From fafc682443fd256d2f299fa8b8cb5d6d4fa4188e Mon Sep 17 00:00:00 2001
From: Taylor Salo <salot@pennmedicine.upenn.edu>
Date: Mon, 9 Feb 2026 15:28:08 -0500
Subject: [PATCH 1/9] Parallelize component figure creation.

---
 tedana/reporting/static_figures.py | 161 ++++++++++++++++-------------
 tedana/workflows/ica_reclassify.py |   2 -
 tedana/workflows/tedana.py         |   3 +-
 3 files changed, 93 insertions(+), 73 deletions(-)

diff --git a/tedana/reporting/static_figures.py b/tedana/reporting/static_figures.py
index ccdfc3a25..1b0065ecd 100644
--- a/tedana/reporting/static_figures.py
+++ b/tedana/reporting/static_figures.py
@@ -9,6 +9,7 @@
 import nibabel as nb
 import numpy as np
 import pandas as pd
+from joblib import Parallel, delayed
 
 matplotlib.use("AGG")
 import matplotlib.pyplot as plt
@@ -320,7 +321,81 @@ def plot_component(
     plt.close(fig)
 
 
-def comp_figures(ts, mask, component_table, mixing, io_generator, png_cmap):
+def _generate_single_component_figure(
+    compnum,
+    component_table,
+    mixing,
+    component_betas_img,
+    tr,
+    png_cmap,
+    out_dir,
+    prefix,
+):
+    """Generate a figure for a single component.
+
+    Parameters
+    ----------
+    compnum : int
+        The component number to plot.
+    component_table : (C x M) :obj:`pandas.DataFrame`
+        Component metric table.
+    mixing : (C x T) array_like
+        Mixing matrix for converting input data to component space.
+    component_betas_img : :obj:`nibabel.Nifti1Image`
+        4D image of the component beta maps.
+    tr : float
+        Repetition time of the time series.
+    png_cmap : str
+        Colormap to use for the spatial map.
+    out_dir : str
+        Output directory path.
+    prefix : str
+        Prefix for the output file name.
+    """
+    classification = component_table.loc[compnum, "classification"]
+    classification_tags = str(component_table.loc[compnum, "classification_tags"])
+
+    color_map = {"accepted": "g", "rejected": "r", "ignored": "k"}
+    line_color = color_map.get(classification, "0.75")
+
+    if classification in color_map:
+        expl_text = f"{classification} reason(s): {classification_tags}"
+    else:
+        expl_text = "other classification"
+
+    # Title will include variance from component_table
+    comp_var = f"{component_table.loc[compnum, 'variance explained']:.2f}"
+    comp_kappa = f"{component_table.loc[compnum, 'kappa']:.2f}"
+    comp_rho = f"{component_table.loc[compnum, 'rho']:.2f}"
+
+    plt_title = (
+        f"Comp. {compnum}: variance: {comp_var}%, kappa: {comp_kappa}, "
+        f"rho: {comp_rho}, {expl_text}"
+    )
+
+    component_timeseries = mixing[:, compnum]
+
+    # Get fft and freqs for this component
+    # adapted from @dangom
+    spectrum, freqs = utils.get_spectrum(component_timeseries, tr)
+
+    plot_name = f"{prefix}comp_{str(compnum).zfill(3)}.png"
+    compplot_name = os.path.join(out_dir, "figures", plot_name)
+
+    plot_component(
+        stat_img=component_betas_img.slicer[..., compnum],
+        component_timeseries=component_timeseries,
+        power_spectrum=spectrum,
+        frequencies=freqs,
+        tr=tr,
+        classification_color=line_color,
+        png_cmap=png_cmap,
+        title=plt_title,
+        out_file=compplot_name,
+    )
+
+
+def comp_figures(component_table, mixing, io_generator, png_cmap, n_threads=1):
     """Create static figures that highlight certain aspects of tedana processing.
 
     This includes a figure for each component showing the component time course,
@@ -328,10 +403,6 @@ def comp_figures(ts, mask, component_table, mixing, io_generator, png_cmap):
 
