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For MIBH run Normalize_full_process_run_processed_data.m
For MIBH run Normalize_full_process_run_processed_data.m
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| clc | ||
| clear | ||
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| cd('/media/panda/RAJA_RP/CSG/Aurore/PET_Results/Manoon/Norm_full') | ||
| control_subjects = dir('control/*.nii'); | ||
| mcs_subjects = dir('mcs/*.nii'); | ||
| mcsstar_subjects = dir('NoLOCA_sws/*.nii'); | ||
| vs_subjects = dir('LOCA_sws/*.nii'); | ||
| % | ||
| for i = 1:length(control_subjects) | ||
| c_subj{i} = strrep(control_subjects(i).name, '.nii', ''); | ||
| end | ||
| control_subjects = c_subj; | ||
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| for i = 1:length(mcs_subjects) | ||
| m_subj{i} = strrep(mcs_subjects(i).name, '.nii', ''); | ||
| end | ||
| mcs_subjects = m_subj; | ||
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| for i = 1:length(mcsstar_subjects) | ||
| ms_subj{i} = strrep(mcsstar_subjects(i).name, '.nii', ''); | ||
| end | ||
| mcsstar_subjects = ms_subj; | ||
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| for i = 1:length(vs_subjects) | ||
| v_subj{i} = strrep(vs_subjects(i).name, '.nii', ''); | ||
| end | ||
| vs_subjects = v_subj; | ||
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| clear c_subj m_subj v_subj ms_subj; | ||
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| %% Register control images, and transform atlases to patient-specific space | ||
| fprintf('Registering control subjects'); | ||
| for i = 1:length(control_subjects) | ||
| fprintf('.'); | ||
| control_input_files{i} = ['control/' control_subjects{i} '.nii']; | ||
| control_atlas_files{i} = ['control/' control_subjects{i} '_atlas.nii.gz']; | ||
| control_normalized_files{i} = ['control/' control_subjects{i} '_normalized.nii.gz']; | ||
| control_normalized_MNI_files{i} = ['control/' control_subjects{i} '_MNI_normalized.nii.gz']; | ||
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| % Register PET to template | ||
| % [~, control_affines{i}, control_warps{i}, control_inverse_warps{i}] = ants(control_input_files{i}, 'control_template.nii.gz', 'preset', 'DOC-PET'); | ||
| % | ||
| % % Warp atlas to PET | ||
| % ants_warp('atlas.nii.gz', control_input_files{i}, 'o', control_atlas_files{i}, 'n', 'NearestNeighbor', '-i', control_affines{i}, control_inverse_warps{i}); | ||
| end | ||
| fprintf(' Done.\n'); | ||
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| %% Create reference distribution | ||
| % fprintf('Creating reference distribution... '); | ||
| % [dist, range] = create_mean_distribution(control_input_files, control_atlas_files); | ||
| % fprintf(' Done.\n'); | ||
| % %% Normalize control images, save normalized and MNI-space registered normalized images. | ||
| % fprintf('Normalizing control subjects'); | ||
| % for i = 1:length(control_subjects) | ||
| % fprintf('.'); | ||
| % | ||
| % % Normalize PET | ||
| % normalize_to_dist(control_input_files{i}, control_atlas_files{i}, range, dist, 'out_file', control_normalized_files{i}); | ||
| % | ||
| % % Warp normalized PET to MNI-space | ||
| % ants_warp(control_normalized_files{i}, 'patient_template.nii.gz', 'o', control_normalized_MNI_files{i}, 'n', 'BSpline', control_warps{i}, control_affines{i}); | ||
| % end | ||
| % fprintf(' Done.\n'); | ||
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| %% Register and normalize mcs images, save normalized and MNI-space registered normalized images. | ||
| fprintf('Registering and normalizing MCS subjects'); | ||
| for i = 1:length(mcs_subjects) | ||
| fprintf('.'); | ||
| mcs_input_files{i} = ['mcs/' mcs_subjects{i} '.nii']; | ||
| mcs_atlas_files{i} = ['mcs/' mcs_subjects{i} '_atlas.nii.gz']; | ||
| mcs_normalized_files{i} = ['mcs/' mcs_subjects{i} '_normalized.nii.gz']; | ||
| mcs_normalized_MNI_files{i} = ['mcs/' mcs_subjects{i} '_MNI_normalized.nii.gz']; | ||
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| % % Register PET to template | ||
