def fmri_qc_workflow(dataset, settings, name='funcMRIQC'): """ The fMRI qc workflow .. workflow:: import os.path as op from mriqc.workflows.functional import fmri_qc_workflow datadir = op.abspath('data') wf = fmri_qc_workflow([op.join(datadir, 'sub-001/func/sub-001_task-rest_bold.nii.gz')], settings={'bids_dir': datadir, 'output_dir': op.abspath('out'), 'no_sub': True}) """ workflow = pe.Workflow(name=name) biggest_file_gb = settings.get("biggest_file_size_gb", 1) # Define workflow, inputs and outputs # 0. Get data, put it in RAS orientation inputnode = pe.Node(niu.IdentityInterface(fields=['in_file']), name='inputnode') WFLOGGER.info( 'Building fMRI QC workflow, datasets list: %s', sorted([d.replace(settings['bids_dir'] + '/', '') for d in dataset])) inputnode.iterables = [('in_file', dataset)] outputnode = pe.Node(niu.IdentityInterface( fields=['qc', 'mosaic', 'out_group', 'out_dvars', 'out_fd']), name='outputnode') non_steady_state_detector = pe.Node(nac.NonSteadyStateDetector(), name="non_steady_state_detector") sanitize = pe.Node(niutils.SanitizeImage(), name="sanitize", mem_gb=biggest_file_gb * 4.0) sanitize.inputs.max_32bit = settings.get("float32", DEFAULTS['float32']) # Workflow -------------------------------------------------------- # 1. HMC: head motion correct if settings.get('hmc_fsl', False): hmcwf = hmc_mcflirt(settings) else: hmcwf = hmc_afni(settings, st_correct=settings.get('correct_slice_timing', False), despike=settings.get('despike', False), deoblique=settings.get('deoblique', False), start_idx=settings.get('start_idx', None), stop_idx=settings.get('stop_idx', None)) # Set HMC settings hmcwf.inputs.inputnode.fd_radius = settings.get('fd_radius', DEFAULT_FD_RADIUS) mean = pe.Node( afni.TStat( # 2. Compute mean fmri options='-mean', outputtype='NIFTI_GZ'), name='mean', mem_gb=biggest_file_gb * 1.5) skullstrip_epi = fmri_bmsk_workflow(use_bet=True) # EPI to MNI registration ema = epi_mni_align(settings) # Compute TSNR using nipype implementation tsnr = pe.Node(nac.TSNR(), name='compute_tsnr', mem_gb=biggest_file_gb * 4.5) # 7. Compute IQMs iqmswf = compute_iqms(settings) # Reports repwf = individual_reports(settings) workflow.connect([ (inputnode, iqmswf, [('in_file', 'inputnode.in_file')]), (inputnode, sanitize, [('in_file', 'in_file')]), (inputnode, non_steady_state_detector, [('in_file', 'in_file')]), (non_steady_state_detector, sanitize, [('n_volumes_to_discard', 'n_volumes_to_discard')]), (sanitize, hmcwf, [('out_file', 'inputnode.in_file')]), (mean, skullstrip_epi, [('out_file', 'inputnode.in_file')]), (hmcwf, mean, [('outputnode.out_file', 'in_file')]), (hmcwf, tsnr, [('outputnode.out_file', 'in_file')]), (mean, ema, [('out_file', 'inputnode.epi_mean')]), (skullstrip_epi, ema, [('outputnode.out_file', 'inputnode.epi_mask')]), (sanitize, iqmswf, [('out_file', 'inputnode.in_ras')]), (mean, iqmswf, [('out_file', 'inputnode.epi_mean')]), (hmcwf, iqmswf, [('outputnode.out_file', 'inputnode.hmc_epi'), ('outputnode.out_fd', 'inputnode.hmc_fd')]), (skullstrip_epi, iqmswf, [('outputnode.out_file', 'inputnode.brainmask')]), (tsnr, iqmswf, [('tsnr_file', 'inputnode.in_tsnr')]), (sanitize, repwf, [('out_file', 'inputnode.in_ras')]), (mean, repwf, [('out_file', 'inputnode.epi_mean')]), (tsnr, repwf, [('stddev_file', 'inputnode.in_stddev')]), (skullstrip_epi, repwf, [('outputnode.out_file', 'inputnode.brainmask') ]), (hmcwf, repwf, [('outputnode.out_fd', 'inputnode.hmc_fd'), ('outputnode.out_file', 