     Parameters
     ----------
-    ts : (S x T) array_like
-        Time series from which to derive ICA betas
-    mask : (S,) array_like
-        Boolean mask array
     component_table : (C x M) :obj:`pandas.DataFrame`
         Component metric table. One row for each component, with a column for
         each metric. The index should be the component number.
@@ -340,78 +411,30 @@ def comp_figures(ts, mask, component_table, mixing, io_generator, png_cmap):
         is components and `T` is the same as in `data`
     io_generator : :obj:`tedana.io.OutputGenerator`
         Output Generator object to use for this workflow
+    png_cmap : str
+        Colormap to use for the spatial map.
+    n_threads : int, optional
+        Number of threads to use for parallel processing. Default is 1.
     """
-    # regenerate the beta images
-    component_maps_arr = stats.get_coeffs(ts, mixing, mask)
-    component_maps_arr = component_maps_arr.reshape(
-        io_generator.reference_img.shape[:3] + component_maps_arr.shape[1:],
-    )
+    component_betas_file = io_generator.get_name("ICA components img")
+    component_betas_img = nb.load(component_betas_file)
 
     # Get repetition time from reference image
     tr = io_generator.reference_img.header.get_zooms()[-1]
 
-    # Remove trailing ';' from rationale column
-    # component_table["rationale"] = component_table["rationale"].str.rstrip(";")
-    for compnum in component_table.index.values:
-        if component_table.loc[compnum, "classification"] == "accepted":
-            line_color = "g"
-            expl_text = "accepted reason(s): " + str(
-                component_table.loc[compnum, "classification_tags"]
-            )
-
-        elif component_table.loc[compnum, "classification"] == "rejected":
-            line_color = "r"
-            expl_text = "rejected reason(s): " + str(
-                component_table.loc[compnum, "classification_tags"]
-            )
-
-        elif component_table.loc[compnum, "classification"] == "ignored":
-            line_color = "k"
-            expl_text = "ignored reason(s): " + str(
-                component_table.loc[compnum, "classification_tags"]
-            )
-
-        else:
-            # Classification not added
-            # If new, this will keep code running
-            line_color = "0.75"
-            expl_text = "other classification"
-
-        # Title will include variance from component_table
-        comp_var = f"{component_table.loc[compnum, 'variance explained']:.2f}"
-        comp_kappa = f"{component_table.loc[compnum, 'kappa']:.2f}"
-        comp_rho = f"{component_table.loc[compnum, 'rho']:.2f}"
-
-        plt_title = (
-            f"Comp. {compnum}: variance: {comp_var}%, kappa: {comp_kappa}, "
-            f"rho: {comp_rho}, {expl_text}"
-        )
-        component_img = nb.Nifti1Image(
-            component_maps_arr[:, :, :, compnum],
-            affine=io_generator.reference_img.affine,
-            header=io_generator.reference_img.header,
-        )
-
-        component_timeseries = mixing[:, compnum]
-
-        # Get fft and freqs for this component
-        # adapted from @dangom
-        spectrum, freqs = utils.get_spectrum(component_timeseries, tr)
-
-        plot_name = f"{io_generator.prefix}comp_{str(compnum).zfill(3)}.png"
-        compplot_name = os.path.join(io_generator.out_dir, "figures", plot_name)
-
-        plot_component(
-            stat_img=component_img,
-            component_timeseries=component_timeseries,
-            power_spectrum=spectrum,
-            frequencies=freqs,
+    Parallel(n_jobs=n_threads)(
+        delayed(_generate_single_component_figure)(
+            compnum=compnum,
+            component_table=component_table,
+            mixing=mixing,
+            component_betas_img=component_betas_img,
             tr=tr,
-            classification_color=line_color,
             png_cmap=png_cmap,
-            title=plt_title,
-            out_file=compplot_name,
+            out_dir=io_generator.out_dir,
+            prefix=io_generator.prefix,
         )
+        for compnum in component_table.index.values
+    )
 
 
 def pca_results(criteria, n_components, all_varex, io_generator):
diff --git a/tedana/workflows/ica_reclassify.py b/tedana/workflows/ica_reclassify.py
index 5e18803ca..7c9776ca5 100644
--- a/tedana/workflows/ica_reclassify.py
+++ b/tedana/workflows/ica_reclassify.py
@@ -590,8 +590,6 @@ def ica_reclassify_workflow(
             gscontrol=gscontrol,
         )
         reporting.static_figures.comp_figures(
-            data_optcom,
-            mask=mask_denoise,
             component_table=component_table,
             mixing=mixing_orig,
             io_generator=io_generator,
diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py
index 4e0918c19..83e3d082e 100644
--- a/tedana/workflows/tedana.py
+++ b/tedana/workflows/tedana.py
@@ -1184,12 +1184,11 @@ def tedana_workflow(
             gscontrol=gscontrol,
         )
         reporting.static_figures.comp_figures(
-            data_optcom,
-            mask=mask_denoise,
             component_table=component_table,
             mixing=mixing_orig,
             io_generator=io_generator,
             png_cmap=png_cmap,
+            n_threads=n_threads,
         )
         reporting.static_figures.plot_t2star_and_s0(io_generator=io_generator, mask=mask_denoise)
         if t2smap is None:

From 536aba45c579fd94289d68a1b4bb708f1c5cf919 Mon Sep 17 00:00:00 2001
From: Taylor Salo <salot@pennmedicine.upenn.edu>
Date: Mon, 9 Feb 2026 15:33:52 -0500
Subject: [PATCH 2/9] Remove unused import.

