diff --git a/.codespell-ignore-words b/.codespell-ignore-words
new file mode 100644
index 00000000..90a99ec5
--- /dev/null
+++ b/.codespell-ignore-words
@@ -0,0 +1,2 @@
+# Citations
+Sepulcre
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index ddfbf143..60601758 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -58,9 +58,9 @@ jobs:
         with:
           path: ~/.cache/templateflow
           key: templateflow
-      - uses: prefix-dev/setup-pixi@1b2de7f3351f171c8b4dfeb558c639cb58ed4ec0
+      - uses: prefix-dev/setup-pixi@5185adfbffb4bd703da3010310260805d89ebb11
         with:
-          pixi-version: v0.53.0
+          pixi-version: v0.70.2
           cache: true
           environments: test
           frozen: true
@@ -88,9 +88,9 @@ jobs:
         with:
           path: ~/.cache/templateflow
           key: templateflow
-      - uses: prefix-dev/setup-pixi@1b2de7f3351f171c8b4dfeb558c639cb58ed4ec0
+      - uses: prefix-dev/setup-pixi@5185adfbffb4bd703da3010310260805d89ebb11
         with:
-          pixi-version: v0.53.0
+          pixi-version: v0.70.2
           cache: true
           environments: test
           frozen: true
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index e20fbe4e..3a7f90b2 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -28,7 +28,7 @@ repos:
     -   id: codespell
         additional_dependencies:
         - tomli
-        args: ["--skip=*.svg"]
+        args: ["--skip=*.svg", "-I .codespell-ignore-words"]
         exclude: 'paper/.*'
 -   repo: https://github.com/hadolint/hadolint
     rev: v2.14.0
diff --git a/CITATION.cff b/CITATION.cff
index 36f93da3..16cd6e45 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -9,7 +9,7 @@ abstract:
 
 message: "If you use this software, please cite it as below."
 
-repository-code: "https://github.com/simexp/wonkyconn"
+repository-code: "https://github.com/HALFpipe/wonkyconn"
 
 contact:
   - email: htwangtw@gmail.com
diff --git a/Dockerfile b/Dockerfile
index 95499d77..5cdc86b3 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,4 +1,4 @@
-FROM ghcr.io/prefix-dev/pixi:0.53.0 AS build
+FROM ghcr.io/prefix-dev/pixi:0.70.2 AS build
 
 SHELL ["/bin/bash", "-o", "pipefail", "-c"]
 
diff --git a/README.md b/README.md
index bc620066..a121fe64 100644
--- a/README.md
+++ b/README.md
@@ -1,19 +1,33 @@
 [![pre-commit.ci status](https://results.pre-commit.ci/badge/github/HALFpipe/wonkyconn/main.svg)](https://results.pre-commit.ci/latest/github/HALFpipe/wonkyconn/main)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-![https://github.com/psf/black](https://img.shields.io/badge/code%20style-black-000000.svg)
+[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 [![Documentation Status](https://readthedocs.org/projects/wonkyconn/badge/?version=latest)](https://wonkyconn.readthedocs.io/en/latest/?badge=latest)
 
 # wonkyconn
 
-Evaluating the residual motion and analytic insights on fMRI connectome and visualise reports.
+Wonkyconn evaluates residual motion and analytic quality in group-level fMRI connectomes and creates visualization reports to compare the generated metrics.
 
 The project is based on the code of [`SIMEXP/fmriprep-denoise-benchmark`](https://github.com/SIMEXP/fmriprep-denoise-benchmark) and the publication by [Wang et al. 2024](http://dx.doi.org/10.1371/journal.pcbi.1011942).
 
 **This project is in alpha stage! This is not a stable project yet.**
 
+## Quick start
+
+Recommended container:
+
+```bash
+apptainer build wonkyconn-edge.sif docker://halfpipe/wonkyconn:edge
+```
+
+See the [documentation](https://wonkyconn.readthedocs.io/en/latest/) for usage, methods, and outputs.
+
 ## Textual GUI (optional)
 
-- Install the optional dependency: `pip install "wonkyconn[textual]"`.
-- Launch the GUI instead of running headless: `wonkyconn --textual`.
-- The GUI requires an interactive terminal (TTY). If you are running inside a non-interactive job (for example, on an HPC batch node), run the CLI without `--textual` instead.
-- All required inputs are collected in the GUI; existing CLI defaults and behaviour are preserved once you click **Run**.
+- Install: `pip install "wonkyconn[textual]`
+- Launch: `wonkyconn --textual`
+- The CLI is required for headless batch jobs or multi-atlas runs.
+
+> [!WARNING]
+>
+> - The textual UI requires an interactive terminal (TTY) if you are running Wonkyconn inside a non-interactive terminal (e.g., HPC batch job), use the CLI with `--textual`.
+> - Current GUI supports one atlas entry. Use the CLI for multi-atlas runs.
diff --git a/docs/source/changes.md b/docs/source/changes.md
index 18213e1f..e99aaed1 100644
--- a/docs/source/changes.md
+++ b/docs/source/changes.md
@@ -53,7 +53,7 @@
 
