def calc_local_metrics(cfg): import os from nipype import config from nipype.pipeline.engine import Node, Workflow, MapNode import nipype.interfaces.utility as util import nipype.interfaces.io as nio import nipype.interfaces.fsl as fsl import nipype.interfaces.freesurfer as freesurfer import CPAC.alff.alff as cpac_alff import CPAC.reho.reho as cpac_reho import CPAC.utils.utils as cpac_utils import CPAC.vmhc.vmhc as cpac_vmhc import CPAC.registration.registration as cpac_registration import CPAC.network_centrality.z_score as cpac_centrality_z_score import utils as calc_metrics_utils # INPUT PARAMETERS dicom_dir = cfg['dicom_dir'] preprocessed_data_dir = cfg['preprocessed_data_dir'] working_dir = cfg['working_dir'] freesurfer_dir = cfg['freesurfer_dir'] template_dir = cfg['template_dir'] script_dir = cfg['script_dir'] ds_dir = cfg['ds_dir'] subject_id = cfg['subject_id'] TR_list = cfg['TR_list'] vols_to_drop = cfg['vols_to_drop'] rois_list = cfg['rois_list'] lp_cutoff_freq = cfg['lp_cutoff_freq'] hp_cutoff_freq = cfg['hp_cutoff_freq'] use_fs_brainmask = cfg['use_fs_brainmask'] use_n_procs = cfg['use_n_procs'] plugin_name = cfg['plugin_name'] ##################################### # GENERAL SETTINGS ##################################### fsl.FSLCommand.set_default_output_type('NIFTI_GZ') freesurfer.FSCommand.set_default_subjects_dir(freesurfer_dir) wf = Workflow(name='LeiCA_metrics') wf.base_dir = os.path.join(working_dir) nipype_cfg = dict(logging=dict(workflow_level='DEBUG'), execution={'stop_on_first_crash': True, 'remove_unnecessary_outputs': True, 'job_finished_timeout': 120}) config.update_config(nipype_cfg) wf.config['execution']['crashdump_dir'] = os.path.join(working_dir, 'crash') ds = Node(nio.DataSink(base_directory=ds_dir), name='ds') ds.inputs.substitutions = [('_TR_id_', 'TR_')] ds.inputs.regexp_substitutions = [('_variabilty_MNIspace_3mm[0-9]*/', ''), ('_z_score[0-9]*/', '')] ##################################### # SET ITERATORS ##################################### # GET SCAN TR_ID ITERATOR scan_infosource = Node(util.IdentityInterface(fields=['TR_id']), name='scan_infosource') scan_infosource.iterables = ('TR_id', TR_list) # get atlas data templates_atlases = { # 'GM_mask_MNI_2mm': 'SPM_GM/SPM_GM_mask_2mm.nii.gz', # 'GM_mask_MNI_3mm': 'SPM_GM/SPM_GM_mask_3mm.nii.gz', 'FSL_MNI_3mm_template': 'MNI152_T1_3mm_brain.nii.gz', 'vmhc_symm_brain': 'cpac_image_resources/symmetric/MNI152_T1_2mm_brain_symmetric.nii.gz', 'vmhc_symm_brain_3mm': 'cpac_image_resources/symmetric/MNI152_T1_3mm_brain_symmetric.nii.gz', 'vmhc_symm_skull': 'cpac_image_resources/symmetric/MNI152_T1_2mm_symmetric.nii.gz', 'vmhc_symm_brain_mask_dil': 'cpac_image_resources/symmetric/MNI152_T1_2mm_brain_mask_symmetric_dil.nii.gz', 'vmhc_config_file_2mm': 'cpac_image_resources/symmetric/T1_2_MNI152_2mm_symmetric.cnf' } selectfiles_anat_templates = Node(nio.SelectFiles(templates_atlases, base_directory=template_dir), name="selectfiles_anat_templates") # GET SUBJECT SPECIFIC