---
 tedana/reporting/static_figures.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tedana/reporting/static_figures.py b/tedana/reporting/static_figures.py
index 1b0065ecd..6cbd2d312 100644
--- a/tedana/reporting/static_figures.py
+++ b/tedana/reporting/static_figures.py
@@ -15,7 +15,7 @@
 import matplotlib.pyplot as plt
 from nilearn import masking, plotting
 
-from tedana import io, stats, utils
+from tedana import io, utils
 
 LGR = logging.getLogger("GENERAL")
 MPL_LGR = logging.getLogger("matplotlib")

From a58de5867a0bd9daab5650145c49e18f1eb2f6af Mon Sep 17 00:00:00 2001
From: Taylor Salo <salot@pennmedicine.upenn.edu>
Date: Tue, 10 Feb 2026 08:25:17 -0500
Subject: [PATCH 3/9] Don't pickle niimgs.

---
 tedana/reporting/static_figures.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/tedana/reporting/static_figures.py b/tedana/reporting/static_figures.py
index 6cbd2d312..f288de587 100644
--- a/tedana/reporting/static_figures.py
+++ b/tedana/reporting/static_figures.py
@@ -325,7 +325,7 @@ def _generate_single_component_figure(
     compnum,
     component_table,
     mixing,
-    component_betas_img,
+    component_betas_file,
     tr,
     png_cmap,
     out_dir,
@@ -341,8 +341,8 @@ def _generate_single_component_figure(
         Component metric table.
     mixing : (C x T) array_like
         Mixing matrix for converting input data to component space.
-    component_betas_img : :obj:`nibabel.Nifti1Image`
-        4D image of the component beta maps.
+    component_betas_file : :obj:`str`
+        Path to the 4D image of the component beta maps.
     tr : float
         Repetition time of the time series.
     png_cmap : str
@@ -382,6 +382,8 @@ def _generate_single_component_figure(
     plot_name = f"{prefix}comp_{str(compnum).zfill(3)}.png"
     compplot_name = os.path.join(out_dir, "figures", plot_name)
 
+    component_betas_img = nb.load(component_betas_file)
+
     plot_component(
         stat_img=component_betas_img.slicer[..., compnum],
         component_timeseries=component_timeseries,
@@ -417,7 +419,6 @@ def comp_figures(component_table, mixing, io_generator, png_cmap, n_threads=1):
         Number of threads to use for parallel processing. Default is 1.
     """
     component_betas_file = io_generator.get_name("ICA components img")
-    component_betas_img = nb.load(component_betas_file)
 
     # Get repetition time from reference image
     tr = io_generator.reference_img.header.get_zooms()[-1]
@@ -427,7 +428,7 @@ def comp_figures(component_table, mixing, io_generator, png_cmap, n_threads=1):
             compnum=compnum,
             component_table=component_table,
             mixing=mixing,
-            component_betas_img=component_betas_img,
+            component_betas_file=component_betas_file,
             tr=tr,
             png_cmap=png_cmap,
             out_dir=io_generator.out_dir,

From 890a098f33eb593697182cc738a985001bca399e Mon Sep 17 00:00:00 2001
From: Taylor Salo <salot@pennmedicine.upenn.edu>
Date: Tue, 10 Feb 2026 09:06:47 -0500
Subject: [PATCH 4/9] Fix docstrings.

---
 tedana/reporting/static_figures.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tedana/reporting/static_figures.py b/tedana/reporting/static_figures.py
index f288de587..40cfffcdf 100644
--- a/tedana/reporting/static_figures.py
+++ b/tedana/reporting/static_figures.py
@@ -339,7 +339,7 @@ def _generate_single_component_figure(
         The component number to plot.
     component_table : (C x M) :obj:`pandas.DataFrame`
         Component metric table.
-    mixing : (C x T) array_like
+    mixing : (T x C) array_like
         Mixing matrix for converting input data to component space.
     component_betas_file : :obj:`str`
         Path to the 4D image of the component beta maps.
@@ -408,7 +408,7 @@ def comp_figures(component_table, mixing, io_generator, png_cmap, n_threads=1):
     component_table : (C x M) :obj:`pandas.DataFrame`
         Component metric table. One row for each component, with a column for
         each metric. The index should be the component number.
-    mixing : (C x T) array_like
+    mixing : (T x C) array_like
         Mixing matrix for converting input data to component space, where `C`
         is components and `T` is the same as in `data`
     io_generator : :obj:`tedana.io.OutputGenerator`