 ## 25.12.0-alpha
 
-**Released December 2026**
+**Released December 2025**
 
 ### New
 
diff --git a/docs/source/conf.py b/docs/source/conf.py
index fd6e4ca6..c355d282 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -5,9 +5,10 @@
 import os
 import sys
 
+sys.path.insert(0, os.path.abspath("../.."))
+
 from wonkyconn import __copyright__, __packagename__, __version__
 
-sys.path.insert(0, os.path.abspath(".."))
 # -- Project information -----------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
@@ -29,7 +30,7 @@
 ]
 
 templates_path = ["_templates"]
-exclude_patterns = []
+exclude_patterns = ["seann_workflow_notes.md"]
 
 
 # -- Options for HTML output -------------------------------------------------
@@ -38,4 +39,4 @@
 html_theme = "sphinx_rtd_theme"
 
 # -- Options for myst_parser -------------------------------------------------
-myst_enable_extensions = ["colon_fence"]
+myst_enable_extensions = ["colon_fence", "alert"]
diff --git a/docs/source/contributing.md b/docs/source/contributing.md
index dac94ec7..1c1c5e95 100644
--- a/docs/source/contributing.md
+++ b/docs/source/contributing.md
@@ -1,14 +1,15 @@
-# Contribution
+# Contributing
 
-## Setting up your environment for development
+## Development environment
 
-1. Fork the repository from github and clone your fork locally (see [here](https://docs.github.com/en/authentication/connecting-to-github-with-ssh/adding-a-new-ssh-key-to-your-github-account) to setup your ssh key):
+Fork the repository from GitHub and clone your fork locally (see [here](https://docs.github.com/en/authentication/connecting-to-github-with-ssh/adding-a-new-ssh-key-to-your-github-account) to set up your SSH key):
 
 ```bash
 git clone git@github.com:<your_username>/wonkyconn.git
+cd wonkyconn
 ```
 
-2. Set up a development environment. We recommend using [pixi](https://pixi.sh):
+Set up a development environment. We recommend using [pixi](https://pixi.sh):
 
 ```bash
 pixi install
@@ -17,74 +18,102 @@ pixi install
 Alternatively, you can use a virtual environment with pip:
 
 ```bash
-python3 -m venv wonkyconn
-source wonkyconn/bin/activate
-pip install -e ".[dev]"
+python3 -m venv .venv
+source .venv/bin/activate
+pip install -e ".[dev,docs]"
 ```
 
-3. Install the data required for testing.
+Install pre-commit hooks to automatically format code and run checks before each commit:
 
-The test data is managed with [datalad](https://www.datalad.org/). After installing datalad, fetch the test data:
+```bash
+pre-commit install
+```
+
+## Downloading test data
+
+Fetch all test data (this will take a bit of time and 1GB of disk space):
 
 ```bash
-datalad get wonkyconn/data/test_data
+# From the root of the repository
+datalad get .
 ```
 
-4. Install pre-commit hooks to run all the checks before each commit.
+### Demo command
+
+Light-mode demo:
 
 ```bash
-pre-commit install
+wonkyconn data/halfpipe test-output group \
+    --phenotypes data/halfpipe/participants.tsv \
+    --atlas Schaefer2018Combined data/atlases/atlas-Schaefer2018Combined_dseg.nii.gz \
+    --light-mode \
+    --verbosity 2
 ```
 
 ## Contributing to code
 
-This is a very generic workflow.
+We use a common [feature branch workflow](https://www.python4data.science/en/latest/productive/git/workflows/feature-branches.html) for development.
 
 1. Comment on an existing issue or open a new issue referencing your addition.
 
-:::{tip}
-Review and discussion on new code can begin well before the work is complete, and the more discussion the better!
-The development team may prefer a different path than you've outlined, so it's better to discuss it and get approval at the early stage of your work.
-:::
+    > [!tip]
+    > Review and discussion on new code can begin well before the work is complete, and the more discussion the better! The development team may prefer a different path than you’ve outlined, so it’s better to discuss it and get approval at the early stage of your work.
 
-2. On your fork, create a new branch from main:
+1. Create a new branch from `main`:
 
-```bash
-git checkout -b your_branch
-```
+    ```bash
+    git switch -c my_feature main
+    ```
+
+1. Make the changes in your branch.
+1. Run the tests locally to confirm your changes don’t break anything. See the [Testing](#running-tests) section below for details.
+1. Push your branch to your fork.
+
+    > [!caution]
+    > If this is the first commit, you might want to set up the remote tracking.
+    >
+    > ```bash
+    > git push origin HEAD --set-upstream
+    > ```
 
-3. Make the changes, lint, and format.
+1. Submit a pull request to the `main` branch of the original repository; follow the guidelines in the [Pull requests](#pull-requests-guidelines) section below.
+1. Check that all continuous integration checks pass. If not, review the logs to identify and fix the issue.
+1. Respond to any review comments and make necessary changes.
 
-4. Commit your changes on this branch.
+(running-tests)=
 
-If you want to make sure all the tests will be run by github continuous integration,
-make sure that your commit message contains `full_test`.
+### Running tests
 
-5. Run the tests locally; you can run specific tests to speed up the process:
+Default unit tests:
 
