diff --git a/tedana/decomposition/ica.py b/tedana/decomposition/ica.py
index a309709b8..7e4902ecb 100644
--- a/tedana/decomposition/ica.py
+++ b/tedana/decomposition/ica.py
@@ -161,7 +161,7 @@ def r_ica(data, n_components, fixed_seed, n_robust_runs, max_it):
             robust_ica = RobustICA(
                 n_components=n_components,
                 robust_runs=n_robust_runs,
-                whiten="arbitrary-variance",
+                whiten="unit-variance",
                 max_iter=max_it,
                 random_state=fixed_seed,
                 robust_dimreduce=False,
@@ -320,6 +320,7 @@ def f_ica(data, n_components, fixed_seed, maxit, maxrestart):
             n_components=n_components,
             algorithm="parallel",
             fun="logcosh",
+            whiten="unit-variance",
             max_iter=maxit,
             random_state=fixed_seed,
         )
diff --git a/tedana/decomposition/pca.py b/tedana/decomposition/pca.py
index 24ac3181e..0c066b196 100644
--- a/tedana/decomposition/pca.py
+++ b/tedana/decomposition/pca.py
@@ -60,6 +60,7 @@ def tedpca(
     kdaw=10.0,
     rdaw=1.0,
     low_mem=False,
+    mean_center_only=False,
 ):
     r"""Use principal components analysis (PCA) to identify and remove thermal noise from data.
 
@@ -104,6 +105,9 @@ def tedpca(
         Whether to use incremental PCA (for low-memory systems) or not.
         This is only compatible with the "kundu" or "kundu-stabilize" algorithms.
         Default: False
+    mean_center_only  : :obj:`bool`, optional
+        Only center the mean time series to 0 instead of also scaling the variance
+        Default: False
 
     Returns
     -------
@@ -210,8 +214,13 @@ def tedpca(
     )
     data = data_optcom[mask, :]
 
-    data_z = ((data.T - data.T.mean(axis=0)) / data.T.std(axis=0)).T  # var normalize ts
-    data_z = (data_z - data_z.mean()) / data_z.std()  # var normalize everything
+    if mean_center_only:
+        LGR.info("Mean centering data before PCA")
+        data_z = data - data.mean(axis=1, keepdims=True)  # mean normalize ts
+    else:
+        LGR.info("Z scaling data before PCA")
+        data_z = ((data.T - data.T.mean(axis=0)) / data.T.std(axis=0)).T  # var normalize ts
+        data_z = (data_z - data_z.mean()) / data_z.std()  # var normalize everything
 
     if algorithm in ["mdl", "aic", "kic"]:
         data_img = io.new_nii_like(io_generator.reference_img, utils.unmask(data, mask))
@@ -450,7 +459,12 @@ def tedpca(
     voxel_kept_comp_weighted = voxel_comp_weights[:, acc] * varex[None, acc]
     kept_data = np.dot(voxel_kept_comp_weighted, comp_ts[:, acc].T)
 
-    kept_data = stats.zscore(kept_data, axis=1)  # variance normalize time series
-    kept_data = stats.zscore(kept_data, axis=None)  # variance normalize everything
+    if mean_center_only:
+        LGR.info("Mean centering data before ICA")
+        kept_data = kept_data - np.mean(kept_data, axis=1, keepdims=True)
+    else:
+        LGR.info("Z scaling data before ICA")
+        kept_data = stats.zscore(kept_data, axis=1)  # variance normalize time series
+        kept_data = stats.zscore(kept_data, axis=None)  # variance normalize everything
 
     return kept_data, n_components
diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py
index 56110593b..2be6a122c 100644
--- a/tedana/workflows/tedana.py
+++ b/tedana/workflows/tedana.py
@@ -369,6 +369,15 @@ def _get_parser():
         default=None,
     )
 
+    optional.add_argument(
+        "--mean_center_only",
+        "--mean-center-only",
+        dest="mean_center_only",
+        action="store_false",
+        help="Only mean center fMRI time series before PCA and ICA. Do not scale by stdev.",
+        default=False,
+    )
+
     optional.add_argument(
         "--quiet", dest="quiet", help=argparse.SUPPRESS, action="store_true", default=False
     )
@@ -431,6 +440,7 @@ def tedana_workflow(
     t2smap=None,
     mixing_file=None,
     tedana_command=None,
+    mean_center_only=False,
 ):
     """Run the "canonical" TE-Dependent ANAlysis workflow.
 
@@ -549,6 +559,9 @@ def tedana_workflow(
     mixing_file : :obj:`str` or None, optional
         File containing mixing matrix, to be used when re-running the workflow.
         If not provided, ME-PCA and ME-ICA are done. Default is None.
+    mean_center_only  : :obj:`bool`, optional
+        Only center the mean time series to 0 instead of also scaling the variance
+        Default: False
     quiet : :obj:`bool`, optional
         If True, suppresses logging/printing of messages. Default is False.
     overwrite : :obj:`bool`, optional
@@ -840,6 +853,7 @@ def tedana_workflow(
             kdaw=10.0,
             rdaw=1.0,
             low_mem=low_mem,
+            mean_center_only=mean_center_only,
         )
         if verbose:
             io_generator.save_file(utils.unmask(data_reduced, mask_clf), "whitened img")