def test_regress_covar(): """ test regress_covar""" _make_tmp_dir() extract_mean_wm_ts = ExtractMeanTS() extract_mean_wm_ts.inputs.file_4D = img_file extract_mean_wm_ts.inputs.mask_file = wm_mask_file extract_mean_wm_ts.inputs.suffix = "wm" mean_wm_ts_file = extract_mean_wm_ts.run().outputs.mean_masked_ts_file extract_mean_csf_ts = ExtractMeanTS() extract_mean_csf_ts.inputs.file_4D = img_file extract_mean_csf_ts.inputs.mask_file = csf_mask_file extract_mean_csf_ts.inputs.suffix = "csf" mean_csf_ts_file = extract_mean_csf_ts.run().outputs.mean_masked_ts_file extra_ts = ExtractTS() extra_ts.inputs.indexed_rois_file = indexed_mask_file extra_ts.inputs.file_4D = img_file masked_ts_file = extra_ts.run().outputs.mean_masked_ts_file regress_covar = RegressCovar() regress_covar.inputs.masked_ts_file = masked_ts_file regress_covar.inputs.mean_wm_ts_file = mean_wm_ts_file regress_covar.inputs.mean_csf_ts_file = mean_csf_ts_file val = regress_covar.run().outputs print(val) os.remove(val.resid_ts_file) os.remove(masked_ts_file) os.remove(mean_csf_ts_file) os.remove(mean_wm_ts_file)
def test_extract_ts(): """ test ExtractTS""" _make_tmp_dir() extra_ts = ExtractTS() extra_ts.inputs.indexed_rois_file = indexed_mask_file extra_ts.inputs.file_4D = img_file val = extra_ts.run().outputs print(val) assert os.path.exists(val.mean_masked_ts_file) os.remove(val.mean_masked_ts_file)
def test_compute_conf_cor_mat(): """test ComputeConfCorMat""" _make_tmp_dir() extract_mean_wm_ts = ExtractMeanTS() extract_mean_wm_ts.inputs.file_4D = img_file extract_mean_wm_ts.inputs.filter_thr = 0.9 extract_mean_wm_ts.inputs.filter_mask_file = csf_mask_file extract_mean_wm_ts.inputs.suffix = "wm" mean_wm_ts_file = extract_mean_wm_ts.run().outputs.mean_masked_ts_file extract_mean_csf_ts = ExtractMeanTS() extract_mean_csf_ts.inputs.file_4D = img_file extract_mean_csf_ts.inputs.filter_thr = 0.9 extract_mean_csf_ts.inputs.filter_mask_file = csf_mask_file extract_mean_csf_ts.inputs.suffix = "csf" mean_csf_ts_file = extract_mean_csf_ts.run().outputs.mean_masked_ts_file extra_ts = ExtractTS() extra_ts.inputs.indexed_rois_file = indexed_mask_file extra_ts.inputs.file_4D = img_file masked_ts_file = extra_ts.run().outputs.mean_masked_ts_file regress_covar = RegressCovar() regress_covar.inputs.masked_ts_file = masked_ts_file regress_covar.inputs.mean_wm_ts_file = mean_wm_ts_file regress_covar.inputs.mean_csf_ts_file = mean_csf_ts_file resid_ts_file = regress_covar.run().outputs.resid_ts_file compute_conf_cor_mat = ComputeConfCorMat() compute_conf_cor_mat.inputs.ts_file = resid_ts_file val = compute_conf_cor_mat.run().outputs print(val) assert os.path.exists(val.cor_mat_file) assert os.path.exists(val.Z_cor_mat_file) assert os.path.exists(val.conf_cor_mat_file) assert os.path.exists(val.Z_conf_cor_mat_file) os.remove(val.cor_mat_file) os.remove(val.conf_cor_mat_file) os.remove(val.Z_cor_mat_file) os.remove(val.Z_conf_cor_mat_file) os.remove(resid_ts_file) os.remove(masked_ts_file) os.remove(mean_csf_ts_file) os.remove(mean_wm_ts_file)
