Multimodal approach to identify neuropsychophysiological subgroups in myalgic encephalomyelitis/chronic fatigue syndrome and their relevance for rehabilitation: protocol for a mechanistic cross-sectional and longitudinal study.

Code ↔ Paper

16 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 16 matches · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted

[1] § CRediT authorship contribution statement ↔ pet/scripts/LaBGAScore_pet_model_TSPO_DPA714.m, lines 1–106 · score 0.69 · Lixin Qiu, Lukas Van Oudenhove, Patrick Dupont
[2] § Methods › Neuroimaging data › Preprocessing › PET ↔ pet/scripts/LaBGAScore_pet_model_TSPO_DPA714.m, lines 1–106 · score 0.68 · endothelial binding, DPA714, SPM, parcels, blood, ROIs
[3] § Methods › Measures › Primary outcome variables › Neuroimaging › PET/MR scan protocol ↔ pet/functions/LCN12_PET_preprocessing.m, lines 1–76 · score 0.67 · attenuation correction, T1 weighted, injection, DPA714, dynamic, scanning
[4] § Methods › Statistical analysis plan › Objective 1: comparison of neuropsychophysiological measures between ME/CFS patients and healthy participants › Brain resting-state fMRI ↔ secondlevel/functions/tfce_volume.m, the whole file · a weak match · score 0.67 · Threshold Free Cluster, Enhancement, TFCE, connectivity, Voxel, error
[5] § Methods › Neuroimaging data › Preprocessing › MRS ↔ mrs/LaBGAScore_mrs_run_osprey_GE.m, lines 230–316 · score 0.66 · linear combination, water scaled, Osprey, spectra, quality, quantification
[6] § Methods › Neuroimaging data › Preprocessing › MRS ↔ mrs/LaBGAScore_mrs_run_osprey_Philips.m, lines 230–316 · score 0.66 · linear combination, water scaled, Osprey, spectra, quality, quantification
[7] § Methods › Statistical analysis plan › Objective 1: comparison of neuropsychophysiological measures between ME/CFS patients and healthy participants › Brain resting-state fMRI ↔ secondlevel/functions/tfce_transform_3d.m, the whole file · a weak match · score 0.66 · Threshold Free Cluster, Enhancement, TFCE, connectivity, Voxel, error
[8] § Methods › Statistical analysis plan › Objective 1: comparison of neuropsychophysiological measures between ME/CFS patients and healthy participants › Brain task-based fMRI ↔ Second_level_analysis_template_scripts/b_copy_to_local_scripts_dir_and_modify/a2_set_default_options.m, lines 383–401 · score 0.66 · multivariate mediation, gray matter mask, FDR correction, PDM, CANlab, threshold
[9] § Methods › Neuroimaging data › Preprocessing › PET ↔ pet/functions/LCN12_write_image.m, the whole file · a weak match · score 0.61 · University College London, Wellcome, SPM12, volume, MATLAB, PET
[10] § Methods › Statistical analysis plan › Objective 4: use these subgroups as potential predictors of longitudinal effects throughout treatment ↔ canlab_mixed_effects_matlab_demo1/canlab_mixed_model_example.r, the whole file · a weak match · score 0.61 · linear mixed models, model fit, intercept
[11] § Methods › Statistical analysis plan › Objective 1: comparison of neuropsychophysiological measures between ME/CFS patients and healthy participants › Brain task-based fMRI ↔ decoding_toolbox/LaBGAScore_decoding_SVM_between_subjects.m, lines 1193–1296 · score 0.61 · Decoding Toolbox, cross validate, shuffling, classifier, train, fold
[12] § CRediT authorship contribution statement ↔ pet/functions/LCN12_PET_preprocessing.m, lines 1–76 · score 0.55 · Lukas Van Oudenhove, Patrick Dupont, editing
[13] § Methods › Neuroimaging data › ROI definition › Task-based fMRI (Montreal Imaging Stress Test) ↔ example_help_files/canlab_help_9_apply_a_multivariate_pattern_of_interest.m, the whole file · a weak match · score 0.54 · anterior cingulate, fMRI, dorsal, ROI
[14] § Methods › Measures › Primary outcome variables › Neuroimaging › MRI scan protocol ↔ mrs/LaBGAScore_mrs_osprey_single_sess_jobfile_Philips.m, lines 262–351 · score 0.54 · T1 weighted, Philips, MRS, scanning, sequence, quantified
[15] § Methods › Measures › Primary outcome variables › Neuroimaging › MRI scan protocol ↔ mrs/LaBGAScore_mrs_osprey_jobfile_Philips.m, lines 262–361 · score 0.54 · T1 weighted, Philips, MRS, scanning, sequence, quantified
[16] § Methods › Statistical analysis plan › Objective 1: comparison of neuropsychophysiological measures between ME/CFS patients and healthy participants › SRS, SCFAs, fatigue/fatigability, immune markers ↔ stats_tools/functions/LaBGAScore_Storey_FDR.m, lines 1–60 · score 0.50 · Benjamini Hochberg, pFDR, spline, bootstrap, CFS

