specification of spike thresholds and decomposition output

Author: rciric

fmriprep/workflows/bold/confounds.py

@@ -41,7 +41,8 @@
 DEFAULT_MEMORY_MIN_GB = 0.01
 
 
-def init_bold_confs_wf(mem_gb, metadata, name="bold_confs_wf"):
+def init_bold_confs_wf(mem_gb, metadata, return_all_components=False,
+                       spike_criteria=None, 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
@@ -87,6 +88,15 @@ def init_bold_confs_wf(mem_gb, metadata, name="bold_confs_wf"):
             BIDS metadata for BOLD file
         name : str
             Name of workflow (default: ``bold_confs_wf``)
+        return_all_components: bool
+            Indicates whether CompCor decompositions should return all
+            components instead of the minimal number of components necessary
+            to explain 50 percent of the variance in the decomposition mask.
+        fd_spike_thr
+            Criterion for flagging framewise displacement outliers
+        dv_spike_thr
+            Criterion for flagging DVARS outliers
+
 
     **Inputs**
 
@@ -144,9 +154,9 @@ def init_bold_confs_wf(mem_gb, metadata, name="bold_confs_wf"):
 The confound time series derived from head motion estimates and global
 signals were expanded with the inclusion of temporal derivatives and
 quadratic terms for each [@confounds_satterthwaite_2013].
-Frames that exceeded a threshold of 0.2 mm FD or 20 DVARS were classified
-as motion outliers [following @power_fd_dvars].
-"""
+Frames that exceeded a threshold of {fd} mm FD or {dv} standardised DVARS
+were classified as motion outliers.
+""".format(fd=fd_spike_thr, dv=dv_spike_thr)
     inputnode = pe.Node(niu.IdentityInterface(
         fields=['bold', 'bold_mask', 'movpar_file', 'skip_vols',
                 't1_mask', 't1_tpms', 't1_bold_xform']),
@@ -194,16 +204,23 @@ def init_bold_confs_wf(mem_gb, metadata, name="bold_confs_wf"):
 
     tcompcor = pe.Node(
         TCompCor(components_file='tcompcor.tsv', header_prefix='t_comp_cor_', pre_filter='cosine',
-                 save_pre_filter=True, num_components='all', save_metadata=True,
-                 percentile_threshold=.05),
+                 save_pre_filter=True, save_metadata=True, percentile_threshold=.05),
         name="tcompcor", mem_gb=mem_gb)
 
     acompcor = pe.Node(
         ACompCor(components_file='acompcor.tsv', header_prefix='a_comp_cor_', pre_filter='cosine',
-                 save_pre_filter=True, num_components='all', save_metadata=True,
-                 mask_names=['combined', 'CSF', 'WM'], merge_method='none'),
+                 save_pre_filter=True, save_metadata=True, mask_names=['combined', 'CSF', 'WM'],
+                 merge_method='none'),
         name="acompcor", mem_gb=mem_gb)
 
+    # Set number of components
+    if return_all_components:
+        acompcor.inputs.num_components = 'all'
+        tcompcor.inputs.num_components = 'all'
+    else:
+        acompcor.inputs.variance_threshold = 0.5
+        tcompcor.inputs.variance_threshold = 0.5
+
     # Set TR if present
     if 'RepetitionTime' in metadata:
         tcompcor.inputs.repetition_time = metadata['RepetitionTime']
@@ -232,7 +249,7 @@ def init_bold_confs_wf(mem_gb, metadata, name="bold_confs_wf"):
                  additional_metadata={'Method': 'tCompCor'}, enforce_case=True),
         name='tcc_metadata_fmt')
     acc_metadata_fmt = pe.Node(
-        TSV2JSON(index_column='component', drop_columns=['mask'],
+        TSV2JSON(index_column='component',
                  additional_metadata={'Method': 'aCompCor'}, enforce_case=True),
         name='acc_metadata_fmt')
 
@@ -244,8 +261,8 @@ def init_bold_confs_wf(mem_gb, metadata, name="bold_confs_wf"):
     # Add spike regressors
     spike_regress = pe.Node(SpikeRegressors(
         criteria={
-            'framewise_displacement': ('>', 0.2),
-            'dvars': ('>', 20)
+            'framewise_displacement': ('>', fd_spike_thr),
+            'std_dvars': ('>', dv_spike_thr)
         }),
         name='spike_regressors')