'inputnode.hmc_epi')]), (ema, repwf, [('outputnode.epi_parc', 'inputnode.epi_parc'), ('outputnode.report', 'inputnode.mni_report')]), (non_steady_state_detector, iqmswf, [('n_volumes_to_discard', 'inputnode.exclude_index')]), (iqmswf, repwf, [('outputnode.out_file', 'inputnode.in_iqms'), ('outputnode.out_dvars', 'inputnode.in_dvars'), ('outputnode.outliers', 'inputnode.outliers')]), (hmcwf, outputnode, [('outputnode.out_fd', 'out_fd')]), ]) if settings.get('fft_spikes_detector', False): workflow.connect([ (iqmswf, repwf, [('outputnode.out_spikes', 'inputnode.in_spikes'), ('outputnode.out_fft', 'inputnode.in_fft')]), ]) if settings.get('ica', False): melodic = pe.Node(nws.MELODICRPT(no_bet=True, no_mask=True, no_mm=True, generate_report=True), name="ICA", mem_gb=biggest_file_gb * 5) workflow.connect([(sanitize, melodic, [('out_file', 'in_files')]), (skullstrip_epi, melodic, [('outputnode.out_file', 'report_mask')]), (melodic, repwf, [('out_report', 'inputnode.ica_report')])]) # Upload metrics if not settings.get('no_sub', False): from ..interfaces.webapi import UploadIQMs upldwf = pe.Node(UploadIQMs(), name='UploadMetrics') upldwf.inputs.url = settings.get('webapi_url') if settings.get('webapi_port'): upldwf.inputs.port = settings.get('webapi_port') upldwf.inputs.email = settings.get('email') upldwf.inputs.strict = settings.get('upload_strict', False) workflow.connect([ (iqmswf, upldwf, [('outputnode.out_file', 'in_iqms')]), ]) return workflow
def init_bold_confs_wf(bold_file_size_gb, use_aroma, ignore_aroma_err, metadata, name="bold_confs_wf"): """ This workflow calculates confounds for a BOLD series, and aggregates them into a :abbr:`TSV (tab-separated value)` file, for use as nuisance regressors in a :abbr:`GLM (general linear model)`. The following confounds are calculated, with column headings in parentheses: #. White matter / global signals (WhiteMatter, GlobalSignal) #. DVARS - standard, nonstandard, and voxel-wise standard variants (stdDVARS, non-stdDVARS, vx-wisestdDVARS) #. Framewise displacement, based on MCFLIRT motion parameters (FramewiseDisplacement) #. tCompCor #. aCompCor #. Cosine basis set for high-pass filtering w/ 0.008 Hz cut-off (CosineXX) #. Non-steady-state volumes (NonSteadyStateXX) #. MCFLIRT motion parameters, in mm and rad (X, Y, Z, RotX, RotY, RotZ) #. ICA-AROMA-identified noise components, if enabled (AROMAAggrCompXX) Prior to estimating aCompCor and tCompCor, non-steady-state volumes are censored and high-pass filtered using a :abbr:`DCT (discrete cosine transform)` basis. The cosine basis, as well as one regressor per censored volume, are included for convenience. .. workflow:: :graph2use: orig :simpleform: yes from fmriprep.workflows.confounds import init_bold_confs_wf wf = init_bold_confs_wf(bold_file_size_gb=1, use_aroma=True, ignore_aroma_err=True, metadata={}) Parameters bold_file_size_gb : float Size of BOLD file in GB use_aroma : bool Perform ICA-AROMA on MNI-resampled functional series ignore_aroma_err : bool Do not fail on ICA-AROMA errors metadata : dict BIDS metadata for BOLD file Inputs bold_t1 BOLD image, resampled in T1w space movpar_file SPM-formatted motion parameters file t1_mask Mask of the skull-stripped template image t1_tpms List of tissue probability maps in T1w space bold_mask_t1 BOLD series mask in T1w space bold_mni BOLD series, resampled to template space bold_mask_mni BOLD series mask in template space Outputs confounds_file TSV of all aggregated confounds confounds_list