 ```bash
-pytest -v wonkyconn/tests/test_connectome.py::test_calculate_intranetwork_correlation
+pixi run unittest
 ```
 
-6. push your changes to your online fork. If this is the first commit, you might want to set up the remote tracking:
+Light smoke tests:
 
 ```bash
-git push origin HEAD --set-upstream
+pixi run smoketestlight
 ```
-In the future you can simply do:
+
+Full non-smoke selection:
 
 ```bash
-git push
+pixi run fulltest
 ```
-7. Submit a pull request from your fork of the repository.
 
-8. Check that all continuous integration tests pass.
+Specific pytest file:
 
-## Contributing to documentation
+```bash
+pytest -v wonkyconn/tests/test_correlation.py
+```
 
-The workflow is the same as code contributions, with some minor differences.
+Learn more about [specifying which test to run](https://docs.pytest.org/en/stable/how-to/usage.html#select-tests)
 
-1. Install the `docs` dependencies.
+### Building the docs
+
+You can build the documentation locally to review your changes before pushing.
+The documentation is built at `docs/build/html`.
 
 Using pixi:
 
@@ -92,71 +121,44 @@ Using pixi:
 pixi run -e docs sphinx-build docs/source docs/build/html
 ```
 
-Or with pip:
+or with a local Python environment:
 
 ```bash
-pip install -e ".[docs]"
 cd docs
 make html
 ```
 
-2. After making changes, review the generated HTML locally.
+(pull-requests-guidelines)=
 
-3. Submit your changes.
+## Pull requests guidelines
 
-## Writing a PR
+Keep pull requests focused. Use draft PRs for early design or method review.
 
-When opening a pull request, please use one of the following prefixes:
+PR prefixes:
 
 - **[ENH]** for enhancements
 - **[FIX]** for bug fixes
 - **[TEST]** for new or updated tests
-- **[DOCS]** for new or updated documentation
+- **[DOCS]** for documentation changes
 - **[STYL]** for stylistic changes
-- **[MAINT]** for refactoring existing code, any maintainace related things
-
-Pull requests should be submitted early and often (please don't mix too many unrelated changes within one PR)!
-If your pull request is not yet ready to be merged, please submit as a drafted PR.
-This tells the development team that your pull request is a "work-in-progress", and that you plan to continue working on it.
+- **[MAINT]** for maintenance or refactoring
 
-One your PR is ready a member of the development team will review your changes to confirm that they can be merged into the main codebase.
+## Making a release
 
-### Running the demo
+This project currently does not publish releases on PyPI. We tag versions on the repository for user reference. Upon pushing a new tag, a GitHub workflow will build and publish a new Docker image.
 
-You can run a demo by downloading some test data.
+### Preparing a release
 
-Run the following from the root of the repository.
+Create a local release branch from `main`:
 
 ```bash
-datalad get wonkyconn/data/test_data
+git fetch upstream main
+git checkout -b REL-x.y.z upstream/main
 ```
 
-```bash
-INPUT=wonkyconn/data/test_data/connectome_Schaefer2018
-OUTPUT=wonkyconn/data/test_data/test_output
-ATLAS_PATH=wonkyconn/data/test_data/connectome_Schaefer2018/atlases/sub-1/func/sub-1_seg-Schaefer2018400Parcels7Networks_dseg.nii.gz
-PHENOTYPE=wonkyconn/data/test_data/connectome_Schaefer2018/participants.tsv
-wonkyconn ${INPUT} ${OUTPUT} group \
-    --phenotypes ${PHENOTYPE} \
-    --atlas Schaefer2018400Parcels7Networks ${ATLAS_PATH} \
-    --group-by seg desc \
-    --verbosity 2
-```
-
-## Prepare a release
-
-Currently this project is not pushed to PyPi.
-We simply tag the version on the repository so users can reference the version of installation.
-The release process will trigger a new tagged docker build of the software.
+First, update the file `docs/source/changes.md` to make sure all the new features, enhancements, and bug fixes are included in their respective sections.
 
-Switch to a new branch locally:
-
-```bash
-git checkout -b REL-x.y.z
-```
-First we need to prepare the release by updating the file `wonkyconn/docs/changes.md` to make sure all the new features, enhancements, and bug fixes are included in their respective sections.
-
-Finally, we need to change the title from x.y.z.dev to x.y.z
+Finally, change the title from x.y.z.dev to x.y.z:
 
 ```markdown
 ## x.y.z
@@ -166,31 +168,31 @@ Finally, we need to change the title from x.y.z.dev to x.y.z
 ### New
 ...
 ```
+
 Add these changes and submit a PR:
 
 ```bash
-git add docs/
+git add docs/source/changes.md
 git commit -m "REL x.y.z"
 git push upstream REL-x.y.z
 ```
 
-Once the PR has been reviewed and merged, pull from master and tag the merge commit:
+Once the PR has been reviewed and merged, pull from `upstream/main` and tag the merge commit:
 
 ```bash
-git checkout main
-git pull upstream main
+git fetch upstream main
+git switch upstream/main
 git tag x.y.z
-git push upstream --tags
+git push origin --tags
 ```
 
