Add Dan0s suggestions

docs/approach.rst

@@ -245,7 +245,10 @@ to select the PCA components based on three widely-used model selection criteria
 .. note::
     Please, bear in mind that this is a data-driven dimensionality reduction approach. The default
     option ``aic`` might not yield perfect results on your data. Consider ``kic``
-    and ``mdl`` options if running ``tedana`` with ``aic`` returns more components than expected. There is no definitively right number of components, but, for typical fMRI datasets, if the PCA explains more than 98% of the variance or if the number of components is more than half the number of time points, then it may be worth considering more aggressive threshold.
+    and ``mdl`` options if running ``tedana`` with ``aic`` returns more components than expected.
+    There is no definitively right number of components, but, for typical fMRI datasets, if the PCA
+    explains more than 98% of the variance or if the number of components is more than half the number
+    of time points, then it may be worth considering more aggressive thresholds.
 
 The simplest approach uses a user-supplied threshold applied to the cumulative variance explained
 by the PCA.

tedana/decomposition/pca.py

@@ -225,7 +225,9 @@ def tedpca(
 
     n_samp, n_echos, n_vols = data_cat.shape
 
-    LGR.info("Computing PCA of optimally combined multi-echo data")
+    LGR.info(
+        f"Computing PCA of optimally combined multi-echo data with selection criteria: {algorithm}"
+    )
     data = data_oc[mask, :]
 
     data_z = ((data.T - data.T.mean(axis=0)) / data.T.std(axis=0)).T  # var normalize ts