List of calculated confounds for reporting acompcor_report Reportlet visualizing white-matter/CSF mask used for aCompCor tcompcor_report Reportlet visualizing ROI identified in tCompCor ica_aroma_report Reportlet visualizing MELODIC ICs, with ICA-AROMA signal/noise labels aroma_noise_ics CSV of noise components identified by ICA-AROMA melodic_mix FSL MELODIC mixing matrix nonaggr_denoised_file BOLD series with non-aggressive ICA-AROMA denoising applied """ inputnode = pe.Node(niu.IdentityInterface(fields=[ 'bold_t1', 'movpar_file', 't1_mask', 't1_tpms', 'bold_mask_t1', 'bold_mni', 'bold_mask_mni' ]), name='inputnode') outputnode = pe.Node(niu.IdentityInterface(fields=[ 'confounds_file', 'confounds_list', 'acompcor_report', 'tcompcor_report', 'ica_aroma_report', 'aroma_noise_ics', 'melodic_mix', 'nonaggr_denoised_file' ]), name='outputnode') # ICA-AROMA if use_aroma: ica_aroma_wf = init_ica_aroma_wf(name='ica_aroma_wf', ignore_aroma_err=ignore_aroma_err) # DVARS dvars = pe.Node(nac.ComputeDVARS(save_all=True, remove_zerovariance=True), name="dvars", mem_gb=bold_file_size_gb * 3) # Frame displacement fdisp = pe.Node(nac.FramewiseDisplacement(parameter_source="SPM"), name="fdisp", mem_gb=bold_file_size_gb * 3) # CompCor non_steady_state = pe.Node(nac.NonSteadyStateDetector(), name='non_steady_state') tcompcor = pe.Node(TCompCorRPT(components_file='tcompcor.tsv', generate_report=True, pre_filter='cosine', save_pre_filter=True, percentile_threshold=.05), name="tcompcor", mem_gb=bold_file_size_gb * 3) acompcor = pe.Node(ACompCorRPT(components_file='acompcor.tsv', pre_filter='cosine', save_pre_filter=True, generate_report=True), name="acompcor", mem_gb=bold_file_size_gb * 3) csf_roi = pe.Node(TPM2ROI(erode_mm=0, mask_erode_mm=30), name='csf_roi') wm_roi = pe.Node(TPM2ROI(erode_mm=6, mask_erode_mm=10), name='wm_roi') merge_rois = pe.Node(niu.Merge(2), name='merge_rois', run_without_submitting=True, mem_gb=0.01) combine_rois = pe.Node(CombineROIs(), name='combine_rois') concat_rois = pe.Node(ConcatROIs(), name='concat_rois') # Global and segment regressors signals = pe.Node(SignalExtraction( detrend=True, class_labels=["WhiteMatter", "GlobalSignal"]), name="signals", mem_gb=bold_file_size_gb * 3) # Arrange confounds add_header = pe.Node( AddTSVHeader(columns=["X", "Y", "Z", "RotX", "RotY", "RotZ"]), name="add_header", mem_gb=0.01, run_without_submitting=True) concat = pe.Node(GatherConfounds(), name="concat", mem_gb=0.01, run_without_submitting=True) # Set TR if present if 'RepetitionTime' in metadata: tcompcor.inputs.repetition_time = metadata['RepetitionTime'] acompcor.inputs.repetition_time = metadata['RepetitionTime'] def _pick_csf(files): return files[0] def _pick_wm(files): return files[2] workflow = pe.Workflow(name=name) workflow.connect([ # connect inputnode to each non-anatomical confound node (inputnode, dvars, [('bold_t1', 'in_file'), ('bold_mask_t1', 'in_mask')]), (inputnode, fdisp, [('movpar_file', 'in_file')]), (inputnode, non_steady_state, [('bold_t1', 'in_file')]), (inputnode, tcompcor, [('bold_t1', 'realigned_file')]), (non_steady_state, tcompcor, [('n_volumes_to_discard', 'ignore_initial_volumes')]), (non_steady_state, acompcor, [('n_volumes_to_discard', 'ignore_initial_volumes')]), (inputnode, csf_roi, [(('t1_tpms', _pick_csf), 't1_tpm'), ('t1_mask', 't1_mask'), ('bold_mask_t1', 'bold_mask')]), (csf_roi, tcompcor, [('eroded_mask', 'mask_files')]), (inputnode, wm_roi, [(('t1_tpms', _pick_wm), 