ME-ICA · tsalo · Oct 22, 2021 · Oct 22, 2021 · Oct 22, 2021 · Oct 22, 2021
diff --git a/docs/approach.rst b/docs/approach.rst
@@ -366,7 +366,7 @@ presented at MRITogether 2022 for a hands-on tutorial.
 Removal of spatially diffuse noise (optional)
 *********************************************
 
-:func:`tedana.gscontrol.gscontrol_raw`, :func:`tedana.gscontrol.gscontrol_mmix`
+:func:`tedana.gscontrol.gscontrol_raw`, :func:`tedana.gscontrol.minimum_image_regression`
 
 Due to the constraints of ICA, TEDICA is able to identify and remove spatially
 localized noise components, but it cannot identify components that are spread

diff --git a/docs/outputs.rst b/docs/outputs.rst
@@ -23,7 +23,7 @@ future processing. ``tedana`` allows for multiple file naming conventions. The k
 and naming options for each convention that can be set using the ``--convention`` option
 are in `outputs.json`_. The output of ``tedana`` also includes a file called
 ``registry.json`` or ``desc-tedana_registry.json`` that includes the keys and the matching
-file names for the output. The table below lists both these keys and the default 
+file names for the output. The table below lists both these keys and the default
 "BIDS Derivatives" file names.
 
 .. _outputs.json: https://github.com/ME-ICA/tedana/blob/main/tedana/resources/config/outputs.json
@@ -107,14 +107,8 @@ tedana_report.html                                                           The
                                                                              status of each component after each node was run.
                                                                              Columns are only added for runs where component
                                                                              statuses can change.
-"ICA accepted components img": desc-ICAAccepted_components.nii.gz            High-kappa ICA coefficient feature set
-"z-scored ICA accepted components img": desc-ICAAcceptedZ_components.nii.gz  Z-normalized spatial component maps
 report.txt                                                                   A summary report for the workflow with relevant
                                                                              citations.
-"low kappa ts img": desc-optcomRejected_bold.nii.gz                          Combined time series from rejected components.
-"high kappa ts img": desc-optcomAccepted_bold.nii.gz                         High-kappa time series. This dataset does not
-                                                                             include thermal noise or low variance components.
-                                                                             Not the recommended dataset for analysis.
 references.bib                                                               The BibTeX entries for references cited in
                                                                              report.txt.
 
@@ -133,7 +127,7 @@ Key: Filename
 "limited s0 img": desc-limited_S0map.nii.gz                                                    Limited S0 map/time series.
                                                                                                Unlike the full S0 maps, if only one 1 echo contains
                                                                                                good data the limited map will have NaN
-"whitened img": desc-optcom_whitened_bold                                                      The optimally combined data after whitening
+"whitened img": desc-optcomWhitened_bold                                                       The optimally combined data after whitening
 "echo weight [PCA|ICA] maps split img": echo-[echo]_desc-[PCA|ICA]_components.nii.gz           Echo-wise PCA/ICA component weight maps.
 "echo T2 [PCA|ICA] split img": echo-[echo]_desc-[PCA|ICA]T2ModelPredictions_components.nii.gz  Component- and voxel-wise R2-model predictions,
                                                                                                separated by echo.
@@ -145,8 +139,6 @@ Key: Filename
 "[PCA|ICA] component F-T2 img": desc-[PCA|ICA]T2_stat-F_statmap.nii.gz                         F-statistic map for each component, for the T2 model.
 "PCA reduced img": desc-optcomPCAReduced_bold.nii.gz                                           Optimally combined data after dimensionality
                                                                                                reduction with PCA. This is the input to the ICA.
-"high kappa ts split img": echo-[echo]_desc-Accepted_bold.nii.gz                               High-Kappa time series for echo number ``echo``
-"low kappa ts split img": echo-[echo]_desc-Rejected_bold.nii.gz                                Low-Kappa time series for echo number ``echo``
 "denoised ts split img": echo-[echo]_desc-Denoised_bold.nii.gz                                 Denoised time series for echo number ``echo``
 =============================================================================================  =====================================================
 
@@ -187,8 +179,6 @@ Key: Filename
 "t1 like img": desc-T1likeEffect_min.nii.gz                                              T1-like effect
 "mir denoised img": desc-optcomMIRDenoised_bold.nii.gz                                   Denoised time series after MIR
 "ICA MIR mixing tsv": desc-ICAMIRDenoised_mixing.tsv                                     ICA mixing matrix after MIR
-"ICA accepted mir component weights img": desc-ICAAcceptedMIRDenoised_components.nii.gz  high-kappa components after MIR
-"ICA accepted mir denoised img": desc-optcomAcceptedMIRDenoised_bold.nii.gz              high-kappa time series after MIR
 =======================================================================================  =====================================================
 
 .. _classification-output-descriptions:

diff --git a/tedana/decomposition/pca.py b/tedana/decomposition/pca.py
@@ -124,55 +124,49 @@ def tedpca(
 
     Notes
     -----
-    ======================    =================================================
+    ======================    ===================================================================
     Notation                  Meaning
-    ======================    =================================================
-    :math:`\\kappa`            Component pseudo-F statistic for TE-dependent
-                              (BOLD) model.
-    :math:`\\rho`              Component pseudo-F statistic for TE-independent
-                              (artifact) model.
+    ======================    ===================================================================
+    :math:`\\kappa`            Component pseudo-F statistic for TE-dependent (BOLD) model.
+    :math:`\\rho`              Component pseudo-F statistic for TE-independent (artifact) model.
     :math:`v`                 Voxel
     :math:`V`                 Total number of voxels in mask
     :math:`\\zeta`             Something
     :math:`c`                 Component
     :math:`p`                 Something else
-    ======================    =================================================
+    ======================    ===================================================================
 
     Steps:
 
-    1.  Variance normalize either multi-echo or optimally combined data,
-        depending on settings.
+    1.  Variance normalize either multi-echo or optimally combined data, depending on settings.
     2.  Decompose normalized data using PCA or SVD.
-    3.  Compute :math:`{\\kappa}` and :math:`{\\rho}`:
+    3.  Compute :math:`{\\kappa}` and :math:`{\\rho}`.
 
