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Aligning robustICA with current Main + (#5)
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* Limit current adaptive mask method to brain mask (ME-ICA#1060)

* Limit adaptive mask calculation to brain mask.

Limit adaptive mask calculation to brain mask.

Expand on logic of first adaptive mask method.

Update tedana/utils.py

Improve docstring.

Update test.

Add decreasing-signal-based adaptive mask.

Keep removing.

Co-Authored-By: Dan Handwerker <[email protected]>

* Use `compute_epi_mask` in t2smap workflow.

* Try fixing the tests.

* Fix make_adaptive_mask.

* Update test_utils.py

* Update test_utils.py

* Improve docstring.

* Update utils.py

* Update test_utils.py

* Revert "Update test_utils.py"

This reverts commit 259b002.

* Don't take absolute value of echo means.

* Log echo-wise thresholds in adaptive mask.

* Add comment about non-zero voxels.

* Update utils.py

* Update test_utils.py

* Update test_utils.py

* Update test_utils.py

* Log the thresholds again.

* Address review.

* Fix test.

---------

Co-authored-by: Dan Handwerker <[email protected]>

* Update nilearn requirement from <=0.10.3,>=0.7 to >=0.7,<=0.10.4 (ME-ICA#1077)

* Add adaptive mask plot to report (ME-ICA#1073)

* Update scikit-learn requirement (ME-ICA#1075)

Updates the requirements on [scikit-learn](https://github.com/scikit-learn/scikit-learn) to permit the latest version.
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](scikit-learn/scikit-learn@0.21.0...1.4.2)

---
updated-dependencies:
- dependency-name: scikit-learn
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* Update pandas requirement from <=2.2.1,>=2.0 to >=2.0,<=2.2.2 (ME-ICA#1076)

Updates the requirements on [pandas](https://github.com/pandas-dev/pandas) to permit the latest version.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Commits](pandas-dev/pandas@v2.0.0...v2.2.2)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* Update bokeh requirement from <=3.4.0,>=1.0.0 to >=1.0.0,<=3.4.1 (ME-ICA#1078)

Updates the requirements on [bokeh](https://github.com/bokeh/bokeh) to permit the latest version.
- [Changelog](https://github.com/bokeh/bokeh/blob/branch-3.5/docs/CHANGELOG)
- [Commits](bokeh/bokeh@1.0.0...3.4.1)

---
updated-dependencies:
- dependency-name: bokeh
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* Load user-defined mask as expected by plot_adaptive_mask (ME-ICA#1079)

* DOC desc-optcomDenoised -> desc-denoised (ME-ICA#1080)

* docs: add mvdoc as a contributor for code, bug, and doc (ME-ICA#1082)

* docs: update README.md

* docs: update .all-contributorsrc

---------

Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com>

* Identify the last good echo in adaptive mask instead of sum of good echoes (ME-ICA#1061)

* Limit adaptive mask calculation to brain mask.

Limit adaptive mask calculation to brain mask.

Expand on logic of first adaptive mask method.

Update tedana/utils.py

Improve docstring.

Update test.

Add decreasing-signal-based adaptive mask.

Keep removing.

Co-Authored-By: Dan Handwerker <[email protected]>

* Use `compute_epi_mask` in t2smap workflow.

* Try fixing the tests.

* Fix make_adaptive_mask.

* Update test_utils.py

* Update test_utils.py

* Improve docstring.

* Identify the last good echo instead of sum.

Improve docstring.

Update test_utils.py

Update test_utils.py

Fix make_adaptive_mask.

Try fixing the tests.

Use `compute_epi_mask` in t2smap workflow.

Limit adaptive mask calculation to brain mask.

Limit adaptive mask calculation to brain mask.

Expand on logic of first adaptive mask method.

Update tedana/utils.py

Improve docstring.

Update test.

Add decreasing-signal-based adaptive mask.

Keep removing.

Co-Authored-By: Dan Handwerker <[email protected]>

* Fix.

* Update utils.py

* Update utils.py

* Try fixing.