't1_tpm'), ('t1_mask', 't1_mask'), ('bold_mask_t1', 'bold_mask')]), (csf_roi, merge_rois, [('roi_file', 'in1')]), (wm_roi, merge_rois, [('roi_file', 'in2')]), (merge_rois, combine_rois, [('out', 'in_files')]), (inputnode, combine_rois, [('bold_t1', 'ref_header')]), # anatomical confound: aCompCor. (inputnode, acompcor, [('bold_t1', 'realigned_file')]), (combine_rois, acompcor, [('out_file', 'mask_files')]), (wm_roi, concat_rois, [('roi_file', 'in_file')]), (inputnode, concat_rois, [('bold_mask_t1', 'in_mask')]), (inputnode, concat_rois, [('bold_t1', 'ref_header')]), # anatomical confound: signal extraction (concat_rois, signals, [('out_file', 'label_files')]), (inputnode, signals, [('bold_t1', 'in_file')]), # connect the confound nodes to the concatenate node (inputnode, add_header, [('movpar_file', 'in_file')]), (signals, concat, [('out_file', 'signals')]), (dvars, concat, [('out_all', 'dvars')]), (fdisp, concat, [('out_file', 'fd')]), (tcompcor, concat, [('components_file', 'tcompcor'), ('pre_filter_file', 'cos_basis')]), (acompcor, concat, [('components_file', 'acompcor')]), (add_header, concat, [('out_file', 'motion')]), (concat, outputnode, [('confounds_file', 'confounds_file'), ('confounds_list', 'confounds_list')]), (acompcor, outputnode, [('out_report', 'acompcor_report')]), (tcompcor, outputnode, [('out_report', 'tcompcor_report')]), ]) if use_aroma: workflow.connect([ (inputnode, ica_aroma_wf, [('bold_mni', 'inputnode.bold_mni'), ('bold_mask_mni', 'inputnode.bold_mask_mni'), ('movpar_file', 'inputnode.movpar_file')]), (ica_aroma_wf, concat, [('outputnode.aroma_confounds', 'aroma')]), (ica_aroma_wf, outputnode, [('outputnode.out_report', 'ica_aroma_report'), ('outputnode.aroma_noise_ics', 'aroma_noise_ics'), ('outputnode.melodic_mix', 'melodic_mix'), ('outputnode.nonaggr_denoised_file', 'nonaggr_denoised_file')]) ]) return workflow
def init_bold_confs_wf(mem_gb, use_aroma, ignore_aroma_err, metadata, name="bold_confs_wf"): """ This workflow calculates confounds for a BOLD series, and aggregates them into a :abbr:`TSV (tab-separated value)` file, for use as nuisance regressors in a :abbr:`GLM (general linear model)`. The following confounds are calculated, with column headings in parentheses: #. Region-wise average signal (``CSF``, ``WhiteMatter``, ``GlobalSignal``) #. DVARS - standard, nonstandard, and voxel-wise standard variants (``stdDVARS``, ``non-stdDVARS``, ``vx-wisestdDVARS``) #. Framewise displacement, based on MCFLIRT motion parameters (``FramewiseDisplacement``) #. Temporal CompCor (``tCompCorXX``) #. Anatomical CompCor (``aCompCorXX``) #. Cosine basis set for high-pass filtering w/ 0.008 Hz cut-off (``CosineXX``) #. Non-steady-state volumes (``NonSteadyStateXX``) #. Estimated head-motion parameters, in mm and rad (``X``, ``Y``, ``Z``, ``RotX``, ``RotY``, ``RotZ``) #. ICA-AROMA-identified noise components, if enabled (``AROMAAggrCompXX``) Prior to estimating aCompCor and tCompCor, non-steady-state volumes are censored and high-pass filtered using a :abbr:`DCT (discrete cosine transform)` basis. The cosine basis, as well as one regressor per censored volume, are included for convenience. .. workflow:: :graph2use: orig :simple_form: yes from fmriprep.workflows.bold.confounds import init_bold_confs_wf wf = init_bold_confs_wf( mem_gb=1, use_aroma=True, ignore_aroma_err=True, metadata={}) **Parameters** mem_gb : float Size of BOLD file in GB - please note that this size should be calculated after resamplings