Include DVARS to the project

narps_open/core/interfaces/confounds.py

@@ -0,0 +1,67 @@
+#!/usr/bin/python
+# coding: utf-8
+
+""" This module defines custom interfaces related to confounds computation """
+
+from os.path import abspath
+
+from nipype.interfaces.base import (
+    BaseInterface, BaseInterfaceInputSpec, traits, TraitedSpec, ImageFile, File
+    )
+from DVARS import DVARS_Calc
+
+class ComputeDVARSInputSpec(BaseInterfaceInputSpec):
+    """ Input specifications of a ComputeDVARS interface """
+    in_file = ImageFile(mandatory = True, desc = '4D nifti input file')
+    nb_time_points = traits.Int(mandatory = True, desc = 'Number of time points in the input file')
+    out_file_name = traits.Str(
+        mandatory = True,
+        desc = 'Base name for the output file, without extension'
+        )
+
+class ComputeDVARSOutputSpec(TraitedSpec):
+    """ Output specifications of a ComputeDVARS interface """
+    out_file = File(
+        exists = True,
+        desc = 'Output file containig a regressor identifying corrupted points'
+        )
+
+class ComputeDVARS(BaseInterface):
+    """ Map the MATLAB code from the following article :
+
+    Afyouni, Soroosh & Nichols, Thomas. (2018).
+    Insight and inference for DVARS. NeuroImage. 172.
+    10.1016/j.neuroimage.2017.12.098.
+
+    Code is available here:
+    https://github.com/asoroosh/DVARS
+
+    Returns
+
+    matlab_output : capture of matlab output which may be
+                    parsed by user to get computation results
+    """
+    input_spec = ComputeDVARSInputSpec
+    output_spec = ComputeDVARSOutputSpec
+
+    def _run_interface(self, runtime):
+        """ Run the DVARS computation and identify corrupted points """
+
+        # Compute DVARS
+        dvars_output = DVARS_Calc(self.inputs.in_file)
+
+        # Identify corrupted points
+        #%   find(Stat.pvals<0.05./(T-1) & Stat.DeltapDvar>5) %print corrupted DVARS data-points
+        pvalues = [e < (0.05/(nb_time_points-1)) for e in dvars_output['Inference']['Pval']]
+        deltapdvar = [e > 5 for e in dvars_output['DVARS']['DeltapDvar']]
+
+        # Write result to file
+        with open(abspath(self.inputs.out_file_name + '.txt'), 'w') as file:
+            file.write(tabulate([a and b for a,b in zip(pvalues, deltapdvar)]))
+
+        return runtime
+
+    def _list_outputs(self):
+        outputs = self._outputs().get()
+        outputs['out_file'] = abspath(self.inputs.out_file_name + '.txt')
+        return outputs

narps_open/pipelines/team_0ED6.py

@@ -21,6 +21,7 @@
 from nipype.algorithms.confounds import ComputeDVARS
 
 from narps_open.pipelines import Pipeline
+from narps_open.core.interfaces.confounds import ComputeDVARS
 from narps_open.data.task import TaskInformation
 from narps_open.data.participants import get_group
 from narps_open.utils.configuration import Configuration
@@ -213,6 +214,7 @@ def get_preprocessing(self):
         select_func.inputs.index = [0] # func file
         preprocessing.connect(smoothing, 'smoothed_files', select_func, 'inlist')
 
+        """
         # MATHS COMMAND - Apply threshold to sbref normalized GM probalility map
         #   and binarise result
         # TODO : add wm in the mask ?
@@ -226,12 +228,19 @@ def get_preprocessing(self):
         reslice_mask = Node(Reslice(), name ='reslice_mask')
         preprocessing.connect(threshold, 'out_file', reslice_mask, 'in_file')
         preprocessing.connect(select_func, 'out', reslice_mask, 'space_defining')
+        """
 
         # COMPUTE DVARS - Identify corrupted time-points from func
-        compute_dvars = Node(ComputeDVARS(), name = 'compute_dvars')
+        """compute_dvars = Node(ComputeDVARS(), name = 'compute_dvars')
         compute_dvars.inputs.series_tr = TaskInformation()['RepetitionTime']
         preprocessing.connect(select_func, 'out', compute_dvars, 'in_file')
-        preprocessing.connect(reslice_mask, 'out_file', compute_dvars, 'in_mask')
+        preprocessing.connect(reslice_mask, 'out_file', compute_dvars, 'in_mask')"""
+
+        # COMPUTE DVARS - Identify corrupted time-points from func
+        compute_dvars = Node(ComputeDVARS(), name = 'compute_dvars')
+        compute_dvars.inputs.nb_time_points = 453
+        compute_dvars.inputs.out_file_name = 'corrupted_points'
+        preprocessing.connect(select_func, 'out', compute_dvars, 'in_file')
 
         # DATA SINK - store the wanted results in the wanted repository
         data_sink = Node(DataSink(), name = 'data_sink')
@@ -240,7 +249,7 @@ def get_preprocessing(self):
         preprocessing.connect(
             realign_unwarp, 'realignment_parameters',
             data_sink, 'preprocessing.@realignement_parameters')
-        preprocessing.connect(compute_dvars, 'out_std', data_sink, 'preprocessing.@dvars_file')
+        preprocessing.connect(compute_dvars, 'out_file', data_sink, 'preprocessing.@dvars_file')
 
         # Remove large files, if requested
         if Configuration()['pipelines']['remove_unused_data']:

setup.py

@@ -31,7 +31,10 @@
         'pytest-helpers-namespace>=2021.12.29,<2021.13',
         'pytest-mock>=3.12.0,<3.13',
         'checksumdir>=1.2.0,<1.3'
-        ]
+        ],
+    'scientific': [
+        'DVARS @ git+https://[email protected]/bclenet/DVARS'
+    ]
 }
 
 setup(