def test_split_ts(): """ test SplitTS""" _make_tmp_dir() extract_mean_wm_ts = ExtractMeanTS() extract_mean_wm_ts.inputs.file_4D = img_file extract_mean_wm_ts.inputs.filter_thr = 0.9 extract_mean_wm_ts.inputs.filter_mask_file = csf_mask_file extract_mean_wm_ts.inputs.suffix = "wm" mean_wm_ts_file = extract_mean_wm_ts.run().outputs.mean_masked_ts_file extract_mean_csf_ts = ExtractMeanTS() extract_mean_csf_ts.inputs.file_4D = img_file extract_mean_csf_ts.inputs.filter_thr = 0.9 extract_mean_csf_ts.inputs.filter_mask_file = csf_mask_file extract_mean_csf_ts.inputs.suffix = "csf" mean_csf_ts_file = extract_mean_csf_ts.run().outputs.mean_masked_ts_file extra_ts = ExtractTS() extra_ts.inputs.indexed_rois_file = indexed_mask_file extra_ts.inputs.file_4D = img_file masked_ts_file = extra_ts.run().outputs.mean_masked_ts_file regress_covar = RegressCovar() regress_covar.inputs.masked_ts_file = masked_ts_file regress_covar.inputs.mean_wm_ts_file = mean_wm_ts_file regress_covar.inputs.mean_csf_ts_file = mean_csf_ts_file resid_ts_file = regress_covar.run().outputs.resid_ts_file split_ts = SplitTS() split_ts.inputs.ts_file = resid_ts_file split_ts.inputs.win_length = 10 split_ts.inputs.offset = 5 splitted_ts_files = split_ts.run().outputs.splitted_ts_files assert len(splitted_ts_files) != 0 assert os.path.exists(splitted_ts_files[0]) for splitted_ts_file in splitted_ts_files: os.remove(splitted_ts_file)
def create_pipeline_nii_to_conmat_seg_template(main_path, pipeline_name="nii_to_conmat", conf_interval_prob=0.05): """ Description: Pipeline from nifti 4D (after preprocessing) to connectivity matrices Inputs (inputnode): * nii_4D_file * rp_file * wm_anat_file * csf_anat_file * ROI_mask_file * ROI_coords_file * ROI_MNI_coords_file * ROI_labels_file Comments: Typically used after nipype preprocessing pipeline and before conmat_to_graph pipeline """ pipeline = pe.Workflow(name=pipeline_name) pipeline.base_dir = main_path inputnode = pe.Node(niu.IdentityInterface(fields=[ 'nii_4D_file', 'rp_file', 'wm_anat_file', 'csf_anat_file', 'ROI_mask_file', 'ROI_coords_file', 'ROI_MNI_coords_file', 'ROI_labels_file' ]), name='inputnode') # Nodes version: use min_BOLD_intensity and # return coords where signal is strong enough extract_mean_ROI_ts = pe.Node(interface=ExtractTS(plot_fig=False), name='extract_mean_ROI_ts') pipeline.connect(inputnode, 'nii_4D_file', extract_mean_ROI_ts, 'file_4D') pipeline.connect(inputnode, 'ROI_mask_file', extract_mean_ROI_ts, 'indexed_rois_file') pipeline.connect(inputnode, 'ROI_coords_file', extract_mean_ROI_ts, 'coord_rois_file') pipeline.connect(inputnode, 'ROI_MNI_coords_file', extract_mean_ROI_ts, 'MNI_coord_rois_file') pipeline.connect(inputnode, 'ROI_labels_file', extract_mean_ROI_ts, 'label_rois_file') # extract white matter signal compute_wm_ts = pe.Node(interface=ExtractMeanTS(plot_fig=False), name='extract_wm_ts') compute_wm_ts.inputs.suffix = 'wm' pipeline.connect(inputnode, 'nii_4D_file', compute_wm_ts, 'file_4D') pipeline.connect(inputnode, 'wm_anat_file', compute_wm_ts, 'filter_mask_file') # extract csf signal compute_csf_ts = pe.Node(interface=ExtractMeanTS(plot_fig=False), name='extract_csf_ts') compute_csf_ts.inputs.suffix = 'csf' pipeline.connect(inputnode, 'nii_4D_file', compute_csf_ts, 'file_4D') pipeline.connect(inputnode, 'csf_anat_file', compute_csf_ts, 'filter_mask_file') regress_covar = pe.Node(interface=RegressCovar(), iterfield=['masked_ts_file', 'rp_file'], name='regress_covar') pipeline.connect(extract_mean_ROI_ts, 'mean_masked_ts_file', regress_covar, 'masked_ts_file') pipeline.connect(compute_wm_ts, 'mean_masked_ts_file', regress_covar, 'mean_wm_ts_file') pipeline.connect(compute_csf_ts, 'mean_masked_ts_file', regress_covar, 'mean_csf_ts_file') pipeline.connect(inputnode, 'rp_file', regress_covar, 'rp_file') # compute correlations compute_conf_cor_mat = pe.Node(interface=ComputeConfCorMat(), name='compute_conf_cor_mat') compute_conf_cor_mat.inputs.conf_interval_prob = conf_interval_prob pipeline.connect(regress_covar, 'resid_ts_file', compute_conf_cor_mat, 'ts_file') pipeline.connect(extract_mean_ROI_ts, 'subj_label_rois_file', compute_conf_cor_mat, 'labels_file') return pipeline