-            .. math::
-                {\\kappa}_c = \\frac{\\sum_{v}^V {\\zeta}_{c,v}^p * \
-                      F_{c,v,R_2^*}}{\\sum {\\zeta}_{c,v}^p}
+        .. math::
+            {\\kappa}_c = \\frac{\\sum_{v}^V {\\zeta}_{c,v}^p * \
+                    F_{c,v,R_2^*}}{\\sum {\\zeta}_{c,v}^p}
 
-                {\\rho}_c = \\frac{\\sum_{v}^V {\\zeta}_{c,v}^p * \
-                      F_{c,v,S_0}}{\\sum {\\zeta}_{c,v}^p}
+            {\\rho}_c = \\frac{\\sum_{v}^V {\\zeta}_{c,v}^p * \
+                    F_{c,v,S_0}}{\\sum {\\zeta}_{c,v}^p}
 
     4.  Some other stuff. Something about elbows.
-    5.  Classify components as thermal noise if they meet both of the
-        following criteria:
+    5.  Classify components as thermal noise if they meet both of the following criteria.
 
             - Nonsignificant :math:`{\\kappa}` and :math:`{\\rho}`.
             - Nonsignificant variance explained.
 
     Generated Files
     ---------------
 
-    ===========================    =============================================
+    ===========================    ======================================================
     Default Filename               Content
-    ===========================    =============================================
+    ===========================    ======================================================
     desc-PCA_metrics.tsv           PCA component table
-    desc-PCA_metrics.json          Metadata sidecar file describing the
-                                   component table
+    desc-PCA_metrics.json          Metadata sidecar file describing the component table
     desc-PCA_mixing.tsv            PCA mixing matrix
     desc-PCA_components.nii.gz     Component weight maps
-    desc-PCA_decomposition.json    Metadata sidecar file describing the PCA
-                                   decomposition
-    ===========================    =============================================
+    desc-PCA_decomposition.json    Metadata sidecar file describing the PCA decomposition
+    ===========================    ======================================================
 
     References
     ----------

diff --git a/tedana/gscontrol.py b/tedana/gscontrol.py
@@ -44,6 +44,12 @@ def gscontrol_raw(catd, optcom, n_echos, io_generator, dtrank=4):
         Input `catd` with global signal removed from time series
     dm_optcom : (S x T) array_like
         Input `optcom` with global signal removed from time series
+
+    Warning
+    -------
+    This GSR approach is rather dissimilar to the more common approach of extracting global signal
+    using a gray matter or whole brain mask, and regressing the mean of that signal from the rest
+    of the brain.
     """
     LGR.info("Applying amplitude-based T1 equilibration correction")
     RepLGR.info(
@@ -160,9 +166,7 @@ def minimum_image_regression(optcom_ts, mmix, mask, comptable, io_generator):
     Filename                  Content
     ======================    =================================================
     sphis_hik.nii             T1-like effect
-    hik_ts_OC_MIR.nii         T1-corrected BOLD (high-Kappa) time series
     dn_ts_OC_MIR.nii          Denoised version of T1-corrected time series
-    betas_hik_OC_MIR.nii      T1 global signal-corrected components
     meica_mix_MIR.1D          T1 global signal-corrected mixing matrix
     ======================    =================================================
 
@@ -204,25 +208,14 @@ def minimum_image_regression(optcom_ts, mmix, mask, comptable, io_generator):
     mehk_noT1gs = mehk_ts - np.dot(
         np.linalg.lstsq(glob_sig.T, mehk_ts.T, rcond=None)[0].T, glob_sig
     )
-    hik_ts = mehk_noT1gs * optcom_std  # rescale
-    io_generator.save_file(utils.unmask(hik_ts, mask), "ICA accepted mir denoised img")
 
     # Make denoised version of T1-corrected time series
     medn_ts = optcom_mean + ((mehk_noT1gs + resid) * optcom_std)
     io_generator.save_file(utils.unmask(medn_ts, mask), "mir denoised img")
 
     # Orthogonalize mixing matrix w.r.t. T1-GS
     mmix_noT1gs = mmix.T - np.dot(np.linalg.lstsq(glob_sig.T, mmix, rcond=None)[0].T, glob_sig)
-    mmix_noT1gs_z = stats.zscore(mmix_noT1gs, axis=-1)
-    mmix_noT1gs_z = np.vstack(
-        (np.atleast_2d(np.ones(max(glob_sig.shape))), glob_sig, mmix_noT1gs_z)
-    )
 
-    # Write T1-corrected components and mixing matrix
-    comp_pes_norm = np.linalg.lstsq(mmix_noT1gs_z.T, optcom_z.T, rcond=None)[0].T
-    io_generator.save_file(
-        utils.unmask(comp_pes_norm[:, 2:], mask),
-        "ICA accepted mir component weights img",
-    )
+    # Write T1-corrected mixing matrix
     mixing_df = pd.DataFrame(data=mmix_noT1gs.T, columns=comptable["Component"].values)
     io_generator.save_file(mixing_df, "ICA MIR mixing tsv")