB, 31 lines
ext_libs/cprintf/ea_cprintf.m, MATLAB, 647 lines
ext_libs/dbsegment/ea_dbsegment.m, MATLAB, 163 lines
ext_libs/dbsegment/ea_dbsegment_menu.m, MATLAB, 27 lines
ext_libs/dcm2nii/ea_dcm2nii.m, MATLAB, 71 lines
ext_libs/dcm2nii/ea_dcm2niix.m, MATLAB, 29 lines
ext_libs/dcm2nii/ea_dcmquery.m, MATLAB, 36 lines
ext_libs/dcm2nii/ea_rocrop.m, MATLAB, 40 lines
ext_libs/dicm2nii/RT_moco.m, MATLAB, 1,048 lines
ext_libs/dicm2nii/anonymize_dicm.m, MATLAB, 143 lines
ext_libs/dicm2nii/dicm2nii.m, MATLAB, 3,103 lines
ext_libs/dicm2nii/dicm_dict.m, MATLAB, 1,353 lines
ext_libs/dicm2nii/dicm_hdr.m, MATLAB, 1,577 lines
ext_libs/dicm2nii/dicm_img.m, MATLAB, 175 lines
ext_libs/dicm2nii/dicm_save.m, MATLAB, 217 lines
ext_libs/dicm2nii/dicm_val_rep.m, MATLAB, 118 lines
ext_libs/dicm2nii/java_dnd.m, MATLAB, 70 lines
ext_libs/dicm2nii/mp4_video.m, MATLAB, 132 lines
ext_libs/dicm2nii/nii_coreg.m, MATLAB, 233 lines
ext_libs/dicm2nii/nii_deface.m, MATLAB, 131 lines
ext_libs/dicm2nii/nii_moco.m, MATLAB, 253 lines
ext_libs/dicm2nii/nii_stc.m, MATLAB, 111 lines
ext_libs/dicm2nii/nii_tool.m, MATLAB, 1,267 lines
ext_libs/dicm2nii/nii_viewer.m, MATLAB, 3,396 lines
ext_libs/dicm2nii/nii_xform.m, MATLAB, 246 lines
ext_libs/dicm2nii/rename_dicm.m, MATLAB, 151 lines
ext_libs/dicm2nii/sort_dicm.m, MATLAB, 86 lines
ext_libs/dicm2nii/write_tsv.m, MATLAB, 60 lines
ext_libs/dragndrop/MLDropTarget.java, Java, 72 lines
ext_libs/dragndrop/dndcontrol.m, MATLAB, 235 lines
ext_libs/ea_columnlegend.m, MATLAB, 140 lines
ext_libs/ea_copyUIAxes.m, MATLAB, 439 lines
ext_libs/ea_crop_img.m, MATLAB, 170 lines
ext_libs/ea_datahash.m, MATLAB, 484 lines
ext_libs/ea_distinguishable_colors.m, MATLAB, 152 lines
ext_libs/ea_intriangulation.m, MATLAB, 328 lines
ext_libs/ea_nonparamreg.m, MATLAB, 151 lines
ext_libs/ea_path_helper.m, MATLAB, 23 lines
ext_libs/ea_plot_arrow.m, MATLAB, 110 lines
ext_libs/ea_plyread.m, MATLAB, 435 lines
ext_libs/ea_prop_test.m, MATLAB, 68 lines
ext_libs/ea_regexpdir.m, MATLAB, 51 lines
ext_libs/ea_robustmean.m, MATLAB, 148 lines
ext_libs/ea_screenshot.m, MATLAB, 310 lines
ext_libs/ea_stlwrite.m, MATLAB, 253 lines
ext_libs/ea_strjoin.m, MATLAB, 55 lines
ext_libs/ea_strsplit.m, MATLAB, 23 lines
ext_libs/ea_trk_read.m, MATLAB, 153 lines
ext_libs/ea_trk_write.m, MATLAB, 82 lines
ext_libs/ea_xcorr.m, MATLAB, 394 lines
ext_libs/export_fig/ImageSelection.java, Java, 38 lines
ext_libs/export_fig/append_pdfs.m, MATLAB, 151 lines
ext_libs/export_fig/copyfig.m, MATLAB, 59 lines
ext_libs/export_fig/crop_borders.m, MATLAB, 157 lines
ext_libs/export_fig/eps2pdf.m, MATLAB, 258 lines
ext_libs/export_fig/export_fig.m, MATLAB, 1,790 lines
ext_libs/export_fig/fix_lines.m, MATLAB, 151 lines
ext_libs/export_fig/ghostscript.m, MATLAB, 208 lines
ext_libs/export_fig/hyperlink.m, MATLAB, 53 lines
ext_libs/export_fig/im2gif.m, MATLAB, 205 lines
ext_libs/export_fig/isolate_axes.m, MATLAB, 130 lines
ext_libs/export_fig/pdf2eps.m, MATLAB, 55 lines
ext_libs/export_fig/pdftops.m, MATLAB, 173 lines
ext_libs/export_fig/print2array.m, MATLAB, 266 lines
ext_libs/export_fig/print2eps.m, MATLAB, 642 lines
ext_libs/export_fig/read_write_entire_textfile.m, MATLAB, 37 lines
ext_libs/export_fig/user_string.m, MATLAB, 117 lines
ext_libs/export_fig/using_hg2.m, MATLAB, 36 lines
ext_libs/fastsurfer/ea_fastsurfer_seg.m, MATLAB, 65 lines
ext_libs/fieldtrip/ea_calc_stiff_matrix_val.m, MATLAB, 7 lines
ext_libs/fig2u3d/dependencies/arclength/arclength.m, MATLAB, 238 lines
ext_libs/fig2u3d/dependencies/cell_extrema/max_cell.m, MATLAB, 55 lines
ext_libs/fig2u3d/dependencies/cell_extrema/min_cell.m, MATLAB, 55 lines
ext_libs/fig2u3d/dependencies/plot_scalings/axes_extremal_xyz.m, MATLAB, 123 lines
ext_libs/fig2u3d/dependencies/plot_scalings/coor_extremals.m, MATLAB, 20 lines
ext_libs/fig2u3d/dependencies/plotmd/plotmd.m, MATLAB, 62 lines
ext_libs/fig2u3d/dependencies/quivermd/quivermd.m, MATLAB, 53 lines
ext_libs/fig2u3d/dependencies/temphold/restorehold.m, MATLAB, 31 lines
ext_libs/fig2u3d/dependencies/temphold/takehold.m, MATLAB, 65 lines
ext_libs/fig2u3d/dependencies/vectorized_meshgrid/domain2meshgrid.m, MATLAB, 107 lines
ext_libs/fig2u3d/dependencies/vectorized_meshgrid/domain2vec.m, MATLAB, 51 lines