Paper

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The authors' code

MATLAB · 1,435 lines · 79 KB · GPL-3.0 · 2 matches

%% LaBGAScore_pet_model_TSPO_DPA714.m
%
%
% *USAGE*
%
% Assumptions:
% Data are organized according to BIDS.
% You have to specify a main directory where al the folders of the
% subjects can be found.
% In the folder of each subject, there might be a folder ses-xx
% which contains the folder anat with the T1 weighted structural MRI
% and a folder pet which contains the dynamic PET images.
% If the folder ses-xx is not existing, we assume that the subfolder
% anat and pet are directly under the main folder of the subject.
%
% The data are preprocessed using LCN12_PET_preprocess_data.m
%
% A template for the PET naming should be specified. PET data are
% acquired dynamically from the start of the injection. The unit of
% the PET data is expresses as Bq/ml.Images are decay corrected to
% the start of the injection/begin of scanning.
% We assume that PET data are in 4D nifti format.
% In the pet folder, there must be a .m file containing the frame
% defintion by specifying the variable frames_timing which is a N x 2
% or N x 3 array (N = number of frames) for which the first column is
% the start time of the frame in seconds post injection, the second
% column is the end time of each frame and the third column is
% optional with the weight for the frame (positive values). If
% weights are not specified, all frames are weighted in the same way.
%
%
% *OPTIONS*
%
% All set in the "PREP WORK, SET INFO AND OPTIONS" section, above the
% "DO NOT CHANGE BELOW THIS LINE" marker:
%
% * sessiondir default '', session subfolder name (empty = no session level)
% * infostring_tracer default 'trc-DPA714', tracer identifier used in filenames
% * infostring_PET default 'rec-acdyn_pet', PET-image filename suffix
% * infostring_frames default 'frames', frame-definition .m filename suffix
% * infostring_input default 'data_blood', arterial input function filename suffix (only needed for models requiring one)
% * infostring_metab default 'data_metab', metabolite filename suffix (only needed for models requiring one)
% * SUBJECTS which subjects to analyze - three alternative strategies in-code (all-PET-in-BIDS, all-in-derivatives, or manual list); default uses "all subjects in derivatives/pet"
% * figures_on default 0, show per-region figures and pause (needs keypress) if 1
% * save_figures default 1, save figures to file (overrules pausing)
% * save_parcelimgs default 0, save parcel-wise images per parameter/model/subject if 1
% * do_voxelwise_logan default 0, also run voxel-wise Logan analysis if 1
% * pet_space_reference default 0, read atlas in PET space (1) vs. atlas space (0)
% * suffix default 'atlas_space', label used when running multiple models
% * atlas_name default 'canlab2024_fine_2mm' - option (a) whole-brain atlas name for load_atlas.m, or option (b, commented) a combined-ROI .nii from LaBGAScore_atlas_rois_from_atlas.m
% * intersect_GM / intersect_WM default 0/0, intersect VOI/parametric image with subject-specific GM/WM mask
% * GM_CUTOFF / WM_CUTOFF default 0.3/0.3, thresholds for the above
% * additional_smooth_parametric default 6 (mm), isotropic Gaussian smoothing kernel for voxel-based parametric Logan images
% * nr_parpool default 12 (LaBGAS server default), number of parallel workers
%
%
% *DEPENDENCIES*
%
% * CanlabCore: load_atlas, downsample_parcellation
% * SPM: spm_vol
% * vendored pet/functions/LCN_*.m: LCN12_read_image, LCN_check_filename, LCN_calc_intact_tracer_hill (and related LCN model-fitting functions)
%
%
% *NOTES*
%
% THIS IS RESEARCH SOFTWARE. Originated as LCN12_PET_TSPO_DPA714.m (v2.0).
%
% History:
% February 2024: an excel file is written which contains for all
% subjects and all regions, the volume of distribution
% and the error of the fit.
% March 2024: adapted to work with standard LaBGAS file
% organization (LVO) and adapted version of
% preprocessing script
% LaBGAScore_pet_preprocess_data
% built in check for enough frames after
% logan_start_time (LVO)
% May 2024 further adaptations to fit LaBGAS file organization (LVO)
% built in more options for automatic subject and ROI definition (LVO)
% November 2024: removal of Global variables and adding parallel processing
% January 2025: weights for excluded frames set to zero and decay
% between measurement samples and start scan is now
% taken into account
% April 2025: Logan is also done voxel based
% May 2025: Built in option to work with canlab atlas objects (LVO)
% Final adaptations to fit LaBGAS file organisation (LVO)
% Added atlas labels automatically to output files (LVO)
% Sept 2025: Integration of LCN12_PET_TSPO_DPA714_compare_models.m (v0.2)
% ~ includes 2T4k models with (ir)reversible endothelial binding
% component and fit statistics to compare models
% (LVO)
%
% -------------------------------------------------------------------------
%
% modified by: Patrick Dupont, Lukas Van Oudenhove, Lixin Qiu
%
% date: October 2023
%
% -------------------------------------------------------------------------
%
% LaBGAScore_pet_model_TSPO_DPA714.m v2.1
%
% last modified: 2026/08/20
clear
close all
%% PREP WORK, SET INFO AND OPTIONS
%--------------------------------------------------------------------------
% SET DIRECTORIES
LaBGAScore_prep_s0_define_directories; % MAKE STUDY-SPECIFIC
maindir = BIDSdir; % directory where the folders of each subject can be found
sessiondir = ''; % if empty, we assume that there is no folder session and the folders anat and pet are directly under the subject folder
infostring_tracer = 'trc-DPA714';
infostring_PET = 'rec-acdyn_pet'; % the PET data are "subjectname"_"sessiondir"_"infostring_tracer"_"infostring_PET".nii (example = sub-test_trc-DPA714_rec-acdyn_pet.nii)
infostring_frames = 'frames'; % in the folder pet, we assume a .m file name "subjectname"_"sessiondir"_"infostring_tracer"_"infostring_PET"_"infostring_frames".m (example sub-test_trc-DPA714_rec-acdyn_pet_frames.m)
% infostring_input and infostring_metab need only be defined if we use
% models which require an arterial input function
infostring_input = 'data_blood'; % % in the folder pet, we assume a .m file name "subjectname"_"sessiondir"_"infostring_tracer"_"infostring_input".m (example sub-test_trc-DPA714_rec-acdyn_data_blood.nii)
infostring_metab = 'data_metab'; % % in the folder pet, we assume a .m file name "subjectname"_"sessiondir"_"infostring_tracer"_"infostring_metab".m (example sub-test_trc-DPA714_rec-acdyn_data_metab.nii)
% Lukas' code to take into account that preprocessed data are in
% derivatives/pet-<infostring_tracer>
derivrootdir = fileparts(derivdir);
derivpetdir = fullfile(derivrootdir,['pet_' infostring_tracer]);
firstlevelpetdir = fullfile(rootdir,'firstlevel',['pet_' infostring_tracer]);
if ~exist(firstlevelpetdir,'dir')
mkdir(firstlevelpetdir);
end
secondlevelpetdir = fullfile(rootdir,'secondlevel',['pet_' infostring_tracer]);
if ~exist(secondlevelpetdir,'dir')
mkdir(secondlevelpetdir);
end
secondlevelpetmaskdir = fullfile(secondlevelpetdir,'masks');
if ~exist(secondlevelpetmaskdir,'dir')
mkdir(secondlevelpetmaskdir);
end
secondlevelpetresultsdir = fullfile(secondlevelpetdir,'results');
if ~exist(secondlevelpetresultsdir,'dir')
mkdir(secondlevelpetresultsdir);
end
secondlevelpetroidir = fullfile(secondlevelpetmaskdir,'rois');
if ~exist(secondlevelpetroidir,'dir')
mkdir(secondlevelpetroidir);
end
% SELECT SUBJECTS
% Lukas' code to automate selection of subjects who have a pet dir in BIDS
% USE THIS FOR ANALYZING ALL PET SUBJECTS AT ONCE
% dir_BIDS = dir(fullfile(BIDSdir,'sub-*'));
%
% petsubcounter = 1;
%
% for sub = 1:size(dir_BIDS,1)
% BIDSsubdir = fullfile(BIDSdir,dir_BIDS(sub).name);
% dir_BIDSsubdir = dir(BIDSsubdir);
% if contains([dir_BIDSsubdir(:).name],'pet')
% SUBJECTS{petsubcounter,1} = dir_BIDS(sub).name;
% petsubcounter = petsubcounter + 1;
% else
% continue
% end
% end
%
% dir_derivpet = dir(fullfile(derivpetdir,'sub-*'));
%
% if ~isequal(SUBJECTS,{dir_derivpet(:).name}')
% error('#subjects with PET data in BIDS and derivatives subdatasets does not match, please preprocess all subjects before proceeding');
% end;
% Lukas' code to automate selection of subjects in derivatives/pet
% USE THIS FOR ANALYZING ALL PET SUBJECTS AT ONCE WITHOUT CHECKING CONSISTENCY BETWEEN BIDS AND DERIVATIVES SUBDATASETS
dir_derivpet = dir(fullfile(derivpetdir,'sub-*'));
SUBJECTS = {dir_derivpet(:).name}';
% Patrick's code to enter subjects manually
% USE THIS FOR ANALYZING SELECTED SUBJECTS
% SUBJECTS = {
% 'sub-KUL034'
% };
% SET FIGURE/IMAGE OPTIONS
figures_on = 0; % if 1, we show the figures for each region and pause. You need to hit a key to proceed.
save_figures = 1; % if 1, we save the figures to file and pausing of figures is overruled.
save_parcelimgs = 0; % if 1, we save parcel-wise images for every parameter in every model for every subject
do_voxelwise_logan = 0; % if 1, we perform a voxel-wise Logan analysis in addition to the parcel-wise analyses
pet_space_reference = 0; % if 1, atlas is read in pet space. Voxels with non-integer values (i.e. voxels at the border of a region) will be excluded. If 0, atlas space is used.
suffix = 'atlas_space'; % use if you want to run multiple models
% DEFINE ATLAS/ROIS
% Lukas' code to flexibly load canlab atlas objects
% option a - atlas name from load_atlas.m for whole-brain parcellation
atlas_name = 'canlab2024_fine_2mm';
atlas = load_atlas(atlas_name);
atlas = downsample_parcellation(atlas,'labels_3'); % downsample to intermediate granularity level, 246 parcels
atlas.probability_maps = [];
atlas_filename = fullfile(secondlevelpetmaskdir,[atlas_name '.nii']);
if ~isfile(atlas_filename)
write(atlas,'fname',atlas_filename);
end
% option b - combined roi .nii file generated by
% https://github.com/labgas/LaBGAScore/blob/main/atlas_mask_tools/LaBGAScore_atlas_rois_from_atlas.m
% for roi analysis
% atlas_name = 'combined_inflammation_regions';
% atlas_name = 'combined_TSPO';
% atlas_mat = 'pet_trc-DPA714_combinedTSPO'; % lixin May26 2025
% atlas_filename = fullfile(secondlevelpetmaskdir,[atlas_name '.nii']);
% atlas_mat = fullfile(secondlevelpetmaskdir,[atlas_mat '.mat']); % lixin May26 2025
% load(atlas_mat);
% SPECIFY BASE NAME FOR THE OUTPUT FILES
% we will extend the name with the name of the model and write as an excel file)
outputfile_excel_region_based = fullfile(secondlevelpetresultsdir,['results_' infostring_tracer '_' atlas_name '_VOIS']);
% SET OPTIONS FOR INTERSECTING WITH GRAY MATTER
% if you want to intersect the VOI region with a subject specific GM or WM map, you need to specify the variables below
% the same will be done for the parametric image with Logan_input in which case the voxels withing GM or WM after thresholding these maps will be calculated.
intersect_GM = 0;
intersect_WM = 0;
GM_CUTOFF = 0.3;
WM_CUTOFF = 0.3;
% SET SMOOTHING KERNEL FOR VOXEL-BASED PARAMETRIC LOGAN IMAGES
additional_smooth_parametric = 6; % kernel size in mm; isotropic Gaussian 3D smoothing; additional smoothing for a voxel based analysis. If GM of WM intersection is selected, smoothing is within this mask
% SET NUMBER OF PARALLEL PROCESSES
nr_parpool = 12; % LaBGAS server default
% nr_parpool = 20;
%+++++++++++++++ DO NOT CHANGE BELOW THIS LINE ++++++++++++++++++++++++++++
%% SETTINGS
%---------------------- settings ------------------------------------------
min_number_voxels = 25; % minimum number of voxels in the parcel that are used for the calculation of the TAC.
% we will use a multigrid search for the optimal parameters for the rate
% constants for each model. Keep in mind that you have
% nr_starting_values_per_dimension^4 number of starting values for a model
% with 4 rate constants that you vary and
% nr_starting_values_per_dimension^6 for a model with 6 parameters that you
% would like to vary.
% the time for the fitting is roughly proportional to the number of
% starting values.
% the starting value for Vb is fixed but it will be fitted.
Vb0 = 0.05;
nr_starting_values_per_dimension = 3;
model_list = {
'Logan_input'
'2T4k'
'2T4k_Vb'
'2T4k_vasc1k'
'2T4k_vasc2k'
};
logan_start_time = 31; % in min (ref Van Weehaeghe et al. J Nucl Med. 2020 Apr;61(4):604-607)
% initial values Hill function
p0_hill = [50 -1]; % see LCN_calc_intact_tracer_hill for details
% boundaries for the models with initial conditions
k0_2T4k_lower_bound = [0.001 0.001 0.001 0.001];
k0_2T4k_upper_bound = [1 1 1 1];
k0_2T4k_Vb_lower_bound = [0.001 0.001 0.001 0.001 0.001];
k0_2T4k_Vb_upper_bound = [1 1 1 1 1];
k0_2T4k_vasc1k_lower_bound = [0.001 0.001 0.001 0.001 0.001 0.001];
k0_2T4k_vasc1k_upper_bound = [1 1 1 1 1 1];
k0_2T4k_vasc2k_lower_bound = [0.001 0.001 0.001 0.001 0.001 0.001 0.001];
k0_2T4k_vasc2k_upper_bound = [1 1 1 1 1 1 1];
nr_params_Logan_input = 2;
nr_params_2T4k = 4;
nr_params_2T4k_Vb = 5;
nr_2T4k_vasc1k = 6;
nr_2T4k_vasc2k = 7;
options = optimoptions('fmincon','Display','off');
STEP = 0.01; % step size for the calculation of integrals in min
% CALC_OPTION: parameter determing the way we calculate the output of a
% model.
% 1 - the model is calculated as the integral of the output concentration
% devided by the frameduration.
