diff --git a/.circleci/tedana_outputs_verbose.txt b/.circleci/tedana_outputs_verbose.txt
index 26eda3590..be908d46f 100644
--- a/.circleci/tedana_outputs_verbose.txt
+++ b/.circleci/tedana_outputs_verbose.txt
@@ -1,4 +1,5 @@
 __meica_mix.1D
+adaptive_mask.nii
 betas_OC.nii
 betas_hik_OC.nii
 comp_table_ica.txt
diff --git a/tedana/tests/test_utils.py b/tedana/tests/test_utils.py
index a9d65d505..280639d6c 100644
--- a/tedana/tests/test_utils.py
+++ b/tedana/tests/test_utils.py
@@ -130,11 +130,11 @@ def test_make_adaptive_mask():
     assert np.allclose(counts, np.array([13564,  3977,  5060, 41749]))
 
     # test user-defined mask
+    # TODO: Add mask file with no bad voxels to test against
     mask, masksum = utils.make_adaptive_mask(data, mask=pjoin(datadir,
                                                               'mask.nii.gz'),
                                              minimum=False, getsum=True)
-    assert np.allclose(mask, nib.load(pjoin(datadir,
-                                            'mask.nii.gz')).get_data().flatten())
+    assert np.allclose(mask, masksum.astype(bool))
 
 
 def test_make_min_mask():
diff --git a/tedana/utils.py b/tedana/utils.py
index c81db91e3..8d1faa8eb 100644
--- a/tedana/utils.py
+++ b/tedana/utils.py
@@ -1,17 +1,20 @@
 """
 Utilities for tedana package
 """
-import nibabel as nib
+import logging
+
 import numpy as np
-from nibabel.filename_parser import splitext_addext
-from nilearn._utils import check_niimg
 from scipy import stats
 from scipy.optimize import leastsq
+import nibabel as nib
+from nilearn._utils import check_niimg
+from nibabel.filename_parser import splitext_addext
 from sklearn.utils import check_array
 
 from tedana.due import due, BibTeX
 
 FORMATS = {'.nii': 'NIFTI'}
+LGR = logging.getLogger(__name__)
 
 
 def get_dtype(data):
@@ -150,6 +153,13 @@ def make_adaptive_mask(data, mask=None, minimum=True, getsum=False):
         # if the user has supplied a binary mask
         mask = load_image(mask).astype(bool)
         masksum = masksum * mask
+        # reduce mask based on masksum
+        # TODO: Use visual report to make checking the reduced mask easier
+        if np.any(masksum[mask] == 0):
+            n_bad_voxels = np.sum(masksum[mask] == 0)
+            LGR.warning('{0} voxels in user-defined mask do not have good '
+                        'signal. Removing voxels from mask.'.format(n_bad_voxels))
+            mask = masksum.astype(bool)
 
     if getsum:
         return mask, masksum
diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py
index d3f5ca014..304d4fae1 100644
--- a/tedana/workflows/tedana.py
+++ b/tedana/workflows/tedana.py
@@ -285,6 +285,8 @@ def tedana_workflow(data, tes, mask=None, mixm=None, ctab=None, manacc=None,
     mask, masksum = utils.make_adaptive_mask(catd, mask=mask,
                                              minimum=False, getsum=True)
     LGR.debug('Retaining {}/{} samples'.format(mask.sum(), n_samp))
+    if verbose:
+        io.filewrite(masksum, op.join(out_dir, 'adaptive_mask.nii'), ref_img)
 
     os.chdir(out_dir)
 
@@ -319,6 +321,8 @@ def tedana_workflow(data, tes, mask=None, mixm=None, ctab=None, manacc=None,
                                                 t2s, t2sG, stabilize, ref_img,
                                                 tes=tes, kdaw=kdaw, rdaw=rdaw,
                                                 ste=ste, wvpca=wvpca)
+
+        LGR.info('Computing ICA of dimensionally reduced data')
         mmix_orig, fixed_seed = decomposition.tedica(n_components, dd,
                                                      fixed_seed)