* Update utils.py

* Update utils.py

* add checks

* Just loop over voxels.

* Update utils.py

* Update utils.py

* Update test_utils.py

* Revert "Update test_utils.py"

This reverts commit 259b002.

* Update test_utils.py

* Update test_utils.py

* Remove checks.

* Don't take absolute value of echo means.

* Log echo-wise thresholds in adaptive mask.

* Add comment about non-zero voxels.

* Update utils.py

* Update utils.py

* Update test_utils.py

* Update test_utils.py

* Update test_utils.py

* Log the thresholds again.

* Update test_utils.py

* Update test_utils.py

* Update test_utils.py

* Add simulated data to adaptive mask test.

* Clean up the tests.

* Add value that tests the base mask.

* Remove print in test.

* Update tedana/utils.py

Co-authored-by: Dan Handwerker <[email protected]>

* Update tedana/utils.py

Co-authored-by: Dan Handwerker <[email protected]>

---------

Co-authored-by: Dan Handwerker <[email protected]>

* Output RMSE map and time series for decay model fit (ME-ICA#1044)

* Draft function to calculate decay model fit.

* Calculate root mean squared error instead.

* Incorporate metrics.

* Output RMSE results.

* Output results in tedana.

* Hopefully fix things.

* Update decay.py

* Try improving performance.

* Update decay.py

* Fix again.

* Use tqdm.

* Update decay.py

* Update decay.py

* Update decay.py

* Update expected outputs.

* Add figures.

* Update outputs.

* Include global signal in confounds file.

* Update fiu_four_echo_outputs.txt

* Rename function.

* Rename function.

* Update tedana.py

* Update tedana/decay.py

Co-authored-by: Dan Handwerker <[email protected]>

* Update decay.py

* Update decay.py

* Whoops.

* Apply suggestions from code review

Co-authored-by: Dan Handwerker <[email protected]>

* Fix things maybe.

* Fix things.

* Update decay.py

* Remove any files that are built through appending.

* Update outputs.

* Add section on plots to docs.

* Fix the description.

* Update docs/outputs.rst

Co-authored-by: Dan Handwerker <[email protected]>

* Update docs/outputs.rst

* Fix docstring.

---------

Co-authored-by: Dan Handwerker <[email protected]>

* minimum nilearn 0.10.3 (ME-ICA#1094)

* Use nearest-neighbors interpolation in `plot_component` (ME-ICA#1098)

* Use nearest-neighbors interpolation in plot_stat_map.

* Only use NN interp for component maps.

* Update scipy requirement from <=1.13.0,>=1.2.0 to >=1.2.0,<=1.13.1 (ME-ICA#1100)

Updates the requirements on [scipy](https://github.com/scipy/scipy) to permit the latest version.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](scipy/scipy@v1.2.0...v1.13.1)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* Update scikit-learn requirement from <=1.4.2,>=0.21 to >=0.21,<=1.5.0 (ME-ICA#1101)

Updates the requirements on [scikit-learn](https://github.com/scikit-learn/scikit-learn) to permit the latest version.
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](scikit-learn/scikit-learn@0.21.0...1.5.0)

---
updated-dependencies:
- dependency-name: scikit-learn
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* Update numpy requirement from <=1.26.4,>=1.16 to >=1.16,<=2.0.0 (ME-ICA#1104)

* Update numpy requirement from <=1.26.4,>=1.16 to >=1.16,<=2.0.0

Updates the requirements on [numpy](https://github.com/numpy/numpy) to permit the latest version.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](numpy/numpy@v1.16.0...v2.0.0)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <[email protected]>

* Use np.nan instead of np.NaN

---------

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Taylor Salo <[email protected]>

* Filter out non-diagonal affine warning (ME-ICA#1103)

* Filter out non-diagonal affine warning.

* Fix warning capture.