% 2 - the model is calculated at the midscantime.
CALC_OPTION = 2;
window_size = 0; % for temporal median filtering
thalf_F18 = 1.82871*60; % half life in min of 18F (REF: García-Toraño E, Medina VP, Ibarra MR. The half-life of 18F. Appl Radiat Isot. (2010) 68(7-8):1561-5)
%--------------------------------------------------------------------------
curdir = pwd;
% determine the path of the prior data of SPM
tmp = which('spm.m');
[spm_pth,~,~] = fileparts(tmp);
spm_pth_priors = fullfile(spm_pth,'tpm');
% define the brain mask
brain_mask_file = fullfile(spm_pth_priors,'mask_ICV.nii');
nr_subjects = size(SUBJECTS,1);
nr_models = size(model_list,1);
%% READ ATLAS PARCELS
%--------------------------------------------------------------------------
if exist('atlas_filename','var') == 1 && ~isempty(atlas_filename) == 1
% read atlas
[atlas_orig,Vatlas_orig] = LCN12_read_image(atlas_filename);
atlas_orig = round(atlas_orig);
parcel_values = setdiff(unique(atlas_orig(:)),0); % assuming 0 is background
% check if there is a .m file with the same name as the atlas
% with the VOIdetails such as the name
[pth,name,ext] = fileparts(atlas_filename);
if exist(fullfile(pth,[name '.m']),'file') == 2
% execute this m-file
curdir = pwd;
cd(pth);
eval(name); % now VOIdetails is known, a n x 2 cell array with each row the value of a parcel in the atlas and the corresponding parcel name
cd(curdir);
parcel_values_atlas = cell2mat(VOIdetails(:,1));
parcel_names = VOIdetails(:,2);
else
parcel_values_atlas = parcel_values;
try
parcel_names = atlas.labels;
catch
parcel_names = roi_atlas.labels;
end
% parcel_names = num2str(parcel_values);
end
nr_VOIS = length(parcel_values);
% initialize output
% varNames = cell(1,nr_VOIS+1);
% for voi = 1:nr_VOIS
% index_value = find(parcel_values_atlas == parcel_values(voi));
% varNames(1,voi+1) = {[num2str(parcel_values(voi)) '-' char(parcel_names(index_value,:))]};
% end
else
fprintf('You need to define an atlas \n');
return;
end
%% INITIALIZE OUTPUT
%--------------------------------------------------------------------------
% PARAMETERS FOR EACH MODEL
sz = [nr_subjects nr_VOIS+1];
varNames = [{'subject'} (parcel_names)]; % lixin may26 2025
varTypes(1,1) = {'string'};
varTypes(1,2:nr_VOIS+1) = {'double'};
results_number_voxels_VOI = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
for m = 1:nr_models
modelname = model_list{m,1};
if strcmp(modelname,'Logan_input')
results_region_based_Logan_input_DV = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_Logan_input_error = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
end
if strcmp(modelname,'2T4k')
results_region_based_2T4k_K1 = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_k2 = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_k3 = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_k4 = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_DV = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_error = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
end
if strcmp(modelname,'2T4k_Vb')
results_region_based_2T4k_Vb_K1 = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_Vb_k2 = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_Vb_k3 = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_Vb_k4 = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_Vb_Vb = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_Vb_DV = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_Vb_error = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
end
if strcmp(modelname,'2T4k_vasc1k')
results_region_based_2T4k_vasc1k_K1 = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_vasc1k_k2 = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_vasc1k_k3 = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_vasc1k_k4 = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_vasc1k_Vb = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_vasc1k_K1v = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_vasc1k_DV = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_vasc1k_error = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
end
if strcmp(modelname,'2T4k_vasc2k')
results_region_based_2T4k_vasc2k_K1 = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_vasc2k_k2 = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_vasc2k_k3 = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_vasc2k_k4 = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_vasc2k_Vb = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_vasc2k_K1v = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_vasc2k_k2v = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_vasc2k_DV = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
results_region_based_2T4k_vasc2k_error = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
end
end
% FIT STATISTICS FOR ALL MODELS
sz2 = [nr_subjects*4 nr_VOIS+2];
varNames2 = [{'subject'} {'method'} (parcel_names)]; % lukasvo sept 22 2025
varTypes2 = cell(1, nr_VOIS + 2);
varTypes2(1:2) = {'string', 'string'};
varTypes2(3:end) = repmat({'double'}, 1, nr_VOIS);
results_region_based_AIC = table('Size', sz2, 'VariableTypes', varTypes2, 'VariableNames', varNames2);
results_region_based_SC = table('Size', sz2, 'VariableTypes', varTypes2, 'VariableNames', varNames2);
%% DETERMINING THE GRID OF STARTING VALUES WITHIN THE SEARCH SPACE
%--------------------------------------------------------------------------
for m = 1:nr_models
modelname = model_list{m,1};
if strcmp(modelname,'2T4k')
k0_2T4k_all_values = zeros(nr_starting_values_per_dimension^4,4);
counter = 0;
for d1 = 1:nr_starting_values_per_dimension
val1 = k0_2T4k_lower_bound(1) + d1*(k0_2T4k_upper_bound(1) - k0_2T4k_lower_bound(1))/(nr_starting_values_per_dimension+1);
for d2 = 1:nr_starting_values_per_dimension
val2 = k0_2T4k_lower_bound(2) + d2*(k0_2T4k_upper_bound(2) - k0_2T4k_lower_bound(2))/(nr_starting_values_per_dimension+1);
for d3 = 1:nr_starting_values_per_dimension
val3 = k0_2T4k_lower_bound(3) + d3*(k0_2T4k_upper_bound(3) - k0_2T4k_lower_bound(3))/(nr_starting_values_per_dimension+1);
for d4 = 1:nr_starting_values_per_dimension
val4 = k0_2T4k_lower_bound(4) + d4*(k0_2T4k_upper_bound(4) - k0_2T4k_lower_bound(4))/(nr_starting_values_per_dimension+1);
counter = counter + 1;
k0_2T4k_all_values(counter,:) = [val1 val2 val3 val4];
end
end
end
end
nr_k0_2T4k = size(k0_2T4k_all_values,1);
end
if strcmp(modelname,'2T4k_Vb')
k0_2T4k_Vb_all_values = zeros(nr_starting_values_per_dimension^4,5);
counter = 0;
val5 = Vb0;
for d1 = 1:nr_starting_values_per_dimension
val1 = k0_2T4k_Vb_lower_bound(1) + d1*(k0_2T4k_Vb_upper_bound(1) - k0_2T4k_Vb_lower_bound(1))/(nr_starting_values_per_dimension+1);
for d2 = 1:nr_starting_values_per_dimension
val2 = k0_2T4k_Vb_lower_bound(2) + d2*(k0_2T4k_Vb_upper_bound(2) - k0_2T4k_Vb_lower_bound(2))/(nr_starting_values_per_dimension+1);
for d3 = 1:nr_starting_values_per_dimension
val3 = k0_2T4k_Vb_lower_bound(3) + d3*(k0_2T4k_Vb_upper_bound(3) - k0_2T4k_Vb_lower_bound(3))/(nr_starting_values_per_dimension+1);
for d4 = 1:nr_starting_values_per_dimension
val4 = k0_2T4k_Vb_lower_bound(4) + d4*(k0_2T4k_Vb_upper_bound(4) - k0_2T4k_Vb_lower_bound(4))/(nr_starting_values_per_dimension+1);
counter = counter + 1;
k0_2T4k_Vb_all_values(counter,:) = [val1 val2 val3 val4 val5];
end
end
end
end
nr_k0_2T4k_Vb = size(k0_2T4k_Vb_all_values,1);
end
if strcmp(modelname,'2T4k_vasc1k')
k0_2T4k_vasc1k_all_values = zeros(nr_starting_values_per_dimension^5,6);
counter = 0;
val5 = Vb0;
for d1 = 1:nr_starting_values_per_dimension
val1 = k0_2T4k_vasc1k_lower_bound(1) + d1*(k0_2T4k_vasc1k_upper_bound(1) - k0_2T4k_vasc1k_lower_bound(1))/(nr_starting_values_per_dimension+1);
for d2 = 1:nr_starting_values_per_dimension
val2 = k0_2T4k_vasc1k_lower_bound(2) + d2*(k0_2T4k_vasc1k_upper_bound(2) - k0_2T4k_vasc1k_lower_bound(2))/(nr_starting_values_per_dimension+1);
for d3 = 1:nr_starting_values_per_dimension
val3 = k0_2T4k_vasc1k_lower_bound(3) + d3*(k0_2T4k_vasc1k_upper_bound(3) - k0_2T4k_vasc1k_lower_bound(3))/(nr_starting_values_per_dimension+1);
for d4 = 1:nr_starting_values_per_dimension
val4 = k0_2T4k_vasc1k_lower_bound(4) + d4*(k0_2T4k_vasc1k_upper_bound(4) - k0_2T4k_vasc1k_lower_bound(4))/(nr_starting_values_per_dimension+1);
for d6 = 1:nr_starting_values_per_dimension
val6 = k0_2T4k_vasc1k_lower_bound(6) + d4*(k0_2T4k_vasc1k_upper_bound(6) - k0_2T4k_vasc1k_lower_bound(6))/(nr_starting_values_per_dimension+1);
counter = counter + 1;
k0_2T4k_vasc1k_all_values(counter,:) = [val1 val2 val3 val4 val5 val6];
end
end
end
end
end
nr_k0_2T4k_vasc1k = size(k0_2T4k_vasc1k_all_values,1);
end
if strcmp(modelname,'2T4k_vasc2k')
k0_2T4k_vasc2k_all_values = zeros(nr_starting_values_per_dimension^6,7);
counter = 0;
val5 = Vb0;
for d1 = 1:nr_starting_values_per_dimension
val1 = k0_2T4k_vasc2k_lower_bound(1) + d1*(k0_2T4k_vasc2k_upper_bound(1) - k0_2T4k_vasc2k_lower_bound(1))/(nr_starting_values_per_dimension+1);
for d2 = 1:nr_starting_values_per_dimension
val2 = k0_2T4k_vasc2k_lower_bound(2) + d2*(k0_2T4k_vasc2k_upper_bound(2) - k0_2T4k_vasc2k_lower_bound(2))/(nr_starting_values_per_dimension+1);
for d3 = 1:nr_starting_values_per_dimension
val3 = k0_2T4k_vasc2k_lower_bound(3) + d3*(k0_2T4k_vasc2k_upper_bound(3) - k0_2T4k_vasc2k_lower_bound(3))/(nr_starting_values_per_dimension+1);
for d4 = 1:nr_starting_values_per_dimension
val4 = k0_2T4k_vasc2k_lower_bound(4) + d4*(k0_2T4k_vasc2k_upper_bound(4) - k0_2T4k_vasc2k_lower_bound(4))/(nr_starting_values_per_dimension+1);
for d6 = 1:nr_starting_values_per_dimension
val6 = k0_2T4k_vasc2k_lower_bound(6) + d4*(k0_2T4k_vasc2k_upper_bound(6) - k0_2T4k_vasc2k_lower_bound(6))/(nr_starting_values_per_dimension+1);
for d7 = 1:nr_starting_values_per_dimension
val7 = k0_2T4k_vasc2k_lower_bound(7) + d4*(k0_2T4k_vasc2k_upper_bound(7) - k0_2T4k_vasc2k_lower_bound(7))/(nr_starting_values_per_dimension+1);
counter = counter + 1;
k0_2T4k_vasc2k_all_values(counter,:) = [val1 val2 val3 val4 val5 val6 val7];
end
end
end
end
end
end
nr_k0_2T4k_vasc2k = size(k0_2T4k_vasc2k_all_values,1);
end
end
%% LOOP OVER SUBJECTS
%--------------------------------------------------------------------------
parpool(nr_parpool);
for subj = 1:nr_subjects
% PREP WORK
clear subjectdir subjectname dir_anat dir_pet dir_deriv* dir_firstlevel dir_tmp dir_anat2 DV_Logan
clear tmp_frames frames_timing nr_frames acqtimes FRAMEDURATION MIDSCANTIMES WEIGHTS_PET
clear name_logfile fid go go_GM go_WM go_CSF go_PET go_frames go_input go_metab Vref Vref_tmp
subjectname = SUBJECTS{subj};
subjectdir = fullfile(maindir,subjectname);
if ~isempty(sessiondir)
dir_pet = fullfile(fullfile(subjectdir,sessiondir),['pet_' infostring_tracer]);
dir_deriv = fullfile(derivpetdir,subjectname,sessiondir);
dir_deriv_tmp = fullfile(dir_deriv,'tmp');
dir_deriv_anat2 = fullfile(dir_deriv,'anat');
dir_firstlevel = fullfile(firstlevelpetdir,subjectname,sessiondir);
if ~exist(dir_firstlevel,'dir')
mkdir(dir_firstlevel);
end
else
dir_pet = fullfile(subjectdir,['pet_' infostring_tracer]);
dir_deriv = fullfile(derivpetdir,subjectname);
dir_deriv_tmp = fullfile(dir_deriv,'tmp');
dir_deriv_anat2 = fullfile(dir_deriv,'anat');
dir_firstlevel = fullfile(firstlevelpetdir,subjectname);
if ~exist(dir_firstlevel,'dir')
mkdir(dir_firstlevel);
end
end
if save_figures == 1
dir_figures = fullfile(dir_deriv,'figures');
if exist(dir_figures,'dir') ~= 7
mkdir(dir_figures);
end
end
fprintf('working on subject %s \n',subjectname);
name_logfile = fullfile(dir_deriv,'LCN12_PET_TSPO_DPA714_log.txt');
fid = fopen(name_logfile,'a+');
fprintf(fid,'subject = %s\n',subjectname);
fprintf(fid,'LCN12_PET_TSPO_DPA714_compare_models.m \n');
fprintf(fid,'%c','-'*ones(1,30));
fprintf(fid,'\n');
fprintf(fid,'Processing started at %s\n',datetime('now'));
fprintf(fid,'\n');
fprintf(fid,'Settings\n');
fprintf(fid,'brain_mask_file = %s\n',brain_mask_file);
fprintf(fid,'figures_on = %i\n',figures_on);
fprintf(fid,'save_figures = %i\n',save_figures);
fprintf(fid,'atlas_filename = %s\n',atlas_filename);
if exist('intersect_GM','var') == 1
fprintf(fid,'intersect_GM = %i\n',intersect_GM);
else
fprintf(fid,'intersect_GM = not defined \n');
end
if exist('intersect_WM','var') == 1
fprintf(fid,'intersect_WM = %i\n',intersect_WM);
else
fprintf(fid,'intersect_WM = not defined \n');
end
if exist('GM_CUTOFF','var') == 1
fprintf(fid,'GM_CUTOFF = %4,2f\n',GM_CUTOFF);
else
fprintf(fid,'GM_CUTOFF = not defined \n');
end
if exist('WM_CUTOFF','var') == 1
fprintf(fid,'WM_CUTOFF = %4.2f\n',WM_CUTOFF);
else
fprintf(fid,'WM_CUTOFF = not defined \n');
end
fprintf(fid,'p0_hill = [%4.2f %4.2f] \n',p0_hill(1),p0_hill(2));
for m = 1:nr_models
modelname = model_list{m,1};
if strcmp(modelname,'Logan_input')
fprintf(fid,'logan_start_time (min) = %i \n',logan_start_time);
end
if strcmp(modelname,'2T4k')
fprintf(fid,'k0_2T4k_lower_bound = [%4.3f %4.3f %4.3f %4.3f %4.3f] \n',k0_2T4k_lower_bound(1),k0_2T4k_lower_bound(2),k0_2T4k_lower_bound(3),k0_2T4k_lower_bound(4));
fprintf(fid,'k0_2T4k_upper_bound = [%4.3f %4.3f %4.3f %4.3f %4.3f] \n',k0_2T4k_upper_bound(1),k0_2T4k_upper_bound(2),k0_2T4k_upper_bound(3),k0_2T4k_upper_bound(4));
end
if strcmp(modelname,'2T4k_Vb')
fprintf(fid,'k0_2T4k_Vb_lower_bound = [%4.3f %4.3f %4.3f %4.3f %4.3f] \n',k0_2T4k_Vb_lower_bound(1),k0_2T4k_Vb_lower_bound(2),k0_2T4k_Vb_lower_bound(3),k0_2T4k_Vb_lower_bound(4),k0_2T4k_Vb_lower_bound(5));
