mcinet_pipeline_MSPC_MSTEPS_2.m

%%% MCINET GLM CONNECTOME SCRIPT for MSTEPS resolutions
%%% This GLM takes into account different scanner models within the ADNI cohort, in addition to the different Montreal samples

clear all
addpath(genpath('/gs/project/gsf-624-aa/quarantaine/niak-boss-0.12.18'));
path_data = '/gs/scratch/atam/';

%%%%%%%%%%%%
%% Grabbing the results from BASC
%%%%%%%%%%%%
files_in = niak_grab_stability_rest([path_data 'adnet/basc_40sc_20141210_msteps/']); 

%%%%%%%%%%%%%%%%%%%%%
%% Grabbing the results from the NIAK fMRI preprocessing pipeline
%%%%%%%%%%%%%%%%%%%%%
opt_g.min_nb_vol = 50;     % The minimum number of volumes for an fMRI dataset to be included. This option is useful when scrubbing is used, and the resulting time series may be too short.
opt_g.min_xcorr_func = 0; % The minimum xcorr score for an fMRI dataset to be included. This metric is a tool for quality control which assess the quality of non-linear coregistration of functional images in stereotaxic space. Manual inspection of the values during QC is necessary to properly set this threshold.
opt_g.min_xcorr_anat = 0; % The minimum xcorr score for an fMRI dataset to be included. This metric is a tool for quality control which assess the quality of non-linear coregistration of the anatomical image in stereotaxic space. Manual inspection of the values during QC is necessary to properly set this threshold.
opt_g.type_files = 'glm_connectome'; % Specify to the grabber to prepare the files for the glm_connectome pipeline
opt_g.filter.session = {'session1'}; % Just grab session 1

% grabbing adni2
files_in.fmri = niak_grab_fmri_preprocess([path_data 'adni2/fmri_preprocess/'],opt_g).fmri; % Replace the folder by the path where the results of the fMRI preprocessing pipeline were stored. 

% grabbing mni
files_in_tmp.fmri = niak_grab_fmri_preprocess([path_data 'ad_mtl/mni_mci/fmri_preprocess/'],opt_g).fmri; % Replace the folder by the path where the results of the fMRI preprocessing pipeline were stored. 
files_in.fmri = psom_merge_pipeline(files_in.fmri,files_in_tmp.fmri);

% grabbing criugma
files_in_tmp.fmri = niak_grab_fmri_preprocess([path_data 'ad_mtl/criugm_mci/fmri_preprocess/'],opt_g).fmri; % Replace the folder by the path where the results of the fMRI preprocessing pipeline were stored. 
files_in.fmri = psom_merge_pipeline(files_in.fmri,files_in_tmp.fmri);

% grabbing criugmb
files_in_tmp.fmri = niak_grab_fmri_preprocess([path_data 'ad_mtl/adpd/fmri_preprocess/'],opt_g).fmri; % Replace the folder by the path where the results of the fMRI preprocessing pipeline were stored. 
files_in.fmri = psom_merge_pipeline(files_in.fmri,files_in_tmp.fmri);


%%%%%%%%%%%%
%% Set the model
%%%%%%%%%%%%

%% Group
files_in.model.group = [path_data 'adnet/models/admci_model_multisite_scanner_fd_snr_20151113.csv'];  %% Note the different csv from mcinet_pipeline_MSPC_MSTEPS.m

%%%%%%%%%%%%
%% Options 
%%%%%%%%%%%%
opt.folder_out = [path_data 'adnet/results/glm30b_scanner_20151113']; % Where to store the results
opt.fdr = 0.1; % The maximal false-discovery rate that is tolerated both for individual (single-seed) maps and whole-connectome discoveries, at each particular scale (multiple comparisons across scales are addressed via permutation testing)
opt.fwe = 0.05; % The overall family-wise error, i.e. the probablity to have the observed number of discoveries, agregated across all scales, under the global null hypothesis of no association.
opt.nb_samps = 1000; % The number of samples in the permutation test. This number has to be multiplied by OPT.NB_BATCH below to get the effective number of samples
opt.nb_batch = 10; % The permutation tests are separated into NB_BATCH independent batches, which can run on parallel if sufficient computational resources are available
opt.flag_rand = false; % if the flag is false, the pipeline is deterministic. Otherwise, the random number generator is initialized based on the clock for each job.


%%%%%%%%%%%%
%% Tests
%%%%%%%%%%%%

%% Group differences

%%% ctlr vs mci

opt.test.ctrlvsmci.group.contrast.ctrlvsmci = 1; 
opt.test.ctrlvsmci.group.contrast.age = 0;     
opt.test.ctrlvsmci.group.contrast.gender = 0;
opt.test.ctrlvsmci.group.contrast.fd = 0;
opt.test.ctrlvsmci.group.multisite = 'mtladni2sites';
opt.test.ctrlvsmci.group.select(1).label = 'diagnosis'; 
opt.test.ctrlvsmci.group.select(1).values = [1 2];
opt.test.ctrlvsmci.group.select(2).label = 'mtladni2sites'; 
opt.test.ctrlvsmci.group.select(2).values = [1 3 4 5 6 7 9];
opt.test.ctrlvsmci.group.select(2).operation = 'and';


%% Group averages

%%% ctrl avg connectivity

opt.test.avg_ctrl.group.contrast.intercept = 1;
opt.test.avg_ctrl.group.contrast.age = 0;
opt.test.avg_ctrl.group.contrast.gender = 0;
opt.test.avg_ctrl.group.contrast.fd = 0;
opt.test.avg_ctrl.group.multisite = 'mtladni2sites';
opt.test.avg_ctrl.group.select(1).label = 'diagnosis';
opt.test.avg_ctrl.group.select(1).values = 1;
opt.test.avg_ctrl.group.select(2).label = 'mtladni2sites'; 
opt.test.avg_ctrl.group.select(2).values = [1 3 4 5 6 7 9];
opt.test.avg_ctrl.group.select(2).operation = 'and';

%%% mci avg connectivity
opt.test.avg_mci.group.contrast.intercept = 1;
opt.test.avg_mci.group.contrast.age = 0;
opt.test.avg_mci.group.contrast.gender = 0;
opt.test.avg_mci.group.contrast.fd = 0;
opt.test.avg_mci.group.multisite = 'mtladni2sites';
opt.test.avg_mci.group.select(1).label = 'diagnosis';
opt.test.avg_mci.group.select(1).values = 2;
opt.test.avg_mci.group.select(2).label = 'mtladni2sites'; 
opt.test.avg_mci.group.select(2).values = [1 3 4 5 6 7 9];
opt.test.avg_mci.group.select(2).operation = 'and';


%%%%%%%%%%%%
%% Run the pipeline
%%%%%%%%%%%%
opt.flag_test = false; % Put this flag to true to just generate the pipeline without running it. Otherwise the region growing will start.
% opt.psom.qsub_options= '-A gsf-624-aa -q sw -l nodes=1:ppn=2 -l walltime=30:00:00';
% opt.psom.max_queued = 10; % Uncomment and change this parameter to set the number of parallel threads used to run the pipeline
[pipeline,opt] = niak_pipeline_glm_connectome(files_in,opt);