* Update tedana/reporting/static_figures.py

Co-authored-by: Dan Handwerker <[email protected]>

* Update static_figures.py

---------

Co-authored-by: Dan Handwerker <[email protected]>

* Update bokeh requirement from <=3.4.1,>=1.0.0 to <=3.5.0,>=3.5.0 (ME-ICA#1109)

* Update bokeh requirement from <=3.4.1,>=1.0.0 to <=3.5.0,>=3.5.0

Updates the requirements on [bokeh](https://github.com/bokeh/bokeh) to permit the latest version.
- [Changelog](https://github.com/bokeh/bokeh/blob/branch-3.6/docs/CHANGELOG)
- [Commits](bokeh/bokeh@1.0.0...3.5.0)

---
updated-dependencies:
- dependency-name: bokeh
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <[email protected]>

* Update pyproject.toml

---------

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Taylor Salo <[email protected]>

* Update scikit-learn requirement from <=1.5.0,>=0.21 to <=1.5.1,>=1.5.1 (ME-ICA#1108)

* Update scikit-learn requirement from <=1.5.0,>=0.21 to <=1.5.1,>=1.5.1

Updates the requirements on [scikit-learn](https://github.com/scikit-learn/scikit-learn) to permit the latest version.
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](scikit-learn/scikit-learn@0.21.0...1.5.1)

---
updated-dependencies:
- dependency-name: scikit-learn
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <[email protected]>

* Update pyproject.toml to restore minimum version of scikit-learn

---------

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Dan Handwerker <[email protected]>

* Update scipy requirement from <=1.13.1,>=1.2.0 to <=1.14.0,>=1.14.0 (ME-ICA#1106)

* Update scipy requirement from <=1.13.1,>=1.2.0 to <=1.14.0,>=1.14.0

Updates the requirements on [scipy](https://github.com/scipy/scipy) to permit the latest version.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](scipy/scipy@v1.2.0...v1.14.0)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <[email protected]>

* Update pyproject.toml to retain minimum version of scipy

---------

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Dan Handwerker <[email protected]>
Co-authored-by: Eneko Uruñuela <[email protected]>

* Update numpy requirement from <=2.0.0,>=1.16 to >=1.16,<=2.0.1 (ME-ICA#1112)

Updates the requirements on [numpy](https://github.com/numpy/numpy) to permit the latest version.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](numpy/numpy@v1.16.0...v2.0.1)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* Cleaning up installation instructions (ME-ICA#1113)

* install instructions

* Update docs/installation.rst

Co-authored-by: Taylor Salo <[email protected]>

* Update docs/installation.rst

Co-authored-by: Eneko Uruñuela <[email protected]>

---------

Co-authored-by: Taylor Salo <[email protected]>
Co-authored-by: Eneko Uruñuela <[email protected]>

* Update bokeh requirement from <=3.5.0,>=1.0.0 to >=1.0.0,<=3.5.1 (ME-ICA#1116)

Updates the requirements on [bokeh](https://github.com/bokeh/bokeh) to permit the latest version.
- [Changelog](https://github.com/bokeh/bokeh/blob/3.5.1/docs/CHANGELOG)
- [Commits](bokeh/bokeh@1.0.0...3.5.1)

---
updated-dependencies:
- dependency-name: bokeh
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* Update list of multi-echo datasets (ME-ICA#1115)

* Generate metrics from external regressors using F stats (ME-ICA#1064)

* Get required metrics from decision tree.

* Continue changes.

* More updates.

* Store necessary_metrics as a list.

* Update selection_nodes.py

* Update selection_utils.py

* Update across the package.

* Keep updating.

* Update tedana.py

* Add extra metrics to list.

* Update ica_reclassify.py

* Draft metric-based regressor correlations.

* Fix typo.

* Work on trees.

* Expand regular expressions in trees.

* Fix up the expansion.

* Really fix it though.

* Fix style issue.

* Added external regress integration test

* Got intregration test with external regressors working

* Added F tests and options

* added corr_no_detrend.json

* updated names and reporting

* Run black.

* Address style issues.

* Try fixing test bugs.

* Update test_component_selector.py

* Update component_selector.py

* Use component table directly in selectcomps2use.

* Fix.

* Include generated metrics in necessary metrics.