fprintf(fid,'k0_2T4k_Vb_upper_bound = [%4.3f %4.3f %4.3f %4.3f %4.3f] \n',k0_2T4k_Vb_upper_bound(1),k0_2T4k_Vb_upper_bound(2),k0_2T4k_Vb_upper_bound(3),k0_2T4k_Vb_upper_bound(4),k0_2T4k_Vb_upper_bound(5));
end
if strcmp(modelname,'2T4k_vasc1k')
fprintf(fid,'k0_2T4k_vasc1k_lower_bound = [%4.3f %4.3f %4.3f %4.3f %4.3f %4.3f] \n',k0_2T4k_vasc1k_lower_bound(1),k0_2T4k_vasc1k_lower_bound(2),k0_2T4k_vasc1k_lower_bound(3),k0_2T4k_vasc1k_lower_bound(4),k0_2T4k_vasc1k_lower_bound(5),k0_2T4k_vasc1k_lower_bound(6));
fprintf(fid,'k0_2T4k_vasc1k_upper_bound = [%4.3f %4.3f %4.3f %4.3f %4.3f %4.3f] \n',k0_2T4k_vasc1k_upper_bound(1),k0_2T4k_vasc1k_upper_bound(2),k0_2T4k_vasc1k_upper_bound(3),k0_2T4k_vasc1k_upper_bound(4),k0_2T4k_vasc1k_upper_bound(5),k0_2T4k_vasc1k_upper_bound(6));
end
if strcmp(modelname,'2T4k_vasc2k')
fprintf(fid,'k0_2T4k_vasc2k_lower_bound = [%4.3f %4.3f %4.3f %4.3f %4.3f %4.3f %4.3f] \n',k0_2T4k_vasc2k_lower_bound(1),k0_2T4k_vasc2k_lower_bound(2),k0_2T4k_vasc2k_lower_bound(3),k0_2T4k_vasc2k_lower_bound(4),k0_2T4k_vasc2k_lower_bound(5),k0_2T4k_vasc2k_lower_bound(6),k0_2T4k_vasc2k_lower_bound(7));
fprintf(fid,'k0_2T4k_vasc2k_upper_bound = [%4.3f %4.3f %4.3f %4.3f %4.3f %4.3f %4.3f] \n',k0_2T4k_vasc2k_upper_bound(1),k0_2T4k_vasc2k_upper_bound(2),k0_2T4k_vasc2k_upper_bound(3),k0_2T4k_vasc2k_upper_bound(4),k0_2T4k_vasc2k_upper_bound(5),k0_2T4k_vasc2k_upper_bound(6),k0_2T4k_vasc2k_upper_bound(7));
end
end
% CHECK IF WE CAN FIND ALL INPUT DATA IN MNI SPACE
[filename_GM,go_GM] = LCN_check_filename(dir_deriv_anat2,['wc1' subjectname '*.nii']);
[filename_WM,go_WM] = LCN_check_filename(dir_deriv_anat2,['wc2' subjectname '*.nii']);
[filename_CSF,go_CSF] = LCN_check_filename(dir_deriv_anat2,['wc3' subjectname '*.nii']);
[filename_wPET,go_PET] = LCN_check_filename(dir_deriv,['w' subjectname '*_' infostring_tracer '*_' infostring_PET '.nii']);
[frame_definition_file,go_frames] = LCN_check_filename(dir_pet,[subjectname '_*' infostring_tracer '_' infostring_PET '_' infostring_frames '.m']);
[input_file,go_input] = LCN_check_filename(dir_pet,[subjectname '_*' infostring_tracer '_' infostring_input '.m']);
[metab_file,go_metab] = LCN_check_filename(dir_pet,[subjectname '_*' infostring_tracer '_' infostring_metab '.m']);
% READ PLASMA AND BLOOD DATA, PERFORM CALCULATIONS, AND ANALYSE DATA
if go_frames == 1 % frame definition file found
% read the frame definition file
fprintf(fid,'Timing file: %s\n',frame_definition_file);
copyfile(frame_definition_file,'tmp_frames.m');
tmp_frames; % the variable frames_timing is now known
delete('tmp_frames.m');
nr_frames = size(frames_timing,1);
% calculate midscan times (in minutes)
acqtimes = frames_timing(:,1:2)/60; % in minutes
FRAMEDURATION = acqtimes(:,2)-acqtimes(:,1);
MIDSCANTIMES = acqtimes(:,1) + FRAMEDURATION/2;
else
fprintf(fid,'frame definition file not found for subject %s \n',subjectname);
continue;
end
if go_input*go_metab == 1
clear ref_time scanner_time
% read the input data and metab data
fprintf(fid,'Input file: %s\n',input_file);
copyfile(input_file,'tmp_input_file.m');
tmp_input_file; % the variable input and calibrationfactor_wellcounter are now known
delete('tmp_input_file.m');
fprintf(fid,'Metab file: %s\n',metab_file);
copyfile(metab_file,'tmp_metab_file.m');
tmp_metab_file; % the variable data_metab is now known
delete('tmp_metab_file.m');
% analyse the metab data
TIME_METAB = data_metab(:,1)/60; % time p.i. in min
FRACTION_INTACT_TRACER = data_metab(:,2)/100; % fraction intact tracer
if size(data_metab,2) == 3
WEIGHTS_METAB = data_metab(:,3);
else
WEIGHTS_METAB = ones(size(data_metab,1),1);
end
% normalize WEIGHTS
WEIGHTS_METAB = WEIGHTS_METAB/sum(WEIGHTS_METAB);
% apply a hill fit for the metabolites
% nr_params_hill = length(p0_hill);
% nr_samples = length(TIME_METAB);
clear pfit res intact_fine
% fit hill model
cost_function = @(p) norm(WEIGHTS_METAB .* (LCN_calc_intact_tracer_hill(p,TIME_METAB)' - FRACTION_INTACT_TRACER));
[pfit, error] = fminsearch(cost_function, p0_hill);
if figures_on == 1 || save_figures == 1
close('all');
hfig1 = figure(1);
set(hfig1,'Name',subjectname);
plot(TIME_METAB,FRACTION_INTACT_TRACER,'o')
axis([0 1.1*max(TIME_METAB) 0 1])
hold on
fine_time = (0:0.01:max(TIME_METAB))';
intact_fine = LCN_calc_intact_tracer_hill(pfit,fine_time);
plot(fine_time,intact_fine)
title('hill');
xlabel('time (min)')
ylabel('fraction intact tracer')
if save_figures == 1
cd(dir_figures);
saveas(hfig1,['fig_metab_' subjectname '.fig']);
else
pause
end
end
% analyse the input data (plasma and blood)
% if the variables ref_time and scanner_time exist, we need to
% correct for decay between measurement of samples and start scan.
% if they are not available, it means that the data are not yet
% corrected for this time difference and it will be done here.
if exist('ref_time','var') == 1 && exist('scanner_time','var') == 1
% correct blood data for decay between start scan and start measurement and well counter
dt = minutes(duration(datetime(ref_time)-datetime(scanner_time),'Format','s'));
decay_factor = exp(log(2)*dt/thalf_F18);
input(:,2) = input(:,2).*decay_factor;
input(:,3) = input(:,3).*decay_factor;
end
% calculate corrected inputcurve
time_input = input(:,1)/60; % time in min
[correction] = LCN_calc_intact_tracer_hill(pfit,time_input);
uncor_input = calibrationfactor_wellcounter.*input(:,2)/(60*1000); % in kBq/ml
corrected_input = calibrationfactor_wellcounter.*correction.*input(:,2)/(60*1000); % in kBq/ml
uncor_blood = calibrationfactor_wellcounter.*input(:,3)/(60*1000); % in kBq/ml
TIME = [0; time_input];
FINETIMES = (0:STEP:max(TIME))';
CA = [0; corrected_input];
CA_WB = [0; uncor_blood];
CA_FINETIMES = interp1q(TIME,CA,FINETIMES);
CA_MIDSCANTIMES = interp1q(TIME,CA,MIDSCANTIMES);
CA_WB_FINETIMES = interp1q(TIME,CA_WB,FINETIMES);
if figures_on == 1 || save_figures == 1
hfig2 = figure(2);
set(hfig2,'Name',subjectname);
subplot(1,2,1);
plot(time_input,uncor_input,'o');
hold on
plot(time_input,corrected_input);
axis([0 1.1*max(time_input) 0 max(uncor_input)]);
title('plasma input function');
xlabel('time (min)');
ylabel('kBq/ml');
subplot(1,2,2);
plot(time_input,uncor_blood,'*r');
axis([0 1.1*max(time_input) 0 max(uncor_blood)]);
title('whole blood');
xlabel('time (min)');
ylabel('kBq/ml');
if save_figures == 1
cd(dir_figures);
saveas(hfig2,['fig_input_' subjectname '.fig']);
else
pause
end
end
end
% READ BRAIN DATA
if go_PET == 1 % dynamic data are found
% reading data in MNI space
fprintf('reading data of subject %s',subjectname);
fprintf(fid,'\n');
fprintf(fid,'Reading data in MNI space started: %s \n',datetime('now'));
if pet_space_reference
Vref_tmp = spm_vol(filename_wPET);
Vref = Vref_tmp(1);
% read dynamic data
dydata = zeros(Vref.dim(1),Vref.dim(2),Vref.dim(3),nr_frames);
for frame = 1:nr_frames
clear tmp
tmp = LCN12_read_image([filename_wPET ',' num2str(frame)],Vref);
dydata(:,:,:,frame) = tmp/1000; %in kBq/ml
end
else
atlas = atlas_orig;
Vref = Vatlas_orig;
dydata = zeros(Vref.dim(1),Vref.dim(2),Vref.dim(3),nr_frames);
for frame = 1:nr_frames
clear tmp
tmp = LCN12_read_image([filename_wPET ',' num2str(frame)],Vref);
dydata(:,:,:,frame) = tmp/1000; %in kBq/ml
end
end
% load the file excluded_frames
[filename_excluded_frames,~] = LCN_check_filename(dir_deriv,'excluded_frames_*pet.mat');
load(filename_excluded_frames); % now the variable excluded_frames is know
frames_ok = setdiff(1:nr_frames,excluded_frames)';
idx_frames = MIDSCANTIMES > logan_start_time;
if size(frames_ok,1) < (size(FRAMEDURATION,1) - sum(idx_frames) + 2) % we need at least 3 points to fit the Logan regression line
fprintf('\nWARNING: all frames after logan start time %d are excluded based on head motion, Logan cannot be fit - skipping subject %s\n', logan_start_time, subjectname);
continue
end
% read brain mask in MNI
fprintf(fid,'brain mask: %s\n',brain_mask_file);
brain_mask_img = LCN12_read_image(brain_mask_file,Vref);
% determine masks
brain_mask = (brain_mask_img > 0.5);
% get the voxel size
tmp = spm_imatrix(Vref.mat); % tmp(7:9) are the voxel sizes
voxelsize_wPET = abs(tmp(7:9));
else
fprintf('Dynamic data of subject %s not found \n',subjectname);
continue;
end
if go_GM*go_WM*go_CSF == 1
% read segmentations
fprintf(fid,'GM: %s\n',filename_GM);
GMimg = LCN12_read_image(filename_GM,Vref);
fprintf(fid,'WM: %s\n',filename_WM);
WMimg = LCN12_read_image(filename_WM,Vref);
fprintf(fid,'CSF: %s\n',filename_CSF);
CSFimg = LCN12_read_image(filename_CSF,Vref);
end
fprintf('... done\n');
fprintf(fid,'reading data ended at %s\n',datetime('now'));
if figures_on == 1 || save_figures == 1
hfig3 = figure(3);
hfig4 = figure(4);
end
% INITIATE VARIABLES FOR MODEL (COMPARISON) RESULTS
for m = 1:nr_models
modelname = model_list{m,1};
if strcmp(modelname,'Logan_input')
results_region_based_Logan_input_DV(subj,1) = {subjectname};
results_region_based_Logan_input_error(subj,1) = {subjectname};
if save_parcelimgs
atlas_Logan_input_DV = zeros(size(atlas_orig));
atlas_Logan_input_error = zeros(size(atlas_orig));
end
end
if strcmp(modelname,'2T4k')
results_region_based_2T4k_K1(subj,1) = {subjectname};
results_region_based_2T4k_k2(subj,1) = {subjectname};
results_region_based_2T4k_k3(subj,1) = {subjectname};
results_region_based_2T4k_k4(subj,1) = {subjectname};
results_region_based_2T4k_DV(subj,1) = {subjectname};
results_region_based_2T4k_error(subj,1) = {subjectname};
if save_parcelimgs
atlas_2T4k_K1 = zeros(size(atlas_orig));
atlas_2T4k_k2 = zeros(size(atlas_orig));
atlas_2T4k_k3 = zeros(size(atlas_orig));
atlas_2T4k_k4 = zeros(size(atlas_orig));
atlas_2T4k_DV = zeros(size(atlas_orig));
atlas_2T4k_error = zeros(size(atlas_orig));
end
end
if strcmp(modelname,'2T4k_Vb')
results_region_based_2T4k_Vb_K1(subj,1) = {subjectname};
results_region_based_2T4k_Vb_k2(subj,1) = {subjectname};
results_region_based_2T4k_Vb_k3(subj,1) = {subjectname};
results_region_based_2T4k_Vb_k4(subj,1) = {subjectname};
results_region_based_2T4k_Vb_Vb(subj,1) = {subjectname};
results_region_based_2T4k_Vb_DV(subj,1) = {subjectname};
results_region_based_2T4k_Vb_error(subj,1) = {subjectname};
if save_parcelimgs
atlas_2T4k_Vb_K1 = zeros(size(atlas_orig));
atlas_2T4k_Vb_k2 = zeros(size(atlas_orig));
atlas_2T4k_Vb_k3 = zeros(size(atlas_orig));
atlas_2T4k_Vb_k4 = zeros(size(atlas_orig));
atlas_2T4k_Vb_Vb = zeros(size(atlas_orig));
atlas_2T4k_Vb_DV = zeros(size(atlas_orig));
atlas_2T4k_Vb_error = zeros(size(atlas_orig));
end
end
if strcmp(modelname,'2T4k_vasc1k')
results_region_based_2T4k_vasc1k_K1(subj,1) = {subjectname};
results_region_based_2T4k_vasc1k_k2(subj,1) = {subjectname};
results_region_based_2T4k_vasc1k_k3(subj,1) = {subjectname};
results_region_based_2T4k_vasc1k_k4(subj,1) = {subjectname};
results_region_based_2T4k_vasc1k_Vb(subj,1) = {subjectname};
results_region_based_2T4k_vasc1k_K1v(subj,1) = {subjectname};
results_region_based_2T4k_vasc1k_DV(subj,1) = {subjectname};
results_region_based_2T4k_vasc1k_error(subj,1) = {subjectname};
if save_parcelimgs
atlas_2T4k_vasc1k_K1 = zeros(size(atlas_orig));
atlas_2T4k_vasc1k_k2 = zeros(size(atlas_orig));
atlas_2T4k_vasc1k_k3 = zeros(size(atlas_orig));
atlas_2T4k_vasc1k_k4 = zeros(size(atlas_orig));
atlas_2T4k_vasc1k_Vb = zeros(size(atlas_orig));
atlas_2T4k_vasc1k_K1v = zeros(size(atlas_orig));
atlas_2T4k_vasc1k_DV = zeros(size(atlas_orig));
atlas_2T4k_vasc1k_error = zeros(size(atlas_orig));
end
end
if strcmp(modelname,'2T4k_vasc2k')
results_region_based_2T4k_vasc2k_K1(subj,1) = {subjectname};
results_region_based_2T4k_vasc2k_k2(subj,1) = {subjectname};
results_region_based_2T4k_vasc2k_k3(subj,1) = {subjectname};
results_region_based_2T4k_vasc2k_k4(subj,1) = {subjectname};
results_region_based_2T4k_vasc2k_Vb(subj,1) = {subjectname};
results_region_based_2T4k_vasc2k_K1v(subj,1) = {subjectname};
results_region_based_2T4k_vasc2k_k2v(subj,1) = {subjectname};
results_region_based_2T4k_vasc2k_DV(subj,1) = {subjectname};
results_region_based_2T4k_vasc2k_error(subj,1) = {subjectname};
if save_parcelimgs
atlas_2T4k_vasc2k_K1 = zeros(size(atlas_orig));
atlas_2T4k_vasc2k_k2 = zeros(size(atlas_orig));
atlas_2T4k_vasc2k_k3 = zeros(size(atlas_orig));
atlas_2T4k_vasc2k_k4 = zeros(size(atlas_orig));
atlas_2T4k_vasc2k_Vb = zeros(size(atlas_orig));
atlas_2T4k_vasc2k_K1v = zeros(size(atlas_orig));
atlas_2T4k_vasc2k_k2v = zeros(size(atlas_orig));
atlas_2T4k_vasc2k_DV = zeros(size(atlas_orig));
atlas_2T4k_vasc2k_error = zeros(size(atlas_orig));
end
end
end
results_number_voxels_VOI(subj,1) = {subjectname};
results_region_based_AIC(4*(subj-1)+1,1) = {subjectname};
results_region_based_SC(4*(subj-1)+1,1) = {subjectname};
% READ ATLAS DATA
if pet_space_reference == 1
% read the atlas in the matrix of the subject
atlas = LCN12_read_image(atlas_filename,Vref);
% atlas contains non-integer values in voxels which are a mixture. We focus
% only on voxels which are of one specific type
% therefore, set the value of other voxels to 0
atlas(mod(atlas,1) > 0) = 0;
end
% adapt weight of the frames
WEIGHTS_PET = ones(length(MIDSCANTIMES),1);
WEIGHTS_PET(excluded_frames) = 0;
WEIGHTS_PET = WEIGHTS_PET./sum(WEIGHTS_PET);
fprintf('atlas based analysis started at %s \n',datetime('now'));