ext_libs/fig2u3d/dependencies/vectorized_meshgrid/meshgrid2vec.m, MATLAB, 51 lines
ext_libs/fig2u3d/dependencies/vnorm/vnorm.m, MATLAB, 69 lines
ext_libs/fig2u3d/fig2idtf/auxiliary/check_face_vertex.m, MATLAB, 29 lines
ext_libs/fig2u3d/fig2idtf/auxiliary/check_file_extension.m, MATLAB, 21 lines
ext_libs/fig2u3d/fig2idtf/auxiliary/clear_file_extension.m, MATLAB, 24 lines
ext_libs/fig2u3d/fig2idtf/auxiliary/compute_normal.m, MATLAB, 55 lines
ext_libs/fig2u3d/fig2idtf/auxiliary/create_marker_lines.m, MATLAB, 167 lines
ext_libs/fig2u3d/fig2idtf/auxiliary/cut_line_to_pieces.m, MATLAB, 25 lines
ext_libs/fig2u3d/fig2idtf/auxiliary/get_line_xyz.m, MATLAB, 19 lines
ext_libs/fig2u3d/fig2idtf/auxiliary/isfilextension.m, MATLAB, 23 lines
ext_libs/fig2u3d/fig2idtf/auxiliary/line_pieces.m, MATLAB, 47 lines
ext_libs/fig2u3d/fig2idtf/auxiliary/verbatim.m, MATLAB, 82 lines
ext_libs/fig2u3d/fig2idtf/fig2idtf.m, MATLAB, 108 lines
ext_libs/fig2u3d/fig2idtf/output/idtf_model_nodes.m, MATLAB, 120 lines
ext_libs/fig2u3d/fig2idtf/output/mesh_diffuse_colors.m, MATLAB, 97 lines
ext_libs/fig2u3d/fig2idtf/output/populate_line_resource_str.m, MATLAB, 118 lines
ext_libs/fig2u3d/fig2idtf/output/populate_mesh_resource_str.m, MATLAB, 33 lines
ext_libs/fig2u3d/fig2idtf/output/populate_point_resource_str.m, MATLAB, 115 lines
ext_libs/fig2u3d/fig2idtf/output/shaders_materials_modifiers.m, MATLAB, 181 lines
ext_libs/fig2u3d/fig2idtf/output/single_mesh_resource_str.m, MATLAB, 122 lines
ext_libs/fig2u3d/fig2idtf/preprocess/u3d_pre_contourgroup.m, MATLAB, 183 lines
ext_libs/fig2u3d/fig2idtf/preprocess/u3d_pre_line.m, MATLAB, 228 lines
ext_libs/fig2u3d/fig2idtf/preprocess/u3d_pre_patch.m, MATLAB, 198 lines
ext_libs/fig2u3d/fig2idtf/preprocess/u3d_pre_quivergroup.m, MATLAB, 216 lines
ext_libs/fig2u3d/fig2idtf/preprocess/u3d_pre_surface.m, MATLAB, 194 lines
ext_libs/fig2u3d/fig2pdf/fig2latex.m, MATLAB, 112 lines
ext_libs/fig2u3d/fig2pdf/fig2pdf3d.m, MATLAB, 82 lines
ext_libs/fig2u3d/fig2pdf/latex2pdf3d.m, MATLAB, 56 lines
ext_libs/fig2u3d/fig2pdf/u3d_in_latex.m, MATLAB, 74 lines
ext_libs/fig2u3d/fig2u3d.m, MATLAB, 327 lines
ext_libs/fig2u3d/idtf2u3d/idtf2u3d.m, MATLAB, 71 lines
ext_libs/fig2u3d/view2vws.m, MATLAB, 172 lines
ext_libs/findjobj/findjobj.m, MATLAB, 3,481 lines
ext_libs/findjobj/findjobj_fast.m, MATLAB, 78 lines
ext_libs/freesurfer/MRIfspec.m, MATLAB, 143 lines
ext_libs/freesurfer/MRIread.m, MATLAB, 278 lines
ext_libs/freesurfer/ea_SegmentThalamicNuclei.m, MATLAB, 2,028 lines
ext_libs/freesurfer/ea_importfssegmentations.m, MATLAB, 139 lines
ext_libs/freesurfer/ea_load_mgh.m, MATLAB, 274 lines
ext_libs/freesurfer/ea_runfreesurfer.m, MATLAB, 82 lines
ext_libs/freesurfer/fread3.m, MATLAB, 30 lines
ext_libs/freesurfer/freesurfer_read_surf.m, MATLAB, 142 lines
ext_libs/freesurfer/load_nifti.m, MATLAB, 166 lines
ext_libs/freesurfer/load_nifti_hdr.m, MATLAB, 216 lines
ext_libs/freesurfer/read_annotation.m, MATLAB, 183 lines
ext_libs/freesurfer/read_surf.m, MATLAB, 78 lines
ext_libs/freesurfer/strlen.m, MATLAB, 35 lines
ext_libs/freesurfer/vox2ras_0to1.m, MATLAB, 49 lines
ext_libs/freesurfer/vox2ras_tkreg.m, MATLAB, 53 lines
ext_libs/fsl/ea_bet.m, MATLAB, 53 lines
ext_libs/fsl/ea_flirt.m, MATLAB, 168 lines
ext_libs/fsl/ea_flirtbbr.m, MATLAB, 173 lines
ext_libs/fsl/ea_fnirt.m, MATLAB, 129 lines
ext_libs/fsl/ea_fsl_apply_coregistration.m, MATLAB, 47 lines
ext_libs/fsl/ea_fsl_apply_normalization.m, MATLAB, 115 lines
ext_libs/fsl/ea_fsl_apply_normalization_to_points.m, MATLAB, 36 lines
ext_libs/fsl/ea_fsl_img2imgcoord.m, MATLAB, 77 lines
ext_libs/fsl/ea_fsl_reslice.m, MATLAB, 34 lines
ext_libs/fsl/ea_fslhd.m, MATLAB, 63 lines
ext_libs/fsl/ea_getfslnormfuns.m, MATLAB, 5 lines
ext_libs/ftracking/ea_DTI.m, MATLAB, 155 lines
ext_libs/ftracking/ea_FT.m, MATLAB, 214 lines
ext_libs/ftracking/ea_plot3t.m, MATLAB, 326 lines
ext_libs/gencode/ea_gencode.m, MATLAB, 223 lines
ext_libs/gencode/ea_gencode_rvalue.m, MATLAB, 133 lines
ext_libs/gencode/ea_gencode_substruct.m, MATLAB, 71 lines
ext_libs/gmsh/gmsh_io/ch_cap.m, MATLAB, 32 lines
ext_libs/gmsh/gmsh_io/ch_eqi.m, MATLAB, 36 lines
ext_libs/gmsh/gmsh_io/gmsh_data_read.m, MATLAB, 127 lines
ext_libs/gmsh/gmsh_io/gmsh_mesh1d_write.m, MATLAB, 95 lines
