diff --git a/examples/fmri_openfmri.py b/examples/fmri_openfmri.py
index 7ed0d0f8c2..9628a8116e 100755
--- a/examples/fmri_openfmri.py
+++ b/examples/fmri_openfmri.py
@@ -121,15 +121,15 @@ def analyze_openfmri_dataset(data_dir, subject=None, model_id=None,
     Set up openfmri data specific components
     """
 
-    subjects = [path.split(os.path.sep)[-1] for path in
-                glob(os.path.join(data_dir, 'sub*'))]
+    subjects = sorted([path.split(os.path.sep)[-1] for path in
+                       glob(os.path.join(data_dir, 'sub*'))])
 
     infosource = pe.Node(niu.IdentityInterface(fields=['subject_id',
                                                        'model_id',
                                                        'task_id']),
                          name='infosource')
     if subject is None:
-        infosource.iterables = [('subject_id', subjects[:2]),
+        infosource.iterables = [('subject_id', subjects),
                                 ('model_id', [model_id]),
                                 ('task_id', [task_id])]
     else:
@@ -200,13 +200,19 @@ def get_highpass(TR, hpcutoff):
     def get_contrasts(contrast_file, task_id, conds):
         import numpy as np
         contrast_def = np.genfromtxt(contrast_file, dtype=object)
+        if len(contrast_def.shape) == 1:
+            contrast_def = contrast_def[None, :]
         contrasts = []
         for row in contrast_def:
             if row[0] != 'task%03d' % task_id:
                 continue
-            con = [row[1], 'T', ['cond%03d' % i  for i in range(len(conds))],
+            con = [row[1], 'T', ['cond%03d' % (i + 1)  for i in range(len(conds))],
                    row[2:].astype(float).tolist()]
             contrasts.append(con)
+        # add auto contrasts for each column
+        for i, cond in enumerate(conds):
+            con = [cond, 'T', ['cond%03d' % (i + 1)], [1]]
+            contrasts.append(con)
         return contrasts
 
     contrastgen = pe.Node(niu.Function(input_names=['contrast_file',