From 44bf91a7ba0b64f4531bedad07daac171e05ec92 Mon Sep 17 00:00:00 2001
From: Taylor Salo <salot@pennmedicine.upenn.edu>
Date: Wed, 11 Feb 2026 09:30:27 -0500
Subject: [PATCH 5/9] Update tedana/reporting/static_figures.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 tedana/reporting/static_figures.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/tedana/reporting/static_figures.py b/tedana/reporting/static_figures.py
index 6cbd2d312..04be8d5f0 100644
--- a/tedana/reporting/static_figures.py
+++ b/tedana/reporting/static_figures.py
@@ -422,7 +422,13 @@ def comp_figures(component_table, mixing, io_generator, png_cmap, n_threads=1):
     # Get repetition time from reference image
     tr = io_generator.reference_img.header.get_zooms()[-1]
 
-    Parallel(n_jobs=n_threads)(
+    # Normalize n_threads to joblib semantics: None/<=0 means use all cores
+    if n_threads is None or n_threads <= 0:
+        n_jobs = -1
+    else:
+        n_jobs = n_threads
+
+    Parallel(n_jobs=n_jobs)(
         delayed(_generate_single_component_figure)(
             compnum=compnum,
             component_table=component_table,

From bbd7f58d113ec3fab4d4cfd6e2318f703661c2e4 Mon Sep 17 00:00:00 2001
From: Taylor Salo <tsalo90@gmail.com>
Date: Fri, 3 Apr 2026 14:26:39 -0400
Subject: [PATCH 6/9] Work on addressing requests.

---
 tedana/io.py                         | 22 +++++++++++++++++++---
 tedana/resources/config/outputs.json |  4 ++++
 tedana/workflows/tedana.py           |  4 +++-
 3 files changed, 26 insertions(+), 4 deletions(-)

diff --git a/tedana/io.py b/tedana/io.py
index 35861f7e2..41267cfaf 100644
--- a/tedana/io.py
+++ b/tedana/io.py
@@ -728,7 +728,7 @@ def write_split_ts(data, mixing, mask, component_table, io_generator, echo=0):
     LGR.info(f"Writing denoised time series: {fout}")
 
 
-def writeresults(data_optcom, mask, component_table, mixing, io_generator):
+def writeresults(data_optcom, mask, component_table, mixing, io_generator, mixing_orig=None):
     """Denoise `ts` and save all resulting files to disk.
 
     Parameters
@@ -746,6 +746,9 @@ def writeresults(data_optcom, mask, component_table, mixing, io_generator):
         is components and `T` is the same as in `data`
     ref_img : :obj:`str` or img_like
         Reference image to dictate how outputs are saved to disk
+    mixing_orig : (C x T) array_like or None, optional
+        Non-orthogonalized mixing matrix.
+        If defined, it will be used for the component maps, but not for denoising.
 
     See Also
     --------
@@ -766,17 +769,30 @@ def writeresults(data_optcom, mask, component_table, mixing, io_generator):
     desc-ICAAccepted_components.nii.gz           Spatial component maps for accepted components.
     desc-ICAAccepted_stat-z_components.nii.gz    Z-normalized spatial component maps
                                                  for accepted components.
+    desc-ICAOrth_components.nii.gz               Spatial component maps for all components from
+                                                 orthogonalized mixing matrix. Only created if
+                                                 tedort and verbose are enabled.
     =========================================    ===============================================
     """
     acc = component_table[component_table.classification == "accepted"].index.values
     write_split_ts(data_optcom, mixing, mask, component_table, io_generator)
 
-    ts_pes = get_coeffs(data_optcom, mixing, mask)
+    if mixing_orig:
+        mixing_for_denoising = mixing
+        mixing_for_components = mixing_orig
+    else:
+        mixing_for_denoising = mixing_for_components = mixing
+
+    ts_pes = get_coeffs(data_optcom, mixing_for_components, mask)
     fout = io_generator.save_file(ts_pes, "ICA components img")
     LGR.info(f"Writing full ICA coefficient feature set: {fout}")
+    if io_generator.verbose and mixing_orig:
+        ts_pes_orth = get_coeffs(data_optcom, mixing_for_denoising, mask)
+        fout = io_generator.save_file(ts_pes_orth, "ICA orthogonalized components img")
+        LGR.info(f"Writing orthogonalized ICA coefficient feature set: {fout}")
 
