fix: test stripped brain image for FLIRT

Author: satra

examples/fmri_openfmri.py

@@ -305,6 +305,7 @@ def num_copes(files):
     wf.connect(preproc, 'outputspec.mean', registration, 'inputspec.mean_image')
     wf.connect(datasource, 'anat', registration, 'inputspec.anatomical_image')
     registration.inputs.inputspec.target_image = fsl.Info.standard_image('MNI152_T1_2mm.nii.gz')
+    registration.inputs.inputspec.target_image_brain = fsl.Info.standard_image('MNI152_T1_2mm_brain.nii.gz')
 
     def merge_files(copes, varcopes):
         out_files = []

nipype/workflows/fmri/fsl/preprocess.py

@@ -1104,7 +1104,8 @@ def create_reg_workflow(name='registration'):
     inputnode = pe.Node(interface=util.IdentityInterface(fields=['source_files',
                                                                  'mean_image',
                                                                  'anatomical_image',
-                                                                 'target_image']),
+                                                                 'target_image',
+                                                                 'target_image_brain']),
                         name='inputspec')
     outputnode = pe.Node(interface=util.IdentityInterface(fields=['func2anat_transform',
                                                               'anat2target_transform',
@@ -1161,8 +1162,11 @@ def create_reg_workflow(name='registration'):
     """
 
     anat2target_affine = pe.Node(fsl.FLIRT(), name='anat2target_linear')
-    register.connect(inputnode, 'anatomical_image', anat2target_affine, 'in_file')
-    register.connect(inputnode, 'target_image', anat2target_affine, 'reference')
+    anat2target.inputs.searchr_x = [-180, 180]
+    anat2target.inputs.searchr_y = [-180, 180]
+    anat2target.inputs.searchr_z = [-180, 180]
+    register.connect(stripper, 'out_file', anat2target_affine, 'in_file')
+    register.connect(inputnode, 'target_image_brain', anat2target_affine, 'reference')
 
     """
     Calculate nonlinear transform from anatomical to target