that may extend the FoV use_aroma : bool Perform ICA-AROMA on MNI-resampled functional series ignore_aroma_err : bool Do not fail on ICA-AROMA errors metadata : dict BIDS metadata for BOLD file **Inputs** bold BOLD image, after the prescribed corrections (STC, HMC and SDC) when available. bold_mask BOLD series mask movpar_file SPM-formatted motion parameters file t1_mask Mask of the skull-stripped template image t1_tpms List of tissue probability maps in T1w space t1_bold_xform Affine matrix that maps the T1w space into alignment with the native BOLD space bold_mni BOLD image resampled in MNI space (only if ``use_aroma`` enabled) bold_mask_mni Brain mask corresponding to the BOLD image resampled in MNI space (only if ``use_aroma`` enabled) **Outputs** confounds_file TSV of all aggregated confounds confounds_list List of calculated confounds for reporting acompcor_report Reportlet visualizing white-matter/CSF mask used for aCompCor tcompcor_report Reportlet visualizing ROI identified in tCompCor ica_aroma_report Reportlet visualizing MELODIC ICs, with ICA-AROMA signal/noise labels aroma_noise_ics CSV of noise components identified by ICA-AROMA melodic_mix FSL MELODIC mixing matrix nonaggr_denoised_file BOLD series with non-aggressive ICA-AROMA denoising applied **Subworkflows** * :py:func:`~fmriprep.workflows.bold.confounds.init_ica_aroma_wf` """ inputnode = pe.Node(niu.IdentityInterface( fields=['bold', 'bold_mask', 'movpar_file', 't1_mask', 't1_tpms', 't1_bold_xform', 'bold_mni', 'bold_mask_mni']), name='inputnode') outputnode = pe.Node(niu.IdentityInterface( fields=['confounds_file', 'confounds_list', 'rois_report', 'ica_aroma_report', 'aroma_noise_ics', 'melodic_mix', 'nonaggr_denoised_file']), name='outputnode') # Get masks ready in T1w space acc_tpm = pe.Node(AddTPMs(indices=[0, 2]), name='tpms_add_csf_wm') # acc stands for aCompCor csf_roi = pe.Node(TPM2ROI(erode_mm=0, mask_erode_mm=30), name='csf_roi') wm_roi = pe.Node(TPM2ROI( erode_prop=0.6, mask_erode_prop=0.6**3), # 0.6 = radius; 0.6^3 = volume name='wm_roi') acc_roi = pe.Node(TPM2ROI( erode_prop=0.6, mask_erode_prop=0.6**3), # 0.6 = radius; 0.6^3 = volume name='acc_roi') # Map ROIs in T1w space into BOLD space csf_tfm = pe.Node(ApplyTransforms(interpolation='NearestNeighbor', float=True), name='csf_tfm', mem_gb=0.1) wm_tfm = pe.Node(ApplyTransforms(interpolation='NearestNeighbor', float=True), name='wm_tfm', mem_gb=0.1) acc_tfm = pe.Node(ApplyTransforms(interpolation='NearestNeighbor', float=True), name='acc_tfm', mem_gb=0.1) tcc_tfm = pe.Node(ApplyTransforms(interpolation='NearestNeighbor', float=True), name='tcc_tfm', mem_gb=0.1) # Ensure ROIs don't go off-limits (reduced FoV) csf_msk = pe.Node(niu.Function(function=_maskroi), name='csf_msk') wm_msk = pe.Node(niu.Function(function=_maskroi), name='wm_msk') acc_msk = pe.Node(niu.Function(function=_maskroi), name='acc_msk') tcc_msk = pe.Node(niu.Function(function=_maskroi), name='tcc_msk') # DVARS dvars = pe.Node(nac.ComputeDVARS(save_all=True, remove_zerovariance=True), name="dvars", mem_gb=mem_gb) # Frame displacement fdisp = pe.Node(nac.FramewiseDisplacement(parameter_source="SPM"), name="fdisp", mem_gb=mem_gb) # a/t-CompCor non_steady_state = pe.Node(nac.NonSteadyStateDetector(), name='non_steady_state') tcompcor = pe.Node(nac.TCompCor( components_file='tcompcor.tsv', pre_filter='cosine', save_pre_filter=True, percentile_threshold=.05), name="tcompcor", mem_gb=mem_gb) acompcor = pe.Node(nac.ACompCor( components_file='acompcor.tsv', pre_filter='cosine', save_pre_filter=True), name="acompcor", mem_gb=mem_gb) # Set TR if present if 'RepetitionTime' in metadata: tcompcor.inputs.repetition_time = metadata['RepetitionTime'] acompcor.inputs.repetition_time = metadata['RepetitionTime'] # Global and segment regressors mrg_lbl = pe.Node(niu.Merge(3), name='merge_rois', run_without_submitting=True) signals = pe.Node(SignalExtraction( detrend=True, class_labels=["CSF", "WhiteMatter", "GlobalSignal"]), name="signals", mem_gb=mem_gb) # Arrange confounds add_header = pe.Node(AddTSVHeader(columns=["X", "Y", "Z", "RotX", "RotY", "RotZ"]), name="add_header", mem_gb=0.01, run_without_submitting=True) concat = pe.Node(GatherConfounds(), name="concat", mem_gb=0.01, run_without_submitting=True) # Generate reportlet mrg_compcor = pe.Node(niu.Merge(2), name='merge_compcor', run_without_submitting=True) rois_plot = pe.Node(ROIsPlot(compress_report=True, colors=['r', 'b', 'magenta'], generate_report=True), name='rois_plot') def _pick_csf(files): return files[0] def _pick_wm(files): return files[-1] workflow = pe.Workflow(name=name) workflow.connect([ # Massage ROIs (in T1w space) (inputnode, acc_tpm, [('t1_tpms', 'in_files')]), (inputnode, csf_roi, [(('t1_tpms', _pick_csf), 'in_tpm'), ('t1_mask', 'in_mask')]), (inputnode, wm_roi, [(('t1_tpms', _pick_wm), 'in_tpm'), ('t1_mask', 'in_mask')]), (inputnode, acc_roi, [('t1_mask', 'in_mask')]), (acc_tpm, acc_roi, [('out_file', 'in_tpm')]), # Map ROIs to BOLD (inputnode, csf_tfm, [('bold_mask', 'reference_image'), ('t1_bold_xform', 'transforms')]), (csf_roi, csf_tfm, [('roi_file', 'input_image')]), (inputnode, wm_tfm, [('bold_mask', 'reference_image'), ('t1_bold_xform', 'transforms')]), (wm_roi, wm_tfm, [('roi_file', 'input_image')]), (inputnode, acc_tfm, [('bold_mask', 'reference_image'), ('t1_bold_xform', 'transforms')]), (acc_roi, acc_tfm, [('roi_file', 'input_image')]), (inputnode, tcc_tfm, [('bold_mask', 'reference_image'), ('t1_bold_xform', 'transforms')]), (csf_roi, tcc_tfm, [('eroded_mask', 'input_image')]), # Mask ROIs with bold_mask (inputnode, csf_msk, [('bold_mask', 'in_mask')]), (inputnode, wm_msk, [('bold_mask', 'in_mask')]), (inputnode, acc_msk, [('bold_mask', 'in_mask')]), (inputnode, tcc_msk, [('bold_mask', 'in_mask')]), # connect inputnode to each non-anatomical confound node (inputnode, dvars, [('bold', 'in_file'), ('bold_mask', 'in_mask')]), (inputnode, fdisp, [('movpar_file', 'in_file')]), # Calculate nonsteady state (inputnode, non_steady_state, [('bold', 'in_file')]), # tCompCor (inputnode, tcompcor, [('bold', 'realigned_file')]), (non_steady_state, tcompcor, [('n_volumes_to_discard', 'ignore_initial_volumes')]), (tcc_tfm, tcc_msk, [('output_image', 'roi_file')]), (tcc_msk, tcompcor, [('out', 'mask_files')]), # aCompCor (inputnode, acompcor, [('bold', 'realigned_file')]), (non_steady_state, acompcor, [('n_volumes_to_discard', 'ignore_initial_volumes')]), (acc_tfm, acc_msk, [('output_image', 'roi_file')]), (acc_msk, acompcor, [('out', 'mask_files')]), # Global signals extraction (constrained by anatomy) (inputnode, signals, [('bold', 'in_file')]), (csf_tfm, csf_msk, [('output_image', 'roi_file')]), (csf_msk, mrg_lbl, [('out', 'in1')]), (wm_tfm, wm_msk, [('output_image', 'roi_file')]), (wm_msk, mrg_lbl, [('out', 'in2')]), (inputnode, mrg_lbl, [('bold_mask', 'in3')]), (mrg_lbl, signals, [('out', 'label_files')]), # Collate computed confounds together (inputnode, add_header, [('movpar_file', 