diff --git a/tedana/model/fit.py b/tedana/model/fit.py
index f772e4cc3..20986a8aa 100644
--- a/tedana/model/fit.py
+++ b/tedana/model/fit.py
@@ -137,14 +137,14 @@ def fitmodels_direct(catd, mmix, mask, t2s, t2sG, tes, combmode, ref_img,
         coeffs_S0 = (B * X1).sum(axis=0) / (X1**2).sum(axis=0)
         SSE_S0 = (B - X1 * np.tile(coeffs_S0, (n_echos, 1)))**2
         SSE_S0 = SSE_S0.sum(axis=0)
-        F_S0 = (alpha - SSE_S0) * 2 / (SSE_S0)
+        F_S0 = (alpha - SSE_S0) * (n_echos - 1) / (SSE_S0)
         F_S0_maps[:, i] = F_S0
 
         # R2 Model
         coeffs_R2 = (B * X2).sum(axis=0) / (X2**2).sum(axis=0)
         SSE_R2 = (B - X2 * np.tile(coeffs_R2, (n_echos, 1)))**2
         SSE_R2 = SSE_R2.sum(axis=0)
-        F_R2 = (alpha - SSE_R2) * 2 / (SSE_R2)
+        F_R2 = (alpha - SSE_R2) * (n_echos - 1) / (SSE_R2)
         F_R2_maps[:, i] = F_R2
 
         # compute weights as Z-values
diff --git a/tedana/utils/utils.py b/tedana/utils/utils.py
index dd7680507..9812c5493 100644
--- a/tedana/utils/utils.py
+++ b/tedana/utils/utils.py
@@ -193,11 +193,13 @@ def make_adaptive_mask(data, minimum=True, getsum=False):
     first_echo = echo_means[echo_means[:, 0] != 0, 0]
 
     # get 33rd %ile of `first_echo` and find corresponding index
-    perc_33 = np.percentile(first_echo, 33, interpolation='higher')  # QUESTION: why 33%ile?
-    med_val = (echo_means[:, 0] == perc_33)
+    # NOTE: percentile is arbitrary
+    perc = np.percentile(first_echo, 33, interpolation='higher')
+    perc_val = (echo_means[:, 0] == perc)
 
     # extract values from all echos at relevant index
-    lthrs = np.squeeze(echo_means[med_val].T) / 3  # QUESTION: why divide by 3?
+    # NOTE: threshold of 1/3 voxel value is arbitrary
+    lthrs = np.squeeze(echo_means[perc_val].T) / 3
 
     # if multiple samples were extracted per echo, keep the one w/the highest signal
     if lthrs.ndim > 1: