fMRI_pre-processing_modeling_ROI-analysis/step_16_roi_analysis_marsbar_native.m at main · jorennig/fMRI_pre-processing_modeling_ROI-analysis · GitHub

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clear all
close all
clc

cf = pwd;

sub = {'AA', 'AB', 'AC', 'AD', 'AE', 'AF', 'AG', 'AH', 'AI', 'AJ', 'AK', 'AL', 'AM', 'AN', 'AO', 'AP', 'AQ', 'AR'};

load('n_vox_native.mat');
con = 16;

marsbar('on');

for i = 1:numel(sub)

    fprintf('-- Subject %d of %d, Sub: %s --\n',i,numel(sub),sub{i})

    % Native space
    results_dir_c = [cf '/POF_nifti/' sub{i}, filesep, 'Results_native'];

    spm_file = fullfile(results_dir_c, 'spm.mat');

    roi_folder = [cf '/POF_nifti/' sub{i}, filesep, 'ROIs_native'];

    rois = dir(fullfile(roi_folder, 'TPJ_*.mat'));
    rois = {rois(:).name}';

    rois_c_vox = n_vox(i,:);
    rois_c_vox = [rois_c_vox(1), rois_c_vox(3), rois_c_vox(2), rois_c_vox(4)];

    for j = 1:numel(rois)

        if rois_c_vox(j) == 0

            data = NaN(1,con);

        else

            roi_c = rois{j};
            roi_c = fullfile(roi_folder, roi_c);

            % Load design
            D = mardo(spm_file);
            %D = autocorr(D, 'fmristat', 2); % to avoid the ReML error
            % Load ROI into analysis
            R = maroi(roi_c);
            % Marsbar data object
            Y = get_marsy(R, D, 'mean');
            % Estimate design on data object
            E = estimate(D, Y);

            % e_specs = 1. row: session, 2. row: number of event; e_names = name of events
            [e_specs, e_names] = event_specs(E);

            % Extraction of event types/names
            ets = event_types_named(E);
            n_event_types = length(ets);
            n_events = size(e_specs, 2);
            dur = 0;

            % extract % signal change, creates % per event
            data = []; % empty data vector at start of each roi assessed
            for e_s = 1:n_events
                pct_ev(e_s) = event_signal(E, e_specs(:,e_s), dur);
                data(e_s) = pct_ev(e_s);
            end

        end

        %data_tot(j,1:con) = data;
        roi_name_c = erase(rois{j}, '_roi.mat');
        psc_rois.(roi_name_c)(i,:) = data;

    end

end

save('psc_rois_native.mat', 'psc_rois');

%% Summary
%load('psc_rois_native.mat')
clearvars -except psc_rois sub

rois = fieldnames(psc_rois);

var_names = {'Global_Run1', 'Global_Run2', 'Scrambled_Run1', 'Scrambled_Run2', 'Places_Run1', 'Faces_Run1', 'Objects_Run1', 'Places_Run2', 'Faces_Run2', 'Objects_Run2', 'Places_Run3', 'Faces_Run3', 'Objects_Run3', 'Places_Run4', 'Faces_Run4', 'Objects_Run4'};

roi_results = 'ROI_Results_native';
mkdir(roi_results)

for i = 1:numel(rois)

    data_c = psc_rois.(rois{i});
    data_ct = data_c(:,[1,3, 2,4, 5,8,11,14, 6,9,12,15, 7,10,13,16]); % Global, Scrambled, Places, Faces, Objects
    data_ct = array2table(data_ct, 'VariableNames', var_names);

    psc_rois.(rois{i}) = data_ct;

    writetable(data_ct, fullfile(roi_results, [rois{i} '.txt']));

end

roi_type = 'Baseline';
rois_baseline = rmfield(psc_rois, rois(find(cellfun(@isempty, strfind(rois, roi_type)))));
roi_type = 'Intact_Scrambled';
rois_intact_scrambled = rmfield(psc_rois, rois(find(cellfun(@isempty, strfind(rois, roi_type)))));

clearvars -except sub