-## Post-release
+### Post-release
 
-At this point, the release has been made.
+The release is done 🎉
 
-We also need to create a new section in `wonkyconn/docs/changes.md` with a title and the usual New, Enhancements, Bug Fixes, and Changes sections for the version currently under development:
+Create a new boilerplate section in `docs/source/changes.md` for the next release:
 
 ```markdown
-
 ## x.y.z+1.dev
 
 **Released MONTH YEAR**
@@ -204,4 +206,7 @@ We also need to create a new section in `wonkyconn/docs/changes.md` with a title
 ### Changes
 ```
 
+> [!caution]
+> Current releases use Git tags and container builds. Update this page if PyPI publishing is added.
+
 Based on contributing guidelines from the [STEMMRoleModels](https://github.com/KirstieJane/STEMMRoleModels/blob/gh-pages/CONTRIBUTING.md) project and [Nilearn contribution guidelines](https://nilearn.github.io/stable/development.html).
diff --git a/docs/source/glossary.md b/docs/source/glossary.md
new file mode 100644
index 00000000..e8b4cb27
--- /dev/null
+++ b/docs/source/glossary.md
@@ -0,0 +1,35 @@
+# Glossary
+
+```{glossary}
+Atlas
+    A parcellation image that maps connectivity matrix rows and columns to brain regions or networks.
+
+[BEP017](https://bids.neuroimaging.io/extensions/beps/bep_017.html)
+    A BIDS Extension Proposal that defines conventions for storing imaging-derived connectivity data.
+
+[BIDS](https://bids.neuroimaging.io/index.html)
+    The Brain Imaging Data Structure, a standard layout for organizing neuroimaging datasets and derivatives.
+
+Connectivity matrix
+    A region-by-region table of connectivity values, usually correlations, that summarizes relationships between brain regions for one subject, task, or run.
+
+DMN
+    The default mode network, one of the Yeo 7 resting-state networks used by Wonkyconn's network organization metrics.
+
+Gradient similarity
+    A group-level metric comparing connectivity gradients from the input data with packaged reference gradient templates.
+
+[HALFpipe](https://github.com/HALFpipe/HALFpipe)
+    A pipeline for fMRI preprocessing and analysis. Wonkyconn supports HALFpipe-style derivative layouts.
+
+QC-FC
+    Quality-control functional connectivity, a test of whether connectivity edge weights are associated with head motion.
+
+    Power, J. D., Schlaggar, B. L., & Petersen, S. E. (2015). Recent progress and outstanding issues in motion correction in resting state fMRI. NeuroImage, 105, 536–551. [https://doi.org/10.1016/j.neuroimage.2014.10.044](https://doi.org/10.1016/j.neuroimage.2014.10.044)
+
+
+[Yeo 7 networks](https://bids.neuroimaging.io/index.html)
+    A seven-network functional brain parcellation used by Wonkyconn to summarize within-network connectivity and DMN-related metrics.
+    Yeo, B. T. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., Fischl, B., Liu, H., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. [https://doi.org/10.1152/jn.00338.2011](https://doi.org/10.1152/jn.00338.2011)
+
+```
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 038e17b0..195a3ffb 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -3,14 +3,14 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-Welcome to wonkyconn's documentation!
-===========================================
+Wonkyconn Documentation
+=======================
 
-Boom
+Wonkyconn evaluates residual motion and analytic quality in group-level fMRI connectomes and creates visualization reports to compare the generated metrics.
 
 .. toctree::
    :maxdepth: 1
-   :caption: Contents
+   :caption: User guide
 
    installation.md
    usage.md
@@ -19,10 +19,11 @@ Boom
 
 .. toctree::
    :maxdepth: 1
-   :caption: Contribution and maintenance
+   :caption: Reference and maintenance
 
-   contributing.md
+   glossary.md
    api.rst
+   contributing.md
    changes.md
 
 Indices and tables
@@ -30,4 +31,3 @@ Indices and tables
 
 * :ref:`genindex`
 * :ref:`modindex`
-* :ref:`search`
diff --git a/docs/source/installation.md b/docs/source/installation.md
index 0039abe9..3c0f917a 100644
--- a/docs/source/installation.md
+++ b/docs/source/installation.md
@@ -1,27 +1,36 @@
 # Installation
 
-## Quick start (container)
+Use the container unless you need a development install.
 
