From a0416487857dc69de54fada9c245ed665d9a094d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=B6rg=20Stadler?= <Stadler@lin-magdeburg.de>
Date: Tue, 19 Apr 2016 08:24:52 +0200
Subject: [PATCH 1/2] autorecon1: convert_modalities output fix

---
 nipype/workflows/smri/freesurfer/autorecon1.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nipype/workflows/smri/freesurfer/autorecon1.py b/nipype/workflows/smri/freesurfer/autorecon1.py
index 3b94315218..84fa6780ce 100644
--- a/nipype/workflows/smri/freesurfer/autorecon1.py
+++ b/nipype/workflows/smri/freesurfer/autorecon1.py
@@ -86,8 +86,8 @@ def convert_modalities(in_file=None, out_file=None):
                 convert.inputs.in_file = in_file
                 convert.inputs.out_file = out_file
                 convert.inputs.no_scale = True
-                convert.run()
-                out_file = os.path.abspath(convert.outputs.out_file)
+                out = convert.run()
+                out_file = os.path.abspath(out.outputs.out_file)
             return out_file
 
         T2_convert = pe.Node(Function(['in_file', 'out_file'],

From e3767b5fbe8f60dc2ca8499005232a409def2044 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=B6rg=20Stadler?= <Stadler@lin-magdeburg.de>
Date: Tue, 19 Apr 2016 10:38:55 +0200
Subject: [PATCH 2/2] In contrast to 'create_featreg_preproc'
 'create_parallelfeat_preproc'  did not add the mean to the highpassed images
 if using FSL >=5.0.7

copy paste addmean fix from create_feat_reg to create_parallelfeat
---
 nipype/workflows/fmri/fsl/preprocess.py | 30 ++++++++++++++++++++-----
 1 file changed, 25 insertions(+), 5 deletions(-)

diff --git a/nipype/workflows/fmri/fsl/preprocess.py b/nipype/workflows/fmri/fsl/preprocess.py
index 4915d93444..272b7118e2 100644
--- a/nipype/workflows/fmri/fsl/preprocess.py
+++ b/nipype/workflows/fmri/fsl/preprocess.py
@@ -113,6 +113,10 @@ def create_parallelfeat_preproc(name='featpreproc', highpass=True):
     >>> preproc.base_dir = '/tmp'
     >>> preproc.run() # doctest: +SKIP
     """
+    version = 0
+    if fsl.Info.version() and \
+            LooseVersion(fsl.Info.version()) > LooseVersion('5.0.6'):
+        version = 507
 
     featpreproc = pe.Workflow(name=name)
 
@@ -358,7 +362,26 @@ def create_parallelfeat_preproc(name='featpreproc', highpass=True):
                               name='highpass')
         featpreproc.connect(inputnode, ('highpass', highpass_operand), highpass, 'op_string')
         featpreproc.connect(meanscale, 'out_file', highpass, 'in_file')
-        featpreproc.connect(highpass, 'out_file', outputnode, 'highpassed_files')
+
+        if version < 507:
+            featpreproc.connect(highpass, 'out_file', outputnode, 'highpassed_files')
+        else:
+            """
+            Add back the mean removed by the highpass filter operation as of FSL 5.0.7
+            """
+            meanfunc4 = pe.MapNode(interface=fsl.ImageMaths(op_string='-Tmean',
+                                                            suffix='_mean'),
+                                   iterfield=['in_file'],
+                                   name='meanfunc4')
+
+            featpreproc.connect(meanscale, 'out_file', meanfunc4, 'in_file')
+            addmean = pe.MapNode(interface=fsl.BinaryMaths(operation='add'),
+                                 iterfield=['in_file', 'operand_file'],
+                                 name='addmean')
+            featpreproc.connect(highpass, 'out_file', addmean, 'in_file')
+            featpreproc.connect(meanfunc4, 'out_file', addmean, 'operand_file')
+            featpreproc.connect(addmean, 'out_file', outputnode, 'highpassed_files')
+
 
     """
     Generate a mean functional image from the first run
@@ -368,11 +391,8 @@ def create_parallelfeat_preproc(name='featpreproc', highpass=True):
                                                     suffix='_mean'),
                            iterfield=['in_file'],
                            name='meanfunc3')
-    if highpass:
-        featpreproc.connect(highpass, 'out_file', meanfunc3, 'in_file')
-    else:
-        featpreproc.connect(meanscale, 'out_file', meanfunc3, 'in_file')
 
+    featpreproc.connect(meanscale, 'out_file', meanfunc3, 'in_file')
     featpreproc.connect(meanfunc3, 'out_file', outputnode, 'mean')
 
     return featpreproc