     data_optcom_z = stats.zscore(data_optcom[mask, :], axis=-1)
-    mixing_z = stats.zscore(mixing, axis=0)
+    mixing_z = stats.zscore(mixing_for_components, axis=0)
     betas_oc = utils.unmask(get_coeffs(data_optcom_z, mixing_z), mask)
     fout = io_generator.save_file(betas_oc, "z-scored ICA components img")
     del data_optcom_z, mixing_z
diff --git a/tedana/resources/config/outputs.json b/tedana/resources/config/outputs.json
index 9a6ad6ea3..ea9548ffe 100644
--- a/tedana/resources/config/outputs.json
+++ b/tedana/resources/config/outputs.json
@@ -43,6 +43,10 @@
         "orig": "ica_components",
         "bidsv1.5.0": "desc-ICA_components"
     },
+    "ICA orthogonalized components img": {
+        "orig": "ica_orth_components",
+        "bidsv1.5.0": "desc-ICAOrth_stat-z_components"
+    },
     "z-scored PCA components img": {
         "orig": "pca_components",
         "bidsv1.5.0": "desc-PCA_stat-z_components"
diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py
index 83e3d082e..23a8b750e 100644
--- a/tedana/workflows/tedana.py
+++ b/tedana/workflows/tedana.py
@@ -1054,6 +1054,7 @@ def tedana_workflow(
     component_table = selector.component_table_
 
     mixing_orig = mixing.copy()
+    mixing_orig_df = mixing_df.copy()
     if tedort:
         comps_accepted = selector.accepted_comps_
         comps_rejected = selector.rejected_comps_
@@ -1082,6 +1083,7 @@ def tedana_workflow(
         component_table=component_table,
         mixing=mixing,
         io_generator=io_generator,
+        mixing_orig=mixing_orig if tedort else None,
     )
 
     if "mir" in gscontrol:
@@ -1213,7 +1215,7 @@ def tedana_workflow(
             # Compute correlations between external regressors and ICA components
             corr_df = metrics.external.compute_external_regressor_correlations(
                 external_regressors=external_regressors,
-                mixing=mixing_df,
+                mixing=mixing_orig_df,
             )
 
             # Plot the heatmap

From d174d674199557826a78b14292521b853597d425 Mon Sep 17 00:00:00 2001
From: Taylor Salo <tsalo90@gmail.com>
Date: Fri, 3 Apr 2026 14:48:59 -0400
Subject: [PATCH 7/9] Update io.py

---
 tedana/io.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tedana/io.py b/tedana/io.py
index d652e7b6f..472ae5ffb 100644
--- a/tedana/io.py
+++ b/tedana/io.py
@@ -818,7 +818,7 @@ def writeresults(data_optcom, mask, component_table, mixing, io_generator, mixin
     acc = component_table[component_table.classification == "accepted"].index.values
     write_split_ts(data_optcom, mixing, mask, component_table, io_generator)
 
-    if mixing_orig:
+    if mixing_orig is not None:
         mixing_for_denoising = mixing
         mixing_for_components = mixing_orig
     else:
@@ -827,7 +827,7 @@ def writeresults(data_optcom, mask, component_table, mixing, io_generator, mixin
     ts_pes = get_coeffs(data_optcom, mixing_for_components)
     fout = io_generator.save_file(ts_pes, "ICA components img")
     LGR.info(f"Writing full ICA coefficient feature set: {fout}")
-    if io_generator.verbose and mixing_orig:
+    if io_generator.verbose and (mixing_orig is not None):
         ts_pes_orth = get_coeffs(data_optcom, mixing_for_denoising)
         fout = io_generator.save_file(ts_pes_orth, "ICA orthogonalized components img")
         LGR.info(f"Writing orthogonalized ICA coefficient feature set: {fout}")

From d99befed122b8c52ba5529bf00bc99fa5768fb1b Mon Sep 17 00:00:00 2001
From: Taylor Salo <salot@pennmedicine.upenn.edu>
Date: Thu, 23 Apr 2026 08:34:35 -0400
Subject: [PATCH 8/9] Update nih_five_echo_outputs_verbose.txt