ext_libs/gmsh/gmsh_io/gmsh_mesh2d_element_data_example.m, MATLAB, 56 lines
ext_libs/gmsh/gmsh_io/gmsh_mesh2d_element_size_example.m, MATLAB, 29 lines
ext_libs/gmsh/gmsh_io/gmsh_mesh2d_node_data_example.m, MATLAB, 52 lines
ext_libs/gmsh/gmsh_io/gmsh_mesh2d_node_size_example.m, MATLAB, 29 lines
ext_libs/gmsh/gmsh_io/gmsh_mesh2d_write.m, MATLAB, 99 lines
ext_libs/gmsh/gmsh_io/gmsh_mesh3d_write.m, MATLAB, 174 lines
ext_libs/gmsh/gmsh_io/gmsh_size_read.m, MATLAB, 146 lines
ext_libs/gmsh/gmsh_io/gmsh_to_fem.m, MATLAB, 726 lines
ext_libs/gmsh/gmsh_io/i4mat_transpose_print.m, MATLAB, 30 lines
ext_libs/gmsh/gmsh_io/i4mat_transpose_print_some.m, MATLAB, 76 lines
ext_libs/gmsh/gmsh_io/mesh_base_one.m, MATLAB, 66 lines
ext_libs/gmsh/gmsh_io/r8mat_transpose_print_some.m, MATLAB, 75 lines
ext_libs/gmsh/gmsh_io/s_begin.m, MATLAB, 110 lines
ext_libs/gmsh/gmsh_io/s_len_trim.m, MATLAB, 35 lines
ext_libs/gmsh/gmsh_io/timestamp.m, MATLAB, 25 lines
ext_libs/graphvar/ea_graphvarmat2mat.m, MATLAB, 29 lines
ext_libs/isMatlabVer/isMatlabVer.c, C, 207 lines
ext_libs/isMatlabVer/isMatlabVer.m, MATLAB, 40 lines
ext_libs/iso2mesh/advancefront.m, MATLAB, 52 lines
ext_libs/iso2mesh/bbxflatsegment.m, MATLAB, 87 lines
ext_libs/iso2mesh/binsurface.m, MATLAB, 94 lines
ext_libs/iso2mesh/bwislands.m, MATLAB, 34 lines
ext_libs/iso2mesh/cgals2m.m, MATLAB, 77 lines
ext_libs/iso2mesh/cgalv2m.m, MATLAB, 103 lines
ext_libs/iso2mesh/decodevarname.m, MATLAB, 72 lines
ext_libs/iso2mesh/deislands2d.m, MATLAB, 56 lines
ext_libs/iso2mesh/deislands3d.m, MATLAB, 35 lines
ext_libs/iso2mesh/delendelem.m, MATLAB, 18 lines
ext_libs/iso2mesh/deletemeshfile.m, MATLAB, 30 lines
ext_libs/iso2mesh/doc/gendoc.sh, Shell, 11 lines
ext_libs/iso2mesh/edgeneighbors.m, MATLAB, 94 lines
ext_libs/iso2mesh/elemvolume.m, MATLAB, 45 lines
ext_libs/iso2mesh/encodevarname.m, MATLAB, 63 lines
ext_libs/iso2mesh/extractloops.m, MATLAB, 75 lines
ext_libs/iso2mesh/faceneighbors.m, MATLAB, 97 lines
ext_libs/iso2mesh/fast_match_bracket.m, MATLAB, 65 lines
ext_libs/iso2mesh/fillholes3d.m, MATLAB, 36 lines
ext_libs/iso2mesh/fillsurf.m, MATLAB, 20 lines
ext_libs/iso2mesh/finddisconnsurf.m, MATLAB, 37 lines
ext_libs/iso2mesh/flatsegment.m, MATLAB, 47 lines
ext_libs/iso2mesh/gendocs.sh, Shell, 89 lines
ext_libs/iso2mesh/getintersecttri.m, MATLAB, 31 lines
ext_libs/iso2mesh/getoptkey.m, MATLAB, 29 lines
ext_libs/iso2mesh/getplanefrom3pt.m, MATLAB, 27 lines
ext_libs/iso2mesh/getvarfrom.m, MATLAB, 33 lines
ext_libs/iso2mesh/highordertet.m, MATLAB, 37 lines
ext_libs/iso2mesh/imedge3d.m, MATLAB, 37 lines
ext_libs/iso2mesh/innersurf.m, MATLAB, 27 lines
ext_libs/iso2mesh/insurface.m, MATLAB, 26 lines
ext_libs/iso2mesh/internalpoint.m, MATLAB, 40 lines
ext_libs/iso2mesh/iso2meshver.m, MATLAB, 41 lines
ext_libs/iso2mesh/isoctavemesh.m, MATLAB, 19 lines
ext_libs/iso2mesh/jdatadecode.m, MATLAB, 464 lines
ext_libs/iso2mesh/jdataencode.m, MATLAB, 398 lines
ext_libs/iso2mesh/jsonopt.m, MATLAB, 36 lines
ext_libs/iso2mesh/latticegrid.m, MATLAB, 74 lines
ext_libs/iso2mesh/loadjson.m, MATLAB, 552 lines
ext_libs/iso2mesh/loadubjson.m, MATLAB, 39 lines
ext_libs/iso2mesh/m2v.m, MATLAB, 16 lines
ext_libs/iso2mesh/match_bracket.m, MATLAB, 48 lines
ext_libs/iso2mesh/maxsurf.m, MATLAB, 46 lines
ext_libs/iso2mesh/mcpath.m, MATLAB, 37 lines
ext_libs/iso2mesh/mergemesh.m, MATLAB, 76 lines
ext_libs/iso2mesh/mergestruct.m, MATLAB, 33 lines
ext_libs/iso2mesh/mergesurf.m, MATLAB, 40 lines
ext_libs/iso2mesh/mesh2mask.m, MATLAB, 137 lines
ext_libs/iso2mesh/mesh2vol.m, MATLAB, 105 lines
ext_libs/iso2mesh/meshabox.m, MATLAB, 34 lines
ext_libs/iso2mesh/meshacylinder.m, MATLAB, 79 lines
ext_libs/iso2mesh/meshanellip.m, MATLAB, 65 lines
ext_libs/iso2mesh/meshasphere.m, MATLAB, 37 lines
ext_libs/iso2mesh/meshcentroid.m, MATLAB, 38 lines
ext_libs/iso2mesh/meshcheckrepair.m, MATLAB, 86 lines
ext_libs/iso2mesh/meshconn.m, MATLAB, 39 lines
ext_libs/iso2mesh/meshcylinders.m, MATLAB, 61 lines
ext_libs/iso2mesh/meshedge.m, MATLAB, 33 lines
ext_libs/iso2mesh/mesheuler.m, MATLAB, 35 lines
ext_libs/iso2mesh/meshgrid5.m, MATLAB, 84 lines
ext_libs/iso2mesh/meshgrid6.m, MATLAB, 79 lines
ext_libs/iso2mesh/meshquality.m, MATLAB, 50 lines
ext_libs/iso2mesh/meshrefine.m, MATLAB, 255 lines