def create_pipeline_nii_to_conmat(main_path, filter_gm_threshold=0.9, pipeline_name="nii_to_conmat", conf_interval_prob=0.05, background_val=-1.0, plot=True, reslice=False, resample=False, min_BOLD_intensity=50, percent_signal=0.5): """ Description: Pipeline from nifti 4D (after preprocessing) to connectivity matrices Inputs (inputnode): * nii_4D_file * rp_file * ROI_mask_file * gm_anat_file * wm_anat_file * csf_anat_file * ROI_coords_file * ROI_MNI_coords_file * ROI_labels_file Comments: Typically used after nipype preprocessing pipeline and before conmat_to_graph pipeline """ if reslice and resample: print("Only reslice OR resample can be true, setting reslice to False") reslice = False pipeline = pe.Workflow(name=pipeline_name) pipeline.base_dir = main_path inputnode = pe.Node(niu.IdentityInterface(fields=[ 'nii_4D_file', 'ROI_mask_file', 'rp_file', 'gm_anat_file', 'wm_anat_file', 'csf_anat_file', 'ROI_coords_file', 'ROI_MNI_coords_file', 'ROI_labels_file' ]), name='inputnode') # reslice gm if reslice: reslice_gm = pe.Node(interface=spmu.Reslice(), name='reslice_gm') pipeline.connect(inputnode, 'ROI_mask_file', reslice_gm, 'space_defining') pipeline.connect(inputnode, 'gm_anat_file', reslice_gm, 'in_file') if resample: resample_gm = pe.Node(interface=RegResample(), name='resample_gm') pipeline.connect(inputnode, 'ROI_mask_file', resample_gm, 'ref_file') pipeline.connect(inputnode, 'gm_anat_file', resample_gm, 'flo_file') # reslice wm if reslice: reslice_wm = pe.Node(interface=spmu.Reslice(), name='reslice_wm') pipeline.connect(inputnode, 'ROI_mask_file', reslice_wm, 'space_defining') pipeline.connect(inputnode, 'wm_anat_file', reslice_wm, 'in_file') if resample: resample_wm = pe.Node(interface=RegResample(), name='resample_wm') pipeline.connect(inputnode, 'ROI_mask_file', resample_wm, 'ref_file') pipeline.connect(inputnode, 'wm_anat_file', resample_wm, 'flo_file') # reslice csf if reslice: reslice_csf = pe.Node(interface=spmu.Reslice(), name='reslice_csf') pipeline.connect(inputnode, 'ROI_mask_file', reslice_csf, 'space_defining') pipeline.connect(inputnode, 'csf_anat_file', reslice_csf, 'in_file') if resample: resample_csf = pe.Node(interface=RegResample(), name='resample_csf') pipeline.connect(inputnode, 'ROI_mask_file', resample_csf, 'ref_file') pipeline.connect(inputnode, 'csf_anat_file', resample_csf, 'flo_file') # Preprocess pipeline, filter_ROI_mask_with_GM = pe.Node(interface=IntersectMask(), name='filter_ROI_mask_with_GM') filter_ROI_mask_with_GM.inputs.filter_thr = filter_gm_threshold filter_ROI_mask_with_GM.inputs.background_val = background_val pipeline.connect(inputnode, 'ROI_mask_file', filter_ROI_mask_with_GM, 'indexed_rois_file') pipeline.connect(inputnode, 'ROI_coords_file', filter_ROI_mask_with_GM, 'coords_rois_file') pipeline.connect(inputnode, 'ROI_MNI_coords_file', filter_ROI_mask_with_GM, 'MNI_coords_rois_file') pipeline.connect(inputnode, 'ROI_labels_file', filter_ROI_mask_with_GM, 'labels_rois_file') if reslice: pipeline.connect(reslice_gm, 