---
 tedana/tests/data/nih_five_echo_outputs_verbose.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tedana/tests/data/nih_five_echo_outputs_verbose.txt b/tedana/tests/data/nih_five_echo_outputs_verbose.txt
index f8c0bf03c..54dfdd56e 100644
--- a/tedana/tests/data/nih_five_echo_outputs_verbose.txt
+++ b/tedana/tests/data/nih_five_echo_outputs_verbose.txt
@@ -14,6 +14,7 @@ sub-01_desc-tedana_registry.json
 sub-01_desc-ICACrossComponent_metrics.json
 sub-01_desc-ICA_status_table.tsv
 sub-01_desc-ICA_decision_tree.json
+sub-01_desc-ICAOrth_stat-z_components.nii.gz
 sub-01_desc-ICAS0_stat-F_statmap.nii.gz
 sub-01_desc-ICAT2_stat-F_statmap.nii.gz
 sub-01_desc-ICA_mixing.tsv

From 74229f968675a799900e872c4e87f40fc34c1b9a Mon Sep 17 00:00:00 2001
From: Taylor Salo <tsalo90@gmail.com>
Date: Mon, 20 Jul 2026 14:21:05 -0400
Subject: [PATCH 9/9] Address review.

---
 tedana/io.py                                  |  6 +-
 tedana/reporting/static_figures.py            | 36 +++++------
 tedana/resources/config/outputs.json          |  2 +-
 .../data/nih_five_echo_outputs_verbose.txt    |  2 +-
 tedana/tests/test_io.py                       | 43 +++++++++++++
 tedana/tests/test_reporting.py                | 62 +++++++++++++++++++
 tedana/workflows/ica_reclassify.py            |  1 +
 tedana/workflows/tedana.py                    |  1 -
 8 files changed, 126 insertions(+), 27 deletions(-)

diff --git a/tedana/io.py b/tedana/io.py
index 472ae5ffb..25a4c7c3f 100644
--- a/tedana/io.py
+++ b/tedana/io.py
@@ -782,12 +782,12 @@ def writeresults(data_optcom, mask, component_table, mixing, io_generator, mixin
         Component metric table. One row for each component, with a column for
         each metric. Requires at least two columns: "component" and
         "classification".
-    mixing : (C x T) array_like
+    mixing : (T x C) array_like
         Mixing matrix for converting input data to component space, where `C`
         is components and `T` is the same as in `data`
-    ref_img : :obj:`str` or img_like
+    io_generator : :obj:`tedana.io.OutputGenerator`
         Reference image to dictate how outputs are saved to disk
-    mixing_orig : (C x T) array_like or None, optional
+    mixing_orig : (T x C) array_like or None, optional
         Non-orthogonalized mixing matrix.
         If defined, it will be used for the component maps, but not for denoising.
 
diff --git a/tedana/reporting/static_figures.py b/tedana/reporting/static_figures.py
index d8bb50967..6704bc7fc 100644
--- a/tedana/reporting/static_figures.py
+++ b/tedana/reporting/static_figures.py
@@ -9,7 +9,6 @@
 import nibabel as nb
 import numpy as np
 import pandas as pd
-from joblib import Parallel, delayed
 
 matplotlib.use("AGG")
 import matplotlib.pyplot as plt
@@ -338,7 +337,7 @@ def _generate_single_component_figure(
     compnum,
     component_table,
     mixing,
-    component_betas_file,
+    component_img,
     tr,
     png_cmap,
     out_dir,
@@ -354,8 +353,8 @@ def _generate_single_component_figure(
         Component metric table.
     mixing : (T x C) array_like
         Mixing matrix for converting input data to component space.
-    component_betas_file : :obj:`str`
-        Path to the 4D image of the component beta maps.
+    component_img : :obj:`nibabel.spatialimages.SpatialImage`
+        Spatial map for the component.
     tr : float
         Repetition time of the time series.
     png_cmap : str
@@ -395,10 +394,8 @@ def _generate_single_component_figure(
     plot_name = f"{prefix}comp_{str(compnum).zfill(3)}.png"
     compplot_name = os.path.join(out_dir, "figures", plot_name)
 
-    component_betas_img = nb.load(component_betas_file)
-
     plot_component(
-        stat_img=component_betas_img.slicer[..., compnum],
+        stat_img=component_img,
         component_timeseries=component_timeseries,
         power_spectrum=spectrum,
         frequencies=freqs,
@@ -410,7 +407,7 @@ def _generate_single_component_figure(
     )
 
 
-def comp_figures(component_table, mixing, io_generator, png_cmap, n_threads=1):
+def comp_figures(component_table, mixing, io_generator, png_cmap):
     """Create static figures that highlight certain aspects of tedana processing.
 
