diff --git a/examples/02_meta-analyses/01_plot_cbma.py b/examples/02_meta-analyses/01_plot_cbma.py
index 93cb6c403..85a89f1d1 100644
--- a/examples/02_meta-analyses/01_plot_cbma.py
+++ b/examples/02_meta-analyses/01_plot_cbma.py
@@ -13,6 +13,7 @@
 For a more detailed introduction to the elements of a coordinate-based
 meta-analysis, see other stuff.
 """
+
 ###############################################################################
 # Load Dataset
 # -----------------------------------------------------------------------------
@@ -57,6 +58,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.1,
 )
 plot_stat_map(
@@ -64,6 +66,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.1,
 )
 
@@ -87,12 +90,14 @@
     results.get_map("z_desc-uniformity"),
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.1,
 )
 plot_stat_map(
     cres.get_map("z_desc-uniformitySize_level-cluster_corr-FWE_method-montecarlo"),
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.1,
 )
 
@@ -117,6 +122,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.1,
 )
 plot_stat_map(
@@ -124,6 +130,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.1,
 )
 
@@ -148,6 +155,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.1,
 )
 plot_stat_map(
@@ -155,6 +163,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.1,
 )
 
@@ -200,6 +209,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.1,
 )
 
diff --git a/examples/02_meta-analyses/02_plot_ibma.py b/examples/02_meta-analyses/02_plot_ibma.py
index 1eac8a7c0..6d0d4458d 100644
--- a/examples/02_meta-analyses/02_plot_ibma.py
+++ b/examples/02_meta-analyses/02_plot_ibma.py
@@ -13,6 +13,7 @@
 For a more detailed introduction to the elements of an image-based
 meta-analysis, see other stuff.
 """
+
 from nilearn.plotting import plot_stat_map
 
 ###############################################################################
@@ -56,6 +57,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 print("Description:")
@@ -74,6 +76,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 print("Description:")
@@ -94,6 +97,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 print("Description:")
@@ -115,6 +119,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 corrector = FWECorrector(method="montecarlo", n_iters=100, n_cores=1)
@@ -125,6 +130,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 print("Description:")
@@ -145,6 +151,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 print("Description:")
@@ -165,6 +172,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 print("Description:")
@@ -185,6 +193,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 print("Description:")
diff --git a/examples/02_meta-analyses/03_plot_kernel_transformers.py b/examples/02_meta-analyses/03_plot_kernel_transformers.py
index 50a162f5d..f0d3232ff 100644
--- a/examples/02_meta-analyses/03_plot_kernel_transformers.py
+++ b/examples/02_meta-analyses/03_plot_kernel_transformers.py
@@ -17,6 +17,7 @@
 This example provides an introduction to the ``KernelTransformer`` class and
 a tour of available types.
 """
+
 # sphinx_gallery_thumbnail_number = 2
 import os
 
@@ -99,6 +100,7 @@
         annotate=False,
         colorbar=False,
         cmap="RdBu_r",
+        symmetric_cbar=True,
     )
 
 ###############################################################################
@@ -115,6 +117,7 @@
     title="MKDA",
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 ###############################################################################
@@ -131,6 +134,7 @@
     title="KDA",
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 ###############################################################################
@@ -150,4 +154,5 @@
     title="ALE",
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
diff --git a/examples/02_meta-analyses/05_plot_correctors.py b/examples/02_meta-analyses/05_plot_correctors.py
index cdd1271a2..83961e0a6 100644
--- a/examples/02_meta-analyses/05_plot_correctors.py
+++ b/examples/02_meta-analyses/05_plot_correctors.py
@@ -8,6 +8,7 @@
 
 Here we take a look at multiple comparisons correction in meta-analyses.
 """
+
 from pprint import pprint
 
 import matplotlib.pyplot as plt
@@ -119,6 +120,7 @@
         cres.get_map(map_name),
         draw_cross=False,
         cmap="RdBu_r",
+        symmetric_cbar=True,
         threshold=0.5,
         cut_coords=[0, 0, -8],
         figure=fig,
@@ -157,6 +159,7 @@
     cres.get_map("z"),
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.5,
     cut_coords=[0, 0, -8],
     figure=fig,
@@ -167,6 +170,7 @@
     cres.get_map("z_corr-FDR_method-indep"),
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     threshold=0.5,
     cut_coords=[0, 0, -8],
     figure=fig,
diff --git a/examples/02_meta-analyses/06_plot_compare_ibma_and_cbma.py b/examples/02_meta-analyses/06_plot_compare_ibma_and_cbma.py
index 433389b51..d43f01576 100644
--- a/examples/02_meta-analyses/06_plot_compare_ibma_and_cbma.py
+++ b/examples/02_meta-analyses/06_plot_compare_ibma_and_cbma.py
@@ -11,6 +11,7 @@
 Collection of NIDM-Results packs downloaded from Neurovault collection 1425,
 uploaded by Dr. Camille Maumet.
 """
+
 import os
 