-Pull the latest image from docker hub (Recommended).
+## Container installation
 
-Apptainer (formerly known as Singularity; recommended):
+Build the latest container image (`halfpipe/wonkyconn:edge`) or a specific tag available on [Docker Hub](https://hub.docker.com/r/halfpipe/wonkyconn/tags).
+
+[Apptainer](https://apptainer.org/docs/user/latest/) (**recommended**):
 
 ```bash
 apptainer build wonkyconn-edge.simg docker://halfpipe/wonkyconn:edge
 ```
 
-Docker:
+> [!tip]
+> Apptainer was formerly known as Singularity. If you have an older version of Singularity, you can use the same command but replace `apptainer` with `singularity`.
+
+[Docker](https://docs.docker.com/):
+
 ```bash
 docker pull halfpipe/wonkyconn:edge
 ```
 
-## Install as a python package
+## Source installation
 
-Install the project in a Python environment:
+Install from Git:
 
 ```bash
-pip install git+https://github.com/halfpipe/wonkyconn.git
+pip install git+https://github.com/HALFpipe/wonkyconn.git
 ```
 
-This method is available for all versions.
-Change the tag based on version you would like to use.
+Install a specific tag:
+
+```bash
+pip install "wonkyconn @ git+https://github.com/HALFpipe/wonkyconn.git@26.02.0-alpha"
+```
diff --git a/docs/source/outputs.md b/docs/source/outputs.md
index c3654088..5fe7f7d9 100644
--- a/docs/source/outputs.md
+++ b/docs/source/outputs.md
@@ -1,30 +1,58 @@
 # Outputs
 
-Wonkyconn performs group-level analysis and writes the following files to the output directory.
+Wonkyconn writes a group-level summary figure, metrics table, and {term}`DMN` similarity tables to the output directory.
+
+> [!NOTE]
+> Grouping columns depend on the input dataset and appear in the output table.
+
+## `metrics.png`
+
+A visual summary of the group-level metrics (see [`metrics.tsv`](#metricstsv)).
+
+(metricstsv)=
 
 ## `metrics.tsv`
 
-A tab-separated file containing group-level quality control and connectivity metrics, indexed by the grouping variable (e.g. atlas segmentation). Columns include:
+One row per group. Grouping fields are included as index columns.
 
-- `median_absolute_qcfc` — Median absolute QC-FC correlation across edges.
-- `percentage_significant_qcfc` — Percentage of edges with significant QC-FC correlation.
-- `distance_dependence` — Correlation between QC-FC values and inter-node distance.
+- `seg` — {term}`Atlas` segmentation group, when present.
+- `feature` — {term}`HALFpipe` feature group, when present.
+- `atlas` — {term}`HALFpipe` {term}`atlas <Atlas>` group, when present.
+- `median_absolute_qcfc` — Median absolute {term}`QC-FC` correlation across edges.
+- `percentage_significant_qcfc` — Percentage of edges with significant {term}`QC-FC` correlation.
+- `distance_dependence` — Correlation between {term}`QC-FC` values and inter-node distance.
 - `gcor` — Global correlation (mean and SEM across subjects).
-- `dmn_vis_distance_vs_dmn_fpn` — Paired t-statistic comparing DMN–visual vs DMN–FPN mean connectivity distance.
-- `dmn_similarity_mean` — Mean correlation of individual connectivity patterns with the DMN template.
-- `dmn_similarity_std` — Standard deviation of the DMN similarity across subjects.
-- `degrees_of_freedom_loss_mean` — Mean degrees of freedom lost to denoising.
-- `degrees_of_freedom_loss_std` — Standard deviation of degrees of freedom loss.
+- `dmn_similarity_mean` — Mean correlation of individual connectivity patterns with the {term}`DMN` template.
+- `dmn_similarity_std` — Standard deviation of {term}`DMN` similarity across subjects.
+- `dmn_vis_distance_vs_dmn_fpn` — Paired t-statistic comparing {term}`DMN`-visual vs {term}`DMN`-FPN mean connectivity distance.
+- `confound_regression_percentage` — Estimated percentage of model DoF associated with confound regression.
+- `motion_scrubbing_percentage` — Estimated percentage of model DoF associated with motion scrubbing.
+- `nonsteady_states_detector_percentage` — Estimated percentage of model DoF associated with non-steady-state volume removal.
 - `sex_auc` — AUC for sex classification from connectivity (with CI bounds).
+- `sex_auc_ci_lower` — Lower confidence interval bound for sex AUC.
+- `sex_auc_ci_upper` — Upper confidence interval bound for sex AUC.
 - `sex_accuracy` — Accuracy for sex classification.
 - `age_mae` — Mean absolute error for age prediction from connectivity (with CI bounds).
-- `age_r2` — R² for age prediction.
-- `gradients_similarity` — Similarity of group-level connectivity gradients to a reference.
+- `age_mae_ci_lower` — Lower confidence interval bound for age MAE.
+- `age_mae_ci_upper` — Upper confidence interval bound for age MAE.
+- `age_r2` — {math}`R^2` for age prediction.
+- `gradients_similarity` — {term}`Gradient similarity` to a reference.
 
-## `metrics.png`
-
-A summary visualization of the group-level metrics.
+> [!NOTE]
+> In `--light-mode`, skipped age/sex and gradient metrics are left empty (`NaN`).
 