fprintf(fid,'atlas based analysis started at %s \n',datetime('now'));
% LOOP OVER VOIS
for voi = 1:nr_VOIS
clear index_value VOIimg VOIname C_MEASURED
index_value = find(parcel_values_atlas == parcel_values(voi));
VOIname = parcel_names{1,index_value};
fprintf(fid,'VOI %i: %s\n',index_value,VOIname);
VOIimg = (atlas == parcel_values(voi));
% intersect VOI with subject specific GM or WM
if intersect_GM == 1
VOIimg = (VOIimg > 0.5).*(GMimg > GM_CUTOFF);
elseif intersect_WM == 1
VOIimg = (VOIimg > 0.5).*(WMimg > WM_CUTOFF);
end
final_VOI_mask = (VOIimg > 0.5).*brain_mask > 0;
results_number_voxels_VOI(subj,1+voi) = {nnz(final_VOI_mask(:) > 0)};
if nnz(final_VOI_mask(:) > 0) < min_number_voxels
continue;
end
C_MEASURED = zeros(nr_frames,1);
for frame = 1:nr_frames
clear tmp values
tmp = dydata(:,:,:,frame);
values = tmp(final_VOI_mask > 0);
C_MEASURED(frame) = mean(values(~isnan(values)));
end
if window_size > 0
% temporal filtering using a median filter
C_MEASURED = medfilt1(C_MEASURED, window_size);
end
if sum(abs(C_MEASURED)) == 0
continue;
end
if sum(isnan(C_MEASURED)) > 0
continue;
end
% CALCULATE ALL THE MODELS
if figures_on == 1 || save_figures == 1
close(3);
hfig3 = figure(3);
set(hfig3,'Name',[subjectname ' - ' VOIname]);
close(4);
hfig4 = figure(4);
set(hfig4,'Name',[subjectname ' - ' VOIname]);
end
for m = 1:nr_models
modelname = model_list{m,1};
if strcmp(modelname,'Logan_input')
clear VD error
if figures_on == 1 || save_figures == 1
figure(3);
[DV,error] = LCN_LOGAN(MIDSCANTIMES(frames_ok),C_MEASURED(frames_ok),time_input,corrected_input,logan_start_time,3);
title('Logan input');
if save_figures == 1
cd(dir_figures);
saveas(hfig3,['fig_Logan_input_VOI_' VOIname '_' suffix '.fig']);
else
pause
end
else
[DV,error] = LCN_LOGAN(MIDSCANTIMES(frames_ok),C_MEASURED(frames_ok),time_input,corrected_input,logan_start_time,0);
end
results_region_based_Logan_input_DV(subj,1+voi) = {DV};
results_region_based_Logan_input_error(subj,1+voi) = {error};
if save_parcelimgs
atlas_Logan_input_DV(atlas_orig == parcel_values(voi)) = DV;
atlas_Logan_input_error(atlas_orig == parcel_values(voi)) = error;
Logan_outputname1 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_Logan_input_DV.nii']);
Logan_outputname2 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_Logan_input_error.nii']);
LCN12_write_image(atlas_Logan_input_DV,Logan_outputname1, '_' + suffix + '_Logan input DV',64,Vatlas_orig);
LCN12_write_image(atlas_Logan_input_error,Logan_outputname2, '_' + suffix + '_Logan input error',64,Vatlas_orig);
end
end
if strcmp(modelname,'2T4k')
clear tmpA tmpB index_min kfit_2T4k cost_function axvalues k0
% start new fit from different starting positions
tmpA = zeros(nr_k0_2T4k,4);
tmpB = zeros(nr_k0_2T4k,1);
cost_function = @(params) norm(WEIGHTS_PET.*(LCN_calc2_model_2T4k(params,MIDSCANTIMES,FRAMEDURATION,CA_FINETIMES,FINETIMES,CALC_OPTION,STEP) - C_MEASURED));
parfor j = 1:nr_k0_2T4k
k0 = k0_2T4k_all_values(j,:);
[tmpA(j,:),tmpB(j)] = fmincon(cost_function,k0,[],[],[],[],k0_2T4k_lower_bound,k0_2T4k_upper_bound,[],options);
end
index_min = find(tmpB == min(tmpB));
kfit_2T4k = tmpA(index_min(1),:);
results_region_based_2T4k_K1(subj,1+voi) = {kfit_2T4k(1)};
results_region_based_2T4k_k2(subj,1+voi) = {kfit_2T4k(2)};
results_region_based_2T4k_k3(subj,1+voi) = {kfit_2T4k(3)};
results_region_based_2T4k_k4(subj,1+voi) = {kfit_2T4k(4)};
results_region_based_2T4k_DV(subj,1+voi) = {kfit_2T4k(1)*(1+kfit_2T4k(3)/kfit_2T4k(4))/kfit_2T4k(2)};
results_region_based_2T4k_error(subj,1+voi) = {tmpB(index_min(1))./mean(C_MEASURED)}; % relative error
if save_parcelimgs
atlas_2T4k_K1(atlas_orig == parcel_values(voi)) = kfit_2T4k(1);
atlas_2T4k_k2(atlas_orig == parcel_values(voi)) = kfit_2T4k(2);
atlas_2T4k_k3(atlas_orig == parcel_values(voi)) = kfit_2T4k(3);
atlas_2T4k_k4(atlas_orig == parcel_values(voi)) = kfit_2T4k(4);
atlas_2T4k_DV(atlas_orig == parcel_values(voi)) = kfit_2T4k(1)*(1+kfit_2T4k(3)/kfit_2T4k(4))/kfit_2T4k(2);
atlas_2T4k_error(atlas_orig == parcel_values(voi)) = tmpB(index_min(1))./mean(C_MEASURED);
twoT4k_outputname1 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_K1.nii']);
twoT4k_outputname2 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_k2.nii']);
twoT4k_outputname3 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_k3.nii']);
twoT4k_outputname4 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_k4.nii']);
twoT4k_outputname6 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_DV.nii']);
twoT4k_outputname7 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_error.nii']);
LCN12_write_image(atlas_2T4k_K1,twoT4k_outputname1,'_' + suffix + '_2T4k_K1',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_k2,twoT4k_outputname2,'_' + suffix + '_2T4k_k2',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_k3,twoT4k_outputname3,'_' + suffix + '_2T4k_k3',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_k4,twoT4k_outputname4,'_' + suffix + '_2T4k_k4',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_DV,twoT4k_outputname6,'_' + suffix + '_2T4k_DV',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_error,twoT4k_outputname7,'_' + suffix + '_2T4k_error',64,Vatlas_orig);
end
[AIC,SC] = LCN_calc_model_selection(nnz(WEIGHTS_PET),tmpB(index_min(1)).^2,nr_params_2T4k);
results_region_based_AIC((subj-1)*4+1,2) = {modelname};
results_region_based_SC((subj-1)*4+1,2) = {modelname};
results_region_based_AIC((subj-1)*4+1,2+voi) = {AIC};
results_region_based_SC((subj-1)*4+1,2+voi) = {SC};
if figures_on == 1 || save_figures == 1
figure(4)
subplot(221)
plot(MIDSCANTIMES,C_MEASURED,'ob:');
hold on
plot(MIDSCANTIMES(excluded_frames),C_MEASURED(excluded_frames),'*r'); % indicating unreliable data due to head movements
xlabel('time in min');
ylabel('kBq/ml');
title('2T4k');
plot(MIDSCANTIMES,LCN_calc2_model_2T4k(kfit_2T4k,MIDSCANTIMES,FRAMEDURATION,CA_FINETIMES,FINETIMES,CALC_OPTION,STEP),'k')
axvalues = axis;
text(axvalues(2)*0.5,axvalues(3)+0.1*(axvalues(4)-axvalues(3)),['VD = ' num2str(kfit_2T4k(1)*(1+kfit_2T4k(3)/kfit_2T4k(4))/kfit_2T4k(2))]);
end
end
if strcmp(modelname,'2T4k_Vb')
clear tmpA tmpB index_min kfit_2T4k_Vb cost_function axvalues k0
% start new fit from different starting positions
tmpA = zeros(nr_k0_2T4k_Vb,5);
tmpB = zeros(nr_k0_2T4k_Vb,1);
cost_function = @(params) norm(WEIGHTS_PET.*(LCN_calc2_model_2T4k_Vb(params,MIDSCANTIMES,FRAMEDURATION,CA_FINETIMES,CA_WB_FINETIMES,FINETIMES,CALC_OPTION,STEP) - C_MEASURED));
parfor j = 1:nr_k0_2T4k_Vb
k0 = k0_2T4k_Vb_all_values(j,:);
[tmpA(j,:),tmpB(j)] = fmincon(cost_function,k0,[],[],[],[],k0_2T4k_Vb_lower_bound,k0_2T4k_Vb_upper_bound,[],options);
end
index_min = find(tmpB == min(tmpB));
kfit_2T4k_Vb = tmpA(index_min(1),:);
results_region_based_2T4k_Vb_K1(subj,1+voi) = {kfit_2T4k_Vb(1)};
results_region_based_2T4k_Vb_k2(subj,1+voi) = {kfit_2T4k_Vb(2)};
results_region_based_2T4k_Vb_k3(subj,1+voi) = {kfit_2T4k_Vb(3)};
results_region_based_2T4k_Vb_k4(subj,1+voi) = {kfit_2T4k_Vb(4)};
results_region_based_2T4k_Vb_Vb(subj,1+voi) = {kfit_2T4k_Vb(5)};
results_region_based_2T4k_Vb_DV(subj,1+voi) = {kfit_2T4k_Vb(1)*(1+kfit_2T4k_Vb(3)/kfit_2T4k_Vb(4))/kfit_2T4k_Vb(2)};
results_region_based_2T4k_Vb_error(subj,1+voi) = {tmpB(index_min(1))./mean(C_MEASURED)}; % relative error
if save_parcelimgs
atlas_2T4k_Vb_K1(atlas_orig == parcel_values(voi)) = kfit_2T4k_Vb(1);
atlas_2T4k_Vb_k2(atlas_orig == parcel_values(voi)) = kfit_2T4k_Vb(2);
atlas_2T4k_Vb_k3(atlas_orig == parcel_values(voi)) = kfit_2T4k_Vb(3);
atlas_2T4k_Vb_k4(atlas_orig == parcel_values(voi)) = kfit_2T4k_Vb(4);
atlas_2T4k_Vb_Vb(atlas_orig == parcel_values(voi)) = kfit_2T4k_Vb(5);
atlas_2T4k_Vb_DV(atlas_orig == parcel_values(voi)) = kfit_2T4k_Vb(1)*(1+kfit_2T4k_Vb(3)/kfit_2T4k_Vb(4))/kfit_2T4k_Vb(2);
atlas_2T4k_Vb_error(atlas_orig == parcel_values(voi)) = tmpB(index_min(1))./mean(C_MEASURED);
twoT4k_Vb_outputname1 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_Vb_K1.nii']);
twoT4k_Vb_outputname2 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_Vb_k2.nii']);
twoT4k_Vb_outputname3 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_Vb_k3.nii']);
twoT4k_Vb_outputname4 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_Vb_k4.nii']);
twoT4k_Vb_outputname5 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_Vb_Vb.nii']);
twoT4k_Vb_outputname6 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_Vb_DV.nii']);
twoT4k_Vb_outputname7 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_Vb_error.nii']);
LCN12_write_image(atlas_2T4k_Vb_K1,twoT4k_Vb_outputname1,'_' + suffix + '2T4k_Vb_K1',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_Vb_k2,twoT4k_Vb_outputname2,'_' + suffix + '2T4k_Vb_k2',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_Vb_k3,twoT4k_Vb_outputname3,'_' + suffix + '2T4k_Vb_k3',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_Vb_k4,twoT4k_Vb_outputname4,'_' + suffix + '2T4k_Vb_k4',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_Vb_Vb,twoT4k_Vb_outputname5,'_' + suffix + '2T4k_Vb_Vb',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_Vb_DV,twoT4k_Vb_outputname6,'_' + suffix + '2T4k_Vb_DV',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_Vb_error,twoT4k_Vb_outputname7,'_' + suffix + '2T4k_Vb_error',64,Vatlas_orig);
end
[AIC,SC] = LCN_calc_model_selection(nnz(WEIGHTS_PET),tmpB(index_min(1)).^2,nr_params_2T4k_Vb);
results_region_based_AIC((subj-1)*4+2,2) = {modelname};
results_region_based_SC((subj-1)*4+2,2) = {modelname};
results_region_based_AIC((subj-1)*4+2,2+voi) = {AIC};
results_region_based_SC((subj-1)*4+2,2+voi) = {SC};
if figures_on == 1 || save_figures == 1
figure(4)
subplot(222)
plot(MIDSCANTIMES,C_MEASURED,'ob:');
hold on
plot(MIDSCANTIMES(excluded_frames),C_MEASURED(excluded_frames),'*r'); % indicating unreliable data due to head movements
xlabel('time in min');
ylabel('kBq/ml');
title('2T4k\_Vb');
plot(MIDSCANTIMES,LCN_calc2_model_2T4k_Vb(kfit_2T4k_Vb,MIDSCANTIMES,FRAMEDURATION,CA_FINETIMES,CA_WB_FINETIMES,FINETIMES,CALC_OPTION,STEP),'k')
axvalues = axis;
text(axvalues(2)*0.5,axvalues(3)+0.1*(axvalues(4)-axvalues(3)),['VD = ' num2str(kfit_2T4k_Vb(1)*(1+kfit_2T4k_Vb(3)/kfit_2T4k_Vb(4))/kfit_2T4k_Vb(2))]);
end
end
if strcmp(modelname,'2T4k_vasc1k')
clear tmpA tmpB index_min kfit_2T4k_vasc1k cost_function axvalues k0
% start new fit from different starting positions
tmpA = zeros(nr_k0_2T4k_vasc1k,6);
tmpB = zeros(nr_k0_2T4k_vasc1k,1);
cost_function = @(params) norm(WEIGHTS_PET.*(LCN_calc_model_2T4k_vasc1k(params,MIDSCANTIMES,FRAMEDURATION,CA_FINETIMES,CA_WB_FINETIMES,FINETIMES,CALC_OPTION,STEP) - C_MEASURED));
parfor j = 1:nr_k0_2T4k_vasc1k
k0 = k0_2T4k_vasc1k_all_values(j,:);
[tmpA(j,:),tmpB(j)] = fmincon(cost_function,k0,[],[],[],[],k0_2T4k_vasc1k_lower_bound,k0_2T4k_vasc1k_upper_bound,[],options);
end
index_min = find(tmpB == min(tmpB));
kfit_2T4k_vasc1k = tmpA(index_min(1),:);
results_region_based_2T4k_vasc1k_K1(subj,1+voi) = {kfit_2T4k_vasc1k(1)};
results_region_based_2T4k_vasc1k_k2(subj,1+voi) = {kfit_2T4k_vasc1k(2)};
results_region_based_2T4k_vasc1k_k3(subj,1+voi) = {kfit_2T4k_vasc1k(3)};
results_region_based_2T4k_vasc1k_k4(subj,1+voi) = {kfit_2T4k_vasc1k(4)};
results_region_based_2T4k_vasc1k_Vb(subj,1+voi) = {kfit_2T4k_vasc1k(5)};
results_region_based_2T4k_vasc1k_K1v(subj,1+voi) = {kfit_2T4k_vasc1k(6)};
results_region_based_2T4k_vasc1k_DV(subj,1+voi) = {kfit_2T4k_vasc1k(1)*(1+kfit_2T4k_vasc1k(3)/kfit_2T4k_vasc1k(4))/kfit_2T4k_vasc1k(2)};
results_region_based_2T4k_vasc1k_error(subj,1+voi) = {tmpB(index_min(1))./mean(C_MEASURED)}; % relative error
if save_parcelimgs
atlas_2T4k_vasc1k_K1(atlas_orig == parcel_values(voi)) = kfit_2T4k_vasc1k(1);
atlas_2T4k_vasc1k_k2(atlas_orig == parcel_values(voi)) = kfit_2T4k_vasc1k(2);
atlas_2T4k_vasc1k_k3(atlas_orig == parcel_values(voi)) = kfit_2T4k_vasc1k(3);
atlas_2T4k_vasc1k_k4(atlas_orig == parcel_values(voi)) = kfit_2T4k_vasc1k(4);
atlas_2T4k_vasc1k_Vb(atlas_orig == parcel_values(voi)) = kfit_2T4k_vasc1k(5);
atlas_2T4k_vasc1k_K1v(atlas_orig == parcel_values(voi)) = kfit_2T4k_vasc1k(6);
atlas_2T4k_vasc1k_DV(atlas_orig == parcel_values(voi)) = kfit_2T4k_vasc1k(1)*(1+kfit_2T4k_vasc1k(3)/kfit_2T4k_vasc1k(4))/kfit_2T4k_vasc1k(2);
atlas_2T4k_vasc1k_error(atlas_orig == parcel_values(voi)) = tmpB(index_min(1))./mean(C_MEASURED);
twoT4k_vasc1k_outputname1 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_vasc1k_K1.nii']);
twoT4k_vasc1k_outputname2 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_vasc1k_k2.nii']);
twoT4k_vasc1k_outputname3 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_vasc1k_k3.nii']);
twoT4k_vasc1k_outputname4 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_vasc1k_k4.nii']);
twoT4k_vasc1k_outputname5 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_vasc1k_Vb.nii']);
twoT4k_vasc1k_outputname6 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_vasc1k_K1v.nii']);
twoT4k_vasc1k_outputname7 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_vasc1k_DV.nii']);
twoT4k_vasc1k_outputname8 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_vasc1k_error.nii']);
LCN12_write_image(atlas_2T4k_vasc1k_K1,twoT4k_vasc1k_outputname1,'_' + suffix + '2T4k_vasc1k_K1',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_vasc1k_k2,twoT4k_vasc1k_outputname2,'_' + suffix + '2T4k_vasc1k_k2',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_vasc1k_k3,twoT4k_vasc1k_outputname3,'_' + suffix + '2T4k_vasc1k_k3',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_vasc1k_k4,twoT4k_vasc1k_outputname4,'_' + suffix + '2T4k_vasc1k_k4',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_vasc1k_Vb,twoT4k_vasc1k_outputname5,'_' + suffix + '2T4k_vasc1k_Vb',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_vasc1k_K1v,twoT4k_vasc1k_outputname6,'_' + suffix + '2T4k_vasc1k_K1v',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_vasc1k_DV,twoT4k_vasc1k_outputname7,'_' + suffix + '2T4k_vasc1k_DV',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_vasc1k_error,twoT4k_vasc1k_outputname8,'_' + suffix + '2T4k_vasc1k_error',64,Vatlas_orig);
end
[AIC,SC] = LCN_calc_model_selection(nnz(WEIGHTS_PET),tmpB(index_min(1)).^2,nr_params_2T4k_Vb);
results_region_based_AIC((subj-1)*4+3,2) = {modelname};
results_region_based_SC((subj-1)*4+3,2) = {modelname};
results_region_based_AIC((subj-1)*4+3,2+voi) = {AIC};
results_region_based_SC((subj-1)*4+3,2+voi) = {SC};
if figures_on == 1 || save_figures == 1
figure(4)
subplot(223)
plot(MIDSCANTIMES,C_MEASURED,'ob:');
hold on
plot(MIDSCANTIMES(excluded_frames),C_MEASURED(excluded_frames),'*r'); % indicating unreliable data due to head movements
xlabel('time in min');
ylabel('kBq/ml');
title('2T4K\_vasc1k');
plot(MIDSCANTIMES,LCN_calc_model_2T4k_vasc1k(kfit_2T4k_vasc1k,MIDSCANTIMES,FRAMEDURATION,CA_FINETIMES,CA_WB_FINETIMES,FINETIMES,CALC_OPTION,STEP),'k')
axvalues = axis;
text(axvalues(2)*0.5,axvalues(3)+0.1*(axvalues(4)-axvalues(3)),['VD = ' num2str(kfit_2T4k_vasc1k(1)*(1+kfit_2T4k_vasc1k(3)/kfit_2T4k_vasc1k(4))/kfit_2T4k_vasc1k(2))]);
end
end
if strcmp(modelname,'2T4k_vasc2k')
clear tmpA tmpB index_min kfit_2T4k_vasc2k cost_function axvalues k0
% start new fit from different starting positions
tmpA = zeros(nr_k0_2T4k_vasc2k,7);
tmpB = zeros(nr_k0_2T4k_vasc2k,1);
cost_function = @(params) norm(WEIGHTS_PET.*(LCN_calc_model_2T4k_vasc2k(params,MIDSCANTIMES,FRAMEDURATION,CA_FINETIMES,CA_WB_FINETIMES,FINETIMES,CALC_OPTION,STEP) - C_MEASURED));
parfor j = 1:nr_k0_2T4k_vasc2k
k0 = k0_2T4k_vasc2k_all_values(j,:);
[tmpA(j,:),tmpB(j)] = fmincon(cost_function,k0,[],[],[],[],k0_2T4k_vasc2k_lower_bound,k0_2T4k_vasc2k_upper_bound,[],options);
end
index_min = find(tmpB == min(tmpB));
kfit_2T4k_vasc2k = tmpA(index_min(1),:);
results_region_based_2T4k_vasc2k_K1(subj,1+voi) = {kfit_2T4k_vasc2k(1)};
results_region_based_2T4k_vasc2k_k2(subj,1+voi) = {kfit_2T4k_vasc2k(2)};
results_region_based_2T4k_vasc2k_k3(subj,1+voi) = {kfit_2T4k_vasc2k(3)};
results_region_based_2T4k_vasc2k_k4(subj,1+voi) = {kfit_2T4k_vasc2k(4)};
results_region_based_2T4k_vasc2k_Vb(subj,1+voi) = {kfit_2T4k_vasc2k(5)};
results_region_based_2T4k_vasc2k_K1v(subj,1+voi) = {kfit_2T4k_vasc2k(6)};
results_region_based_2T4k_vasc2k_k2v(subj,1+voi) = {kfit_2T4k_vasc2k(7)};
results_region_based_2T4k_vasc2k_DV(subj,1+voi) = {kfit_2T4k_vasc2k(1)*(1+kfit_2T4k_vasc2k(3)/kfit_2T4k_vasc2k(4))/kfit_2T4k_vasc2k(2)};
results_region_based_2T4k_vasc2k_error(subj,1+voi) = {tmpB(index_min(1))./mean(C_MEASURED)}; % relative error
if save_parcelimgs
atlas_2T4k_vasc2k_K1(atlas_orig == parcel_values(voi)) = kfit_2T4k_vasc2k(1);
atlas_2T4k_vasc2k_k2(atlas_orig == parcel_values(voi)) = kfit_2T4k_vasc2k(2);
atlas_2T4k_vasc2k_k3(atlas_orig == parcel_values(voi)) = kfit_2T4k_vasc2k(3);
atlas_2T4k_vasc2k_k4(atlas_orig == parcel_values(voi)) = kfit_2T4k_vasc2k(4);
atlas_2T4k_vasc2k_Vb(atlas_orig == parcel_values(voi)) = kfit_2T4k_vasc2k(5);
atlas_2T4k_vasc2k_K1v(atlas_orig == parcel_values(voi)) = kfit_2T4k_vasc2k(6);
atlas_2T4k_vasc2k_k2v(atlas_orig == parcel_values(voi)) = kfit_2T4k_vasc2k(7);
atlas_2T4k_vasc2k_DV(atlas_orig == parcel_values(voi)) = kfit_2T4k_vasc2k(1)*(1+kfit_2T4k_vasc2k(3)/kfit_2T4k_vasc2k(4))/kfit_2T4k_vasc2k(2);
atlas_2T4k_vasc2k_error(atlas_orig == parcel_values(voi)) = tmpB(index_min(1))./mean(C_MEASURED);
twoT4k_vasc2k_outputname1 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_vasc2k_K1.nii']);
twoT4k_vasc2k_outputname2 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_vasc2k_k2.nii']);
twoT4k_vasc2k_outputname3 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_vasc2k_k3.nii']);
twoT4k_vasc2k_outputname4 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_vasc2k_k4.nii']);
twoT4k_vasc2k_outputname5 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_vasc2k_Vb.nii']);
twoT4k_vasc2k_outputname6 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_vasc2k_K1v.nii']);
twoT4k_vasc2k_outputname7 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_vasc2k_k2v.nii']);
twoT4k_vasc2k_outputname8 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_vasc2k_DV.nii']);
twoT4k_vasc2k_outputname9 = fullfile(dir_firstlevel,['w' subjectname '_' suffix '_atlas_2T4k_vasc2k_error.nii']);
LCN12_write_image(atlas_2T4k_vasc2k_K1,twoT4k_vasc2k_outputname1,'_' + suffix + '2T4k_vasc2k_K1',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_vasc2k_k2,twoT4k_vasc2k_outputname2,'_' + suffix + '2T4k_vasc2k_k2',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_vasc2k_k3,twoT4k_vasc2k_outputname3,'_' + suffix + '2T4k_vasc2k_k3',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_vasc2k_k4,twoT4k_vasc2k_outputname4,'_' + suffix + '2T4k_vasc2k_k4',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_vasc2k_Vb,twoT4k_vasc2k_outputname5,'_' + suffix + '2T4k_vasc2k_Vb',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_vasc2k_K1v,twoT4k_vasc2k_outputname6,'_' + suffix + '2T4k_vasc2k_K1v',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_vasc2k_k2v,twoT4k_vasc2k_outputname7,'_' + suffix + '2T4k_vasc2k_k2v',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_vasc2k_DV,twoT4k_vasc2k_outputname8,'_' + suffix + '2T4k_vasc2k_DV',64,Vatlas_orig);
LCN12_write_image(atlas_2T4k_vasc2k_error,twoT4k_vasc2k_outputname9,'_' + suffix + '2T4k_vasc2k_error',64,Vatlas_orig);
end
[AIC,SC] = LCN_calc_model_selection(nnz(WEIGHTS_PET),tmpB(index_min(1)).^2,nr_params_2T4k_Vb);
results_region_based_AIC((subj-1)*4+4,2) = {modelname};
results_region_based_SC((subj-1)*4+4,2) = {modelname};
results_region_based_AIC((subj-1)*4+4,2+voi) = {AIC};
results_region_based_SC((subj-1)*4+4,2+voi) = {SC};
if figures_on == 1 || save_figures == 1
figure(4)
subplot(224)
plot(MIDSCANTIMES,C_MEASURED,'ob:');
hold on
plot(MIDSCANTIMES(excluded_frames),C_MEASURED(excluded_frames),'*r'); % indicating unreliable data due to head movements
xlabel('time in min');
ylabel('kBq/ml');
title('2T4K\_vasc2k');
plot(MIDSCANTIMES,LCN_calc_model_2T4k_vasc2k(kfit_2T4k_vasc2k,MIDSCANTIMES,FRAMEDURATION,CA_FINETIMES,CA_WB_FINETIMES,FINETIMES,CALC_OPTION,STEP),'k')
axvalues = axis;
text(axvalues(2)*0.5,axvalues(3)+0.1*(axvalues(4)-axvalues(3)),['VD = ' num2str(kfit_2T4k_vasc2k(1)*(1+kfit_2T4k_vasc2k(3)/kfit_2T4k_vasc2k(4))/kfit_2T4k_vasc2k(2))]);
end
end
end % for loop models
if save_figures == 1
cd(dir_figures);
saveas(hfig4,['fig_4models_' VOIname '_' suffix '.fig']);
else
pause
end
end % for loop VOIs
fprintf('atlas based analysis ended at %s \n',datetime('now'));
fprintf(fid,'atlas based analysis ended at %s \n',datetime('now'));
% GENERATE A VOXEL-BASED PARAMETRIC IMAGE FOR LOGAN INPUT
if do_voxelwise_logan
clear DV_Logan DV_Logan_error outputname1 outputname2 Vout1 Vout2
dimx = Vref.dim(1);
dimy = Vref.dim(2);
dimz = Vref.dim(3);
fprintf('start additional smoothing at %s \n',datetime('now'));
fprintf(fid,'start additional smoothing at %s \n',datetime('now'));
% smooth the images
sdydata = zeros(dimx*dimy*dimz,size(dydata,4));
if intersect_GM == 1
brain_mask = (brain_mask > 0).*(GMimg > GM_CUTOFF);
end
if intersect_WM == 1
brain_mask = (brain_mask > 0).*(WMimg > WM_CUTOFF);
end
for frame = 1:nr_frames
clear tmp stmp
tmp = squeeze(dydata(:,:,:,frame)).*(brain_mask > 0);
stmp = LCN12_smooth(tmp,[additional_smooth_parametric additional_smooth_parametric additional_smooth_parametric]./voxelsize_wPET,brain_mask);
sdydata(:,frame) = stmp(:);
end
fprintf('additional smoothing ended at %s \n',datetime('now'));
fprintf(fid,'additional smoothing ended at %s \n',datetime('now'));
% run voxel-based Logan analysis
indexlist = find(brain_mask(:) == 1);
datavalues = sdydata(indexlist,:);
tmpA = [];
tmpB = [];
tmp1 = zeros(dimx*dimy*dimz,1);
tmp2 = zeros(dimx*dimy*dimz,1);
fprintf('voxel based analysis for Logan_input started at %s \n',datetime('now'));
fprintf(fid,'voxel based analysis for Logan_input started at %s \n',datetime('now'));
datavalues_ok = datavalues(:,frames_ok);
midscantimes_ok = MIDSCANTIMES(frames_ok);
parfor i = 1:length(indexlist)
C_MEASURED = datavalues_ok(i,:)';
[DV,error] = LCN_LOGAN(midscantimes_ok,C_MEASURED,time_input,corrected_input,logan_start_time,0);
tmpA(i) = DV;
tmpB(i) = error;
end
tmp1(indexlist) = tmpA;
tmp2(indexlist) = tmpB;
DV_Logan = reshape(tmp1,dimx,dimy,dimz);
DV_Logan_error = reshape(tmp2,dimx,dimy,dimz);
% save images
outputname1 = fullfile(dir_firstlevel,['w' subjectname '_Logan_input_DV.nii']);
outputname2 = fullfile(dir_firstlevel,['w' subjectname '_Logan_input_error.nii']);
Vout1 = LCN12_write_image(DV_Logan,outputname1,'DV Logan',Vref.dt(1),Vref);
Vout2 = LCN12_write_image(DV_Logan_error,outputname2,'DV Logan error',Vref.dt(1),Vref);
fprintf('voxel based analysis for Logan_input ended at %s \n',datetime('now'));
fprintf(fid,'voxel based analysis for Logan_input ended at %s \n',datetime('now'));
end
end % for loop over subjects
%% SAVE THE RESULTS FOR THE REGION-BASED ANALYSIS
%--------------------------------------------------------------------------
for m = 1:nr_models
modelname = model_list{m,1};
if strcmp(modelname,'Logan_input')
outputfile_excel = [outputfile_excel_region_based '_' suffix '_Logan_input.xlsx'];
writetable(results_region_based_Logan_input_DV,outputfile_excel,'Sheet','DV');
writetable(results_region_based_Logan_input_error,outputfile_excel,'Sheet','error');
end
if strcmp(modelname,'2T4k')
outputfile_excel = [outputfile_excel_region_based '_' suffix '_2T4k.xlsx'];
writetable(results_region_based_2T4k_K1,outputfile_excel,'Sheet','K1');
writetable(results_region_based_2T4k_k2,outputfile_excel,'Sheet','k2');
writetable(results_region_based_2T4k_k3,outputfile_excel,'Sheet','k3');
writetable(results_region_based_2T4k_k4,outputfile_excel,'Sheet','k4');
writetable(results_region_based_2T4k_DV,outputfile_excel,'Sheet','DV');
writetable(results_region_based_2T4k_error,outputfile_excel,'Sheet','error');
end
if strcmp(modelname,'2T4k_Vb')
outputfile_excel = [outputfile_excel_region_based '_' suffix '_2T4k_Vb.xlsx'];
writetable(results_region_based_2T4k_Vb_K1,outputfile_excel,'Sheet','K1');
writetable(results_region_based_2T4k_Vb_k2,outputfile_excel,'Sheet','k2');
writetable(results_region_based_2T4k_Vb_k3,outputfile_excel,'Sheet','k3');
writetable(results_region_based_2T4k_Vb_k4,outputfile_excel,'Sheet','k4');
writetable(results_region_based_2T4k_Vb_Vb,outputfile_excel,'Sheet','Vb');
writetable(results_region_based_2T4k_Vb_DV,outputfile_excel,'Sheet','DV');
writetable(results_region_based_2T4k_Vb_error,outputfile_excel,'Sheet','error');
end
if strcmp(modelname,'2T4k_vasc1k')
outputfile_excel = [outputfile_excel_region_based '_' suffix '_2T4k_vasc1k.xlsx'];
writetable(results_region_based_2T4k_vasc1k_K1,outputfile_excel,'Sheet','K1');
writetable(results_region_based_2T4k_vasc1k_k2,outputfile_excel,'Sheet','k2');
writetable(results_region_based_2T4k_vasc1k_k3,outputfile_excel,'Sheet','k3');
writetable(results_region_based_2T4k_vasc1k_k4,outputfile_excel,'Sheet','k4');
writetable(results_region_based_2T4k_vasc1k_Vb,outputfile_excel,'Sheet','Vb');
writetable(results_region_based_2T4k_vasc1k_K1v,outputfile_excel,'Sheet','K1v');
writetable(results_region_based_2T4k_vasc1k_DV,outputfile_excel,'Sheet','DV');
writetable(results_region_based_2T4k_vasc1k_error,outputfile_excel,'Sheet','error');
end
if strcmp(modelname,'2T4k_vasc2k')
outputfile_excel = [outputfile_excel_region_based '_' suffix '_2T4k_vasc2k.xlsx'];
writetable(results_region_based_2T4k_vasc2k_K1,outputfile_excel,'Sheet','K1');
writetable(results_region_based_2T4k_vasc2k_k2,outputfile_excel,'Sheet','k2');
writetable(results_region_based_2T4k_vasc2k_k3,outputfile_excel,'Sheet','k3');
writetable(results_region_based_2T4k_vasc2k_k4,outputfile_excel,'Sheet','k4');
writetable(results_region_based_2T4k_vasc2k_Vb,outputfile_excel,'Sheet','Vb');
writetable(results_region_based_2T4k_vasc2k_K1v,outputfile_excel,'Sheet','K1v');
writetable(results_region_based_2T4k_vasc2k_k2v,outputfile_excel,'Sheet','k2v');
writetable(results_region_based_2T4k_vasc2k_DV,outputfile_excel,'Sheet','DV');
writetable(results_region_based_2T4k_vasc2k_error,outputfile_excel,'Sheet','error');
end
end
outputfile_excel = [outputfile_excel_region_based '_' suffix '_model_comparison_' char(datetime('today')) '.xlsx'];
writetable(results_region_based_AIC,outputfile_excel,'Sheet','AIC');
writetable(results_region_based_SC,outputfile_excel,'Sheet','SC');
outputfile_excel = [outputfile_excel_region_based '_' suffix '_nr_voxels_used_' char(datetime('today')) '.xlsx'];
writetable(results_number_voxels_VOI,outputfile_excel,'Sheet','nr of voxels');
cd(curdir);
fprintf('ALL DONE\n');