FUNCTIONAL AND STRUCTURAL DATA selectfiles_templates = { 'epi_2_MNI_warp': '{subject_id}/rsfMRI_preprocessing/registration/epi_2_MNI_warp/TR_{TR_id}/*.nii.gz', 'epi_mask': '{subject_id}/rsfMRI_preprocessing/masks/brain_mask_epiSpace/TR_{TR_id}/*.nii.gz', 'preproc_epi_full_spectrum': '{subject_id}/rsfMRI_preprocessing/epis/01_denoised/TR_{TR_id}/*.nii.gz', 'preproc_epi_bp': '{subject_id}/rsfMRI_preprocessing/epis/02_denoised_BP/TR_{TR_id}/*.nii.gz', 'preproc_epi_bp_tNorm': '{subject_id}/rsfMRI_preprocessing/epis/03_denoised_BP_tNorm/TR_{TR_id}/*.nii.gz', 'epi_2_struct_mat': '{subject_id}/rsfMRI_preprocessing/registration/epi_2_struct_mat/TR_{TR_id}/*.mat', 't1w': '{subject_id}/raw_niftis/sMRI/t1w_reoriented.nii.gz', 't1w_brain': '{subject_id}/rsfMRI_preprocessing/struct_prep/t1w_brain/t1w_reoriented_maths.nii.gz', } selectfiles = Node(nio.SelectFiles(selectfiles_templates, base_directory=preprocessed_data_dir), name="selectfiles") wf.connect(scan_infosource, 'TR_id', selectfiles, 'TR_id') selectfiles.inputs.subject_id = subject_id # CREATE TRANSFORMATIONS # creat MNI 2 epi warp MNI_2_epi_warp = Node(fsl.InvWarp(), name='MNI_2_epi_warp') MNI_2_epi_warp.inputs.reference = fsl.Info.standard_image('MNI152_T1_2mm.nii.gz') wf.connect(selectfiles, 'epi_mask', MNI_2_epi_warp, 'reference') wf.connect(selectfiles, 'epi_2_MNI_warp', MNI_2_epi_warp, 'warp') # # CREATE GM MASK IN EPI SPACE # GM_mask_epiSpace = Node(fsl.ApplyWarp(), name='GM_mask_epiSpace') # GM_mask_epiSpace.inputs.out_file = 'GM_mask_epiSpace.nii.gz' # # wf.connect(selectfiles_anat_templates, 'GM_mask_MNI_2mm', GM_mask_epiSpace, 'in_file') # wf.connect(selectfiles, 'epi_mask', GM_mask_epiSpace, 'ref_file') # wf.connect(MNI_2_epi_warp, 'inverse_warp', GM_mask_epiSpace, 'field_file') # wf.connect(GM_mask_epiSpace, 'out_file', ds, 'GM_mask_epiSpace') # fixme # # CREATE TS IN MNI SPACE # # is it ok to apply the 2mm warpfield to the 3mm template? # # seems ok: https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind0904&L=FSL&P=R14011&1=FSL&9=A&J=on&d=No+Match%3BMatch%3BMatches&z=4 # epi_bp_MNIspace_3mm = Node(fsl.ApplyWarp(), name='epi_bp_MNIspace_3mm') # epi_bp_MNIspace_3mm.inputs.interp = 'spline' # epi_bp_MNIspace_3mm.plugin_args = {'submit_specs': 'request_memory = 4000'} # wf.connect(selectfiles_anat_templates, 'FSL_MNI_3mm_template', epi_bp_MNIspace_3mm, 'ref_file') # wf.connect(selectfiles, 'preproc_epi_bp', epi_bp_MNIspace_3mm, 'in_file') # wf.connect(selectfiles, 'epi_2_MNI_warp', epi_bp_MNIspace_3mm, 'field_file') # CREATE EPI MASK IN MNI SPACE epi_mask_MNIspace_3mm = Node(fsl.ApplyWarp(), name='epi_mask_MNIspace_3mm') epi_mask_MNIspace_3mm.inputs.interp = 'nn' epi_mask_MNIspace_3mm.plugin_args = {'submit_specs': 'request_memory = 4000'} wf.connect(selectfiles_anat_templates, 'FSL_MNI_3mm_template', epi_mask_MNIspace_3mm, 'ref_file') wf.connect(selectfiles, 'epi_mask', epi_mask_MNIspace_3mm, 'in_file') wf.connect(selectfiles, 'epi_2_MNI_warp', epi_mask_MNIspace_3mm, 