ext_libs/iso2mesh/meshreorient.m, MATLAB, 27 lines
ext_libs/iso2mesh/meshresample.m, MATLAB, 84 lines
ext_libs/iso2mesh/meshunitsphere.m, MATLAB, 55 lines
ext_libs/iso2mesh/mwpath.m, MATLAB, 47 lines
ext_libs/iso2mesh/neighborelem.m, MATLAB, 36 lines
ext_libs/iso2mesh/nestbracket2dim.m, MATLAB, 79 lines
ext_libs/iso2mesh/nodevolume.m, MATLAB, 31 lines
ext_libs/iso2mesh/orderloopedge.m, MATLAB, 37 lines
ext_libs/iso2mesh/orthdisk.m, MATLAB, 43 lines
ext_libs/iso2mesh/outersurf.m, MATLAB, 40 lines
ext_libs/iso2mesh/plotedges.m, MATLAB, 75 lines
ext_libs/iso2mesh/plotmesh.m, MATLAB, 231 lines
ext_libs/iso2mesh/plotsurf.m, MATLAB, 135 lines
ext_libs/iso2mesh/plottetra.m, MATLAB, 77 lines
ext_libs/iso2mesh/qmeshcut.m, MATLAB, 205 lines
ext_libs/iso2mesh/raysurf.m, MATLAB, 73 lines
ext_libs/iso2mesh/raytrace.m, MATLAB, 72 lines
ext_libs/iso2mesh/readasc.m, MATLAB, 46 lines
ext_libs/iso2mesh/readgts.m, MATLAB, 43 lines
ext_libs/iso2mesh/readinr.m, MATLAB, 93 lines
ext_libs/iso2mesh/readmedit.m, MATLAB, 39 lines
ext_libs/iso2mesh/readoff.m, MATLAB, 70 lines
ext_libs/iso2mesh/readsmf.m, MATLAB, 38 lines
ext_libs/iso2mesh/readtetgen.m, MATLAB, 56 lines
ext_libs/iso2mesh/remeshsurf.m, MATLAB, 75 lines
ext_libs/iso2mesh/removedupelem.m, MATLAB, 21 lines
ext_libs/iso2mesh/removedupnodes.m, MATLAB, 30 lines
ext_libs/iso2mesh/removeisolatednode.m, MATLAB, 38 lines
ext_libs/iso2mesh/removeisolatedsurf.m, MATLAB, 39 lines
ext_libs/iso2mesh/rotatevec3d.m, MATLAB, 42 lines
ext_libs/iso2mesh/rotmat2vec.m, MATLAB, 35 lines
ext_libs/iso2mesh/s2m.m, MATLAB, 48 lines
ext_libs/iso2mesh/s2v.m, MATLAB, 51 lines
ext_libs/iso2mesh/sample/demo_cgalmesher.m, MATLAB, 52 lines
ext_libs/iso2mesh/sample/demo_directplc_ex1.m, MATLAB, 51 lines
ext_libs/iso2mesh/sample/demo_grayscale_ex1.m, MATLAB, 28 lines
ext_libs/iso2mesh/sample/demo_helloworld.m, MATLAB, 59 lines
ext_libs/iso2mesh/sample/demo_insert_nodes.m, MATLAB, 35 lines
ext_libs/iso2mesh/sample/demo_label_sizing.m, MATLAB, 50 lines
ext_libs/iso2mesh/sample/demo_merge_surfaces.m, MATLAB, 37 lines
ext_libs/iso2mesh/sample/demo_mesh_smoothing.m, MATLAB, 110 lines
ext_libs/iso2mesh/sample/demo_qmeshcut_ex1.m, MATLAB, 79 lines
ext_libs/iso2mesh/sample/demo_refine_spherebox_mesh.m, MATLAB, 72 lines
ext_libs/iso2mesh/sample/demo_remesh_surface.m, MATLAB, 33 lines
ext_libs/iso2mesh/sample/demo_shortcut_ex1.m, MATLAB, 39 lines
ext_libs/iso2mesh/sample/demo_surf2mesh_ex1.m, MATLAB, 21 lines
ext_libs/iso2mesh/sample/demo_surf2vol_ex1.m, MATLAB, 60 lines
ext_libs/iso2mesh/sample/demo_vol2mesh_ex1.m, MATLAB, 29 lines
ext_libs/iso2mesh/sample/demo_vol2mesh_ex1b.m, MATLAB, 30 lines
ext_libs/iso2mesh/sample/demo_vol2mesh_ex1c.m, MATLAB, 30 lines
ext_libs/iso2mesh/sample/demo_vol2mesh_ex2.m, MATLAB, 24 lines
ext_libs/iso2mesh/sample/demo_vol2mesh_ex3.m, MATLAB, 53 lines
ext_libs/iso2mesh/saveabaqus.m, MATLAB, 108 lines
ext_libs/iso2mesh/saveasc.m, MATLAB, 26 lines
ext_libs/iso2mesh/savebinstl.m, MATLAB, 69 lines
ext_libs/iso2mesh/savedxf.m, MATLAB, 59 lines
ext_libs/iso2mesh/savegts.m, MATLAB, 35 lines
ext_libs/iso2mesh/saveinr.m, MATLAB, 46 lines
ext_libs/iso2mesh/savejmesh.m, MATLAB, 135 lines
ext_libs/iso2mesh/savejson.m, MATLAB, 813 lines
ext_libs/iso2mesh/savemedit.m, MATLAB, 47 lines
ext_libs/iso2mesh/savemphtxt.m, MATLAB, 68 lines
ext_libs/iso2mesh/savemsh.m, MATLAB, 148 lines
ext_libs/iso2mesh/saveoff.m, MATLAB, 29 lines
ext_libs/iso2mesh/savesmf.m, MATLAB, 21 lines
ext_libs/iso2mesh/savestl.m, MATLAB, 59 lines
ext_libs/iso2mesh/savesurfpoly.m, MATLAB, 238 lines
ext_libs/iso2mesh/savetetgenele.m, MATLAB, 36 lines
ext_libs/iso2mesh/savetetgennode.m, MATLAB, 35 lines
ext_libs/iso2mesh/saveubjson.m, MATLAB, 71 lines
ext_libs/iso2mesh/savevrml.m, MATLAB, 54 lines
ext_libs/iso2mesh/smoothbinvol.m, MATLAB, 56 lines
ext_libs/iso2mesh/smoothsurf.m, MATLAB, 98 lines
ext_libs/iso2mesh/sms.m, MATLAB, 34 lines
ext_libs/iso2mesh/sortmesh.m, MATLAB, 55 lines
ext_libs/iso2mesh/struct2jdata.m, MATLAB, 96 lines
ext_libs/iso2mesh/surf2mesh.m, MATLAB, 148 lines
ext_libs/iso2mesh/surf2vol.m, MATLAB, 84 lines
ext_libs/iso2mesh/surf2volz.m, MATLAB, 65 lines
ext_libs/iso2mesh/surfaceclean.m, MATLAB, 43 lines
ext_libs/iso2mesh/surfacenorm.m, MATLAB, 31 lines