LaBGAScore_pet_model_TSPO_DPA714.m at commit 75a8bc9, under GPL-3.0 · at the source

Overview

Authors: Ynse Dooms^1,2,3, Lixin Qiu^1,3, Iris Coppieters^1,3,4,5, Elfi Vergaelen^6,7, Stephan Claes^6,7, Patrick Dupont^3,8, Melina Hehl^2,3,9, Koen Cuypers^2,3,10, Harald Engler¹¹, Kirsten Dombrowski¹¹, Kristin Verbeke¹², Omer Van den Bergh¹³, Jeroen Raes¹⁴, Lukas Van Oudenhove^1,3,15, Maaike Van Den Houte^1,2,3, Katleen Bogaerts^2,13

ORCID iDs: Ynse Dooms, Patrick Dupont, Melina Hehl, Koen Cuypers, Omer Van den Bergh, Jeroen Raes, Lukas Van Oudenhove, Maaike Van Den Houte

15 affiliations

Laboratory for Brain-Gut Axis Studies (LaBGAS), Translational Research in Gastrointestinal Disorders (TARGID), Department of Chronic Diseases and Metabolism (CHROMETA), KU Leuven, Leuven, Belgium
REVAL – Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium
Leuven Brain Institute, KU Leuven, Leuven, Belgium
Pain in Motion Research Group (PAIN), Department of Physiotherapy, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel, Brussels, Belgium
Experimental Health Psychology Research Group, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, 6211 LK, the Netherlands
University Psychiatric Center KU Leuven, University Hospitals Leuven, Leuven, Belgium
Mind Body Research, Psychiatry Research Group, Department of Neurosciences, KU Leuven, Leuven, Belgium
Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
Translational MRI, Department of Imaging & Pathology, KU Leuven, Leuven, Belgium
Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Heverlee, Belgium
Institute of Medical Psychology and Behavioral Immunobiology, Center for Translational Neuro- and Behavioral Sciences (C-TNBS), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
Laboratory of Digestion and Absorption (DigALab), Translational Research Center in Gastrointestinal Disorders (TARGID), Department of Chronic Diseases and Metabolism (CHROMETA), KU Leuven, Leuven, Belgium
Health Psychology, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
Laboratory of Molecular Bacteriology, Rega Institute, KU Leuven, Leuven, Belgium
Cognitive & Affective Neuroscience Lab (CANLab), Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA

Institutions: Hasselt University (Belgium); KU Leuven (Belgium); Vrije Universiteit Brussel (Belgium); Maastricht University (Netherlands); Pain in Motion (Belgium); Universitair Ziekenhuis Leuven (Belgium); Essen University Hospital (Germany); University of Duisburg-Essen (Germany); Dartmouth College (United States)

Journal: Brain, behavior, & immunity - health, volume 56, article 101299

Dates: received 27 February 2026; accepted 5 July 2026; published online 6 July 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1016/j.bbih.2026.101299 · PMID 42472232 · PMCID PMC13380062 · OpenAlex W7167514348

Open access: gold, a free copy (OpenAlex)

Status: code verified

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

Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging, Physiology & signal measures

Keywords: Fatigue, Inflammation, Neurophysiology, Gastrointestinal microbiome, Psychological stress

Topic: Fibromyalgia and Chronic Fatigue Syndrome Research (Psychiatry and Mental health, Medicine), according to OpenAlex

Funding: Research Foundation Flanders

Citations: not cited yet (Europe PMC); 170 references in the paper

Research resources: 2011 RRID:SCR_007037, 2012 RRID:SCR_009550, RRID:SCR_016216

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 16 matches between paragraphs and lines of code.

labgas/labgascore

License: GPL-3.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 75a8bc9e5bc708c2d5ca78efd2c05602220140b2, 24 September 2026

Languages: MATLAB (150), Python (5), Shell (3), SAS (2)

Size: 196 files, 160 scripts

Software Heritage: not archived

Found in: “Data availability”

Holds: README, license file

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

Tools: Statistics and Machine Learning Toolbox (26 files), SPM (23 files), Parallel Computing Toolbox (4 files), CoSMoMVPA (1 file), DataLad (1 file), Image Processing Toolbox (1 file), Optimization Toolbox (1 file), Signal Processing Toolbox (1 file), Tools for NIfTI and ANALYZE image (MATLAB) (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

162 files

atlas_mask_tools/LaBGAScore_atlas_binary_mask_from_atlas.m, MATLAB, 261 lines
atlas_mask_tools/LaBGAScore_atlas_rois_from_atlas.m, MATLAB, 434 lines
clean/LaBGAScore_check_all_scripts.m, MATLAB, 114 lines
clean/LaBGAScore_check_display.m, MATLAB, 200 lines
clean/LaBGAScore_clean_gzip_all_nii.m, MATLAB, 83 lines
clean/LaBGAScore_clean_sourcedata.m, MATLAB, 34 lines
clean/LaBGAScore_dep_build_index.m, MATLAB, 319 lines
clean/LaBGAScore_dep_map.m, MATLAB, 682 lines
clean/LaBGAScore_dep_report.m, MATLAB, 368 lines
clean/LaBGAScore_move_repos_matlabpath.m, MATLAB, 21 lines
clean/LaBGAScore_prov_gitinfo.m, MATLAB, 259 lines
clean/LaBGAScore_prov_gitstatus.m, MATLAB, 268 lines
clean/LaBGAScore_prov_protect_reflogs.m, MATLAB, 210 lines
clean/LaBGAScore_prov_publish.m, MATLAB, 439 lines
clean/LaBGAScore_prov_resolve_retrospective.m, MATLAB, 1,237 lines
clean/LaBGAScore_prov_snapshot.m, MATLAB, 533 lines
clean/LaBGAScore_run_reports.m, MATLAB, 284 lines
clean/LaBGAScore_smart_parallel_pool_setup.m, MATLAB, 54 lines
clean/checker_positive_controls/control_mvpa_reg_cov_permutation.m, MATLAB, 137 lines
clean/checker_positive_controls/control_set_after_use.m, MATLAB, 36 lines
clean/checker_positive_controls/control_use_before_def.m, MATLAB, 32 lines
clean/checker_positive_controls/run_controls.sh, Shell, 34 lines
clean/labgascore_prov_protect_reflogs.sh, Shell, 66 lines
clean/labgascore_run_headless.sh, Shell, 230 lines
clean/set_after_use.py, Python, 87 lines
clean/use_before_def.py, Python, 69 lines
cosmomvpa/LaBGAScore_cosmomvpa_searchlight_rsa.m, MATLAB, 626 lines
decoding_toolbox/LaBGAScore_decoding_SVM_between_subjects.m, MATLAB, 2,004 lines, 1 match
decoding_toolbox/LaBGAScore_decoding_template_xclass_acc.m, MATLAB, 412 lines
decoding_toolbox/LaBGAScore_export_scaled_contrasts.m, MATLAB, 119 lines
figures/canlabCmap.m, MATLAB, 3 lines
figures/cluster_surface_plots.m, MATLAB, 81 lines
figures/plugin_set_figure_size.m, MATLAB, 381 lines
figures/save_all_open_figures_smart.m, MATLAB, 199 lines
firstlevel/LaBGAScore_firstlevel_s1_options_dsgn_struct.m, MATLAB, 462 lines
firstlevel/LaBGAScore_firstlevel_s1a_options_dsgn_multisess_multitask.m, MATLAB, 475 lines
firstlevel/LaBGAScore_firstlevel_s1b_fit_phMRI_model.m, MATLAB, 569 lines
firstlevel/LaBGAScore_firstlevel_s2_fit_model.m, MATLAB, 1,102 lines
firstlevel/LaBGAScore_firstlevel_s2a_fit_model_multisess_multitask.m, MATLAB, 2,019 lines
firstlevel/LaBGAScore_firstlevel_s2b_phMRI_covar_contrasts.m, MATLAB, 326 lines
firstlevel/LaBGAScore_firstlevel_s3_diagnose_model.m, MATLAB, 98 lines
firstlevel/LaBGAScore_firstlevel_s3b_phMRI_signature_responses.m, MATLAB, 254 lines
firstlevel/LaBGAScore_firstlevel_s3c_phMRI_neurotransmitter_maps.m, MATLAB, 261 lines
firstlevel/functions/LaBGAScore_firstlevel_find_events.m, MATLAB, 111 lines
firstlevel/functions/LaBGAScore_firstlevel_refit_motion_comparison.m, MATLAB, 710 lines
firstlevel/functions/LaBGAScore_firstlevel_task_motion_diagnostics.m, MATLAB, 680 lines
firstlevel/functions/canlab_glm_subject_levels_old.m, MATLAB, 690 lines
firstlevel/functions/canlab_glm_subject_levels_run1subject_old.m, MATLAB, 766 lines
graphvar/LaBGAScore_prep_graphvar_input_from_conn.m, MATLAB, 360 lines
juspace/LaBGAScore_juspace_corr_behav.m, MATLAB, 141 lines
juspace/LaBGAScore_prep_juspace_input.m, MATLAB, 308 lines
mrs/LaBGAScore_mrs_osprey_jobfile_GE.m, MATLAB, 393 lines
mrs/LaBGAScore_mrs_osprey_jobfile_Philips.m, MATLAB, 395 lines, 1 match
mrs/LaBGAScore_mrs_osprey_single_sess_jobfile_GE.m, MATLAB, 383 lines
mrs/LaBGAScore_mrs_osprey_single_sess_jobfile_Philips.m, MATLAB, 385 lines, 1 match
mrs/LaBGAScore_mrs_run_osprey_GE.m, MATLAB, 317 lines, 1 match
mrs/LaBGAScore_mrs_run_osprey_Philips.m, MATLAB, 317 lines, 1 match
mrs/LaBGAScore_prep_mrs2bids.m, MATLAB, 392 lines
pet/functions/LCN12_PET_preprocessing.m, MATLAB, 430 lines, 2 matches
pet/functions/LCN12_analyze_headmovement_PET.m, MATLAB, 128 lines
pet/functions/LCN12_generate_qc_from4D.m, MATLAB, 44 lines
pet/functions/LCN12_read_image.m, MATLAB, 57 lines
pet/functions/LCN12_realign2_dy_PET.m, MATLAB, 146 lines
pet/functions/LCN12_realign_dy_PET.m, MATLAB, 178 lines
pet/functions/LCN12_smooth.m, MATLAB, 56 lines
pet/functions/LCN12_write_image.m, MATLAB, 85 lines, 1 match
pet/functions/LCN_3Dimage_dilate.m, MATLAB, 28 lines
pet/functions/LCN_LOGAN.m, MATLAB, 86 lines
pet/functions/LCN_calc2_model_2T4k.m, MATLAB, 57 lines
pet/functions/LCN_calc2_model_2T4k_Vb.m, MATLAB, 62 lines
pet/functions/LCN_calc_intact_tracer_hill.m, MATLAB, 21 lines
pet/functions/LCN_calc_model_2T4k_vasc1k.m, MATLAB, 63 lines
pet/functions/LCN_calc_model_2T4k_vasc2k.m, MATLAB, 62 lines
pet/functions/LCN_calc_model_selection.m, MATLAB, 22 lines
pet/functions/LCN_check_filename.m, MATLAB, 23 lines
pet/functions/LCN_cost_intact_tracer_hill.m, MATLAB, 32 lines
pet/functions/LCN_string.m, MATLAB, 30 lines
pet/scripts/LaBGAScore_pet_a2_set_default_options.m, MATLAB, 333 lines
pet/scripts/LaBGAScore_pet_a_set_up_paths_always_run_first.m, MATLAB, 252 lines
pet/scripts/LaBGAScore_pet_dcm2bids.m, MATLAB, 137 lines
pet/scripts/LaBGAScore_pet_model_TSPO_DPA714.m, MATLAB, 1,435 lines, 2 matches
pet/scripts/LaBGAScore_pet_prep_1_dat_behavioral_data.m, MATLAB, 140 lines
pet/scripts/LaBGAScore_pet_prep_2_load_image_data_and_save.m, MATLAB, 309 lines
pet/scripts/LaBGAScore_pet_prep_4_apply_signatures_and_save.m, MATLAB, 137 lines
pet/scripts/LaBGAScore_pet_preprocess_data.m, MATLAB, 221 lines
pet/scripts/LaBGAScore_pet_run_plot_PLS_ENet_pipeline.m, MATLAB, 531 lines
power/holmthreshold.m, MATLAB, 18 lines
power/medianIQR_to_meanSD.m, MATLAB, 47 lines
power/print_LEAR_matrix.m, MATLAB, 57 lines
power/sd_from_ci.m, MATLAB, 32 lines
prep/LaBGAScore_prep_parrec2bids.m, MATLAB, 430 lines
prep/LaBGAScore_prep_s0_define_directories.m, MATLAB, 111 lines
prep/LaBGAScore_prep_s1_write_events_tsv.m, MATLAB, 411 lines
prep/LaBGAScore_prep_s1_write_events_tsv_multisess_multitask.m, MATLAB, 1,029 lines
prep/LaBGAScore_prep_s2_smooth.m, MATLAB, 134 lines
prep/LaBGAScore_prep_s2_smooth_multisess.m, MATLAB, 156 lines
qr_code/QRtoPDF.py, Python, 22 lines
qr_code/emailQR.py, Python, 21 lines
qr_code/email_QR_Input.py, Python, 22 lines
secondlevel/LaBGAScore_pdm_regenerate_reports.m, MATLAB, 85 lines
secondlevel/classes/ProgressTracker.m, MATLAB, 64 lines
secondlevel/functions/ENet_neuroimaging_pipeline.m, MATLAB, 729 lines
secondlevel/functions/LaBGAScore_blob_montage.m, MATLAB, 91 lines
secondlevel/functions/LaBGAScore_pdm_report.m, MATLAB, 111 lines
secondlevel/functions/LaBGAScore_pdm_report_image.m, MATLAB, 94 lines
secondlevel/functions/LaBGAScore_region_table.m, MATLAB, 558 lines
secondlevel/functions/LaBGAScore_region_table_safe.m, MATLAB, 109 lines
secondlevel/functions/PLSDA_neuroimaging_pipeline.m, MATLAB, 647 lines
secondlevel/functions/PLSDA_paired_neuroimaging_pipeline.m, MATLAB, 468 lines
secondlevel/functions/PLSR_neuroimaging_pipeline.m, MATLAB, 669 lines
secondlevel/functions/applyScaling.m, MATLAB, 47 lines
secondlevel/functions/bootstrapOOB_ENet.m, MATLAB, 238 lines
secondlevel/functions/bootstrapOOB_PLSDA.m, MATLAB, 122 lines
secondlevel/functions/bootstrapOOB_PLSR.m, MATLAB, 125 lines
secondlevel/functions/capLV.m, MATLAB, 37 lines
secondlevel/functions/dice_statistic_image.m, MATLAB, 113 lines
secondlevel/functions/dice_statistic_image_by_roi.m, MATLAB, 158 lines
secondlevel/functions/enetLambdaGrid.m, MATLAB, 32 lines
secondlevel/functions/foldPreprocess.m, MATLAB, 62 lines
secondlevel/functions/globalBaselineCV.m, MATLAB, 123 lines
secondlevel/functions/group_tfce_from_subject_maps.m, MATLAB, 402 lines
secondlevel/functions/logitSafe.m, MATLAB, 13 lines
secondlevel/functions/makeGroupedFolds.m, MATLAB, 27 lines
secondlevel/functions/maskToSignificant.m, MATLAB, 83 lines
secondlevel/functions/plot_ENet_diagnostics_neuroimaging.m, MATLAB, 659 lines
secondlevel/functions/plot_PLSDA_diagnostics_neuroimaging.m, MATLAB, 552 lines
secondlevel/functions/plot_PLSR_diagnostics_neuroimaging.m, MATLAB, 1,006 lines
secondlevel/functions/quickCV_ENet.m, MATLAB, 333 lines
secondlevel/functions/quickCV_ENet_PR.m, MATLAB, 334 lines
secondlevel/functions/quickCV_PLSDA.m, MATLAB, 23 lines
secondlevel/functions/quickCV_PLSDA_PR.m, MATLAB, 20 lines
secondlevel/functions/quickCV_PLSDA_core.m, MATLAB, 163 lines
secondlevel/functions/quickCV_PLSR.m, MATLAB, 134 lines
secondlevel/functions/quickGroupedCV.m, MATLAB, 50 lines
secondlevel/functions/residualizeFold.m, MATLAB, 107 lines
secondlevel/functions/residualizeY.m, MATLAB, 42 lines
secondlevel/functions/selectENetHyperparams.m, MATLAB, 167 lines
secondlevel/functions/setParforStream.m, MATLAB, 46 lines
secondlevel/functions/swapWithinSubjectLabels.m, MATLAB, 38 lines
secondlevel/functions/tfce_fwe_from_null.m, MATLAB, 58 lines
secondlevel/functions/tfce_one_fmri_dat.m, MATLAB, 49 lines
secondlevel/functions/tfce_transform_3d.m, MATLAB, 69 lines, 1 match
secondlevel/functions/tfce_volume.m, MATLAB, 93 lines, 1 match
secondlevel/functions/thresholded_fmri_data_from_pval_nii.m, MATLAB, 111 lines
secondlevel/functions/thresholded_fmri_data_from_statistic_image.m, MATLAB, 110 lines
secondlevel/functions/validateAtlasLabels.m, MATLAB, 78 lines
secondlevel/functions/validateCovariates.m, MATLAB, 100 lines
secondlevel/functions/warnUnknownOptions.m, MATLAB, 47 lines
secondlevel/scripts/LaBGAScore_secondlevel_MS_mat_pipeline.m, MATLAB, 148 lines
secondlevel/scripts/LaBGAScore_secondlevel_extractparcels_sessions.m, MATLAB, 223 lines
secondlevel/scripts/LaBGAScore_secondlevel_mvpa_beta_maps_conn.m, MATLAB, 583 lines
secondlevel/scripts/LaBGAScore_secondlevel_ooFmriDataObjML_example.m, MATLAB, 233 lines
secondlevel/scripts/LaBGAScore_secondlevel_roi_run_plot_PLSR_pipeline.m, MATLAB, 364 lines
secondlevel/scripts/LaBGAScore_secondlevel_roi_run_plot_PLS_ENet_pipeline.m, MATLAB, 413 lines
stats_tools/functions/LaBGAScore_Storey_FDR.m, MATLAB, 652 lines, 1 match
stats_tools/functions/LaBGAScore_combat_apply.m, MATLAB, 73 lines
stats_tools/functions/LaBGAScore_combat_fit.m, MATLAB, 148 lines
stats_tools/functions/LaBGAScore_dummy_code.m, MATLAB, 78 lines
stats_tools/sas_macros/es_identify.sas, SAS, 381 lines
stats_tools/sas_macros/mixed_effectsize.sas, SAS, 1,000 lines
LICENSE, License, 674 lines
README.md, Text, 342 lines