'out_file', filter_ROI_mask_with_GM, 'filter_mask_file') elif resample: pipeline.connect(resample_gm, 'out_file', filter_ROI_mask_with_GM, 'filter_mask_file') else: pipeline.connect(inputnode, 'gm_anat_file', filter_ROI_mask_with_GM, 'filter_mask_file') # Nodes version: use min_BOLD_intensity and # return coords where signal is strong enough extract_mean_ROI_ts = pe.Node(interface=ExtractTS(plot_fig=plot), name='extract_mean_ROI_ts') extract_mean_ROI_ts.inputs.percent_signal = percent_signal extract_mean_ROI_ts.inputs.min_BOLD_intensity = min_BOLD_intensity pipeline.connect(inputnode, 'nii_4D_file', extract_mean_ROI_ts, 'file_4D') pipeline.connect(filter_ROI_mask_with_GM, 'filtered_indexed_rois_file', extract_mean_ROI_ts, 'indexed_rois_file') pipeline.connect(filter_ROI_mask_with_GM, 'filtered_MNI_coords_rois_file', extract_mean_ROI_ts, 'MNI_coord_rois_file') pipeline.connect(filter_ROI_mask_with_GM, 'filtered_coords_rois_file', extract_mean_ROI_ts, 'coord_rois_file') pipeline.connect(filter_ROI_mask_with_GM, 'filtered_labels_rois_file', extract_mean_ROI_ts, 'label_rois_file') # extract white matter signal compute_wm_ts = pe.Node(interface=ExtractMeanTS(plot_fig=plot), name='extract_wm_ts') compute_wm_ts.inputs.suffix = 'wm' pipeline.connect(inputnode, 'nii_4D_file', compute_wm_ts, 'file_4D') if reslice: pipeline.connect(reslice_wm, 'out_file', compute_wm_ts, 'filter_mask_file') elif resample: pipeline.connect(resample_wm, 'out_file', compute_wm_ts, 'filter_mask_file') else: pipeline.connect(inputnode, 'wm_anat_file', compute_wm_ts, 'filter_mask_file') # extract csf signal compute_csf_ts = pe.Node(interface=ExtractMeanTS(plot_fig=plot), name='extract_csf_ts') compute_csf_ts.inputs.suffix = 'csf' pipeline.connect(inputnode, 'nii_4D_file', compute_csf_ts, 'file_4D') if reslice: pipeline.connect(reslice_csf, 'out_file', compute_csf_ts, 'filter_mask_file') elif resample: pipeline.connect(resample_csf, 'out_file', compute_csf_ts, 'filter_mask_file') else: pipeline.connect(inputnode, 'csf_anat_file', compute_csf_ts, 'filter_mask_file') # regress covariates regress_covar = pe.Node(interface=RegressCovar(plot_fig=plot), iterfield=[ 'masked_ts_file', 'rp_file', 'mean_wm_ts_file', 'mean_csf_ts_file' ], name='regress_covar') pipeline.connect(extract_mean_ROI_ts, 'mean_masked_ts_file', regress_covar, 'masked_ts_file') pipeline.connect(inputnode, 'rp_file', regress_covar, 'rp_file') pipeline.connect(compute_wm_ts, 'mean_masked_ts_file', regress_covar, 'mean_wm_ts_file') pipeline.connect(compute_csf_ts, 'mean_masked_ts_file', regress_covar, 'mean_csf_ts_file') # compute correlations compute_conf_cor_mat = pe.Node(interface=ComputeConfCorMat(plot_mat=plot), name='compute_conf_cor_mat') compute_conf_cor_mat.inputs.conf_interval_prob = conf_interval_prob pipeline.connect(regress_covar, 'resid_ts_file', compute_conf_cor_mat, 'ts_file') pipeline.connect(extract_mean_ROI_ts, 'subj_label_rois_file', compute_conf_cor_mat, 'labels_file') return pipeline