 ## `dmn_similarity_*.tsv`
 
-Per-subject DMN similarity statistics, including within-network mean connectivity, standard deviation, and correlation with the DMN template for each Yeo 7 network.
+Per-matrix {term}`DMN` similarity summaries. Current filenames:
+
+- `dmn_similarity_seg.tsv` for {term}`BEP017`-style inputs.
+- `dmn_similarity_feature-atlas.tsv` for {term}`HALFpipe` inputs.
+
+- `mean_yeo7-1` through `mean_yeo7-7` — Mean connectivity summaries for each {term}`Yeo 7 network <Yeo 7 networks>`.
+- `sd_yeo7-1` through `sd_yeo7-7` — Standard-deviation summaries for each {term}`Yeo 7 network <Yeo 7 networks>`.
+- `corr_with_dmn` — Average correlation of seed-based connectivity patterns with the {term}`DMN` mask.
+- `mean-diff_dmn_visual` — {term}`DMN`-visual connectivity difference.
+- `mean-diff_dmn_fpn` — {term}`DMN`-FPN connectivity difference.
+
+> [!WARNING]
+> {term}`DMN` TSV filenames are format-based. Check output files carefully for multi-group runs.
diff --git a/docs/source/usage.md b/docs/source/usage.md
index 826701f6..c04e6074 100644
--- a/docs/source/usage.md
+++ b/docs/source/usage.md
@@ -1,4 +1,65 @@
-# Usage Notes
+# Usage
+
+Wonkyconn runs group-level analyses on datasets with time-series files and {term}`connectivity matrices <Connectivity matrix>`.
+
+## Run with Apptainer (recommended)
+
+Build the latest container image (`halfpipe/wonkyconn:edge`) or a specific tag available on [Docker Hub](https://hub.docker.com/r/halfpipe/wonkyconn/tags):
+
+```bash
+VERSION="edge"; apptainer build wonkyconn-${VERSION}.sif docker://halfpipe/wonkyconn:${VERSION}
+```
+
+Run with explicit host-path bindings:
+
+```bash
+BIDS_DIR="/path/to/halfpipe/derivatives"
+OUTPUT_DIR="/path/to/wonkyconn_output"
+PHENOTYPES="/path/to/participants.tsv"
+ATLAS_PATH="/path/to/atlas-Schaefer2018Combined_dseg.nii.gz"
+SIF_IMG="/path/to/wonkyconn.sif"
+
+apptainer run --contain --cleanenv \
+    --bind ${BIDS_DIR} \
+    --bind ${ATLAS_PATH} \
+    --bind ${OUTPUT_DIR} \
+    ${SIF_IMG} ${BIDS_DIR} ${OUTPUT_DIR} group \
+    --phenotypes ${PHENOTYPES} \
+    --atlas SchaeferCombined ${ATLAS_PATH}
+```
+
+> [!IMPORTANT]
+>
+> - Your `participants.tsv` file must include `participant_id`, `age`, and `gender` columns.
+> - Wonkyconn expects the {term}`HALFpipe` input folder to be named `derivatives/halfpipe`.
+> - Mount every directory the container needs with `--bind`, for example `--bind /path/to/atlas:atlas`.
+> - For `--atlas`, use the same {term}`atlas <Atlas>` name used in {term}`HALFpipe`, as shown in filenames such as `sub-xxx_task-xxx_feature-xxx_atlas-NAME_desc-correlation.tsv`.
+
+## CPU and memory requirements
+
+As a general starting point, allocate 1–2 CPU cores and 10 GB of RAM for a typical group-level run. If resources are limited, consider using `--light-mode`, which skips the more compute- and memory-intensive metrics: age/sex prediction and {term}`gradient similarity <Gradient similarity>`.
+
+> [!WARNING]
+> Wonkyconn has not yet been benchmarked systematically across datasets.
+
+## Grouping behavior
+
+Wonkyconn groups results from the input filenames automatically. These groups appear in `metrics.tsv` and `metrics.png`. There is no `--group-by` option.
+
+## Textual user interface
+
+You can run wonkyconn with a textual user interface, using the `--textual` flag, to select the input files and options interactively. This is useful for users who are new to the command line or want to explore the options before running the analysis.
+
+First, you must install the optional dependencies:
+
+```bash
+pip install "wonkyconn[textual] @ git+https://github.com/HALFpipe/wonkyconn.git"
+```
+
+> [!WARNING]
+>
+> - The textual UI requires an interactive terminal (TTY) if you are running Wonkyconn inside a non-interactive terminal (e.g., HPC batch job), use the CLI with `--textual`.
+> - The current GUI supports one {term}`atlas <Atlas>` entry. Use the CLI for multi-atlas runs.
 
 ## Command line interface
 
diff --git a/docs/source/workflow.md b/docs/source/workflow.md
index 1cc19a91..b4dbfab6 100644
--- a/docs/source/workflow.md
+++ b/docs/source/workflow.md
@@ -6,7 +6,7 @@ The processing pipeline computes the following metrics for each group of connect
 
 - **QC-FC correlation** — correlation between framewise displacement and each edge, with median absolute value and percentage of significant edges.
 - **Distance dependence** — correlation between QC-FC values and Euclidean distance between atlas nodes.
-- **Global correlation (GCOR)** — average correlation across all pairs of time series, summarised as mean and SEM.
+- **Global correlation (GCOR)** — average correlation across all pairs of time series, summarized as mean and SEM.
 - **DMN similarity** — within-network mean connectivity for each Yeo 7 network, and correlation of seed-based connectivity patterns with the default mode network template.
 - **DMN distance t-statistic** — paired t-test comparing DMN–visual vs DMN–FPN mean connectivity distances.
 - **Degrees of freedom loss** — mean and standard deviation of temporal degrees of freedom lost to confound regression.
diff --git a/pixi.lock b/pixi.lock
index ca3a0cec..18a4f745 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -1,4 +1,7 @@
-version: 6
+version: 7
+platforms:
+- name: linux-64
+- name: osx-arm64
 environments:
   default:
     channels:
@@ -8,708 +11,718 @@ environments:
     - https://pypi.org/simple
     packages:
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+  - python >=3.10
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   license: Apache-2.0
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-  md5: c05d2d4b438ef09c55b291e062eddf79
+  - pkg:pypi/vtk?source=hash-mapping
+  size: 62382783
+  timestamp: 1780078470441
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/vtk-io-ffmpeg-9.6.2-py314he2b832b_2.conda
+  sha256: 9e1514376b1c9dd43324ab31d9a2d08fee8c1338f7e0191b62c564a139910aa7
+  md5: 6329fcd2e2991f645310d0f196ce72fe
   depends:
-  - python
-  - cffi >=1.11
-  - zstd >=1.5.7,<1.5.8.0a0
-  - __osx >=11.0
-  - python 3.12.* *_cpython
-  - python_abi 3.12.* *_cp312
-  - zstd >=1.5.7,<1.6.0a0
+  - vtk-base ==9.6.2 py314haf0c3d6_2
+  - ffmpeg
+  - ffmpeg >=8.1.1,<9.0a0
+  - python_abi 3.14.* *_cp314
   license: BSD-3-Clause
   license_family: BSD
-  purls:
-  - pkg:pypi/zstandard?source=hash-mapping
-  size: 391011
-  timestamp: 1757930161367
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.25.0-py313h9734d34_0.conda
-  sha256: 8a1bf8a66c05f724e8a56cb1918eae70bcb467a7c5d43818e37e04d86332c513
-  md5: ce17795bf104a29a2c7ed0bba7a804cb
+  purls: []
+  size: 95167
+  timestamp: 1780078470441
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.2.1-py314h6c2aa35_0.conda
+  sha256: 2f419160ec31de3ca3501e8a831a386a471f6c5e1dc43a57f3f3bcd1cf77e6b0
+  md5: b7af50eb697b32997d38bc0af06010d3
   depends:
-  - python
-  - cffi >=1.11
-  - zstd >=1.5.7,<1.5.8.0a0
   - __osx >=11.0
-  - python 3.13.* *_cp313
-  - zstd >=1.5.7,<1.6.0a0
-  - python_abi 3.13.* *_cp313
-  license: BSD-3-Clause
+  - python >=3.14,<3.15.0a0
+  - python >=3.14,<3.15.0a0 *_cp314
+  - python_abi 3.14.* *_cp314
+  license: BSD-2-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/zstandard?source=hash-mapping
-  size: 396477
-  timestamp: 1757930170468
-- conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda
-  sha256: a4166e3d8ff4e35932510aaff7aa90772f84b4d07e9f6f83c614cba7ceefe0eb
-  md5: 6432cb5d4ac0046c3ac0a8a0f95842f9
+  - pkg:pypi/wrapt?source=hash-mapping
+  size: 114356
+  timestamp: 1779477743708
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/x264-1!164.3095-h57fd34a_2.tar.bz2
+  sha256: debdf60bbcfa6a60201b12a1d53f36736821db281a28223a09e0685edcce105a
+  md5: b1f6dccde5d3a1f911960b6e567113ff
+  license: GPL-2.0-or-later
+  license_family: GPL
+  purls: []
+  size: 717038
+  timestamp: 1660323292329
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/x265-3.5-hbc6ce65_3.tar.bz2
+  sha256: 2fed6987dba7dee07bd9adc1a6f8e6c699efb851431bcb6ebad7de196e87841d
+  md5: b1f7f2780feffe310b068c021e8ff9b2
   depends:
-  - __glibc >=2.17,<3.0.a0
-  - libgcc >=13
-  - libstdcxx >=13
-  - libzlib >=1.3.1,<2.0a0
+  - libcxx >=12.0.1
+  license: GPL-2.0-or-later
+  license_family: GPL
+  purls: []
+  size: 1832744
+  timestamp: 1646609481185
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxau-1.0.12-hc919400_1.conda
+  sha256: adae11db0f66f86156569415ed79cda75b2dbf4bea48d1577831db701438164f
+  md5: 78b548eed8227a689f93775d5d23ae09
+  depends:
+  - __osx >=11.0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 14105
+  timestamp: 1762976976084
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxdmcp-1.1.5-hc919400_1.conda
+  sha256: f7fa0de519d8da589995a1fe78ef74556bb8bc4172079ae3a8d20c3c81354906
+  md5: 9d1299ace1924aa8f4e0bc8e71dd0cf7
+  depends:
+  - __osx >=11.0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 19156
+  timestamp: 1762977035194
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/yaml-0.2.5-h925e9cb_3.conda
+  sha256: b03433b13d89f5567e828ea9f1a7d5c5d697bf374c28a4168d71e9464f5dafac
+  md5: 78a0fe9e9c50d2c381e8ee47e3ea437d
+  depends:
+  - __osx >=11.0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 83386
+  timestamp: 1753484079473
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zfp-1.0.1-ha86207d_5.conda
+  sha256: 5b8bc86ca206f456ca9fe9e1a629f68b949ac47070211bccf4b44d29141c85d7
+  md5: 581bd74656ccd460cf2bbe152292a1eb
+  depends:
+  - __osx >=11.0
+  - libcxx >=19
+  - llvm-openmp >=19.1.7
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 567578
-  timestamp: 1742433379869
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-h6491c7d_2.conda
-  sha256: 0d02046f57f7a1a3feae3e9d1aa2113788311f3cf37a3244c71e61a93177ba67
-  md5: e6f69c7bcccdefa417f056fa593b40f0
+  size: 204043
+  timestamp: 1766549790975
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-ng-2.3.3-hed4e4f5_1.conda
+  sha256: a339606a6b224bb230ff3d711e801934f3b3844271df9720165e0353716580d4
+  md5: d99c2a23a31b0172e90f456f580b695e
+  depends:
+  - __osx >=11.0
+  - libcxx >=19
+  license: Zlib
+  license_family: Other
+  purls: []
+  size: 94375
+  timestamp: 1770168363685
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda
+  sha256: 9485ba49e8f47d2b597dd399e88f4802e100851b27c21d7525625b0b4025a5d9
+  md5: ab136e4c34e97f34fb621d2592a393d8
   depends:
   - __osx >=11.0
   - libzlib >=1.3.1,<2.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 399979
-  timestamp: 1742433432699
+  size: 433413
+  timestamp: 1764777166076
+- conda: https://conda.fmri.science/noarch/brainspace-0.1.20-py_1.conda
+  sha256: 6319144b362be8942818427e303c211df41c3adbb0e1de7cc76cd69ea916a111
+  md5: 035669ff0d573ad54c3eed0ef4c749ca
+  depends:
+  - matplotlib >=2.0.0
+  - nibabel
+  - nilearn
+  - numpy >=1.11.0
+  - python
+  - scikit-learn >=0.22.0
+  - scipy >=0.17.0
+  - vtk >=9.1.0
+  size: 58374395
+  timestamp: 1760878645747
+- pypi: .
+  name: wonkyconn
+  requires_dist:
+  - brainspace
+  - matplotlib
+  - nibabel
+  - nilearn
+  - numba
+  - numpy
+  - pandas>=2.0
+  - patsy
+  - rich
+  - scipy
+  - seaborn
+  - statsmodels
+  - tqdm
+  - mypy ; extra == 'dev'
+  - pandas-stubs ; extra == 'dev'
+  - pre-commit ; extra == 'dev'
+  - pytest ; extra == 'dev'
+  - pytest-cov ; extra == 'dev'
+  - ruff ; extra == 'dev'
+  - types-tqdm ; extra == 'dev'
+  - myst-parser>=5.1.0 ; extra == 'docs'
+  - sphinx ; extra == 'docs'
+  - sphinx-argparse ; extra == 'docs'
+  - sphinx-rtd-theme ; extra == 'docs'
+  - textual>=0.58 ; extra == 'textual'
+  requires_python: '>=3.11'
+- pypi: https://files.pythonhosted.org/packages/d4/4e/229b628b0dd57a240b9a2d0154bbf871cd9018281f2b38756f27e55c6c99/brainspace-0.2.1-py3-none-any.whl
+  name: brainspace
+  version: 0.2.1
+  sha256: da887894b69d5a425d4eae641080995d946833f2a2b7e9209bce831fa1449d94
+  requires_dist:
+  - numpy>=1.20
+  - scipy>=1.6
+  - scikit-learn>=0.22
+  - matplotlib>=3.3
+  - vtk>=9.1.0,<9.7.0
+  - nibabel>=3.2
+  - pytest ; extra == 'test'
+  - coverage ; extra == 'test'
+  - pytest-cov ; extra == 'test'
+  - coveralls ; extra == 'test'
+  - nilearn>=0.9 ; extra == 'examples'
+  requires_python: '>=3.9'
diff --git a/pyproject.toml b/pyproject.toml
index 8a9d67e4..f162fb4c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,7 +36,7 @@ dependencies = [
 wonkyconn = "wonkyconn.run:main"
 
 [project.urls]
-"Homepage" = "https://github.com/SIMEXP/wonkyconn"
+"Homepage" = "https://github.com/HALFpipe/wonkyconn"
 "Documentation" = "https://wonkyconn.readthedocs.io/en/latest/"
 
 [project.optional-dependencies]
@@ -44,7 +44,7 @@ textual = ["textual>=0.58"]
 docs = [
     "sphinx",
     "sphinx_rtd_theme",
-    "myst-parser",
+    "myst-parser>=5.1.0", # >= 5.1.0 for the GitHub-like callout boxes
     "sphinx-argparse",
 ]
 dev = [