'field_file') wf.connect(epi_mask_MNIspace_3mm, 'out_file', ds, 'epi_mask_MNIspace_3mm') ##################### # CALCULATE METRICS ##################### # f/ALFF alff = cpac_alff.create_alff('alff') alff.inputs.hp_input.hp = 0.01 alff.inputs.lp_input.lp = 0.1 wf.connect(selectfiles, 'preproc_epi_full_spectrum', alff, 'inputspec.rest_res') # wf.connect(GM_mask_epiSpace, 'out_file', alff, 'inputspec.rest_mask') wf.connect(selectfiles, 'epi_mask', alff, 'inputspec.rest_mask') wf.connect(alff, 'outputspec.alff_img', ds, 'alff.alff') wf.connect(alff, 'outputspec.falff_img', ds, 'alff.falff') # f/ALFF 2 MNI # fixme spline or default? alff_MNIspace_3mm = Node(fsl.ApplyWarp(), name='alff_MNIspace_3mm') alff_MNIspace_3mm.inputs.interp = 'spline' alff_MNIspace_3mm.plugin_args = {'submit_specs': 'request_memory = 4000'} wf.connect(selectfiles_anat_templates, 'FSL_MNI_3mm_template', alff_MNIspace_3mm, 'ref_file') wf.connect(alff, 'outputspec.alff_img', alff_MNIspace_3mm, 'in_file') wf.connect(selectfiles, 'epi_2_MNI_warp', alff_MNIspace_3mm, 'field_file') wf.connect(alff_MNIspace_3mm, 'out_file', ds, 'alff.alff_MNI_3mm') falff_MNIspace_3mm = Node(fsl.ApplyWarp(), name='falff_MNIspace_3mm') falff_MNIspace_3mm.inputs.interp = 'spline' falff_MNIspace_3mm.plugin_args = {'submit_specs': 'request_memory = 4000'} wf.connect(selectfiles_anat_templates, 'FSL_MNI_3mm_template', falff_MNIspace_3mm, 'ref_file') wf.connect(alff, 'outputspec.falff_img', falff_MNIspace_3mm, 'in_file') wf.connect(selectfiles, 'epi_2_MNI_warp', falff_MNIspace_3mm, 'field_file') wf.connect(falff_MNIspace_3mm, 'out_file', ds, 'alff.falff_MNI_3mm') # f/ALFF_MNI Z-SCORE alff_MNIspace_3mm_Z = cpac_utils.get_zscore(input_name='alff_MNIspace_3mm', wf_name='alff_MNIspace_3mm_Z') wf.connect(alff_MNIspace_3mm, 'out_file', alff_MNIspace_3mm_Z, 'inputspec.input_file') # wf.connect(selectfiles_anat_templates, 'GM_mask_MNI_3mm', alff_MNIspace_3mm_Z, 'inputspec.mask_file') wf.connect(epi_mask_MNIspace_3mm, 'out_file', alff_MNIspace_3mm_Z, 'inputspec.mask_file') wf.connect(alff_MNIspace_3mm_Z, 'outputspec.z_score_img', ds, 'alff.alff_MNI_3mm_Z') falff_MNIspace_3mm_Z = cpac_utils.get_zscore(input_name='falff_MNIspace_3mm', wf_name='falff_MNIspace_3mm_Z') wf.connect(falff_MNIspace_3mm, 'out_file', falff_MNIspace_3mm_Z, 'inputspec.input_file') # wf.connect(selectfiles_anat_templates, 'GM_mask_MNI_3mm', falff_MNIspace_3mm_Z, 'inputspec.mask_file') wf.connect(epi_mask_MNIspace_3mm, 'out_file', falff_MNIspace_3mm_Z, 'inputspec.mask_file') wf.connect(falff_MNIspace_3mm_Z, 'outputspec.z_score_img', ds, 'alff.falff_MNI_3mm_Z') # f/ALFF_MNI STANDARDIZE BY MEAN alff_MNIspace_3mm_standardized_mean = calc_metrics_utils.standardize_divide_by_mean( wf_name='alff_MNIspace_3mm_standardized_mean') wf.connect(alff_MNIspace_3mm, 'out_file', alff_MNIspace_3mm_standardized_mean, 'inputnode.in_file') wf.connect(epi_mask_MNIspace_3mm, 'out_file', alff_MNIspace_3mm_standardized_mean, 'inputnode.mask_file') wf.connect(alff_MNIspace_3mm_standardized_mean, 