'in_file')]), (signals, concat, [('out_file', 'signals')]), (dvars, concat, [('out_all', 'dvars')]), (fdisp, concat, [('out_file', 'fd')]), (tcompcor, concat, [('components_file', 'tcompcor'), ('pre_filter_file', 'cos_basis')]), (acompcor, concat, [('components_file', 'acompcor')]), (add_header, concat, [('out_file', 'motion')]), # Set outputs (concat, outputnode, [('confounds_file', 'confounds_file'), ('confounds_list', 'confounds_list')]), (inputnode, rois_plot, [('bold', 'in_file'), ('bold_mask', 'in_mask')]), (tcompcor, mrg_compcor, [('high_variance_masks', 'in1')]), (acc_msk, mrg_compcor, [('out', 'in2')]), (mrg_compcor, rois_plot, [('out', 'in_rois')]), (rois_plot, outputnode, [('out_report', 'rois_report')]), ]) if use_aroma: # ICA-AROMA ica_aroma_wf = init_ica_aroma_wf(name='ica_aroma_wf', ignore_aroma_err=ignore_aroma_err) workflow.connect([ (inputnode, ica_aroma_wf, [('bold_mni', 'inputnode.bold_mni'), ('bold_mask_mni', 'inputnode.bold_mask_mni'), ('movpar_file', 'inputnode.movpar_file')]), (ica_aroma_wf, concat, [('outputnode.aroma_confounds', 'aroma')]), (ica_aroma_wf, outputnode, [('outputnode.out_report', 'ica_aroma_report'), ('outputnode.aroma_noise_ics', 'aroma_noise_ics'), ('outputnode.melodic_mix', 'melodic_mix'), ('outputnode.nonaggr_denoised_file', 'nonaggr_denoised_file')]) ]) return workflow
def init_bold_confs_wf(bold_file_size_gb, use_aroma, ignore_aroma_err, metadata, name="bold_confs_wf"): ''' All input fields are required. Calculates global regressor and tCompCor from motion-corrected fMRI ('inputnode.fmri_file'). Calculates DVARS from the fMRI and an EPI brain mask ('inputnode.bold_mask') Calculates frame displacement from MCFLIRT movement parameters ('inputnode.movpar_file') Calculates segment regressors and aCompCor from the fMRI and a white matter/gray matter/CSF segmentation ('inputnode.t1_seg'), after applying the transform to the images. Transforms should be fsl-formatted. Calculates noise components identified from ICA-AROMA (if ``use_aroma=True``) Saves the confounds in a file ('outputnode.confounds_file')''' inputnode = pe.Node(niu.IdentityInterface(fields=[ 'fmri_file', 'movpar_file', 't1_mask', 't1_tpms', 'bold_mask', 'bold_mni', 'bold_mask_mni' ]), name='inputnode') outputnode = pe.Node(niu.IdentityInterface(fields=[ 'confounds_file', 'confounds_list', 'acompcor_report', 'tcompcor_report', 'ica_aroma_report', 'aroma_noise_ics', 'melodic_mix', 'nonaggr_denoised_file' ]), name='outputnode') # ICA-AROMA if use_aroma: ica_aroma_wf = init_ica_aroma_wf(name='ica_aroma_wf', ignore_aroma_err=ignore_aroma_err) # DVARS dvars = pe.Node(nac.ComputeDVARS(save_all=True, remove_zerovariance=True), name="dvars", mem_gb=bold_file_size_gb * 3) # Frame displacement fdisp = pe.Node(nac.FramewiseDisplacement(parameter_source="SPM"), name="fdisp", mem_gb=bold_file_size_gb * 3) # CompCor non_steady_state = pe.Node(nac.NonSteadyStateDetector(), name='non_steady_state') tcompcor = pe.Node(TCompCorRPT(components_file='tcompcor.tsv', generate_report=True, pre_filter='cosine', save_pre_filter=True, percentile_threshold=.05), name="tcompcor", mem_gb=bold_file_size_gb * 3) acompcor = pe.Node(ACompCorRPT(components_file='acompcor.tsv', pre_filter='cosine', save_pre_filter=True, generate_report=True), name="acompcor", mem_gb=bold_file_size_gb * 3) csf_roi = pe.Node(TPM2ROI(erode_mm=0, mask_erode_mm=30), name='csf_roi') wm_roi = pe.Node(TPM2ROI(erode_mm=6, mask_erode_mm=10), name='wm_roi') merge_rois = pe.Node(niu.Merge(2), name='merge_rois', run_without_submit=True, mem_gb=0.01) combine_rois = pe.Node(CombineROIs(), name='combine_rois') concat_rois = pe.Node(ConcatROIs(), name='concat_rois') # Global and segment regressors signals = pe.Node(SignalExtraction( detrend=True, class_labels=["WhiteMatter", "GlobalSignal"]), name="signals", mem_gb=bold_file_size_gb * 3) # Arrange confounds add_header = pe.Node( AddTSVHeader(columns=["X", "Y", "Z", "RotX", "RotY", "RotZ"]), name="add_header", mem_gb=0.01, run_without_submit=True) concat = pe.Node(GatherConfounds(), name="concat", mem_gb=0.01, run_without_submit=True) # Set TR if present if 'RepetitionTime' in metadata: tcompcor.inputs.repetition_time = metadata['RepetitionTime'] acompcor.inputs.repetition_time = metadata['RepetitionTime'] def _pick_csf(files): return files[0] def _pick_wm(files): return files[2] workflow = pe.Workflow(name=name) workflow.connect([ # connect inputnode to each non-anatomical confound node (inputnode, dvars, [('fmri_file', 'in_file'), ('bold_mask', 'in_mask')]), (inputnode, fdisp, [('movpar_file', 'in_file')]), (inputnode, non_steady_state, [('fmri_file', 'in_file')]), (inputnode, tcompcor, [('fmri_file', 'realigned_file')]), (non_steady_state, tcompcor, [('n_volumes_to_discard', 'ignore_initial_volumes')]), (non_steady_state, acompcor, [('n_volumes_to_discard', 'ignore_initial_volumes')]), (inputnode, csf_roi, [(('t1_tpms', _pick_csf), 't1_tpm'), ('t1_mask', 't1_mask'), ('bold_mask', 'bold_mask')]), (csf_roi, tcompcor, [('eroded_mask', 'mask_files')]), (inputnode, wm_roi, [(('t1_tpms', _pick_wm), 't1_tpm'), ('t1_mask', 't1_mask'), ('bold_mask', 'bold_mask')]), (csf_roi, merge_rois, [('roi_file', 'in1')]), (wm_roi, merge_rois, [('roi_file', 'in2')]), (merge_rois, combine_rois, [('out', 'in_files')]), (inputnode, combine_rois, [('fmri_file', 'ref_header')]), # anatomical confound: aCompCor. (inputnode, acompcor, [('fmri_file', 'realigned_file')]), (combine_rois, acompcor, [('out_file', 'mask_files')]), (wm_roi, concat_rois, [('roi_file', 'in_file')]), (inputnode, concat_rois, [('bold_mask', 'in_mask')]), (inputnode, concat_rois, [('fmri_file', 'ref_header')]), # anatomical confound: signal extraction (concat_rois, signals, [('out_file', 'label_files')]), (inputnode, signals, [('fmri_file', 'in_file')]), # connect the confound nodes to the concatenate node (inputnode, add_header, [('movpar_file', 'in_file')]), (signals, concat, [('out_file', 'signals')]), (dvars, concat, [('out_all', 'dvars')]), (fdisp, concat, [('out_file', 'fd')]), (tcompcor, concat, [('components_file', 'tcompcor'), ('pre_filter_file', 'cos_basis')]), (acompcor, concat, [('components_file', 'acompcor')]), (add_header, concat, [('out_file', 'motion')]), (concat, outputnode, [('confounds_file', 'confounds_file'), ('confounds_list', 'confounds_list')]), (acompcor, outputnode, [('out_report', 'acompcor_report')]), (tcompcor, outputnode, [('out_report', 'tcompcor_report')]), ]) if use_aroma: workflow.connect([ (inputnode, ica_aroma_wf, [('bold_mni', 'inputnode.bold_mni'), ('bold_mask_mni', 'inputnode.bold_mask_mni'), ('movpar_file', 'inputnode.movpar_file')]), (ica_aroma_wf, concat, [('outputnode.aroma_confounds', 'aroma')]), (ica_aroma_wf, outputnode, [('outputnode.out_report', 'ica_aroma_report'), ('outputnode.aroma_noise_ics', 'aroma_noise_ics'), ('outputnode.melodic_mix', 'melodic_mix'), ('outputnode.nonaggr_denoised_file', 'nonaggr_denoised_file')]) ]) return workflow