 import pandas as pd
@@ -53,6 +54,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 ###############################################################################
@@ -65,6 +67,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 ###############################################################################
diff --git a/examples/02_meta-analyses/07_macm.py b/examples/02_meta-analyses/07_macm.py
index 07b4a9d92..d73691320 100644
--- a/examples/02_meta-analyses/07_macm.py
+++ b/examples/02_meta-analyses/07_macm.py
@@ -12,6 +12,7 @@
 analysis in which task-independent "connectivity" is assessed by selecting
 studies within a larger database based on locations of report coordinates.
 """
+
 import nibabel as nib
 import numpy as np
 from nilearn import datasets, image, plotting
@@ -71,6 +72,7 @@
     threshold=3.09,
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
 )
 
 ###############################################################################
@@ -85,4 +87,4 @@
 xyz = dset.coordinates[["x", "y", "z"]].values
 scale = SCALE(xyz=xyz, n_iters=10000, n_cores=1, kernel__n=20)
 results = scale.fit(dset_sel)
-plotting.plot_stat_map(results.get_map("z"), draw_cross=False, cmap="RdBu_r")
+plotting.plot_stat_map(results.get_map("z"), draw_cross=False, cmap="RdBu_r", symmetric_cbar=True)
diff --git a/examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py b/examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py
index dd6bc02c3..5f544d87a 100644
--- a/examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py
+++ b/examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py
@@ -19,6 +19,7 @@
 5. Compare the two samples with a subtraction analysis.
 6. Compare the two within-sample meta-analyses with a conjunction analysis.
 """
+
 import os
 from pathlib import Path
 
@@ -71,6 +72,7 @@
     title="Semantic knowledge",
     threshold=2.326,  # cluster-level p < .01, one-tailed
     cmap="RdBu_r",
+    symmetric_cbar=True,
     vmax=4,
     axes=axes[0],
     figure=fig,
@@ -86,6 +88,7 @@
     title="Semantic relatedness",
     threshold=2.326,  # cluster-level p < .01, one-tailed
     cmap="RdBu_r",
+    symmetric_cbar=True,
     vmax=4,
     axes=axes[1],
     figure=fig,
@@ -189,6 +192,7 @@
     title="Conjunction",
     threshold=2.326,  # cluster-level p < .01, one-tailed
     cmap="RdBu_r",
+    symmetric_cbar=True,
     vmax=4,
 )
 
diff --git a/examples/02_meta-analyses/10_plot_cbma_workflow.py b/examples/02_meta-analyses/10_plot_cbma_workflow.py
index d153727a5..5b0bbdccf 100644
--- a/examples/02_meta-analyses/10_plot_cbma_workflow.py
+++ b/examples/02_meta-analyses/10_plot_cbma_workflow.py
@@ -11,6 +11,7 @@
 This tutorial will walk you through using a CBMA workflow function which puts together
 the fundamental steps of a CBMA meta-analysis.
 """
+
 import os
 from pathlib import Path
 