     This includes a figure for each component showing the component time course,
@@ -428,33 +425,30 @@ def comp_figures(component_table, mixing, io_generator, png_cmap, n_threads=1):
         Output Generator object to use for this workflow
     png_cmap : str
         Colormap to use for the spatial map.
-    n_threads : int, optional
-        Number of threads to use for parallel processing. Default is 1.
     """
     component_betas_file = io_generator.get_name("ICA components img")
+    component_betas_img = nb.load(component_betas_file)
+    component_betas_arr = np.asanyarray(component_betas_img.dataobj)
 
     # Get repetition time from reference image
     tr = io_generator.reference_img.header.get_zooms()[-1]
 
-    # Normalize n_threads to joblib semantics: None/<=0 means use all cores
-    if n_threads is None or n_threads <= 0:
-        n_jobs = -1
-    else:
-        n_jobs = n_threads
-
-    Parallel(n_jobs=n_jobs)(
-        delayed(_generate_single_component_figure)(
+    for compnum in component_table.index.values:
+        component_img = nb.Nifti1Image(
+            component_betas_arr[..., compnum],
+            affine=component_betas_img.affine,
+            header=component_betas_img.header,
+        )
+        _generate_single_component_figure(
             compnum=compnum,
             component_table=component_table,
             mixing=mixing,
-            component_betas_file=component_betas_file,
+            component_img=component_img,
             tr=tr,
             png_cmap=png_cmap,
             out_dir=io_generator.out_dir,
             prefix=io_generator.prefix,
         )
-        for compnum in component_table.index.values
-    )
 
 
 def pca_results(criteria, n_components, all_varex, io_generator):
diff --git a/tedana/resources/config/outputs.json b/tedana/resources/config/outputs.json
index ea9548ffe..c28c13e6e 100644
--- a/tedana/resources/config/outputs.json
+++ b/tedana/resources/config/outputs.json
@@ -45,7 +45,7 @@
     },
     "ICA orthogonalized components img": {
         "orig": "ica_orth_components",
-        "bidsv1.5.0": "desc-ICAOrth_stat-z_components"
+        "bidsv1.5.0": "desc-ICAOrth_components"
     },
     "z-scored PCA components img": {
         "orig": "pca_components",
diff --git a/tedana/tests/data/nih_five_echo_outputs_verbose.txt b/tedana/tests/data/nih_five_echo_outputs_verbose.txt
index 54dfdd56e..21e5f3e7a 100644
--- a/tedana/tests/data/nih_five_echo_outputs_verbose.txt
+++ b/tedana/tests/data/nih_five_echo_outputs_verbose.txt
@@ -14,7 +14,7 @@ sub-01_desc-tedana_registry.json
 sub-01_desc-ICACrossComponent_metrics.json
 sub-01_desc-ICA_status_table.tsv
 sub-01_desc-ICA_decision_tree.json
-sub-01_desc-ICAOrth_stat-z_components.nii.gz
+sub-01_desc-ICAOrth_components.nii.gz
 sub-01_desc-ICAS0_stat-F_statmap.nii.gz
 sub-01_desc-ICAT2_stat-F_statmap.nii.gz
 sub-01_desc-ICA_mixing.tsv
diff --git a/tedana/tests/test_io.py b/tedana/tests/test_io.py
index 305e96b8f..5e337ed61 100644
--- a/tedana/tests/test_io.py
+++ b/tedana/tests/test_io.py
@@ -118,6 +118,49 @@ def test_smoke_write_split_ts():
     os.remove(fname)
 
 
+def test_writeresults_uses_original_mixing_for_component_maps():
+    """Component maps should use the original mixing matrix when one is provided."""
+    rng = np.random.default_rng(42)
+    n_samples, n_times, n_components = 5, 8, 3
+    data = rng.normal(size=(n_samples, n_times))
+    mask = np.ones(n_samples, dtype=bool)
+    mixing = rng.normal(size=(n_times, n_components))
+    mixing_orig = rng.normal(size=(n_times, n_components))
+    component_table = pd.DataFrame(
+        {"classification": ["rejected", "ignored", "rejected"]},
+    )
+    saved = {}
+
+    class StubOutputGenerator:
+        verbose = True
+
+        def save_file(self, data, description, **kwargs):
+            del kwargs
+            saved[description] = np.asarray(data).copy()
+            return description
+
+    with mock.patch.object(me, "write_split_ts") as write_split_ts:
+        me.writeresults(
+            data_optcom=data,
+            mask=mask,
+            component_table=component_table,
+            mixing=mixing,
+            io_generator=StubOutputGenerator(),
+            mixing_orig=mixing_orig,
+        )
+
+    write_split_ts.assert_called_once()
+    np.testing.assert_allclose(
+        saved["ICA components img"],
+        me.get_coeffs(data, mixing_orig),
+    )
+    np.testing.assert_allclose(
+        saved["ICA orthogonalized components img"],
+        me.get_coeffs(data, mixing),
+    )
+    assert "z-scored ICA components img" in saved
+
+
 def test_load_data_nilearn_multi_echo_fastpath(tmp_path):
     """`load_data_nilearn` should return (Mb, E, T) for multi-echo files."""
     affine = np.eye(4)
diff --git a/tedana/tests/test_reporting.py b/tedana/tests/test_reporting.py
index fddd443a4..111ebc520 100644
--- a/tedana/tests/test_reporting.py
+++ b/tedana/tests/test_reporting.py
@@ -4,7 +4,10 @@
 import re
 import shutil
 from os.path import dirname, join
+from pathlib import Path
+from types import SimpleNamespace
 
