The commands I use look like this, and I run it separately for patients and controls indeed.
The jobfile is nearly identical for both groups, I only correct the exact path to each folder of data.
%% jobSDAT.m
%
% This function describes an Osprey job defined in a MATLAB script.
%
% AUTHOR:
% Dr. Georg Oeltzschner (Johns Hopkins University, 2019-07-15)
% goeltzs1@jhmi.edu
% 2019-07-15: First version of the code.
%
% ADAPTED:
% Dra. Kia Puustinen (UHasselt, 2023-02-16)
% kia.puustinen@uhasselt.be
%
% ADAPTED:
% Melina Hehl (KUL/UHasselt, 2023-02-19)
% melina.hehl@kuleuven.be.
% → Adapted to fit BIDS structure with .sdat and .spar files
% (see markings with %MH 2023-02-19)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% 1. SPECIFY SEQUENCE INFORMATION %%%
% Specify sequence type
seqType = ‘HERCULES’; % OPTIONS: - ‘unedited’ (default)
% - ‘MEGA’
% - ‘HERMES’
% - ‘HERCULES’
% Specify editing targets
editTarget = {‘GABA’, ‘GSH’}; % OPTIONS: - {‘none’} (default if ‘unedited’)
% - {‘GABA’}, {‘GSH’}, {‘Lac’}, {‘PE322’}, {‘PE398’} (for ‘MEGA’)
% - {‘GABA’, ‘GSH’}, {‘GABA’, ‘Lac’}, {‘NAA’, ‘NAAG’} (for 'HERMES’and ‘HERCULES’)
% Specify data scenario
dataScenario = ‘invivo’; % OPTIONS: - ‘invivo’ (default)
% - ‘phantom’
% - ‘PRIAM’
% - ‘MRSI’
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% 2. SPECIFY DATA HANDLING AND MODELING OPTIONS %%%
% Which spectral registration method should be used? Robust spectral
% registration is default, a frequency restricted spectral registration
% method is also availaible and is linked to the fit range.
opts.SpecReg = ‘RobSpecReg’; % OPTIONS: - ‘RobSpecReg’ (default) Spectral aligment with Water/Lipid removal, using simialrity meric, and weighted averaging
% - ‘ProbSpecReg’ Probabilistic aligment to median target and weighted averaging
% - ‘RestrSpecReg’ Frequency restricted (fit range) spectral aligment, using simialrity meric, and weighted averaging
% - ‘none’
% Which algorithm do you want to align the sub spectra? L2 norm
% optimization is the default. This is only used for edited MRS!
%Perform correction on the metabolite data (raw) or metabolite
%-nulled data (mm).
opts.SubSpecAlignment.mets = ‘L1Norm’; % OPTIONS: - ‘L2Norm’
% - ‘L1Norm’
% - ‘none’
%Perform eddy-current correction on the metabolite data (raw) or metabolite
%-nulled data (mm). This can either be done similar for all data sets by
%supplying a single value or specified for each dataset individually by supplying
% multiple entries (number has to match the number of datasets) e.g. to perform ECC
% for the second dataset only:
% opts.ECC.raw = [0 1];
% opts.ECC.mm = [0 1];
opts.ECC.raw = 1; % OPTIONS: - ‘1’ (default)
opts.ECC.mm = 1; % - ‘0’ (no)
% - array
% Save LCModel-exportable files for each spectrum?
opts.saveLCM = 1; % OPTIONS: - 0 (no, default)
% - 1 (yes)
% Save jMRUI-exportable files for each spectrum?
opts.savejMRUI = 0; % OPTIONS: - 0 (no, default)
% - 1 (yes)
% Save processed spectra in vendor-specific format (SDAT/SPAR, RDA, P)?
opts.saveVendor = 0; % OPTIONS: - 0 (no, default)
% - 1 (yes)
% Save processed spectra in NIfTI-MRS format?
opts.saveNII = 0; % OPTIONS: - 0 (no, default)
% - 1 (yes)
% Save PDF output for all Osprey modules and subjects?
opts.savePDF = 0; % OPTIONS: - 0 (no, default)
% - 1 (yes)
% Select the metabolites to be included in the basis set as a cell array,
% with entries separates by commas.
% With default Osprey basis sets, you can select the following metabolites:
% Ala, Asc, Asp, bHB, bHG, Cit, Cr, Cystat, CrCH2, EtOH, GABA, GPC, GSH, Glc, Gln,
% Glu, Gly, H2O, mI, Lac, NAA, NAAG, PCh, PCr, PE, Phenyl, sI, Ser,
% Tau, Tyros, MM09, MM12, MM14, MM17, MM20, Lip09, Lip13, Lip20.
% If you enter ‘default’, the basis set will include all of the above
% except for Ala, bHB, bHG, Cit, Cystat, EtOH, Glc, Gly, Phenyl, Ser, and Tyros.
