diff --git a/pydfc/data_loader.py b/pydfc/data_loader.py
index 98f9cdf..1d8fe89 100644
--- a/pydfc/data_loader.py
+++ b/pydfc/data_loader.py
@@ -361,9 +361,9 @@ def nifti2array(
 
     return time_series, labels, locs
 
-
 def nifti2timeseries(
     nifti_file,
+    n_rois,
     Fs,
     subj_id,
     confound_strategy="none",
diff --git a/pydfc/dfc_methods/cap.py b/pydfc/dfc_methods/cap.py
index ed0b21c..f3dfeb8 100644
--- a/pydfc/dfc_methods/cap.py
+++ b/pydfc/dfc_methods/cap.py
@@ -10,6 +10,7 @@
 import numpy as np
 from scipy.special import softmax
 from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_samples
 
 from ..dfc import DFC
 from ..time_series import TIME_SERIES
@@ -38,6 +39,7 @@ class CAP(BaseDFCMethod):
     def __init__(self, **params):
         self.logs_ = ""
         self.FCS_ = []
+        self.silhouette_sc_ = []
         self.mean_act = []
         self.FCS_fit_time_ = None
         self.dFC_assess_time_ = None
@@ -126,6 +128,9 @@ def estimate_FCS(self, time_series):
         self.FCS_ = self.act_vec2FCS(group_act_centroids)
         self.Z = self.kmeans_.predict(time_series.data.T.astype(np.float32))
 
+        # silhouette coefficient
+        self.silhouette_sc_ = silhouette_samples(time_series.data.T.astype(np.float32),self.Z)
+
         # mean activation of states
         self.set_mean_activity(time_series)