At the source: github.com/labgas/LaBGAScore/tree/main/pet/scripts

labgas/CANlab_help_examples

License: GPL-2.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 9c03690272e0e4cb534333588b19c337e5e4b228, 24 September 2026

Languages: MATLAB (222), Python (11), R (2)

Size: 393 files, 235 scripts

Software Heritage: not archived

Found in: “Data availability”

Holds: README, license file, documentation, 16 notebooks

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

Tools: Statistics and Machine Learning Toolbox (43 files), Nipype (8 files), SPM (8 files), FSL (5 files), Parallel Computing Toolbox (5 files), NumPy (5 files), FreeSurfer (4 files), Image Processing Toolbox (4 files), pandas (4 files), cifti-matlab (2 files), GIfTI library for MATLAB (1 file), lme4 (1 file), lmerTest (1 file), Stan (1 file), statsmodels (1 file), Violinplot-Matlab (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

237 files

Canlab_simple_image_load_prep/canlab_simple_find_outliers.m, MATLAB, 26 lines
Canlab_simple_image_load_prep/canlab_simple_mean_image_montage_by_condition.m, MATLAB, 25 lines
Canlab_simple_image_load_prep/canlab_simple_reload.m, MATLAB, 53 lines
Canlab_simple_image_load_prep/canlab_simple_set_conditions_contrasts_colors.m, MATLAB, 38 lines
Canlab_simple_image_load_prep/canlab_simple_set_paths.m, MATLAB, 62 lines
Canlab_simple_image_load_prep/canlab_simple_workflow.m, MATLAB, 45 lines
RSA_example/plot_rsa_results.m, MATLAB, 25 lines
RSA_example/reorder_studies_and_subjects.m, MATLAB, 19 lines
RSA_example/rsa_analysis_example.m, MATLAB, 36 lines
Second_level_analysis_template_scripts/a0_begin_here_readme.m, MATLAB, 293 lines
Second_level_analysis_template_scripts/a2_second_level_toolbox_check_dependencies.m, MATLAB, 207 lines
Second_level_analysis_template_scripts/a_set_up_new_analysis_folder_and_scripts.m, MATLAB, 92 lines
Second_level_analysis_template_scripts/b_copy_to_local_scripts_dir_and_modify/a2_set_default_options.m, MATLAB, 446 lines, 1 match
Second_level_analysis_template_scripts/b_copy_to_local_scripts_dir_and_modify/a_set_up_paths_always_run_first.m, MATLAB, 310 lines
Second_level_analysis_template_scripts/b_copy_to_local_scripts_dir_and_modify/b1_behavioral_analysis.m, MATLAB, 90 lines
Second_level_analysis_template_scripts/b_copy_to_local_scripts_dir_and_modify/prep_0_batch_run_once.m, MATLAB, 26 lines
Second_level_analysis_template_scripts/b_copy_to_local_scripts_dir_and_modify/prep_1_set_conditions_contrasts_colors.m, MATLAB, 295 lines
Second_level_analysis_template_scripts/b_copy_to_local_scripts_dir_and_modify/prep_1b_prep_behavioral_data.m, MATLAB, 419 lines
Second_level_analysis_template_scripts/b_copy_to_local_scripts_dir_and_modify/prep_1b_prep_behavioral_data_example2.m, MATLAB, 115 lines
Second_level_analysis_template_scripts/b_copy_to_local_scripts_dir_and_modify/prep_1c_normalize_to_MNI.m, MATLAB, 63 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/b2_show_data_vs_underlay.m, MATLAB, 28 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/b3_eliminate_subjects_with_outlier_images.m, MATLAB, 57 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/b_reload_saved_matfiles.m, MATLAB, 164 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/c2_SVM_contrasts.m, MATLAB, 258 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/c2_SVM_contrasts_masked.m, MATLAB, 946 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/c2a_second_level_regression.m, MATLAB, 1,883 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/c2b_SVM_betweenperson_contrasts.m, MATLAB, 147 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/c2c_SVM_between_condition_contrasts.m, MATLAB, 175 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/c2d_lassoPCR_contrasts.m, MATLAB, 139 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/c2e_second_level_robust_parcelwise_regression.m, MATLAB, 290 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/c2f_run_MVPA_regression_single_trial.m, MATLAB, 1,573 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/c2g_run_multivariate_mediation_single_trial.m, MATLAB, 896 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/c2h_run_multivariate_mediation.m, MATLAB, 430 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/c3_univariate_contrast_maps_scaleddata.m, MATLAB, 63 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/c4_univ_contrast_wedge_plot_and_lateralization_test.m, MATLAB, 61 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/c5_univ_contrast_wedge_plot_and_lateralization_test_scaled_data.m, MATLAB, 61 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/c_univariate_contrast_maps.m, MATLAB, 179 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/c_univariate_contrast_maps_scaledcontrastdata.m, MATLAB, 168 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/c_univariate_contrast_maps_scaleddata.m, MATLAB, 180 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/c_univariate_ttests_each_condition.m, MATLAB, 23 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/d10_signature_riverplots.m, MATLAB, 241 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/d11_signature_similarity_barplots.m, MATLAB, 166 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/d12_kragel_emotion_signature_responses.m, MATLAB, 5 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/d13_kragel_emotion_riverplots.m, MATLAB, 77 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/d14_kragel_emotion_signature_similarity_barplots.m, MATLAB, 166 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/d15_plot_local_nps_responses_by_region.m, MATLAB, 15 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/d1_pain_signature_responses_dotproduct.m, MATLAB, 5 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/d1b_FAPS_signature_response_dotproduct.m, MATLAB, 5 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/d2_pain_signature_responses.m, MATLAB, 5 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/d3_plot_nps_subregions.m, MATLAB, 107 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/d3_plot_nps_subregions_bars.m, MATLAB, 208 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/d4_compare_NPS_SIIPS_scaling_similarity_metrics.m, MATLAB, 74 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/d5_emotion_signature_responses.m, MATLAB, 5 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/d6_empathy_signature_responses.m, MATLAB, 5 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/d7_autonomic_signature_responses.m, MATLAB, 5 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/d8_fibromyalgia_signature_responses.m, MATLAB, 5 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/d9_nps_correlations_with_global_signal.m, MATLAB, 89 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/d_signature_responses_generic.m, MATLAB, 166 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/e1_corr_patterns.m, MATLAB, 395 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/f2_bucknerlab_network_barplots.m, MATLAB, 174 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/f2_bucknerlab_network_wedgeplots.m, MATLAB, 180 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/g2_bucknerlab_network_riverplots.m, MATLAB, 78 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/h1_group_differences_FAPS.m, MATLAB, 90 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/h2_group_differences_emosignatures.m, MATLAB, 90 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/h2_group_differences_emosignatures_conditions.m, MATLAB, 91 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/h3_kragel_patterns_group_differences.m, MATLAB, 111 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/h_group_differences_conditions.m, MATLAB, 236 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/h_signature_responses_group_diff.m, MATLAB, 1,815 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/j1_display_parcels.m, MATLAB, 41 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/k2_neurosynth_cogcontrol_pattern_and_region_analyses.m, MATLAB, 372 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/k_emotionmeta_pattern_and_region_analyses.m, MATLAB, 348 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/mvpa_reg_cov_benchmark_algorithms.m, MATLAB, 142 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/mvpa_reg_cov_predictive_model.m, MATLAB, 181 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/older/z_batch_publish_svm_analyses.m, MATLAB, 38 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_add_metadata_to_DATA_OBJ.m, MATLAB, 145 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_canlab_condition_contrast_plot.m, MATLAB, 309 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_check_or_create_slice_display.m, MATLAB, 33 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_check_required_fields.m, MATLAB, 30 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_display_study_info_json.m, MATLAB, 54 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_extract_local_nps_region_vals.m, MATLAB, 145 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_find_master_script_directory.m, MATLAB, 12 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_get_group_names_colors.m, MATLAB, 62 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_get_holdout_sets.m, MATLAB, 78 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_get_holdout_sets_balanced_groups.m, MATLAB, 99 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_get_holdout_sets_lasso.m, MATLAB, 70 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_get_options_for_analysis_script.m, MATLAB, 41 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_get_parcelwise_statistic_images.m, MATLAB, 68 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_oofmridataobj_svm_contrasts_get_results_per_subject.m, MATLAB, 436 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_plot_local_nps_region_vals.m, MATLAB, 103 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_save_figure.m, MATLAB, 43 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_signature_condition_contrast_plot.m, MATLAB, 298 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_signature_condition_contrast_plot_kragel_emotion.m, MATLAB, 256 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_svm_contrasts_get_fitted_obs_per_subject.m, MATLAB, 72 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_svm_contrasts_get_results_per_subject.m, MATLAB, 388 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/plugin_unzip_images_if_needed.m, MATLAB, 57 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/prep_2_load_image_data_and_save.m, MATLAB, 805 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/prep_3_calc_univariate_contrast_maps_and_save.m, MATLAB, 1,072 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/prep_3a_run_second_level_regression_and_save.m, MATLAB, 3,283 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/prep_3b_run_SVMs_on_contrasts_and_save.m, MATLAB, 190 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/prep_3b_run_second_level_regression_on_conditions_and_save.m, MATLAB, 288 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/prep_3c_run_SVMs_on_contrasts_masked.m, MATLAB, 959 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/prep_3d_run_SVM_betweenperson_contrasts.m, MATLAB, 226 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/prep_3e_run_SVM_betweenperson_contrasts_on_conditions.m, MATLAB, 366 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/prep_3f_create_fmri_data_single_trial_object.m, MATLAB, 444 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/prep_3g_create_fmri_data_runwise_contrast_object.m, MATLAB, 388 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/prep_4_apply_signatures_and_save.m, MATLAB, 316 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/prep_5_apply_shen_parcellation_and_save.m, MATLAB, 169 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/prep_5b_apply_spmanatomy_parcellation_and_save.m, MATLAB, 176 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/prep_6_apply_kragel_emotion_signatures_and_save.m, MATLAB, 58 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/prep_7_extract_painpathways_data.m, MATLAB, 176 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/test_figs_prep_3a_run_second_level_regression_and_save.m, MATLAB, 2,138 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/z_batch_bucknerlab_network_analyses.m, MATLAB, 29 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/z_batch_coverage_and_contrasts.m, MATLAB, 51 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/z_batch_load_and_prep.m, MATLAB, 103 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/z_batch_meta_analysis_mask_analyses.m, MATLAB, 27 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/z_batch_publish_analyses.m, MATLAB, 243 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/z_batch_publish_everything.m, MATLAB, 10 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/z_batch_publish_image_prep_and_qc.m, MATLAB, 37 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/z_batch_signature_analyses.m, MATLAB, 115 lines
Second_level_analysis_template_scripts/core_scripts_to_run_without_modifying/z_batch_svm_analysis.m, MATLAB, 55 lines
Second_level_analysis_template_scripts/d_additional_optional_copy_to_local_dir/h_NPS_contrasts_between_person_correlations.m, MATLAB, 92 lines
Second_level_analysis_template_scripts/d_additional_optional_copy_to_local_dir/i_ancova_stimintensity_nps.m, MATLAB, 148 lines
Second_level_analysis_template_scripts/d_additional_optional_copy_to_local_dir/prep_2_data_from_fmri_objects.m, MATLAB, 115 lines
Second_level_analysis_template_scripts/list_of_scripts_and_workflow.m, MATLAB, 129 lines
Second_level_analysis_template_scripts/move_betas_to_secondlvl.m, MATLAB, 145 lines
Second_level_analysis_template_scripts/older/0_begin_here_readme.m, MATLAB, 298 lines
active_inference/Simplified_Step_by_Step_Active_Inference_Guide.mlx, MATLAB, not shown here
bayes_factors/bayes_factor_test_with_rstanarm.r, R, 34 lines
canlab_dataset/create_a_canlab_dataset_and_import_data.m, MATLAB, 80 lines
canlab_mixed_effects_matlab_demo1/canlab_mixed_model_example.m, MATLAB, 504 lines
canlab_mixed_effects_matlab_demo1/canlab_mixed_model_example.mlx, MATLAB, not shown here
canlab_mixed_effects_matlab_demo1/canlab_mixed_model_example.r, R, 46 lines, 1 match
example_help_files/BIC_model_selection.mlx, MATLAB, not shown here
example_help_files/Comparing_model_variance_explained_BMRK4_painTasteVision/OLD/anj_script1 copy.m, MATLAB, 11 lines
example_help_files/Comparing_model_variance_explained_BMRK4_painTasteVision/OLD/anj_script1.m, MATLAB, 11 lines
example_help_files/Comparing_model_variance_explained_BMRK4_painTasteVision/OLD/glmfit_multilevel_vardecomp_old.m, MATLAB, 232 lines
example_help_files/Comparing_model_variance_explained_BMRK4_painTasteVision/OLD/tmp_varexp.m, MATLAB, 54 lines
example_help_files/Comparing_model_variance_explained_BMRK4_painTasteVision/bmrk4_PTV_variance_decomp.m, MATLAB, 135 lines
example_help_files/Voxel_and_image_spaces_in_CANlab_fmri_data_objects.mlx, MATLAB, not shown here
example_help_files/atlas_2012_behavioral_plot_example_figure.m, MATLAB, 152 lines
example_help_files/canlab_dataset_basic_usage.m, MATLAB, 66 lines
example_help_files/canlab_dual_regression_example.mlx, MATLAB, not shown here
example_help_files/canlab_help_10_BayesFactor_map_walkthrough.m, MATLAB, 142 lines
example_help_files/canlab_help_1_installing_tools.m, MATLAB, 210 lines
example_help_files/canlab_help_1b_getting_help.mlx, MATLAB, not shown here
example_help_files/canlab_help_2_load_a_sample_dataset.m, MATLAB, 199 lines
example_help_files/canlab_help_2b_basic_image_visualization.m, MATLAB, 149 lines
example_help_files/canlab_help_2c_loading_datasets.m, MATLAB, 276 lines
example_help_files/canlab_help_2d_publish_a_report.m, MATLAB, 98 lines
example_help_files/canlab_help_3_voxelwise_t_test_walkthrough.m, MATLAB, 222 lines
example_help_files/canlab_help_3b_atlases_and_ROI_analysis.m, MATLAB, 319 lines
example_help_files/canlab_help_4_masking_and_writing_nifti_files.m, MATLAB, 163 lines
example_help_files/canlab_help_4b_3D_visualization.m, MATLAB, 531 lines
example_help_files/canlab_help_5_regression_walkthrough.m, MATLAB, 561 lines
example_help_files/canlab_help_7_multivariate_prediction_basics.m, MATLAB, 391 lines
example_help_files/canlab_help_8_riverplot_cerebellar_atlas_example.m, MATLAB, 197 lines
example_help_files/canlab_help_9_apply_a_multivariate_pattern_of_interest.m, MATLAB, 103 lines, 1 match
example_help_files/canlab_help_MPathI_multivariate_pathway.mlx, MATLAB, not shown here
example_help_files/canlab_help_annotate_thresholded_results_map.m, MATLAB, 10 lines
example_help_files/canlab_help_connectivity_with_brainpathway_starter_demo.m, MATLAB, 147 lines
example_help_files/canlab_help_discriminable_cluster_identification.m, MATLAB, 39 lines
example_help_files/canlab_help_prep_for_connectivity.mlx, MATLAB, not shown here
example_help_files/canlab_help_second_level_batch_example.m, MATLAB, 30 lines
example_help_files/canlab_help_x_network_based_analysis_and_lateralization.m, MATLAB, 137 lines
example_help_files/canlab_mediation_walkthrough1.m, MATLAB, 20 lines
example_help_files/canlab_meta_analysis_walkthrough1.m, MATLAB, 255 lines
example_help_files/canlab_meta_analysis_walkthrough2.m, MATLAB, 194 lines
example_help_files/cifti_image_loading_and_plotting.mlx, MATLAB, not shown here
example_help_files/compare_pain_patterns_sens_spec.m, MATLAB, 78 lines
example_help_files/conf_region_example.m, MATLAB, 57 lines
example_help_files/first_level_design_matrix_exploration.m, MATLAB, 463 lines
example_help_files/generalizability_example.m, MATLAB, 57 lines
example_help_files/linear_filtering_a_timeseries.m, MATLAB, 74 lines
example_help_files/make_polar_wedge_plots_for_signatures.m, MATLAB, 143 lines
example_help_files/map_surface2volume_with_CBIG_tools.m, MATLAB, 23 lines
example_help_files/map_surface2volume_with_CBIG_tools.mlx, MATLAB, not shown here
example_help_files/mediation_example_script_2.m, MATLAB, 109 lines
example_help_files/mnisurf_projection_tutorial.mlx, MATLAB, not shown here
example_help_files/mnisurf_surface_projection_tutorial.mlx, MATLAB, not shown here
example_help_files/neurosynth_topic_similarity_and_wedge_plot.m, MATLAB, 93 lines
example_help_files/older/CANlab_set_paths_example_script.m, MATLAB, 41 lines
example_help_files/older/canlab_help_2_load_a_sample_dataset_v1.mlx, MATLAB, not shown here
example_help_files/older/canlab_help_2b_using_the_plot_function.m, MATLAB, 148 lines
example_help_files/older/canlab_help_3_voxelwise_t_test.m, MATLAB, 188 lines
example_help_files/older/canlab_help_3_voxelwise_t_test_walkthrough_v1.mlx, MATLAB, not shown here
example_help_files/older/canlab_help_4b_3D_visualization_old.m, MATLAB, 342 lines
example_help_files/older/canlab_help_6_multilevel_regression.m, MATLAB, 35 lines
example_help_files/older/canlab_help_8_apply_pattern_of_interest.m, MATLAB, 52 lines
example_help_files/older/create_and_plot_a_design_matrix.m, MATLAB, 414 lines
example_help_files/older/surface_cutaways_walkthrough_old.m, MATLAB, 29 lines
example_help_files/plot_atlas_parcels_on_slice_montage.m, MATLAB, 86 lines
example_help_files/run_permutation_tests.m, MATLAB, 136 lines
example_help_files/using_canlab_atlases.m, MATLAB, 661 lines
example_help_files/variable_dur_and_param_mod_example_code.mlx, MATLAB, not shown here
example_help_files/visualize_neuroimaging_data.m, MATLAB, 155 lines
figure_gallery/CANlab_Dartmouth_logo/CANlab_images/canlab_dartmouth_brainlogo.m, MATLAB, 176 lines
figure_gallery/CANlab_Dartmouth_logo/Dpine_viz_tor.m, MATLAB, 142 lines
figure_gallery/CANlab_Dartmouth_logo/Dpine_viz_tor2.m, MATLAB, 166 lines
figure_gallery/CANlab_Dartmouth_logo/Dpine_viz_tor3.m, MATLAB, 110 lines
figure_gallery/CANlab_Dartmouth_logo/canlab_dartmouth_brainlogo.m, MATLAB, 218 lines
figure_gallery/NPS_SIIPS_surface_figures_2025.mlx, MATLAB, not shown here
figure_gallery/SPM_HRF_3_basis_functions/hrf_fits_3bf.m, MATLAB, 112 lines
hyp_opt_and_mlpcr/hyp_opt_and_mlpcr_demo.m, MATLAB, 1,172 lines
nipype/libraries/glm/__init__.py, Python, 1 line
nipype/libraries/glm/designs.py, Python, 520 lines
nipype/libraries/glm/preproc.py, Python, 349 lines
nipype/libraries/glm/utils.py, Python, 57 lines
nipype/libraries/nipype_ext/__init__.py, Python, 1 line
nipype/libraries/nipype_ext/vifs.py, Python, 48 lines
nipype/libraries/nipype_ext/workbench.py, Python, 576 lines
nipype/subjectlevel/hcp_glm_basic.py, Python, 486 lines
nipype/subjectlevel/hcp_glm_grayord.py, Python, 804 lines
nipype/subjectlevel/hcp_single_blocks_w_confounds_grayordinate.py, Python, 390 lines
nipype/subjectlevel/hcp_single_trials_w_confounds.py, Python, 288 lines
publish_help_files/CANlab_help_examples_TEMPLATE.m, MATLAB, 126 lines
publish_help_files/canlab_help_publish.m, MATLAB, 30 lines
publish_help_files/publish_canlab_help_set1.m, MATLAB, 47 lines
published_html/canlab_help_surface_projection/mnisurf_projection_tutorial.m, MATLAB, 343 lines
published_html/mlpcr_demo.m/mlpcr_demo.m, MATLAB, 885 lines
published_html/plotting_parcellations/plotting_parcellations.m, MATLAB, 87 lines
unfinished_examples/BayesFactorsDemo.m, MATLAB, 39 lines
unfinished_examples/EmoReg_BayesFactor_walkthrough.m, MATLAB, 176 lines
unfinished_examples/Multivariate_Connectivity_Walkthrough.m, MATLAB, 174 lines
unfinished_examples/bmrk3_analysis_by_temp_walkthrough.m, MATLAB, 253 lines
unfinished_examples/bmrk3_compare_feature_sets.m, MATLAB, 5 lines
unfinished_examples/extract_and_prep_for_connectomics.m, MATLAB, 32 lines
unfinished_examples/extract_gray_white_csf_tissue_compartments.m, MATLAB, 13 lines
unfinished_examples/fMRI2015_sim_walkthrough_wani_2.m, MATLAB, 136 lines
unfinished_examples/fMRI_regression_walkthrough1_3d.m, MATLAB, 129 lines
unfinished_examples/misbie_mri_show_ROIs.m, MATLAB, 72 lines
unfinished_examples/multidimensional_scaling_mds_example_vivek.m, MATLAB, 63 lines
unfinished_examples/predict_example_wani.m, MATLAB, 98 lines
unfinished_examples/publish_analysis_by_temp_walkthrough.m, MATLAB, 18 lines
unfinished_examples/regression_walkthrough.m, MATLAB, 215 lines
unfinished_examples/render_default_mode_network.m, MATLAB, 65 lines
LICENSE, License, 339 lines
README.md, Text, 358 lines

At the source: github.com/labgas/CANlab_help_examples

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:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
395 scripts, each with its path and the digest of its content;
16 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.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to the authors' code: labgas/CANlab_help_examples, labgas/labgascore
it says that the data are available on request
it says that the code is available on request

Read it in the paper: doi.org/10.1016/j.bbih.2026.101299.

Versions

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

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 16 authors, 5 keywords, 1 funder, 156 references, 3 RRIDs.

Cite

This paper

Dooms, Y., Qiu, L., Coppieters, I., Vergaelen, E., Claes, S., Dupont, P., Hehl, M., Cuypers, K., Engler, H., Dombrowski, K., Verbeke, K., Van den Bergh, O., Raes, J., Van Oudenhove, L., Van Den Houte, M., & Bogaerts, K. (2026). Multimodal approach to identify neuropsychophysiological subgroups in myalgic encephalomyelitis/chronic fatigue syndrome and their relevance for rehabilitation: protocol for a mechanistic cross-sectional and longitudinal study. Brain, behavior, & immunity - health, 56, 101299. https://doi.org/10.1016/j.bbih.2026.101299

BibTeX

@article{dooms2026multimodal, author = {Dooms, Ynse and Qiu, Lixin and Coppieters, Iris and Vergaelen, Elfi and Claes, Stephan and Dupont, Patrick and Hehl, Melina and Cuypers, Koen and Engler, Harald and Dombrowski, Kirsten and Verbeke, Kristin and Van den Bergh, Omer and Raes, Jeroen and Van Oudenhove, Lukas and Van Den Houte, Maaike and Bogaerts, Katleen}, title = {{Multimodal approach to identify neuropsychophysiological subgroups in myalgic encephalomyelitis/chronic fatigue syndrome and their relevance for rehabilitation: protocol for a mechanistic cross-sectional and longitudinal study}}, journal = {Brain, behavior, \& immunity - health}, year = {2026}, month = jul, volume = {56}, pages = {101299}, publisher = {Elsevier}, issn = {2666-3546}, doi = {10.1016/j.bbih.2026.101299}, url = {https://doi.org/10.1016/j.bbih.2026.101299}, pmid = {42472232}, pmcid = {PMC13380062} }

RIS

TY - JOUR AU - Dooms, Ynse AU - Qiu, Lixin AU - Coppieters, Iris AU - Vergaelen, Elfi AU - Claes, Stephan AU - Dupont, Patrick AU - Hehl, Melina AU - Cuypers, Koen AU - Engler, Harald AU - Dombrowski, Kirsten AU - Verbeke, Kristin AU - Van den Bergh, Omer AU - Raes, Jeroen AU - Van Oudenhove, Lukas AU - Van Den Houte, Maaike AU - Bogaerts, Katleen TI - Multimodal approach to identify neuropsychophysiological subgroups in myalgic encephalomyelitis/chronic fatigue syndrome and their relevance for rehabilitation: protocol for a mechanistic cross-sectional and longitudinal study T2 - Brain, behavior, & immunity - health J2 - Brain Behav Immun Health PY - 2026 DA - 2026/07/06 VL - 56 SP - 101299 SN - 2666-3546 PB - Elsevier DO - 10.1016/j.bbih.2026.101299 UR - https://doi.org/10.1016/j.bbih.2026.101299 LA - en ER -

CSL-JSON

{ "id": "10.1016/j.bbih.2026.101299", "type": "article-journal", "title": "Multimodal approach to identify neuropsychophysiological subgroups in myalgic encephalomyelitis/chronic fatigue syndrome and their relevance for rehabilitation: protocol for a mechanistic cross-sectional and longitudinal study", "container-title": "Brain, behavior, & immunity - health", "author": [ { "family": "Dooms", "given": "Ynse" }, { "family": "Qiu", "given": "Lixin" }, { "family": "Coppieters", "given": "Iris" }, { "family": "Vergaelen", "given": "Elfi" }, { "family": "Claes", "given": "Stephan" }, { "family": "Dupont", "given": "Patrick" }, { "family": "Hehl", "given": "Melina" }, { "family": "Cuypers", "given": "Koen" }, { "family": "Engler", "given": "Harald" }, { "family": "Dombrowski", "given": "Kirsten" }, { "family": "Verbeke", "given": "Kristin" }, { "family": "Van den Bergh", "given": "Omer" }, { "family": "Raes", "given": "Jeroen" }, { "family": "Van Oudenhove", "given": "Lukas" }, { "family": "Van Den Houte", "given": "Maaike" }, { "family": "Bogaerts", "given": "Katleen" } ], "container-title-short": "Brain Behav Immun Health", "volume": "56", "page": "101299", "DOI": "10.1016/j.bbih.2026.101299", "PMID": "42472232", "PMCID": "PMC13380062", "ISSN": "2666-3546", "publisher": "Elsevier", "URL": "https://doi.org/10.1016/j.bbih.2026.101299", "language": "en", "issued": { "date-parts": [ [ 2026, 7, 6 ] ] } }

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

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