(enh) (#1618) pass ICA-AROMA metadata TSV forward

Author: rciric

fmriprep/interfaces/confounds.py

@@ -107,6 +107,7 @@ class ICAConfoundsOutputSpec(TraitedSpec):
         File(exists=True, desc='output confounds file extracted from ICA-AROMA'))
     aroma_noise_ics = File(exists=True, desc='ICA-AROMA noise components')
     melodic_mix = File(exists=True, desc='melodic mix file')
+    aroma_metadata = File(exists=True, desc='tabulated ICA-AROMA metadata')
 
 
 class ICAConfounds(SimpleInterface):
@@ -116,8 +117,10 @@ class ICAConfounds(SimpleInterface):
     output_spec = ICAConfoundsOutputSpec
 
     def _run_interface(self, runtime):
-        aroma_confounds, motion_ics_out, melodic_mix_out = _get_ica_confounds(
-            self.inputs.in_directory, self.inputs.skip_vols, newpath=runtime.cwd)
+        (aroma_confounds, motion_ics_out, melodic_mix_out, aroma_metadata
+            ) = _get_ica_confounds(self.inputs.in_directory,
+                                   self.inputs.skip_vols,
+                                   newpath=runtime.cwd)
 
         if self.inputs.err_on_aroma_warn and aroma_confounds is None:
             raise RuntimeError('ICA-AROMA failed')
@@ -126,6 +129,7 @@ def _run_interface(self, runtime):
 
         self._results['aroma_noise_ics'] = motion_ics_out
         self._results['melodic_mix'] = melodic_mix_out
+        self._results['aroma_metadata'] = aroma_metadata
         return runtime
 
 
@@ -218,10 +222,12 @@ def _get_ica_confounds(ica_out_dir, skip_vols, newpath=None):
     # load the txt files from ICA-AROMA
     melodic_mix = os.path.join(ica_out_dir, 'melodic.ica/melodic_mix')
     motion_ics = os.path.join(ica_out_dir, 'classified_motion_ICs.txt')
+    aroma_metadata = os.path.join(ica_out_dir, 'classification_overview.txt')
 
     # Change names of motion_ics and melodic_mix for output
     melodic_mix_out = os.path.join(newpath, 'MELODICmix.tsv')
     motion_ics_out = os.path.join(newpath, 'AROMAnoiseICs.csv')
+    aroma_metadata_out = os.path.join(newpath, 'classification_overview.tsv')
 
     # copy metion_ics file to derivatives name
     shutil.copyfile(motion_ics, motion_ics_out)
@@ -238,18 +244,27 @@ def _get_ica_confounds(ica_out_dir, skip_vols, newpath=None):
     # save melodic_mix_arr
     np.savetxt(melodic_mix_out, melodic_mix_arr, delimiter='\t')
 
+    # process the metadata so that the IC column entries match the BIDS name of
+    # the regressor
+    aroma_metadata = pd.read_csv(aroma_metadata, sep='\t')
+    aroma_metadata['IC'] = [
+        'aroma_motion_{}'.format(name) for name in aroma_metadata['IC']]
+    aroma_metadata.columns = [
+        re.sub('[ |\-|\/]', '_', c) for c in aroma_metadata.columns]
+    aroma_metadata.to_csv(aroma_metadata_out, sep='\t', index=False)
+
     # Return dummy list of ones if no noise compnents were found
     if motion_ic_indices.size == 0:
         LOGGER.warning('No noise components were classified')
-        return None, motion_ics_out, melodic_mix_out
+        return None, motion_ics_out, melodic_mix_out, aroma_metadata_out
 
     # the "good" ics, (e.g., not motion related)
     good_ic_arr = np.delete(melodic_mix_arr, motion_ic_indices, 1).T
 
     # return dummy lists of zeros if no signal components were found
     if good_ic_arr.size == 0:
         LOGGER.warning('No signal components were classified')
-        return None, motion_ics_out, melodic_mix_out
+        return None, motion_ics_out, melodic_mix_out, aroma_metadata_out
 
     # transpose melodic_mix_arr so x refers to the correct dimension
     aggr_confounds = np.asarray([melodic_mix_arr.T[x] for x in motion_ic_indices])
@@ -260,7 +275,7 @@ def _get_ica_confounds(ica_out_dir, skip_vols, newpath=None):
                  columns=['aroma_motion_%02d' % (x + 1) for x in motion_ic_indices]).to_csv(
         aroma_confounds, sep="\t", index=None)
 
-    return aroma_confounds, motion_ics_out, melodic_mix_out
+    return aroma_confounds, motion_ics_out, melodic_mix_out, aroma_metadata_out
 
 
 class FMRISummaryInputSpec(BaseInterfaceInputSpec):