diff --git a/mne_connectivity/spectral/epochs_multivariate.py b/mne_connectivity/spectral/epochs_multivariate.py
index 7265ef9b..2881cc00 100644
--- a/mne_connectivity/spectral/epochs_multivariate.py
+++ b/mne_connectivity/spectral/epochs_multivariate.py
@@ -819,3 +819,4 @@ class _GCTREst(_GCEstBase):
 
 _multivariate_methods = ["mic", "mim", "gc", "gc_tr"]
 _gc_methods = ["gc", "gc_tr"]
+_patterns_methods = ["mic"]  # methods with spatial patterns
diff --git a/mne_connectivity/spectral/time.py b/mne_connectivity/spectral/time.py
index b5d5a648..58b49cb1 100644
--- a/mne_connectivity/spectral/time.py
+++ b/mne_connectivity/spectral/time.py
@@ -21,6 +21,7 @@
     _check_rank_input,
     _gc_methods,
     _multivariate_methods,
+    _patterns_methods,
 )
 from .smooth import _create_kernel, _smooth_spectra
 
@@ -541,10 +542,14 @@ def spectral_connectivity_time(
     conn_patterns = dict()
     for m in method:
         conn[m] = np.zeros((n_epochs, n_cons, n_freqs))
-        # patterns shape of [epochs x seeds/targets x cons x channels x freqs]
-        conn_patterns[m] = np.full(
-            (n_epochs, 2, n_cons, max_n_channels, n_freqs), np.nan
-        )
+        # prevent allocating memory for a huge array if not required
+        if m in _patterns_methods:
+            # patterns shape of [epochs x seeds/targets x cons x channels x freqs]
+            conn_patterns[m] = np.full(
+                (n_epochs, 2, n_cons, max_n_channels, n_freqs), np.nan
+            )
+        else:
+            conn_patterns[m] = None
     logger.info("Connectivity computation...")
 
     # parameters to pass to the connectivity function
@@ -577,12 +582,10 @@ def spectral_connectivity_time(
         scores, patterns = _spectral_connectivity(data[epoch_idx], **call_params)
         for m in method:
             conn[m][epoch_idx] = np.stack(scores[m], axis=0)
-            if multivariate_con and patterns[m] is not None:
+            if patterns[m] is not None:
                 conn_patterns[m][epoch_idx] = np.stack(patterns[m], axis=0)
     for m in method:
-        if np.isnan(conn_patterns[m]).all():
-            conn_patterns[m] = None
-        else:
+        if conn_patterns[m] is not None:
             # transpose to [seeds/targets x epochs x cons x channels x freqs]
             conn_patterns[m] = conn_patterns[m].transpose((1, 0, 2, 3, 4))