FIX: Update DVARS headers, remove vxwise-stdDVARS, fix carpetplot entries

Author: effigies

fmriprep/interfaces/confounds.py

@@ -10,6 +10,7 @@
 
 """
 import os
+import re
 import shutil
 import numpy as np
 import pandas as pd
@@ -27,6 +28,7 @@
 class GatherConfoundsInputSpec(BaseInterfaceInputSpec):
     signals = File(exists=True, desc='input signals')
     dvars = File(exists=True, desc='file containing DVARS')
+    std_dvars = File(exists=True, desc='file containing standardized DVARS')
     fd = File(exists=True, desc='input framewise displacement')
     tcompcor = File(exists=True, desc='input tCompCorr')
     acompcor = File(exists=True, desc='input aCompCorr')
@@ -124,7 +126,7 @@ def _run_interface(self, runtime):
         return runtime
 
 
-def _gather_confounds(signals=None, dvars=None, fdisp=None,
+def _gather_confounds(signals=None, dvars=None, std_dvars=None, fdisp=None,
                       tcompcor=None, acompcor=None, cos_basis=None,
                       motion=None, aroma=None, newpath=None):
     """
@@ -134,16 +136,16 @@ def _gather_confounds(signals=None, dvars=None, fdisp=None,
     >>> from tempfile import TemporaryDirectory
     >>> tmpdir = TemporaryDirectory()
     >>> os.chdir(tmpdir.name)
-    >>> pd.DataFrame({'a': [0.1]}).to_csv('signals.tsv', index=False, na_rep='n/a')
-    >>> pd.DataFrame({'b': [0.2]}).to_csv('dvars.tsv', index=False, na_rep='n/a')
+    >>> pd.DataFrame({'Global Signal': [0.1]}).to_csv('signals.tsv', index=False, na_rep='n/a')
+    >>> pd.DataFrame({'stdDVARS': [0.2]}).to_csv('dvars.tsv', index=False, na_rep='n/a')
     >>> out_file, confound_list = _gather_confounds('signals.tsv', 'dvars.tsv')
     >>> confound_list
     ['Global signals', 'DVARS']
 
     >>> pd.read_csv(out_file, sep='\s+', index_col=None,
     ...             engine='python')  # doctest: +NORMALIZE_WHITESPACE
-         a    b
-    0  0.1  0.2
+       global_signal  std_dvars
+    0            0.1        0.2
     >>> tmpdir.cleanup()
 
 
@@ -153,6 +155,12 @@ def less_breakable(a_string):
         ''' hardens the string to different envs (i.e. case insensitive, no whitespace, '#' '''
         return ''.join(a_string.split()).strip('#')
 
+    # Taken from https://stackoverflow.com/questions/1175208/
+    # If we end up using it more than just here, probably worth pulling in a well-tested package
+    def camel_to_snake(name):
+        s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name)
+        return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower()
+
     def _adjust_indices(left_df, right_df):
         # This forces missing values to appear at the beggining of the DataFrame
         # instead of the end
@@ -167,6 +175,7 @@ def _adjust_indices(left_df, right_df):
     all_files = []
     confounds_list = []
     for confound, name in ((signals, 'Global signals'),
+                           (std_dvars, 'Standardized DVARS'),
                            (dvars, 'DVARS'),
                            (fdisp, 'Framewise displacement'),
                            (tcompcor, 'tCompCor'),
@@ -183,7 +192,7 @@ def _adjust_indices(left_df, right_df):
     for file_name in all_files:  # assumes they all have headings already
         new = pd.read_csv(file_name, sep="\t")
         for column_name in new.columns:
-            new.rename(columns={column_name: less_breakable(column_name)},
+            new.rename(columns={column_name: camel_to_snake(less_breakable(column_name))},
                        inplace=True)
 
         _adjust_indices(confounds_data, new)

fmriprep/workflows/bold/confounds.py

@@ -171,7 +171,7 @@ def init_bold_confs_wf(mem_gb, metadata, name="bold_confs_wf"):
     tcc_msk = pe.Node(niu.Function(function=_maskroi), name='tcc_msk')
 
     # DVARS
-    dvars = pe.Node(nac.ComputeDVARS(save_all=True, remove_zerovariance=True),
+    dvars = pe.Node(nac.ComputeDVARS(save_nstd=True, save_std=True, remove_zerovariance=True),
                     name="dvars", mem_gb=mem_gb)
 
     # Frame displacement
@@ -200,9 +200,15 @@ def init_bold_confs_wf(mem_gb, metadata, name="bold_confs_wf"):
                       name="signals", mem_gb=mem_gb)
 
     # Arrange confounds
-    add_header = pe.Node(
+    add_dvars_header = pe.Node(
+        AddTSVHeader(columns=["dvars"]),
+        name="add_dvars_header", mem_gb=0.01, run_without_submitting=True)
+    add_std_dvars_header = pe.Node(
+        AddTSVHeader(columns=["std_dvars"]),
+        name="add_std_dvars_header", mem_gb=0.01, run_without_submitting=True)
+    add_motion_headers = pe.Node(
         AddTSVHeader(columns=["trans_x", "trans_y", "trans_z", "rot_x", "rot_y", "rot_z"]),
-        name="add_header", mem_gb=0.01, run_without_submitting=True)
+        name="add_motion_headers", mem_gb=0.01, run_without_submitting=True)
     concat = pe.Node(GatherConfounds(), name="concat", mem_gb=0.01, run_without_submitting=True)
 
     # Generate reportlet
@@ -275,14 +281,17 @@ def _pick_wm(files):
         (mrg_lbl, signals, [('out', 'label_files')]),
 
         # Collate computed confounds together
-        (inputnode, add_header, [('movpar_file', 'in_file')]),
+        (inputnode, add_motion_headers, [('movpar_file', 'in_file')]),
+        (dvars, add_dvars_header, [('out_nstd', 'in_file')]),
+        (dvars, add_std_dvars_header, [('out_std', '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')]),
+        (add_motion_headers, concat, [('out_file', 'motion')]),
+        (add_dvars_header, concat, [('out_file', 'dvars')]),
+        (add_std_dvars_header, concat, [('out_file', 'std_dvars')]),
 
         # Set outputs
         (concat, outputnode, [('confounds_file', 'confounds_file')]),
@@ -358,11 +367,11 @@ def init_carpetplot_wf(mem_gb, metadata, name="bold_carpet_wf"):
     conf_plot = pe.Node(FMRISummary(
         tr=metadata['RepetitionTime'],
         confounds_list=[
-            ('GlobalSignal', None, 'GS'),
-            ('CSF', None, 'GSCSF'),
-            ('WhiteMatter', None, 'GSWM'),
-            ('stdDVARS', None, 'DVARS'),
-            ('FramewiseDisplacement', 'mm', 'FD')]),
+            ('global_signal', None, 'GS'),
+            ('csf', None, 'GSCSF'),
+            ('white_matter', None, 'GSWM'),
+            ('std_dvars', None, 'DVARS'),
+            ('framewise_displacement', 'mm', 'FD')]),
         name='conf_plot', mem_gb=mem_gb)
     ds_report_bold_conf = pe.Node(
         DerivativesDataSink(suffix='carpetplot'),