ext_libs/iso2mesh/surfboolean.m, MATLAB, 214 lines
ext_libs/iso2mesh/surfdiffuse.m, MATLAB, 62 lines
ext_libs/iso2mesh/surfedge.m, MATLAB, 65 lines
ext_libs/iso2mesh/surfinterior.m, MATLAB, 45 lines
ext_libs/iso2mesh/surfpart.m, MATLAB, 43 lines
ext_libs/iso2mesh/surfplane.m, MATLAB, 25 lines
ext_libs/iso2mesh/surfreorient.m, MATLAB, 22 lines
ext_libs/iso2mesh/surfseeds.m, MATLAB, 26 lines
ext_libs/iso2mesh/surfvolume.m, MATLAB, 29 lines
ext_libs/iso2mesh/thickenbinvol.m, MATLAB, 34 lines
ext_libs/iso2mesh/thinbinvol.m, MATLAB, 49 lines
ext_libs/iso2mesh/uniqedges.m, MATLAB, 33 lines
ext_libs/iso2mesh/v2m.m, MATLAB, 18 lines
ext_libs/iso2mesh/v2s.m, MATLAB, 35 lines
ext_libs/iso2mesh/varargin2struct.m, MATLAB, 40 lines
ext_libs/iso2mesh/vol2mesh.m, MATLAB, 75 lines
ext_libs/iso2mesh/vol2restrictedtri.m, MATLAB, 54 lines
ext_libs/iso2mesh/vol2surf.m, MATLAB, 240 lines
ext_libs/iso2mesh/volface.m, MATLAB, 22 lines
ext_libs/iso2mesh/volmap2mesh.m, MATLAB, 30 lines
ext_libs/mArrow3.m, MATLAB, 162 lines
ext_libs/massunivariate/ea_fast_t1.m, MATLAB, 90 lines
ext_libs/massunivariate/ea_fast_t2.m, MATLAB, 98 lines
ext_libs/mesoft/GTdefaults.m, MATLAB, 125 lines
ext_libs/mesoft/KummerComplex/KummerComplex.m, MATLAB, 231 lines
ext_libs/mesoft/KummerComplex/cgama.m, MATLAB, 127 lines
ext_libs/mesoft/SH2IC_mises.m, MATLAB, 45 lines
ext_libs/mesoft/aux_mfiles/chooseThreshold_stackview.m, MATLAB, 153 lines
ext_libs/mesoft/aux_mfiles/cmult.m, MATLAB, 30 lines
ext_libs/mesoft/aux_mfiles/colPlot3D.m, MATLAB, 27 lines
ext_libs/mesoft/aux_mfiles/fileexists.m, MATLAB, 19 lines
ext_libs/mesoft/aux_mfiles/myhist.m, MATLAB, 8 lines
ext_libs/mesoft/aux_mfiles/reslice_nifti.m, MATLAB, 33 lines
ext_libs/mesoft/aux_mfiles/save_mrstruct_as_nifti.m, MATLAB, 14 lines
ext_libs/mesoft/aux_mfiles/sfigure.m, MATLAB, 18 lines
ext_libs/mesoft/aux_mfiles/showbTensor.m, MATLAB, 11 lines
ext_libs/mesoft/ccode/AccumulateTrilin.cpp, C++, 115 lines
ext_libs/mesoft/ccode/AccumulateTrilinWeighted.cpp, C++, 91 lines
ext_libs/mesoft/ccode/BuildFibres.cpp, C++, 276 lines
ext_libs/mesoft/ccode/EnergyComputerBase.cpp, C++, 539 lines
ext_libs/mesoft/ccode/EnergyComputer_connec.cpp, C++, 714 lines
ext_libs/mesoft/ccode/MersenneTwister.h, C/C++, 607 lines
ext_libs/mesoft/ccode/ParticleGrid.cpp, C++, 778 lines
ext_libs/mesoft/ccode/RJMCMCBase.cpp, C++, 438 lines
ext_libs/mesoft/ccode/RJMCMC_randshift.cpp, C++, 856 lines
ext_libs/mesoft/ccode/SelectCorticalFibers.cpp, C++, 95 lines
ext_libs/mesoft/ccode/SphereInterpolator.cpp, C++, 150 lines
ext_libs/mesoft/ccode/auxilary_classes.cpp, C++, 395 lines
ext_libs/mesoft/ccode/ft_make.m, MATLAB, 19 lines
ext_libs/mesoft/ccode/pcRJMCMC.cpp, C++, 308 lines
ext_libs/mesoft/ccode/printTOstderr.cpp, C++, 27 lines
ext_libs/mesoft/ccode/reparametrize_arclen.cpp, C++, 154 lines
ext_libs/mesoft/ccode/wintime.h, C/C++, 45 lines
ext_libs/mesoft/chiLogLik.m, MATLAB, 9 lines
ext_libs/mesoft/compMesoParams.m, MATLAB, 110 lines
ext_libs/mesoft/computeParameterMaps.m, MATLAB, 460 lines
ext_libs/mesoft/computeWatsonSH.m, MATLAB, 35 lines
ext_libs/mesoft/correctRician.m, MATLAB, 18 lines
ext_libs/mesoft/createDWIHashnum.m, MATLAB, 22 lines
ext_libs/mesoft/createInteractionLUTs.m, MATLAB, 27 lines
ext_libs/mesoft/createModelModelLUT.m, MATLAB, 279 lines
ext_libs/mesoft/createModelModelLUT_classical.m, MATLAB, 88 lines
ext_libs/mesoft/createModelSignalLUT.m, MATLAB, 155 lines
ext_libs/mesoft/createModelSignalLUT_classical.m, MATLAB, 30 lines
ext_libs/mesoft/createStopButton.m, MATLAB, 24 lines
ext_libs/mesoft/createWeightingScheme.m, MATLAB, 29 lines
ext_libs/mesoft/createjointLUTs.m, MATLAB, 231 lines
ext_libs/mesoft/csd.m, MATLAB, 217 lines
ext_libs/mesoft/dispstick.m, MATLAB, 258 lines
ext_libs/mesoft/editParamStruct.m, MATLAB, 65 lines
ext_libs/mesoft/erfi.m, MATLAB, 21 lines
ext_libs/mesoft/fitDispersion.m, MATLAB, 141 lines
ext_libs/mesoft/fitWholeBrainDispersion.m, MATLAB, 66 lines
ext_libs/mesoft/ftr2FDmaps.m, MATLAB, 126 lines
ext_libs/mesoft/genPhan.m, MATLAB, 154 lines
ext_libs/mesoft/init_mesoft.m, MATLAB, 12 lines
ext_libs/mesoft/loadData_mat.m, MATLAB, 58 lines
ext_libs/mesoft/loadData_nii.m, MATLAB, 162 lines
ext_libs/mesoft/loadmask.m, MATLAB, 88 lines