+import nibabel as nb
 import numpy as np
 import pandas as pd
 import pytest
@@ -21,6 +24,65 @@ def test_smoke_trim_edge_zeros():
     assert reporting.static_figures._trim_edge_zeros(arr) is not None
 
 
+def test_comp_figures_smoke(tmp_path, monkeypatch):
+    """Load component maps once and generate one PNG per component."""
+    component_maps = np.arange(16, dtype=np.float32).reshape(2, 2, 2, 2)
+    component_maps_file = tmp_path / "components.nii.gz"
+    nb.save(nb.Nifti1Image(component_maps, np.eye(4)), component_maps_file)
+
+    reference_img = nb.Nifti1Image(np.zeros((2, 2, 2, 4)), np.eye(4))
+    component_table = pd.DataFrame(
+        {
+            "classification": ["accepted", "rejected"],
+            "classification_tags": ["Likely BOLD", "Low variance"],
+            "variance explained": [60.0, 40.0],
+            "kappa": [10.0, 2.0],
+            "rho": [2.0, 10.0],
+        },
+    )
+    mixing = np.arange(8, dtype=float).reshape(4, 2)
+    figures_dir = tmp_path / "figures"
+    figures_dir.mkdir()
+    io_generator = SimpleNamespace(
+        get_name=lambda name: (str(component_maps_file) if name == "ICA components img" else None),
+        reference_img=reference_img,
+        out_dir=str(tmp_path),
+        prefix="sub-01_",
+    )
+    original_load = nb.load
+    load_calls = []
+    plotted_maps = []
+
+    def tracked_load(filename):
+        load_calls.append(filename)
+        return original_load(filename)
+
+    def fake_plot_component(**kwargs):
+        assert kwargs["stat_img"].shape == (2, 2, 2)
+        plotted_maps.append(kwargs["stat_img"].get_fdata())
+        Path(kwargs["out_file"]).touch()
+
+    monkeypatch.setattr(reporting.static_figures.nb, "load", tracked_load)
+    monkeypatch.setattr(reporting.static_figures, "plot_component", fake_plot_component)
+
+    reporting.static_figures.comp_figures(
+        component_table=component_table,
+        mixing=mixing,
+        io_generator=io_generator,
+        png_cmap="coolwarm",
+    )
+
+    assert load_calls == [str(component_maps_file)]
+    np.testing.assert_array_equal(
+        np.stack(plotted_maps, axis=-1),
+        component_maps,
+    )
+    assert sorted(path.name for path in figures_dir.glob("*.png")) == [
+        "sub-01_comp_000.png",
+        "sub-01_comp_001.png",
+    ]
+
+
 def test_calculate_rejected_components_impact():
     selector = sample_selector()
     mixing = sample_mixing_matrix()
diff --git a/tedana/workflows/ica_reclassify.py b/tedana/workflows/ica_reclassify.py
index d5ed6aa7c..fdd3c3178 100644
--- a/tedana/workflows/ica_reclassify.py
+++ b/tedana/workflows/ica_reclassify.py
@@ -586,6 +586,7 @@ def ica_reclassify_workflow(
         component_table=component_table,
         mixing=mixing,
         io_generator=io_generator,
+        mixing_orig=mixing_orig if tedort else None,
     )
 
     if "mir" in gscontrol:
diff --git a/tedana/workflows/tedana.py b/tedana/workflows/tedana.py
index af6870768..d56e8574e 100644
--- a/tedana/workflows/tedana.py
+++ b/tedana/workflows/tedana.py
@@ -1174,7 +1174,6 @@ def tedana_workflow(
             mixing=mixing_orig,
             io_generator=io_generator,
             png_cmap=png_cmap,
-            n_threads=n_threads,
         )
         reporting.static_figures.plot_t2star_and_s0(
             io_generator=io_generator,