diff --git a/demos/fwe_control.m b/demos/fwe_control.m
index 83b93d4..05a18f1 100644
--- a/demos/fwe_control.m
+++ b/demos/fwe_control.m
@@ -41,6 +41,16 @@
     fp(i) = sum(pcorr_pos(:)<.05)+sum(pcorr_neg(:)<.05);
 end
 sum(fp)/nsim % false positive rate
+%% correlation, one-sided
+nsim = 1000;
+fp = NaN(nsim,1);
+for i = 1:nsim
+    imgs = randn(4,4,4,20);
+    covariate = randn(20,1);
+    pcorr = matlab_tfce('correlation',1,imgs,[],covariate);
+    fp(i) = sum(pcorr(:)<.05)+sum(pcorr(:)<.05);
+end
+sum(fp)/nsim % false positive rate
 
 %% correlation, two-sided
 nsim = 1000;
diff --git a/matlab_tfce_correlation.m b/matlab_tfce_correlation.m
index f2ca171..7c2d63c 100644
--- a/matlab_tfce_correlation.m
+++ b/matlab_tfce_correlation.m
@@ -3,8 +3,7 @@
 % individual difference correlation between a covariate and brain activity.
 %
 %   Arguments:
-%   imgs -- a 4D (x,y,z,subject) matrix of images. Like tfce_permutation,
-%   these should have had tfce_transform applied already.
+%   imgs -- a 4D (x,y,z,subject) matrix of images.
 %   covariate -- a vector of length = number of subjects containing values
 %   to be correlated with brain activity
 %	tails -- 1 or 2 tailed test
@@ -54,6 +53,7 @@
 trueimg(implicitmask) = truestat;
 trueimg = transform(trueimg,H,E,C,dh);
 tfcestat = trueimg(implicitmask);
+cvals = tfcestat;
 if tails == 2
 	cvals = abs(tfcestat);
 end
@@ -96,7 +96,7 @@
 	pcorr_neg(pos) = 1;
 end
 
-% assigne output to varargout
+% assign output to varargout
 if tails == 1
 	varargout{1} = pcorr;
 else