ext_libs/mesoft/mesoGT_tool.m, MATLAB, 1,483 lines
ext_libs/mesoft/mf_sample.m, MATLAB, 51 lines
ext_libs/mesoft/myerf.m, MATLAB, 6 lines
ext_libs/mesoft/numevalsol.m, MATLAB, 18 lines
ext_libs/mesoft/saveFTR.m, MATLAB, 170 lines
ext_libs/mesoft/savemovie_asgif.m, MATLAB, 17 lines
ext_libs/mesoft/selectCortexFibers.m, MATLAB, 16 lines
ext_libs/mesoft/showTractStats.m, MATLAB, 286 lines
ext_libs/mesoft/sphereInterpolation/computeLUTs.m, MATLAB, 11 lines
ext_libs/mesoft/sphereInterpolation/sphereInterpolLUT.m, MATLAB, 207 lines
ext_libs/mesoft/stackview_compare.m, MATLAB, 322 lines
ext_libs/mesoft/stackview_compare_test.m, MATLAB, 224 lines
ext_libs/mesoft/stackview_meso.m, MATLAB, 383 lines
ext_libs/mesoft/stopButtonOnTop.m, MATLAB, 3 lines
ext_libs/mesoft/stopButtonPressed.m, MATLAB, 2 lines
ext_libs/mesoft/testDispEffect.m, MATLAB, 51 lines
ext_libs/mesoft/trainmodel.m, MATLAB, 145 lines
ext_libs/mesoft/trainmodel2.m, MATLAB, 180 lines
ext_libs/mesoft/trainmodel3.m, MATLAB, 211 lines
ext_libs/mesoft/trainmodel_PS.m, MATLAB, 247 lines
ext_libs/mesoft/trainmodel_PS2.m, MATLAB, 212 lines
ext_libs/myslicer/image3.m, MATLAB, 152 lines
ext_libs/myslicer/slice3i.m, MATLAB, 223 lines
ext_libs/nan/ea_nanmax.m, MATLAB, 67 lines
ext_libs/nan/ea_nanmean.m, MATLAB, 54 lines
ext_libs/nan/ea_nanmedian.m, MATLAB, 78 lines
ext_libs/nan/ea_nanmin.m, MATLAB, 67 lines
ext_libs/nan/ea_nansem.m, MATLAB, 49 lines
ext_libs/nan/ea_nanstd.m, MATLAB, 80 lines
ext_libs/nan/ea_nansum.m, MATLAB, 51 lines
ext_libs/nan/ea_nanvar.m, MATLAB, 80 lines
ext_libs/nan/ea_nanzscore.m, MATLAB, 27 lines
ext_libs/nan/ea_nanzscore_sampled.m, MATLAB, 20 lines
ext_libs/nii_lib/bipolar.m, MATLAB, 94 lines
ext_libs/nii_lib/bresenham_line3d.m, MATLAB, 189 lines
ext_libs/nii_lib/clip_nii.m, MATLAB, 115 lines
ext_libs/nii_lib/collapse_nii_scan.m, MATLAB, 260 lines
ext_libs/nii_lib/ea_affine.m, MATLAB, 509 lines
ext_libs/nii_lib/ea_flip_lr.m, MATLAB, 84 lines
ext_libs/nii_lib/ea_load_nii_hdr.m, MATLAB, 280 lines
ext_libs/nii_lib/ea_load_untouch_nii.m, MATLAB, 197 lines
ext_libs/nii_lib/ea_make_nii.m, MATLAB, 256 lines
ext_libs/nii_lib/ea_ori_load_nii.m, MATLAB, 198 lines
ext_libs/nii_lib/ea_pad_nii.m, MATLAB, 141 lines
ext_libs/nii_lib/ea_reslice_nii.m, MATLAB, 361 lines
ext_libs/nii_lib/ea_save_nii.m, MATLAB, 286 lines
ext_libs/nii_lib/ea_save_untouch_nii.m, MATLAB, 232 lines
ext_libs/nii_lib/expand_nii_scan.m, MATLAB, 48 lines
ext_libs/nii_lib/extra_nii_hdr.m, MATLAB, 255 lines
ext_libs/nii_lib/flip_lr.m, MATLAB, 84 lines
ext_libs/nii_lib/get_nii_frame.m, MATLAB, 164 lines
ext_libs/nii_lib/load_nii.m, MATLAB, 198 lines
ext_libs/nii_lib/load_nii_ext.m, MATLAB, 207 lines
ext_libs/nii_lib/load_nii_img.m, MATLAB, 392 lines
ext_libs/nii_lib/load_untouch0_nii_hdr.m, MATLAB, 200 lines
ext_libs/nii_lib/load_untouch_header_only.m, MATLAB, 187 lines
ext_libs/nii_lib/load_untouch_nii.m, MATLAB, 191 lines
ext_libs/nii_lib/load_untouch_nii_hdr.m, MATLAB, 217 lines
ext_libs/nii_lib/load_untouch_nii_img.m, MATLAB, 468 lines
ext_libs/nii_lib/make_ana.m, MATLAB, 210 lines
ext_libs/nii_lib/make_nii.m, MATLAB, 256 lines
ext_libs/nii_lib/mat_into_hdr.m, MATLAB, 83 lines
ext_libs/nii_lib/rri_file_menu.m, MATLAB, 176 lines
ext_libs/nii_lib/rri_orient.m, MATLAB, 95 lines
ext_libs/nii_lib/rri_orient_ui.m, MATLAB, 251 lines
ext_libs/nii_lib/rri_select_file.m, MATLAB, 636 lines
ext_libs/nii_lib/rri_xhair.m, MATLAB, 92 lines
ext_libs/nii_lib/rri_zoom_menu.m, MATLAB, 33 lines
ext_libs/nii_lib/save_nii.m, MATLAB, 286 lines
ext_libs/nii_lib/save_nii_ext.m, MATLAB, 38 lines
ext_libs/nii_lib/save_nii_hdr.m, MATLAB, 227 lines
ext_libs/nii_lib/save_untouch0_nii_hdr.m, MATLAB, 219 lines
ext_libs/nii_lib/save_untouch_nii.m, MATLAB, 232 lines
ext_libs/nii_lib/save_untouch_nii_hdr.m, MATLAB, 207 lines
ext_libs/nii_lib/save_untouch_slice.m, MATLAB, 580 lines
ext_libs/nii_lib/unxform_nii.m, MATLAB, 40 lines
ext_libs/nii_lib/verify_nii_ext.m, MATLAB, 45 lines
ext_libs/nii_lib/view_nii.m, MATLAB, 4,902 lines
ext_libs/nii_lib/view_nii_menu.m, MATLAB, 480 lines
ext_libs/nii_lib/xform_nii.m, MATLAB, 521 lines
ext_libs/othercolor/othercolor.m, MATLAB, 155 lines
ext_libs/plywrite.m, MATLAB, 123 lines
ext_libs/pol2vox/polygon2voxel.m, MATLAB, 163 lines