@@ -62,6 +63,7 @@
     display_mode="z",
     threshold=1.65,  # voxel_thresh p < .05, one-tailed
     cmap="RdBu_r",
+    symmetric_cbar=True,
     vmax=4,
 )
 plt.show()
diff --git a/examples/02_meta-analyses/11_plot_cbmr.py b/examples/02_meta-analyses/11_plot_cbmr.py
index e3ac5e248..2740b8303 100644
--- a/examples/02_meta-analyses/11_plot_cbmr.py
+++ b/examples/02_meta-analyses/11_plot_cbmr.py
@@ -113,6 +113,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     title="Schizophrenia with drug treatment",
     threshold=1e-4,
     vmax=1e-3,
@@ -122,6 +123,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     title="Schizophrenia without drug treatment",
     threshold=1e-4,
     vmax=1e-3,
@@ -131,6 +133,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     title="Depression with drug treatment",
     threshold=1e-4,
     vmax=1e-3,
@@ -140,6 +143,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     title="Depression without drug treatment",
     threshold=1e-4,
     vmax=1e-3,
@@ -173,6 +177,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     title="SchizophreniaYes",
     threshold=scipy.stats.norm.isf(0.05),
     vmax=30,
@@ -183,6 +188,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     title="SchizophreniaNo",
     threshold=scipy.stats.norm.isf(0.05),
     vmax=30,
@@ -193,6 +199,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     title="DepressionYes",
     threshold=scipy.stats.norm.isf(0.05),
     vmax=30,
@@ -203,6 +210,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     title="DepressionNo",
     threshold=scipy.stats.norm.isf(0.05),
     vmax=30,
@@ -235,6 +243,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     title="Schizophrenia with drug treatment (FDR corrected)",
     threshold=scipy.stats.norm.isf(0.05),
     vmax=30,
@@ -245,6 +254,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     title="Schizophrenia without drug treatment (FDR corrected)",
     threshold=scipy.stats.norm.isf(0.05),
     vmax=30,
@@ -255,6 +265,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     title="Depression with drug treatment (FDR corrected)",
     threshold=scipy.stats.norm.isf(0.05),
     vmax=30,
@@ -265,6 +276,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     title="Depression without drug treatment (FDR corrected)",
     threshold=scipy.stats.norm.isf(0.05),
     vmax=30,
@@ -300,6 +312,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     title="Drug Treatment Effect for Schizophrenia",
     threshold=scipy.stats.norm.isf(0.4),
     vmax=2,
@@ -310,6 +323,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     title="Untreated Schizophrenia vs. Untreated Depression",
     threshold=scipy.stats.norm.isf(0.4),
     vmax=2,
@@ -320,6 +334,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     title="Drug Treatment Effect for Depression",
     threshold=scipy.stats.norm.isf(0.4),
     vmax=2,
@@ -365,6 +380,7 @@
     cut_coords=[0, 0, -8],
     draw_cross=False,
     cmap="RdBu_r",
+    symmetric_cbar=True,
     title="GLH_groups_0",
     threshold=scipy.stats.norm.isf(0.4),
 )
diff --git a/examples/02_meta-analyses/12_plot_ibma_workflow.py b/examples/02_meta-analyses/12_plot_ibma_workflow.py
index a98beed41..ea0899a39 100644
--- a/examples/02_meta-analyses/12_plot_ibma_workflow.py
+++ b/examples/02_meta-analyses/12_plot_ibma_workflow.py
@@ -11,6 +11,7 @@
 This tutorial will walk you through using a IBMA workflow function which puts together
 the fundamental steps of a IBMA meta-analysis.
 """
+
 import os
 from pathlib import Path
 
@@ -64,6 +65,7 @@
     display_mode="z",
     threshold=1.65,  # voxel_thresh p < .05, one-tailed
     cmap="RdBu_r",
+    symmetric_cbar=True,
     vmax=4,
 )
 plt.show()
diff --git a/examples/03_annotation/04_plot_gclda.py b/examples/03_annotation/04_plot_gclda.py
index f6da02b39..7396625fe 100644
--- a/examples/03_annotation/04_plot_gclda.py
+++ b/examples/03_annotation/04_plot_gclda.py
@@ -15,6 +15,7 @@
     If you are interested in using GCLDA, we recommend using a large dataset
     like Neurosynth, and training with at least 10k iterations.
 """
+
 import os
 
 import nibabel as nib
@@ -81,6 +82,7 @@
         draw_cross=False,
         colorbar=False,
         annotate=False,
+        symmetric_cbar=True,
         title=f"Topic {i_topic + 1}",
     )
 
@@ -89,7 +91,7 @@
 # -----------------------------------------------------------------------------
 text = "dorsal anterior cingulate cortex"
 encoded_img, _ = decode.encode.gclda_encode(model, text)
-plotting.plot_stat_map(encoded_img, draw_cross=False)
+plotting.plot_stat_map(encoded_img, draw_cross=False, symmetric_cbar=True)
 
 ###############################################################################
 #
diff --git a/nimare/reports/figures.py b/nimare/reports/figures.py
index 4e896a51b..44f038d50 100644
--- a/nimare/reports/figures.py
+++ b/nimare/reports/figures.py
@@ -139,6 +139,7 @@ def plot_static_brain(img, out_filename, threshold=1e-06):
         draw_cross=False,
         threshold=threshold,
         display_mode="mosaic",
+        symmetric_cbar=True,
     )
     fig.savefig(out_filename, dpi=300)
     fig.close()
@@ -270,7 +271,13 @@ def plot_interactive_brain(img, out_filename, threshold=1e-06):
     _check_extention(out_filename, [".html"])
 
     template = datasets.load_mni152_template(resolution=1)
-    html_view = view_img(img, bg_img=template, black_bg=False, threshold=threshold)
+    html_view = view_img(
+        img,
+        bg_img=template,
+        black_bg=False,
+        threshold=threshold,
+        symmetric_cbar=True,
+    )
     html_view.save_as_html(out_filename)
 
 
@@ -608,6 +615,7 @@ def _plot_relcov_map(maps_arr, masker, out_filename):
         alpha=0.7,
         colorbar=True,
         cmap="Blues",
+        symmetric_cbar=False,
         vmin=0,
         vmax=1,
         display_mode="mosaic",
@@ -637,6 +645,7 @@ def _plot_dof_map(dof_map, out_filename):
         alpha=0.7,
         colorbar=True,
         cmap="YlOrRd",
+        symmetric_cbar=False,
         vmin=0,
         display_mode="mosaic",
     )