diff --git a/mriqc_learn/viz/metrics.py b/mriqc_learn/viz/metrics.py
index f79debe..ef8366f 100644
--- a/mriqc_learn/viz/metrics.py
+++ b/mriqc_learn/viz/metrics.py
@@ -184,6 +184,7 @@ def plot_corrmat(
     cbarlabel="",
     symmetric=True,
     figsize=None,
+    sorted=False,
     **kwargs,
 ):
     """
@@ -204,14 +205,33 @@ def plot_corrmat(
         A dictionary with arguments to `matplotlib.Figure.colorbar`.  Optional.
     cbarlabel
         The label for the colorbar.  Optional.
+    sorted : :obj:`bool`
+        Flag to perform hierachical clustering on the correlation plot
     **kwargs
         All other arguments are forwarded to `imshow`.
 
     """
     from mpl_toolkits.axes_grid1.inset_locator import inset_axes
 
+    # Cluster rows and columns (if arguments enabled)
+    if sorted:
+        from scipy.cluster.hierarchy import linkage, dendrogram, fcluster
+
+        Z = linkage(data, "complete", optimal_ordering=True)
+
+        dendrogram(Z, labels=data.columns, no_plot=True)
+
+        # Clusterize the data
+        threshold = 0.1
+        labels = fcluster(Z, threshold, criterion="distance")
+        # Keep the indices to sort labels
+        labels_order = np.argsort(labels)
+
+        # Reorder data
+        data = data.take(labels_order, axis=0).take(labels_order, axis=1)
+
     if hasattr(data, "columns"):
-        col_labels = data.columns.tolist()
+        col_labels = data.columns
         data = data.values
 
     if figsize is not None:
@@ -220,6 +240,7 @@ def plot_corrmat(
     if not ax:
         ax = plt.gca()
 
+    # If matrix is symmetric, keep only lower triangle
     if symmetric:
         data[np.triu(np.ones(data.shape, dtype=bool))] = np.nan
 
@@ -252,10 +273,16 @@ def plot_corrmat(
     ax.tick_params(top=False, bottom=True, labeltop=False, labelbottom=True)
 
     # Rotate the tick labels and set their alignment.
-    plt.setp(ax.get_xticklabels(), rotation=90, ha="right", rotation_mode="anchor")
+    plt.setp(
+        ax.get_xticklabels(),
+        rotation=90,
+        ha="right",
+        va="center",
+        rotation_mode="anchor",
+    )
 
     # Turn spines off and create white grid.
-    ax.spines[:].set_visible(False)
+    plt.setp(ax.spines.values(), visible=False)
 
     ax.set_xticks(np.arange(data.shape[1] + 1) - 0.5, minor=True)
     ax.set_yticks(np.arange(data.shape[0] + 1) - 0.5, minor=True)