'outputnode.out_file', ds, 'alff.alff_MNI_3mm_standardized_mean') falff_MNIspace_3mm_standardized_mean = calc_metrics_utils.standardize_divide_by_mean( wf_name='falff_MNIspace_3mm_standardized_mean') wf.connect(falff_MNIspace_3mm, 'out_file', falff_MNIspace_3mm_standardized_mean, 'inputnode.in_file') wf.connect(epi_mask_MNIspace_3mm, 'out_file', falff_MNIspace_3mm_standardized_mean, 'inputnode.mask_file') wf.connect(falff_MNIspace_3mm_standardized_mean, 'outputnode.out_file', ds, 'alff.falff_MNI_3mm_standardized_mean') # REHO reho = cpac_reho.create_reho() reho.inputs.inputspec.cluster_size = 27 wf.connect(selectfiles, 'preproc_epi_bp', reho, 'inputspec.rest_res_filt') # wf.connect(GM_mask_epiSpace, 'out_file', reho, 'inputspec.rest_mask') wf.connect(selectfiles, 'epi_mask', reho, 'inputspec.rest_mask') wf.connect(reho, 'outputspec.raw_reho_map', ds, 'reho.reho') # REHO 2 MNI # fixme spline or default? reho_MNIspace_3mm = Node(fsl.ApplyWarp(), name='reho_MNIspace_3mm') reho_MNIspace_3mm.inputs.interp = 'spline' reho_MNIspace_3mm.plugin_args = {'submit_specs': 'request_memory = 4000'} wf.connect(selectfiles_anat_templates, 'FSL_MNI_3mm_template', reho_MNIspace_3mm, 'ref_file') wf.connect(reho, 'outputspec.raw_reho_map', reho_MNIspace_3mm, 'in_file') wf.connect(selectfiles, 'epi_2_MNI_warp', reho_MNIspace_3mm, 'field_file') wf.connect(reho_MNIspace_3mm, 'out_file', ds, 'reho.reho_MNI_3mm') # REHO_MNI Z-SCORE reho_MNIspace_3mm_Z = cpac_utils.get_zscore(input_name='reho_MNIspace_3mm', wf_name='reho_MNIspace_3mm_Z') wf.connect(alff_MNIspace_3mm, 'out_file', reho_MNIspace_3mm_Z, 'inputspec.input_file') # wf.connect(selectfiles_anat_templates, 'GM_mask_MNI_3mm', reho_MNIspace_3mm_Z, 'inputspec.mask_file') wf.connect(epi_mask_MNIspace_3mm, 'out_file', reho_MNIspace_3mm_Z, 'inputspec.mask_file') wf.connect(reho_MNIspace_3mm_Z, 'outputspec.z_score_img', ds, 'reho.reho_MNI_3mm_Z') # REHO_MNI STANDARDIZE BY MEAN reho_MNIspace_3mm_standardized_mean = calc_metrics_utils.standardize_divide_by_mean( wf_name='reho_MNIspace_3mm_standardized_mean') wf.connect(reho_MNIspace_3mm, 'out_file', reho_MNIspace_3mm_standardized_mean, 'inputnode.in_file') wf.connect(epi_mask_MNIspace_3mm, 'out_file', reho_MNIspace_3mm_standardized_mean, 'inputnode.mask_file') wf.connect(reho_MNIspace_3mm_standardized_mean, 'outputnode.out_file', ds, 'reho.reho_MNI_3mm_standardized_mean') # VMHC # create registration to symmetrical MNI template struct_2_MNI_symm = cpac_registration.create_nonlinear_register(name='struct_2_MNI_symm') wf.connect(selectfiles_anat_templates, 'vmhc_config_file_2mm', struct_2_MNI_symm, 'inputspec.fnirt_config') wf.connect(selectfiles_anat_templates, 'vmhc_symm_brain', struct_2_MNI_symm, 'inputspec.reference_brain') wf.connect(selectfiles_anat_templates, 'vmhc_symm_skull', struct_2_MNI_symm, 'inputspec.reference_skull') wf.connect(selectfiles_anat_templates, 'vmhc_symm_brain_mask_dil', struct_2_MNI_symm, 'inputspec.ref_mask') wf.connect(selectfiles, 't1w', struct_2_MNI_symm, 'inputspec.input_skull') wf.connect(selectfiles, 't1w_brain', struct_2_MNI_symm, 'inputspec.input_brain') wf.connect(struct_2_MNI_symm, 'outputspec.output_brain', ds, 'vmhc.symm_reg.