diff --git a/_episodes/05-data-cleaning-with-nilearn.md b/_episodes/05-data-cleaning-with-nilearn.md
index aa6b8f54..7f687079 100644
--- a/_episodes/05-data-cleaning-with-nilearn.md
+++ b/_episodes/05-data-cleaning-with-nilearn.md
@@ -33,7 +33,7 @@ Let's load in some modules as we've done before
 import os
 from nilearn import image as nimg
 from nilearn import plotting as nplot
-import matplotilb.pyplot as plt
+import matplotlib.pyplot as plt
 import numpy as np
 import nibabel as nib
 %matplotlib inline
@@ -205,7 +205,7 @@ Now we'll implement our **Dummy TR Drop**. Remember this means that we are remov
 
 ~~~
 #First we'll load in our data and check the shape
-raw_func_img = nimg.load_img(func)
+raw_func_img = nimg.load_img(func_file)
 raw_func_img.shape
 ~~~
 {: .language-python}
@@ -296,7 +296,7 @@ t_r = 2
 
 #Clean!
 clean_img = nimg.clean_img(func_img,confounds=confounds_matrix,detrend=True,standardize=True,
-                         low_pass=low_pass,high_pass=high_pass,t_r=t_r, mask_img=mask)
+                         low_pass=low_pass,high_pass=high_pass,t_r=t_r, mask_img=mask_file)
 
 #Let's visualize our result! Doesn't really tell us much, but that's the data we're using for analysis!
 nplot.plot_epi(clean_img.slicer[:,:,:,50])