ext_libs/pol2vox/polygon2voxel_double.c, C, 192 lines
ext_libs/pol2vox/polygon2voxel_double.m, MATLAB, 109 lines
ext_libs/rdir/enhanced_rdir.m, MATLAB, 65 lines
ext_libs/rdir/rdir.m, MATLAB, 438 lines
ext_libs/readyaml/readyaml.m, MATLAB, 291 lines
ext_libs/regexpdir.m, MATLAB, 69 lines
ext_libs/segment/ea_newseg.m, MATLAB, 94 lines
ext_libs/segment/ea_spm_preproc_run.m, MATLAB, 288 lines
ext_libs/segment/ea_spm_preproc_write8.m, MATLAB, 479 lines
ext_libs/smoothpatch/ea_smoothpatch.m, MATLAB, 93 lines
ext_libs/smoothpatch/ea_smoothpatch_curvature_double.c, C, 231 lines
ext_libs/smoothpatch/ea_smoothpatch_inversedistance_double.c, C, 213 lines
ext_libs/smoothpatch/ea_vertex_neighbours.m, MATLAB, 8 lines
ext_libs/smoothpatch/ea_vertex_neighbours_double.c, C, 215 lines
ext_libs/smoothpatch/ea_vertex_neighbours_double.m, MATLAB, 76 lines
ext_libs/smoothpatch/make.m, MATLAB, 11 lines
ext_libs/spm/ea_patch_spm.m, MATLAB, 75 lines
ext_libs/spm/ea_spm_reslice.m, MATLAB, 382 lines
ext_libs/support/ea_fix_runtimepath.m, MATLAB, 31 lines
ext_libs/support/ea_libs_helper.m, MATLAB, 47 lines
ext_libs/surfice/Resources/script/basic_paint_surface.py, Python, 12 lines
ext_libs/surfice/Resources/script/contour.py, Python, 14 lines
ext_libs/surfice/Resources/script/create_atlas.py, Python, 14 lines
ext_libs/surfice/Resources/script/fmri_mesh.py, Python, 8 lines
ext_libs/surfice/Resources/script/frontal_atlas.py, Python, 11 lines
ext_libs/surfice/Resources/script/help.py, Python, 7 lines
ext_libs/surfice/Resources/script/hide_curves.py, Python, 8 lines
ext_libs/surfice/Resources/script/matcapMesh.py, Python, 11 lines
ext_libs/surfice/Resources/script/matcapPainted.py, Python, 12 lines
ext_libs/surfice/Resources/script/matcapSubcortical.py, Python, 7 lines
ext_libs/surfice/Resources/script/mesh.py, Python, 9 lines
ext_libs/surfice/Resources/script/node.py, Python, 19 lines
ext_libs/surfice/Resources/script/scalp.py, Python, 18 lines
ext_libs/surfice/Resources/script/shaders.py, Python, 34 lines
ext_libs/surfice/Resources/script/subcortical.py, Python, 38 lines
ext_libs/surfice/Resources/script/track.py, Python, 10 lines
ext_libs/surfice/Resources/script/walnut.py, Python, 17 lines
ext_libs/surfice/ea_atlvis_surfice.m, MATLAB, 13 lines
ext_libs/surfice/ea_elvis_surfice.m, MATLAB, 15 lines
ext_libs/surfice/ea_gensurfice_temps.m, MATLAB, 60 lines
ext_libs/surfice/ea_getsurficeplots.m, MATLAB, 27 lines
ext_libs/surfice/ea_sfc_getautothresh.m, MATLAB, 29 lines
ext_libs/surfice/ea_sfc_setthreshs.m, MATLAB, 131 lines
ext_libs/surfice/ea_surfice.m, MATLAB, 65 lines
ext_libs/surfice/ea_surfice_heatmap_menu.m, MATLAB, 22 lines
ext_libs/surfice/ea_surficeoverlay.m, MATLAB, 140 lines
ext_libs/surfice/surfice.app/Contents/Resources/script/basic_paint_surface.py, Python, 12 lines
ext_libs/surfice/surfice.app/Contents/Resources/script/contour.py, Python, 14 lines
ext_libs/surfice/surfice.app/Contents/Resources/script/create_atlas.py, Python, 14 lines
ext_libs/surfice/surfice.app/Contents/Resources/script/fmri_mesh.py, Python, 8 lines
ext_libs/surfice/surfice.app/Contents/Resources/script/frontal_atlas.py, Python, 11 lines
ext_libs/surfice/surfice.app/Contents/Resources/script/help.py, Python, 7 lines
ext_libs/surfice/surfice.app/Contents/Resources/script/hide_curves.py, Python, 8 lines
ext_libs/surfice/surfice.app/Contents/Resources/script/matcapMesh.py, Python, 11 lines
ext_libs/surfice/surfice.app/Contents/Resources/script/matcapPainted.py, Python, 12 lines
ext_libs/surfice/surfice.app/Contents/Resources/script/matcapSubcortical.py, Python, 7 lines
ext_libs/surfice/surfice.app/Contents/Resources/script/mesh.py, Python, 9 lines
ext_libs/surfice/surfice.app/Contents/Resources/script/node.py, Python, 19 lines
ext_libs/surfice/surfice.app/Contents/Resources/script/scalp.py, Python, 18 lines
ext_libs/surfice/surfice.app/Contents/Resources/script/shaders.py, Python, 34 lines
ext_libs/surfice/surfice.app/Contents/Resources/script/subcortical.py, Python, 38 lines
ext_libs/surfice/surfice.app/Contents/Resources/script/track.py, Python, 10 lines
ext_libs/surfice/surfice.app/Contents/Resources/script/walnut.py, Python, 17 lines
ext_libs/unring/ea_unring.m, MATLAB, 23 lines