| % [~, mcs_affines{i}, mcs_warps{i}, mcs_inverse_warps{i}] = ants(mcs_input_files{i}, 'patient_template.nii.gz', 'preset', 'DOC-PET'); | ||
| % | ||
| % % Warp atlas to PET | ||
| % ants_warp('atlas.nii.gz', mcs_input_files{i}, 'o', mcs_atlas_files{i}, 'n', 'NearestNeighbor', '-i', mcs_affines{i}, mcs_inverse_warps{i}); | ||
| % | ||
| % % Normalize PET | ||
| % normalize_to_dist(mcs_input_files{i}, mcs_atlas_files{i}, range, dist, 'out_file', mcs_normalized_files{i}); | ||
| % | ||
| % % Warp normalized PET to MNI-space | ||
| % ants_warp(mcs_normalized_files{i}, 'patient_template.nii.gz', 'o', mcs_normalized_MNI_files{i}, 'n', 'BSpline', mcs_warps{i}, mcs_affines{i}); | ||
| end | ||
| fprintf(' Done.\n'); | ||
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| %% Register and normalize vs images, save normalized and MNI-space registered normalized images. | ||
| fprintf('Registering and normalizing LOCA_sws subjects'); | ||
| for i = 1:length(vs_subjects) | ||
| fprintf('.'); | ||
| vs_input_files{i} = ['LOCA_sws/' vs_subjects{i} '.nii']; | ||
| vs_atlas_files{i} = ['LOCA_sws/' vs_subjects{i} '_atlas.nii.gz']; | ||
| vs_normalized_files{i} = ['LOCA_sws/' vs_subjects{i} '_normalized.nii.gz']; | ||
| vs_normalized_MNI_files{i} = ['LOCA_sws/' vs_subjects{i} '_MNI_normalized.nii.gz']; | ||
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| % % Register PET to template | ||
| % [~, vs_affines{i}, vs_warps{i}, vs_inverse_warps{i}] = ants(vs_input_files{i}, 'patient_template.nii.gz', 'preset', 'DOC-PET'); | ||
| % | ||
| % % Warp atlas to PET | ||
| % ants_warp('atlas.nii.gz', vs_input_files{i}, 'o', vs_atlas_files{i}, 'n', 'NearestNeighbor', '-i', vs_affines{i}, vs_inverse_warps{i}); | ||
| % | ||
| % % Normalize PET | ||
| % normalize_to_dist(vs_input_files{i}, vs_atlas_files{i}, range, dist, 'out_file', vs_normalized_files{i}); | ||
| % | ||
| % % Warp normalized PET to MNI-space | ||
| % ants_warp(vs_normalized_files{i}, 'patient_template.nii.gz', 'o', vs_normalized_MNI_files{i}, 'n', 'BSpline', vs_warps{i}, vs_affines{i}); | ||
| end | ||
| fprintf(' Done.\n'); | ||
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| %% Register and normalize NoLOCA_sws images, save normalized and MNI-space registered normalized images. | ||
| fprintf('Registering and normalizing NoLOCA_sws subjects'); | ||
| for i = 1:length(mcsstar_subjects) | ||
| fprintf('.'); | ||
| mcsstar_input_files{i} = ['NoLOCA_sws/' mcsstar_subjects{i} '.nii']; | ||
| mcsstar_atlas_files{i} = ['NoLOCA_sws/' mcsstar_subjects{i} '_atlas.nii.gz']; | ||
| mcsstar_normalized_files{i} = ['NoLOCA_sws/' mcsstar_subjects{i} '_normalized.nii.gz']; | ||
| mcsstar_normalized_MNI_files{i} = ['NoLOCA_sws/' mcsstar_subjects{i} '_MNI_normalized.nii.gz']; | ||
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| % % Register PET to template | ||
| % [~, mcsstar_affines{i}, mcsstar_warps{i}, mcsstar_inverse_warps{i}] = ants(mcsstar_input_files{i}, 'patient_template.nii.gz', 'preset', 'DOC-PET'); | ||
| % | ||
| % % Warp atlas to PET | ||
| % ants_warp('atlas.nii.gz', mcsstar_input_files{i}, 'o', mcsstar_atlas_files{i}, 'n', 'NearestNeighbor', '-i', mcsstar_affines{i}, mcsstar_inverse_warps{i}); | ||
| % | ||
| % % Normalize PET | ||
| % normalize_to_dist(mcsstar_input_files{i}, mcsstar_atlas_files{i}, range, dist, 'out_file', mcsstar_normalized_files{i}); | ||
| % | ||
| % % Warp normalized PET to MNI-space | ||
| % ants_warp(mcsstar_normalized_files{i}, 'patient_template.nii.gz', 'o', mcsstar_normalized_MNI_files{i}, 'n', 'BSpline', mcsstar_warps{i}, mcsstar_affines{i}); | ||
| end | ||
| fprintf(' Done.\n'); | ||
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| %% Get high hemisphere means | ||
| for i = 1:length(control_subjects) | ||
| pet = load_nii(control_normalized_files{i}); %pet = reslice_nii(control_normalized_files{i}); | ||
| atlas = load_nii(control_atlas_files{i}); %atlas = reslice_nii(control_atlas_files{i}); | ||
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| l_norm_mean(i) = mean(pet.img(0 < atlas.img & atlas.img < 49)); | ||