@output_brain') wf.connect(struct_2_MNI_symm, 'outputspec.linear_xfm', ds, 'vmhc.symm_reg.@linear_xfm') wf.connect(struct_2_MNI_symm, 'outputspec.invlinear_xfm', ds, 'vmhc.symm_reg.@invlinear_xfm') wf.connect(struct_2_MNI_symm, 'outputspec.nonlinear_xfm', ds, 'vmhc.symm_reg.@nonlinear_xfm') # fixme vmhc = cpac_vmhc.create_vmhc(use_ants=False, name='vmhc') vmhc.inputs.fwhm_input.fwhm = 4 wf.connect(selectfiles_anat_templates, 'vmhc_symm_brain_3mm', vmhc, 'inputspec.standard_for_func') wf.connect(selectfiles, 'preproc_epi_bp_tNorm', vmhc, 'inputspec.rest_res') wf.connect(selectfiles, 'epi_2_struct_mat', vmhc, 'inputspec.example_func2highres_mat') wf.connect(struct_2_MNI_symm, 'outputspec.nonlinear_xfm', vmhc, 'inputspec.fnirt_nonlinear_warp') # wf.connect(GM_mask_epiSpace, 'out_file', vmhc, 'inputspec.rest_mask') wf.connect(selectfiles, 'epi_mask', vmhc, 'inputspec.rest_mask') wf.connect(vmhc, 'outputspec.rest_res_2symmstandard', ds, 'vmhc.rest_res_2symmstandard') wf.connect(vmhc, 'outputspec.VMHC_FWHM_img', ds, 'vmhc.VMHC_FWHM_img') wf.connect(vmhc, 'outputspec.VMHC_Z_FWHM_img', ds, 'vmhc.VMHC_Z_FWHM_img') wf.connect(vmhc, 'outputspec.VMHC_Z_stat_FWHM_img', ds, 'vmhc.VMHC_Z_stat_FWHM_img') # VARIABILITY SCORES variability = Node(util.Function(input_names=['in_file'], output_names=['out_file_list'], function=calc_metrics_utils.calc_variability), name='variability') wf.connect(selectfiles, 'preproc_epi_bp', variability, 'in_file') wf.connect(variability, 'out_file_list', ds, 'variability.subjectSpace.@out_files') # #fixme spline? variabilty_MNIspace_3mm = MapNode(fsl.ApplyWarp(), iterfield=['in_file'], name='variabilty_MNIspace_3mm') variabilty_MNIspace_3mm.inputs.interp = 'spline' variabilty_MNIspace_3mm.plugin_args = {'submit_specs': 'request_memory = 4000'} wf.connect(selectfiles_anat_templates, 'FSL_MNI_3mm_template', variabilty_MNIspace_3mm, 'ref_file') wf.connect(selectfiles, 'epi_2_MNI_warp', variabilty_MNIspace_3mm, 'field_file') wf.connect(variability, 'out_file_list', variabilty_MNIspace_3mm, 'in_file') wf.connect(variabilty_MNIspace_3mm, 'out_file', ds, 'variability.MNI_3mm.@out_file') # CALC Z SCORE variabilty_MNIspace_3mm_Z = cpac_centrality_z_score.get_cent_zscore(wf_name='variabilty_MNIspace_3mm_Z') wf.connect(variabilty_MNIspace_3mm, 'out_file', variabilty_MNIspace_3mm_Z, 'inputspec.input_file') # wf.connect(selectfiles_anat_templates, 'GM_mask_MNI_3mm', variabilty_MNIspace_3mm_Z, 'inputspec.mask_file') wf.connect(epi_mask_MNIspace_3mm, 'out_file', variabilty_MNIspace_3mm_Z, 'inputspec.mask_file') wf.connect(variabilty_MNIspace_3mm_Z, 'outputspec.z_score_img', ds, 'variability.MNI_3mm_Z.