def create_pipeline_nii_to_subj_ROI(main_path, filter_gm_threshold=0.9, pipeline_name="nii_to_subj_ROI", background_val=-1.0, plot=True, reslice=False, resample=False, min_BOLD_intensity=50, percent_signal=0.5): """ Description: Pipeline from nifti 4D (after preprocessing) to connectivity matrices Use Grey matter for having a mask for each subject Inputs (inputnode): * nii_4D_file * gm_anat_file * ROI_mask_file * ROI_coords_file * ROI_MNI_coords_file * ROI_labels_file Comments: Typically used after nipype preprocessing pipeline and before conmat_to_graph pipeline """ if reslice and resample: print("Only reslice OR resample can be true, setting reslice to False") reslice = False pipeline = pe.Workflow(name=pipeline_name) pipeline.base_dir = main_path inputnode = pe.Node(niu.IdentityInterface(fields=[ 'nii_4D_file', 'ROI_mask_file', 'gm_anat_file', 'ROI_coords_file', 'ROI_MNI_coords_file', 'ROI_labels_file' ]), name='inputnode') # reslice gm if reslice: reslice_gm = pe.Node(interface=spmu.Reslice(), name='reslice_gm') pipeline.connect(inputnode, 'ROI_mask_file', reslice_gm, 'space_defining') pipeline.connect(inputnode, 'gm_anat_file', reslice_gm, 'in_file') if resample: resample_gm = pe.Node(interface=RegResample(), name='resample_gm') pipeline.connect(inputnode, 'ROI_mask_file', resample_gm, 'ref_file') pipeline.connect(inputnode, 'gm_anat_file', resample_gm, 'flo_file') # Preprocess pipeline, filter_ROI_mask_with_GM = pe.Node(interface=IntersectMask(), name='filter_ROI_mask_with_GM') filter_ROI_mask_with_GM.inputs.filter_thr = filter_gm_threshold filter_ROI_mask_with_GM.inputs.background_val = background_val pipeline.connect(inputnode, 'ROI_mask_file', filter_ROI_mask_with_GM, 'indexed_rois_file') pipeline.connect(inputnode, 'ROI_coords_file', filter_ROI_mask_with_GM, 'coords_rois_file') pipeline.connect(inputnode, 'ROI_MNI_coords_file', filter_ROI_mask_with_GM, 'MNI_coords_rois_file') pipeline.connect(inputnode, 'ROI_labels_file', filter_ROI_mask_with_GM, 'labels_rois_file') if reslice: pipeline.connect(reslice_gm, 'out_file', filter_ROI_mask_with_GM, 'filter_mask_file') elif resample: pipeline.connect(resample_gm, 'out_file', filter_ROI_mask_with_GM, 'filter_mask_file') else: pipeline.connect(inputnode, 'gm_anat_file', filter_ROI_mask_with_GM, 'filter_mask_file') # Nodes version: use min_BOLD_intensity and # return coords where signal is strong enough extract_mean_ROI_ts = pe.Node(interface=ExtractTS(plot_fig=plot), name='extract_mean_ROI_ts') extract_mean_ROI_ts.inputs.percent_signal = percent_signal extract_mean_ROI_ts.inputs.min_BOLD_intensity = min_BOLD_intensity pipeline.connect(inputnode, 'nii_4D_file', extract_mean_ROI_ts, 'file_4D') pipeline.connect(filter_ROI_mask_with_GM, 'filtered_indexed_rois_file', extract_mean_ROI_ts, 'indexed_rois_file') pipeline.connect(filter_ROI_mask_with_GM, 'filtered_MNI_coords_rois_file', extract_mean_ROI_ts, 'MNI_coord_rois_file') pipeline.connect(filter_ROI_mask_with_GM, 'filtered_coords_rois_file', extract_mean_ROI_ts, 'coord_rois_file') pipeline.connect(filter_ROI_mask_with_GM, 'filtered_labels_rois_file', extract_mean_ROI_ts, 'label_rois_file') return pipeline
def create_pipeline_nii_to_conmat_simple(main_path, pipeline_name="nii_to_conmat", conf_interval_prob=0.05, background_val=-1.0, plot=True): """ Description: Pipeline from nifti 4D (after preprocessing) to connectivity matrices, no segmentation in tissues given, but coords for wm and csf are available and regressed. coords / labels o indexed mask are also available Inputs (inputnode): * nii_4D_file * ROI_mask_file Optional inputs (inputnode) : * rp_file * ROI_coords_file * ROI_MNI_coords_file * ROI_labels_file Comments: Typically used after nipype preprocessing pipeline and before conmat_to_graph pipeline """ pipeline = pe.Workflow(name=pipeline_name) pipeline.base_dir = main_path inputnode = pe.Node(niu.IdentityInterface(fields=[ 'nii_4D_file', 'ROI_mask_file', 'rp_file', 'ROI_coords_file', 'ROI_MNI_coords_file', 'ROI_labels_file' ]), name='inputnode') # Nodes version: use min_BOLD_intensity and # return coords where signal is strong enough extract_mean_ROI_ts = pe.Node(interface=ExtractTS(plot_fig=False), name='extract_mean_ROI_ts') extract_mean_ROI_ts.inputs.background_val = background_val pipeline.connect(inputnode, 'nii_4D_file', extract_mean_ROI_ts, 'file_4D') pipeline.connect(inputnode, 'ROI_mask_file', extract_mean_ROI_ts, 'indexed_rois_file') pipeline.connect(inputnode, 'ROI_coords_file', extract_mean_ROI_ts, 'coord_rois_file') pipeline.connect(inputnode, 'ROI_MNI_coords_file', extract_mean_ROI_ts, 'MNI_coord_rois_file') pipeline.connect(inputnode, 'ROI_labels_file', extract_mean_ROI_ts, 'label_rois_file') # regress covariates regress_covar = pe.Node(interface=RegressCovar(plot_fig=plot), iterfield=['masked_ts_file', 'rp_file'], name='regress_covar') pipeline.connect(extract_mean_ROI_ts, 'mean_masked_ts_file', regress_covar, 'masked_ts_file') pipeline.connect(inputnode, 'rp_file', regress_covar, 'rp_file') # compute correlations compute_conf_cor_mat = pe.Node(interface=ComputeConfCorMat(plot_mat=plot), name='compute_conf_cor_mat') compute_conf_cor_mat.inputs.conf_interval_prob = conf_interval_prob pipeline.connect(regress_covar, 'resid_ts_file', compute_conf_cor_mat, 'ts_file') pipeline.connect(extract_mean_ROI_ts, 'subj_label_rois_file', compute_conf_cor_mat, 'labels_file') return pipeline
def create_pipeline_nii_to_conmat_no_seg(main_path, pipeline_name="nii_to_conmat", conf_interval_prob=0.05): """ Description: Pipeline from nifti 4D (after preprocessing) to connectivity matrices Inputs (inputnode): * nii_4D_file * rp_file * ROI_mask_file * ROI_coords_file * ROI_MNI_coords_file * ROI_labels_file Comments: Typically used after nipype preprocessing pipeline and before conmat_to_graph pipeline """ pipeline = pe.Workflow(name=pipeline_name) pipeline.base_dir = main_path inputnode = pe.Node(niu.IdentityInterface(fields=[ 'nii_4D_file', 'rp_file', 'ROI_mask_file', 'ROI_coords_file', 'ROI_MNI_coords_file', 'ROI_labels_file' ]), name='inputnode') #### Nodes version: use min_BOLD_intensity and return coords where signal is strong enough extract_mean_ROI_ts = pe.Node(interface=ExtractTS(plot_fig=False), name='extract_mean_ROI_ts') #pipeline.connect(inputnode, 'ROI_coords_file', filter_ROI_mask_with_GM, 'coords_rois_file') #pipeline.connect(inputnode, 'ROI_MNI_coords_file', filter_ROI_mask_with_GM, 'MNI_coords_rois_file') #pipeline.connect(inputnode, 'ROI_labels_file', filter_ROI_mask_with_GM, 'labels_rois_file') #extract_mean_ROI_ts.inputs.indexed_rois_file = ROI_mask_file #extract_mean_ROI_ts.inputs.coord_rois_file = ROI_coords_file #extract_mean_ROI_ts.inputs.min_BOLD_intensity = min_BOLD_intensity pipeline.connect(inputnode, 'nii_4D_file', extract_mean_ROI_ts, 'file_4D') pipeline.connect(inputnode, 'ROI_mask_file', extract_mean_ROI_ts, 'indexed_rois_file') pipeline.connect(inputnode, 'ROI_coords_file', extract_mean_ROI_ts, 'coord_rois_file') pipeline.connect(inputnode, 'ROI_labels_file', extract_mean_ROI_ts, 'label_rois_file') #### extract white matter signal compute_wm_ts = pe.Node(interface=ExtractMeanTS(plot_fig=False), name='extract_wm_ts') compute_wm_ts.inputs.suffix = 'wm' pipeline.connect(inputnode, 'nii_4D_file', compute_wm_ts, 