opts.fit.includeMetabs = { ‘Asc’ ‘Asp’ ‘Cr’ ‘CrCH2’ ‘GABA’ ‘GPC’ ‘GSH’ ‘Gln’ ‘Glu’ ‘H2O’ ‘mI’ ‘Lac’ ‘NAA’ ‘NAAG’ ‘PCh’ ‘PCr’ ‘PE’ ‘sI’ ‘Tau’ ‘MM09’ ‘MM12’ ‘MM14’ ‘MM17’ ‘MM20’ ‘Lip09’ ‘Lip13’ ‘Lip20’}; % OPTIONS: - {‘default’}
% - {custom}
% Choose the fitting algorithm
opts.fit.method = ‘Osprey’; % OPTIONS: - ‘Osprey’ (default)
% - ‘LCModel’
% Choose the fitting style for difference-edited datasets (MEGA, HERMES, HERCULES)
% (only available for the Osprey fitting method)
opts.fit.style = ‘Separate’; % OPTIONS: - ‘Concatenated’ (default) - will fit DIFF and SUM simultaneously)
% - ‘Separate’ - will fit DIFF and OFF separately
% Determine fitting range (in ppm) for the metabolite and water spectra
opts.fit.range = [0.5 4]; % [ppm] Default: [0.5 4]
opts.fit.rangeWater = [2.0 7.4]; % [ppm] Default: [2.0 7.4]
% Determine the baseline knot spacing (in ppm) for the metabolite spectra
opts.fit.bLineKnotSpace = 0.4; % [ppm] Default: 0.4.
% Add macromolecule and lipid basis functions to the fit?
opts.fit.fitMM = 1; % OPTIONS: - 0 (no)
% - 1 (yes, default)
opts.UnstableWater = 1;
% Optional: In case the automatic basisset picker is not working you can manually
% select the path to the basis set in the osprey/fit/basis, i.e.:
% opts.fit.basisSetFile = ‘osprey/fit/basis/3T/philips/mega/press/gaba68/basis_philips_megapress_gaba68.mat’;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% 3. SPECIFY MRS DATA AND STRUCTURAL IMAGING FILES %%
% Clear existing files
clear files files_ref files_w files_nii files_mm
% Data folder in BIDS format
% The filparts(which()) comment is needed to find the data on your machine. If you set
% up the jobFile for your own data you can set a direct path to your data
% folder e.g., data_folder = /Volumes/MyProject/data/’
data_folder = ‘C:\Users\kiapu\Desktop\S65741\BIDS_new\patient\HERCULES’;
% The following lines perform an automated set-up of the jobFile which
% takes advatage of the BIDS foramt. If you are not using BIDS (highly
% recommended) you can look at the definitions below the loop to see how to
% set up direct path links to your data.
subs = dir(data_folder);
subs(1:2) = ;
subs = subs([subs.isdir]);
subs = subs(contains({subs.name},‘sub’));
counter = 1;
for kk = 1:length(subs)
% Loop over sessions
sess = dir([subs(kk).folder filesep subs(kk).name]);
sess(1:2) = ;
sess = sess([sess.isdir]);
sess = sess(contains({sess.name},‘ses’));
for ll = 1:length(sess)
% Specify metabolite data
% (MANDATORY)
dir_metabolite = dir([sess(ll).folder filesep sess(ll).name filesep 'mrs' filesep subs(kk).name '_' sess(ll).name '_acq-ACChercules_svs.SDAT']); %MH 2023-02-19
files(counter) = {[dir_metabolite(end).folder filesep dir_metabolite(end).name]};
% Specify water reference data for eddy-current correction (same sequence as metabolite data!)
% (OPTIONAL)
% Leave empty for GE P-files (.7) - these include water reference data by
% default.
dir_ref = dir([sess(ll).folder filesep sess(ll).name filesep 'mrs' filesep subs(kk).name '_' sess(ll).name '_acq-ACChercules_ref.SDAT']); %MH 2023-02-19
files_ref(counter) = {[dir_ref(end).folder filesep dir_ref(end).name]};
% Specify water data for quantification (e.g. short-TE water scan)
% (OPTIONAL)
files_w = {};
% Specify metabolite-nulled data for quantification
% (OPTIONAL)
files_mm = {};
% Specify T1-weighted structural imaging data
% (OPTIONAL)
% Link to single NIfTI (*.nii) files for Siemens and Philips data
% Link to DICOM (*.dcm) folders for GE data
%files_nii(counter) = {[sess(ll).folder filesep sess(ll).name filesep 'anat' filesep subs(kk).name filesep sess(ll).name '_T1w.nii']};
files_nii(counter) = {[sess(ll).folder filesep sess(ll).name filesep 'anat' filesep subs(kk).name '_' sess(ll).name '_T1w.nii']};
% External segmentation results
% (OPTIONAL)
% Link to NIfTI (*.nii or *.nii.gz) files with segmentation results
% Add supply gray matter, white matter, and CSF as 1 x 3 cell within a
% cell array or a single 4D file in the same order supplied as 1 x 1 cell;
% files_seg(counter) = {{[sess(ll).folder filesep sess(ll).name filesep ‘anat’ filesep subs(kk).name filesep ‘c1’ sess(ll).name ‘_T1w.nii.gz’],…
% [sess(ll).folder filesep sess(ll).name filesep ‘anat’ filesep subs(kk).name filesep ‘c2’ sess(ll).name ‘_T1w.nii.gz’],…
% [sess(ll).folder filesep sess(ll).name filesep ‘anat’ filesep subs(kk).name filesep ‘c3’ sess(ll).name ‘_T1w.nii.gz’]}};
% files_seg(counter) = {{[sess(ll).folder filesep sess(ll).name filesep ‘anat’ filesep subs(kk).name filesep ‘4D’ sess(ll).name ‘_T1w.nii.gz’]}};
counter = counter + 1;
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% 4. SPECIFY STAT FILE %%%
% file_stat = fullfile(data_folder, ‘stat.csv’);
file_stat = fullfile(data_folder, ‘stat.csv’);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% 5. SPECIFY OUTPUT FOLDER %%
outputFolder = ‘C:\Users\kiapu\Documents\Osprey output’;
Sorry for the long reply. Have a great day!