@out_file') # STANDARDIZE BY MEAN variabilty_MNIspace_3mm_standardized_mean = calc_metrics_utils.standardize_divide_by_mean( wf_name='variabilty_MNIspace_3mm_standardized_mean') wf.connect(variabilty_MNIspace_3mm, 'out_file', variabilty_MNIspace_3mm_standardized_mean, 'inputnode.in_file') wf.connect(epi_mask_MNIspace_3mm, 'out_file', variabilty_MNIspace_3mm_standardized_mean, 'inputnode.mask_file') wf.connect(variabilty_MNIspace_3mm_standardized_mean, 'outputnode.out_file', ds, 'variability.MNI_3mm_standardized_mean.@out_file') wf.write_graph(dotfilename=wf.name, graph2use='colored', format='pdf') # 'hierarchical') wf.write_graph(dotfilename=wf.name, graph2use='orig', format='pdf') wf.write_graph(dotfilename=wf.name, graph2use='flat', format='pdf') if plugin_name == 'CondorDAGMan': wf.run(plugin=plugin_name) if plugin_name == 'MultiProc': wf.run(plugin=plugin_name, plugin_args={'n_procs': use_n_procs})
def calc_centrality_metrics(cfg): import os from nipype import config from nipype.pipeline.engine import Node, Workflow import nipype.interfaces.utility as util import nipype.interfaces.io as nio import nipype.interfaces.fsl as fsl import nipype.interfaces.freesurfer as freesurfer import CPAC.network_centrality.resting_state_centrality as cpac_centrality import CPAC.network_centrality.z_score as cpac_centrality_z_score # INPUT PARAMETERS dicom_dir = cfg['dicom_dir'] preprocessed_data_dir = cfg['preprocessed_data_dir'] working_dir = cfg['working_dir'] freesurfer_dir = cfg['freesurfer_dir'] template_dir = cfg['template_dir'] script_dir = cfg['script_dir'] ds_dir = cfg['ds_dir'] subjects_list = cfg['subjects_list'] TR_list = cfg['TR_list'] use_n_procs = cfg['use_n_procs'] plugin_name = cfg['plugin_name'] ##################################### # GENERAL SETTINGS ##################################### fsl.FSLCommand.set_default_output_type('NIFTI_GZ') freesurfer.FSCommand.set_default_subjects_dir(freesurfer_dir) wf = Workflow(name='LeiCA_metrics') wf.base_dir = os.path.join(working_dir) nipype_cfg = dict(logging=dict(workflow_level='DEBUG'), execution={'stop_on_first_crash': False, 'remove_unnecessary_outputs': True, 'job_finished_timeout': 120}) config.update_config(nipype_cfg) wf.config['execution']['crashdump_dir'] = os.path.join(working_dir, 'crash') ds = Node(nio.DataSink(), name='ds') ds.inputs.substitutions = [('_TR_id_', 'TR_')] ds.inputs.regexp_substitutions = [('_subject_id_[A0-9]*/', ''), ( '_z_score[0-9]*/', '')] # , #('dc/_TR_id_[0-9]*/', ''), ('evc/_TR_id_[0-9]*/','')] ##################################### # SET ITERATORS ##################################### # GET SCAN TR_ID ITERATOR scan_infosource = Node(util.IdentityInterface(fields=['TR_id']), name='scan_infosource') scan_infosource.iterables = ('TR_id', TR_list) subjects_infosource = Node(util.IdentityInterface(fields=['subject_id']), name='subjects_infosource') subjects_infosource.iterables = ('subject_id', subjects_list) def add_subject_id_to_ds_dir_fct(subject_id, ds_path): import os out_path = os.path.join(ds_path, subject_id) return out_path add_subject_id_to_ds_dir = Node(util.Function(input_names=['subject_id', 