* Update component_selector.py

* responding to feedback from tsalo

* Update component_selector.py

* Update test_component_selector.py

* fixed some testing failures

* fixed test_check_null_succeeds

* fixed ica_reclassify bug and selector_properties test

* ComponentSelector initialized before loading data

* fixed docstrings

* updated building decision tree docs

* using external regressors and most tests passing

* removed corr added tasks

* fit_model moved to stats

* removed and cleaned up external_regressors_config option

* Added task regressors and some tests. Now alll in decision tree

* cleaning up decision tree json files

* removed mot12_csf.json changed task to signal

* fixed tests with task_keep signal

* Update tedana/metrics/external.py

Co-authored-by: Taylor Salo <[email protected]>

* Update tedana/metrics/_utils.py

Co-authored-by: Taylor Salo <[email protected]>

* Update tedana/metrics/collect.py

Co-authored-by: Taylor Salo <[email protected]>

* Update tedana/metrics/external.py

Co-authored-by: Taylor Salo <[email protected]>

* Update tedana/metrics/external.py

Co-authored-by: Taylor Salo <[email protected]>

* Responding to review comments

* reworded docstring

* Added type hints to external.py

* fixed external.py type hints

* type hints to _utils collect and component_selector

* type hints and doc improvements in selection_utils

* no expand_node recursion

* removed expand_nodes expand_node expand_dict

* docstring lines break on punctuation

* updating external tests and docs

* moved test data downloading to tests.utils.py and started test for fit_regressors

* fixed bug where task regressors retained in partial models

* matched testing external regressors to included mixing and fixed bugs

* Made single function for detrending regressors

* added tests for external fit_regressors and fix_mixing_to_regressors

* Full tests in test_external_metrics.py

* adding tests

* fixed extern regress validation warnings and added tests

* sorting set values for test outputs

* added to test_metrics

* Added docs to building_decision_trees.rst

* Added motion task decision tree flow chart

* made recommended change to external_regressor_config

* Finished documentation and renamed demo decision trees

* added link to example external regressors tsv file

* Apply suggestions from code review

Fixed nuissance typos

Co-authored-by: Taylor Salo <[email protected]>

* Minor documentation edits

---------

Co-authored-by: Taylor Salo <[email protected]>
Co-authored-by: Taylor Salo <[email protected]>
Co-authored-by: Neha Reddy <[email protected]>

* Link to the open-multi-echo-data website (ME-ICA#1117)

* Update multi-echo.rst

* Update multi-echo.rst

* Refactor `metrics.dependence` module (ME-ICA#1088)

* Add type hints to metric functions.

* Use keyword arguments.

* Update tests.

* Update dependence.py

* Update collect.py

* Fix other stuff.

* documentation and resource updates (ME-ICA#1114)

* documentation and resource updates

* Fixed citation numbering and updated posters

---------

Co-authored-by: Neha Reddy <[email protected]>

* Adding already requested changes

* fixed failing tests

* updated documentation in faq.rst

* more documentation changes

---------

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: Taylor Salo <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Matteo Visconti di Oleggio Castello <[email protected]>
Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com>
Co-authored-by: Eneko Uruñuela <[email protected]>
Co-authored-by: Taylor Salo <[email protected]>
Co-authored-by: Taylor Salo <[email protected]>
Co-authored-by: Neha Reddy <[email protected]>
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9 people authored Aug 30, 2024
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11 changes: 11 additions & 0 deletions .all-contributorsrc
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"contributions": [
"code"
]
},
{
"login": "mvdoc",
"name": "Matteo Visconti di Oleggio Castello",
"avatar_url": "https://avatars.githubusercontent.com/u/6150554?v=4",
"profile": "https://github.com/mvdoc",
"contributions": [
"code",
"bug",
"doc"
]
}
],
"contributorsPerLine": 5,
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.pytest_cache/
.testing_data_cache/

# For decision tree .tex flow charts do not archive intermediate files
decision_tree*.aux
decision_tree*.fdb_latexmk
decision_tree*.fls
decision_tree*.log
decision_tree*.synctex.gz

# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
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Expand Up @@ -177,6 +177,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
<td align="center" valign="top" width="20%"><a href="https://github.com/martinezeguiluz"><img src="https://avatars.githubusercontent.com/u/129765987?v=4?s=100" width="100px;" alt="Maitane Martinez Eguiluz"/><br /><sub><b>Maitane Martinez Eguiluz</b></sub></a><br /><a href="https://github.com/ME-ICA/tedana/commits?author=martinezeguiluz" title="Code">💻</a></td>
<td align="center" valign="top" width="20%"><a href="https://github.com/martaarbizu"><img src="https://avatars.githubusercontent.com/u/127724722?v=4?s=100" width="100px;" alt="Marta Arbizu Gómez"/><br /><sub><b>Marta Arbizu Gómez</b></sub></a><br /><a href="https://github.com/ME-ICA/tedana/commits?author=martaarbizu" title="Code">💻</a></td>
</tr>
<tr>
<td align="center" valign="top" width="20%"><a href="https://github.com/mvdoc"><img src="https://avatars.githubusercontent.com/u/6150554?v=4?s=100" width="100px;" alt="Matteo Visconti di Oleggio Castello"/><br /><sub><b>Matteo Visconti di Oleggio Castello</b></sub></a><br /><a href="https://github.com/ME-ICA/tedana/commits?author=mvdoc" title="Code">💻</a> <a href="https://github.com/ME-ICA/tedana/issues?q=author%3Amvdoc" title="Bug reports">🐛</a> <a href="https://github.com/ME-ICA/tedana/commits?author=mvdoc" title="Documentation">📖</a></td>
</tr>
</tbody>
</table>

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122 changes: 122 additions & 0 deletions docs/_static/decision_tree_demo_external_regressors_motion_task_models.tex
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\documentclass[border=2pt]{standalone}
\usepackage[utf8]{inputenc} % Required for inserting images
\usepackage{tikz}
\usepackage{helvet}
\usetikzlibrary{shapes.geometric, arrows}
\pagecolor{white}

%-------------------------defining colorblind friendly colors
% Using pale color scheme in Figure 6
% by Paul Tol https://personal.sron.nl/~pault/
\definecolor{cbblue}{HTML}{BBCCEE}
\definecolor{cbcyan}{HTML}{CCEEFF}
\definecolor{cbgreen}{HTML}{CCDDAA}
\definecolor{cbyellow}{HTML}{EEEEBB}
\definecolor{cbred}{HTML}{FFCCCC}
\definecolor{cbgrey}{HTML}{DDDDDD}

% -------------------------defining nodes
\tikzstyle{input} = [trapezium, trapezium left angle =80, trapezium right angle = 100,
minimum width= 3cm, minimum height=0.5cm, text centered, draw=black, fill=cbblue]
\tikzstyle{process} = [rectangle, minimum width = 3cm, minimum height = 1cm,
text centered, , text width=4cm,draw=black, fill=cbgrey]
\tikzstyle{decision} = [diamond, minimum width = 3cm, minimum height = 1cm,
text centered, , text width=3cm, draw=black, fill=cbcyan]
\tikzstyle{changeclass} = [rectangle, rounded corners, minimum width=3cm, minimum height=1cm,
text centered, draw = black, fill=cbyellow]
\tikzstyle{reject} = [trapezium, trapezium left angle =80, trapezium right angle = 100,
minimum width= 1cm, minimum height=0.5cm, text centered, draw=black, fill=cbred]
\tikzstyle{accept} = [trapezium, trapezium left angle =80, trapezium right angle = 100,
minimum width= 1cm, minimum height=0.5cm, text centered, draw=black, fill=cbgreen]

% -------------------------defining connectors
\tikzstyle{arrow} = [thick,->, >=stealth]
\tikzstyle{line} = [thick,-,>=stealth]
\begin{document}

% ------------------------- tikz image (flow chart)
\begin{tikzpicture}[node distance = 2cm]