| r_norm_mean(i) = mean(pet.img(48 < atlas.img & atlas.img < 100)); | ||
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| control_high_hemisphere(i) = max([l_norm_mean(i), r_norm_mean(i)]); | ||
| control_low_hemisphere(i) = min([l_norm_mean(i), r_norm_mean(i)]); | ||
| end | ||
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| for i = 1:length(mcs_subjects) | ||
| pet = load_nii(mcs_normalized_files{i}); % pet = reslice_nii(mcs_normalized_files{i}); % | ||
| atlas = load_nii(mcs_atlas_files{i}); % atlas = reslice_nii(mcs_atlas_files{i}); % | ||
| l_norm_mean(i) = mean(pet.img(0 < atlas.img & atlas.img < 49)); | ||
| r_norm_mean(i) = mean(pet.img(48 < atlas.img & atlas.img < 100)); | ||
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| mcs_high_hemisphere(i) = max([l_norm_mean(i), r_norm_mean(i)]); | ||
| mcs_low_hemisphere(i) = min([l_norm_mean(i), r_norm_mean(i)]); | ||
| end | ||
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| for i = 1:length(mcsstar_subjects) | ||
| pet = load_nii(mcsstar_normalized_files{i}); % pet = reslice_nii(mcsstar_normalized_files{i}); % | ||
| atlas = load_nii(mcsstar_atlas_files{i}); % atlas = reslice_nii(mcsstar_atlas_files{i}); % | ||
| l_norm_mean(i) = mean(pet.img(0 < atlas.img & atlas.img < 49)); | ||
| r_norm_mean(i) = mean(pet.img(48 < atlas.img & atlas.img < 100)); | ||
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| mcsstar_high_hemisphere(i) = max([l_norm_mean(i), r_norm_mean(i)]); | ||
| mcsstar_low_hemisphere(i) = min([l_norm_mean(i), r_norm_mean(i)]); | ||
| end | ||
| % | ||
| for i = 1:length(vs_subjects) | ||
| pet = load_nii(vs_normalized_files{i}); % pet = reslice_nii(vs_normalized_files{i}); % | ||
| atlas = load_nii(vs_atlas_files{i}); % atlas = reslice_nii(vs_atlas_files{i}); % | ||
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| l_norm_mean(i) = mean(pet.img(0 < atlas.img & atlas.img < 49)); | ||
| r_norm_mean(i) = mean(pet.img(48 < atlas.img & atlas.img < 100)); | ||
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| vs_high_hemisphere(i) = max([l_norm_mean(i), r_norm_mean(i)]); | ||
| vs_low_hemisphere(i) = min([l_norm_mean(i), r_norm_mean(i)]); | ||
| end | ||
| fprintf('Hemispheric metabolisim value computation..... Done.\n'); | ||
| %% Plot results | ||
| f = figure; | ||
| bar([mean(control_high_hemisphere), mean(mcs_high_hemisphere), mean(mcsstar_high_hemisphere), mean(vs_high_hemisphere)]); | ||
| hold on; | ||
| errorbar([mean(control_high_hemisphere), mean(mcs_high_hemisphere), mean(mcsstar_high_hemisphere), mean(vs_high_hemisphere)], [std(control_high_hemisphere), std(mcs_high_hemisphere), std(mcsstar_high_hemisphere), std(vs_high_hemisphere)], '.') | ||
| set(gca, 'XTickLabel', {'Control', 'MCS', 'MCS*', 'VS'}); | ||
| [h,p_cm]=ttest2(control_high_hemisphere,mcs_high_hemisphere) | ||
| [h,p_cms]=ttest2(control_high_hemisphere,mcsstar_high_hemisphere) | ||
| [h,p_cv]=ttest2(control_high_hemisphere,vs_high_hemisphere) | ||
| [h,p_mms]=ttest2(mcs_high_hemisphere,mcsstar_high_hemisphere) | ||
| [h,p_mv]=ttest2(mcs_high_hemisphere,vs_high_hemisphere) | ||
| [h,p_mstarv]=ttest2(mcsstar_high_hemisphere,vs_high_hemisphere) | ||
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| %% Plot results with doted bar | ||
| Grp=[1*ones(size(mcsstar_high_hemisphere,2),1);2*ones(size(vs_high_hemisphere,2),1);3*ones(size(mcs_high_hemisphere,2),1);4*ones(size(control_high_hemisphere,2),1);]; | ||
| high_hemisphere=[mcsstar_high_hemisphere';vs_high_hemisphere';mcs_high_hemisphere';control_high_hemisphere']; | ||
| figure(); notBoxPlot(high_hemisphere,Grp,0.5,'patch',ones(length(high_hemisphere),1)); | ||
| set(gca, 'XTickLabel', {'No-Loca', 'LOCA', 'MCS-', 'Control'}); ylabel('MIBH') | ||
| %low_hemisphere=[control_low_hemisphere';mcs_low_hemisphere';mcsstar_low_hemisphere';vs_low_hemisphere']; | ||
| %figure(); notBoxPlot(low_hemisphere,Grp,0.5,'patch',ones(length(low_hemisphere),1)); | ||
| %set(gca, 'XTickLabel', {'Controls', 'MCS', 'MCS*', 'UWS'}); ylabel('Low Hemisphere') |