ext_libs/unring/ringRm.cpp, C++, 639 lines
ext_libs/unring/ur_make.m, MATLAB, 9 lines
ext_libs/uw_overlay/uw_overlay_bwgui.m, MATLAB, 932 lines
ext_libs/uw_overlay/uw_overlay_bwthresh.m, MATLAB, 251 lines
ext_libs/uw_overlay/uw_overlay_convert2dicom.m, MATLAB, 705 lines
ext_libs/uw_overlay/xform_nii.m, MATLAB, 349 lines
ext_libs/wavelet_fusion/dwt3.m, MATLAB, 34 lines
ext_libs/wavelet_fusion/ea_waveletfusion.m, MATLAB, 166 lines
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ext_libs/wavelet_fusion/idwt3.m, MATLAB, 35 lines
ext_libs/wavelet_fusion/size3.m, MATLAB, 6 lines
genetics/ea_checkgenesim.m, MATLAB, 87 lines
genetics/ea_exportgenemap.m, MATLAB, 100 lines
genetics/ea_probeexpressions.m, MATLAB, 42 lines
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At the source: github.com/leaddbs/leaddbs

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:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
1,998 scripts, each with its path and the digest of its content;
7 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

All code used to analyze the dataset is openly available within Lead‐DBS (including sweet spot mapping, fiber filtering, and network mapping software: https://github.com/leaddbs/leaddbs). Cohort‐wise demographic and clinical outcomes from the training and the test cohorts are available in Supplementary Tables S2 and S3. We cannot openly share patient imaging data due to data sharing and privacy regulations, but they can be made available upon request to the corresponding primary investigator. The corresponding author and the principal investigator (P.Z. and A.H.) commit to returning data requests within a time frame of 30 days.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 28 authors, 13 MeSH terms, 6 funders, 43 references.

Cite

This paper

Zvarova, P., van der Linden, C., Li, N., Butenko, K., Berger, T., Meyer, G. M., Sahin, I. A., Goede, L. L., Bahners, B. H., Hollunder, B., Dembek, T. A., Pines, A. R., Reich, M., Volkmann, J., Odekerken, V. J. J., de Bie, R. M. A., Xu, X., Ling, Z., Yao, C., . . . Horn, A. (2026). Multimodal Image Guidance in Subthalamic Deep Brain Stimulation for Parkinson's Disease. Annals of neurology, 100(1), 22-35. https://doi.org/10.1002/ana.78206

BibTeX

@article{zvarova2026multimodal, author = {Zvarova, Patricia and van der Linden, Christina and Li, Ningfei and Butenko, Konstantin and Berger, Thea and Meyer, Garance M and Sahin, Ilkem Aysu and Goede, Lukas L and Bahners, Bahne H and Hollunder, Barbara and Dembek, Till A and Pines, Andrew R and Reich, Martin and Volkmann, Jens and Odekerken, Vincent J J and de Bie, Rob M A and Xu, Xin and Ling, Zhipei and Yao, Chen and Kühn, Andrea A and Soekadar, Surjo R and Ritter, Kerstin and Barbe, Michael T and Visser‐Vandewalle, Veerle and Fox, Michael D and Petry‐Schmelzer, Jan Niklas and Rajamani, Nanditha and Horn, Andreas}, title = {{Multimodal Image Guidance in Subthalamic Deep Brain Stimulation for Parkinson's Disease}}, journal = {Annals of neurology}, year = {2026}, month = apr, volume = {100}, number = {1}, pages = {22--35}, publisher = {Wiley}, issn = {0364-5134}, doi = {10.1002/ana.78206}, url = {https://doi.org/10.1002/ana.78206}, pmid = {41992934}, pmcid = {PMC13327559} }

RIS

TY - JOUR AU - Zvarova, Patricia AU - van der Linden, Christina AU - Li, Ningfei AU - Butenko, Konstantin AU - Berger, Thea AU - Meyer, Garance M AU - Sahin, Ilkem Aysu AU - Goede, Lukas L AU - Bahners, Bahne H AU - Hollunder, Barbara AU - Dembek, Till A AU - Pines, Andrew R AU - Reich, Martin AU - Volkmann, Jens AU - Odekerken, Vincent J J AU - de Bie, Rob M A AU - Xu, Xin AU - Ling, Zhipei AU - Yao, Chen AU - Kühn, Andrea A AU - Soekadar, Surjo R AU - Ritter, Kerstin AU - Barbe, Michael T AU - Visser‐Vandewalle, Veerle AU - Fox, Michael D AU - Petry‐Schmelzer, Jan Niklas AU - Rajamani, Nanditha AU - Horn, Andreas TI - Multimodal Image Guidance in Subthalamic Deep Brain Stimulation for Parkinson's Disease T2 - Annals of neurology J2 - Ann Neurol PY - 2026 DA - 2026/04/17 VL - 100 IS - 1 SP - 22 EP - 35 SN - 0364-5134 PB - Wiley DO - 10.1002/ana.78206 UR - https://doi.org/10.1002/ana.78206 LA - en ER -

CSL-JSON

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