'ds_path'], output_names=['out_path'], function=add_subject_id_to_ds_dir_fct), name='add_subject_id_to_ds_dir') wf.connect(subjects_infosource, 'subject_id', add_subject_id_to_ds_dir, 'subject_id') add_subject_id_to_ds_dir.inputs.ds_path = ds_dir wf.connect(add_subject_id_to_ds_dir, 'out_path', ds, 'base_directory') # get atlas data templates_atlases = {'GM_mask_MNI_2mm': 'SPM_GM/SPM_GM_mask_2mm.nii.gz', 'GM_mask_MNI_3mm': 'SPM_GM/SPM_GM_mask_3mm.nii.gz', 'FSL_MNI_3mm_template': 'MNI152_T1_3mm_brain.nii.gz', 'vmhc_symm_brain': 'cpac_image_resources/symmetric/MNI152_T1_2mm_brain_symmetric.nii.gz', 'vmhc_symm_brain_3mm': 'cpac_image_resources/symmetric/MNI152_T1_3mm_brain_symmetric.nii.gz', 'vmhc_symm_skull': 'cpac_image_resources/symmetric/MNI152_T1_2mm_symmetric.nii.gz', 'vmhc_symm_brain_mask_dil': 'cpac_image_resources/symmetric/MNI152_T1_2mm_brain_mask_symmetric_dil.nii.gz', 'vmhc_config_file_2mm': 'cpac_image_resources/symmetric/T1_2_MNI152_2mm_symmetric.cnf' } selectfiles_anat_templates = Node(nio.SelectFiles(templates_atlases, base_directory=template_dir), name="selectfiles_anat_templates") # GET SUBJECT SPECIFIC FUNCTIONAL AND STRUCTURAL DATA selectfiles_templates = { 'epi_2_MNI_warp': '{subject_id}/rsfMRI_preprocessing/registration/epi_2_MNI_warp/TR_{TR_id}/*.nii.gz', 'epi_mask': '{subject_id}/rsfMRI_preprocessing/masks/brain_mask_epiSpace/TR_{TR_id}/*.nii.gz', 'preproc_epi_full_spectrum': '{subject_id}/rsfMRI_preprocessing/epis/01_denoised/TR_{TR_id}/*.nii.gz', 'preproc_epi_bp': '{subject_id}/rsfMRI_preprocessing/epis/02_denoised_BP/TR_{TR_id}/*.nii.gz', 'preproc_epi_bp_tNorm': '{subject_id}/rsfMRI_preprocessing/epis/03_denoised_BP_tNorm/TR_{TR_id}/*.nii.gz', 'epi_2_struct_mat': '{subject_id}/rsfMRI_preprocessing/registration/epi_2_struct_mat/TR_{TR_id}/*.mat', 't1w': '{subject_id}/raw_niftis/sMRI/t1w_reoriented.nii.gz', 't1w_brain': '{subject_id}/rsfMRI_preprocessing/struct_prep/t1w_brain/t1w_reoriented_maths.nii.gz', 'epi_bp_tNorm_MNIspace_3mm': '{subject_id}/rsfMRI_preprocessing/epis_MNI_3mm/03_denoised_BP_tNorm/TR_645/residual_filt_norm_warp.nii.gz' } selectfiles = Node(nio.SelectFiles(selectfiles_templates, base_directory=preprocessed_data_dir), name="selectfiles") wf.connect(scan_infosource, 'TR_id', selectfiles, 'TR_id') wf.connect(subjects_infosource, 'subject_id', selectfiles, 'subject_id') # selectfiles.inputs.subject_id = subject_id # CREATE TRANSFORMATIONS # creat MNI 2 epi warp MNI_2_epi_warp = Node(fsl.InvWarp(), name='MNI_2_epi_warp') MNI_2_epi_warp.inputs.reference = fsl.Info.standard_image('MNI152_T1_2mm.nii.gz') wf.connect(selectfiles, 'epi_mask', MNI_2_epi_warp, 'reference') wf.connect(selectfiles, 'epi_2_MNI_warp', MNI_2_epi_warp, 'warp') ##################### # CALCULATE METRICS ##################### # DEGREE # fixme # a_mem = 5 # fixme a_mem = 20 dc = cpac_centrality.create_resting_state_graphs(allocated_memory=a_mem, wf_name='dc') # allocated_memory = a_mem, wf_name = 'dc') # dc.plugin_args = {'submit_specs': 'request_memory = 6000'} # fixme dc.plugin_args = {'submit_specs': 'request_memory = 20000'} dc.inputs.inputspec.method_option = 0 # 0 for degree centrality, 1 for eigenvector centrality, 2 for lFCD dc.inputs.inputspec.threshold_option = 0 # 0 for probability p_value, 1 for sparsity threshold, any other for threshold value dc.inputs.inputspec.threshold = 0.0001 dc.inputs.inputspec.weight_options = [True, True] # list of two booleans for binarize and weighted options respectively wf.connect(selectfiles, 'epi_bp_tNorm_MNIspace_3mm', dc, 'inputspec.subject') wf.connect(selectfiles_anat_templates, 'GM_mask_MNI_3mm', dc, 'inputspec.template') wf.connect(dc, 'outputspec.centrality_outputs', ds, 'metrics.centrality.dc.@centrality_outputs') wf.connect(dc, 'outputspec.correlation_matrix', ds, 'metrics.centrality.dc.@correlation_matrix') wf.connect(dc, 'outputspec.graph_outputs', ds, 'metrics.centrality.dc.@graph_outputs') # DC Z-SCORE dc_Z = cpac_centrality_z_score.get_cent_zscore(wf_name='dc_Z') wf.connect(dc, 'outputspec.centrality_outputs', dc_Z, 'inputspec.input_file') wf.connect(selectfiles_anat_templates, 'GM_mask_MNI_3mm', dc_Z, 'inputspec.mask_file') wf.connect(dc_Z, 'outputspec.z_score_img', ds, 'metrics.centrality.dc_z.@output') a_mem = 20 evc = cpac_centrality.create_resting_state_graphs(allocated_memory=a_mem, wf_name='evc') evc.plugin_args = {'submit_specs': 'request_memory = 20000'} evc.inputs.inputspec.method_option = 1 # 0 for degree centrality, 1 for eigenvector centrality, 2 for lFCD evc.inputs.inputspec.threshold_option = 0 # 0 for probability p_value, 1 for sparsity threshold, any other for threshold value evc.inputs.inputspec.threshold = 0.0001 evc.inputs.inputspec.weight_options = [True, True] # list of two booleans for binarize and weighted options respectively wf.connect(selectfiles, 'epi_bp_tNorm_MNIspace_3mm', evc, 'inputspec.subject') wf.connect(selectfiles_anat_templates, 'GM_mask_MNI_3mm', evc, 'inputspec.template') wf.connect(evc, 'outputspec.centrality_outputs', ds, 'metrics.centrality.evc.@centrality_outputs') wf.connect(evc, 'outputspec.correlation_matrix', ds, 'metrics.centrality.evc.@correlation_matrix') wf.connect(evc, 'outputspec.graph_outputs', ds, 'metrics.centrality.evc.@graph_outputs') # EVC Z-SCORE evc_Z = cpac_centrality_z_score.get_cent_zscore(wf_name='evc_Z') wf.connect(evc, 'outputspec.centrality_outputs', evc_Z, 'inputspec.input_file') wf.connect(selectfiles_anat_templates, 'GM_mask_MNI_3mm', evc_Z, 'inputspec.mask_file') wf.connect(evc_Z, 'outputspec.z_score_img', ds, 'metrics.centrality.evc_z.@output') wf.write_graph(dotfilename=wf.name, graph2use='colored', format='pdf') # 'hierarchical') wf.write_graph(dotfilename=wf.name, graph2use='orig', format='pdf') wf.write_graph(dotfilename=wf.name, graph2use='flat', format='pdf') if plugin_name == 'CondorDAGMan': wf.run(plugin=plugin_name) if plugin_name == 'MultiProc': wf.run(plugin=plugin_name, plugin_args={'n_procs': use_n_procs})