'file_4D') compute_wm_ts.inputs.ROI_coord = [46, 40, 76] #### extract csf signal compute_csf_ts = pe.Node(interface=ExtractMeanTS(plot_fig=False), name='extract_csf_ts') compute_csf_ts.inputs.suffix = 'csf' pipeline.connect(inputnode, 'nii_4D_file', compute_csf_ts, 'file_4D') compute_csf_ts.inputs.ROI_coord = [47, 18, 83] #### regress covariates ### use R linear model to regress movement parameters, white matter and ventricule signals, and compute Z-score of the residuals #regress_covar = pe.MapNode(interface = RegressCovar(filtered = False, normalized = False),iterfield = ['masked_ts_file','rp_file','mean_wm_ts_file','mean_csf_ts_file'],name='regress_covar') regress_covar = pe.Node(interface=RegressCovar(), iterfield=['masked_ts_file', 'rp_file'], name='regress_covar') pipeline.connect(extract_mean_ROI_ts, 'mean_masked_ts_file', regress_covar, 'masked_ts_file') pipeline.connect(compute_wm_ts, 'mean_masked_ts_file', regress_covar, 'mean_wm_ts_file') pipeline.connect(compute_csf_ts, 'mean_masked_ts_file', regress_covar, 'mean_csf_ts_file') pipeline.connect(inputnode, 'rp_file', regress_covar, 'rp_file') ##################################### compute correlations #################################################### compute_conf_cor_mat = pe.Node(interface=ComputeConfCorMat(), name='compute_conf_cor_mat') compute_conf_cor_mat.inputs.conf_interval_prob = conf_interval_prob pipeline.connect(regress_covar, 'resid_ts_file', compute_conf_cor_mat, 'ts_file') pipeline.connect(extract_mean_ROI_ts, 'subj_label_rois_file', compute_conf_cor_mat, 'labels_file') return pipeline
def create_pipeline_nii_to_conmat_simple(main_path, ROI_mask_file, pipeline_name="nii_to_conmat", conf_interval_prob=0.05, background_val=-1.0): """ Description: Pipeline from nifti 4D (after preprocessing) to connectivity matrices Inputs (inputnode): * nii_4D_file * rp_file as arguments; * ROI_mask_file Comments: Typically used after nipype preprocessing pipeline and before conmat_to_graph pipeline """ pipeline = pe.Workflow(name=pipeline_name) pipeline.base_dir = main_path inputnode = pe.Node( niu.IdentityInterface(fields=['nii_4D_file', 'rp_file']), name='inputnode') #### Nodes version: use min_BOLD_intensity and return coords where signal is strong enough extract_mean_ROI_ts = pe.Node(interface=ExtractTS(plot_fig=True), name='extract_mean_ROI_ts') extract_mean_ROI_ts.inputs.indexed_rois_file = ROI_mask_file extract_mean_ROI_ts.inputs.background_val = background_val pipeline.connect(inputnode, 'nii_4D_file', extract_mean_ROI_ts, 'file_4D') ##### regress covariates #### use R linear model to regress movement parameters, white matter and ventricule signals, and compute Z-score of the residuals regress_covar = pe.Node(interface=RegressCovar(), iterfield=['masked_ts_file', 'rp_file'], name='regress_covar') pipeline.connect(extract_mean_ROI_ts, 'mean_masked_ts_file', regress_covar, 'masked_ts_file') pipeline.connect(inputnode, 'rp_file', regress_covar, 'rp_file') ###################################### compute correlations #################################################### compute_conf_cor_mat = pe.Node(interface=ComputeConfCorMat(), name='compute_conf_cor_mat') compute_conf_cor_mat.inputs.conf_interval_prob = conf_interval_prob pipeline.connect(regress_covar, 'resid_ts_file', compute_conf_cor_mat, 'ts_file') pipeline.connect(extract_mean_ROI_ts, 'subj_label_rois_file', compute_conf_cor_mat, 'labels_file') return pipeline