% ------------------------- nodes -------------------------
% ----- node: 0
\node(0)[input,label={90:\textbf{Demo Decision Tree with External Regressors, Motion, and Task Models}}, label={180:$node\ 0$}]{Set all components to unclassified};
% ----- node: 1
\node(1)[decision, below of=0,label={180:$node\ 1$}, yshift=-1.5cm]{$\rho$ $>$ $\kappa$};
\node(rej1)[changeclass, right of=1, xshift=3cm, align=center]{Unlikely BOLD\\$\rightarrow$ Provisional reject};
% ----- node: 2
\node(2)[decision, below of=1,label={180:$node\ 2$}, label={[align=center] 315: voxel counts for signif fit\\of multi-echo data\\to $T_2$ or $S_0$ decay models}, yshift=-4.0cm]{$n \, FS_0 \, > \, n \, FT_2$ \& $n \,FT_2$ $>$ 0};
\node(rej2)[changeclass, right of=2, xshift=3cm, align=center]{Unlikely BOLD\\$\rightarrow$ Provisional Reject};
% ----- node: 3
\node(3)[process, below of=2, label={180:$node\ 3$}, label={[align=center] 315: varex: variance explained\\by each component}, yshift=-2.0cm]{Calculate median(varex) across all components};
% ----- node: 4
\node(4)[decision, below of=3,label={180:$node\ 4$},label={[align=center] 315:DICE overlap between $T_2$ or $S_0$\\decay models and ICA component\\peak clusters}, yshift=-1.5cm]{dice $FS_0$ $>$ dice $FT_2$ \& varex $>$ median(varex)
};
\node(rej4)[changeclass, right of=4, xshift=3cm, align=center]{Unlikely BOLD\\$\rightarrow$ Provisional Reject};
% ----- node: 5
\node(5)[decision, below of=4,label={180:$node\ 5$}, label={[align=center] 315: $t-statistic$ of $FT_2$ values\\in component peak clusters vs\\peak voxels outside of clusters}, yshift=-4.0cm]{ $0 \, >$ signal-noise \& varex $>$ median(varex)};
\node(rej5)[changeclass, right of=5, xshift=3cm, align=center]{Unlikely BOLD\\$\rightarrow$ Provisional Reject};
% ----- node: 6
\node(6)[process, below of=5, label={180:$node\ 6$}, label={0: Uses all components}, yshift=-2.0cm]{Calculate $\kappa$ elbow};
% ----- node: 7
\node(7)[process, below of=6, label={180:$node\ 7$}, label={[align=center] 0: Uses all components and subset\\of unclassified components}]{Calculate $\rho$ elbow\\(liberal method)};
% ----- node: 8
\node(8)[decision, below of=7,label={180:$node\ 8$}, yshift=-1.5cm]{$\kappa \geq \kappa$ elbow\\$\rho$ $<$ $\rho$ elbow};
\node(chrej8)[changeclass, below of=8, xshift=0cm, yshift=-2cm]{Provisional reject};
\node(chacc8)[changeclass, right of=8, xshift=3cm, yshift=0cm]{Provisional accept};
% ----- node: 9
\node(9)[decision, below of=chrej8,label={180:$node\ 9$},label={20: Accept even if $\rho < \rho\ elbow$},yshift=-1.5cm]{$\kappa > 2\rho$\\$\kappa \geq \kappa$ elbow};
\node(chrej9)[changeclass, below of=9, xshift=0cm, yshift=-2cm]{Provisional reject};
\node(chacc9)[changeclass, right of=9, xshift=3cm, yshift=0cm]{Provisional accept};
% ----- node: 10
\node(10)[decision, below of=chacc9,label={150:$node\ 10$},label={[align=center] 310: Reject if\\fits external\\nuisance\\regressors},yshift=-2cm]{F test for\\Nuisance Regressors\\$p_{Full} \leq 0.05$\\$R^2_{Full} \geq 0.5$};
\node(chrej10)[changeclass, below of=10, xshift=0cm, yshift=-2cm, align=center]{External regressors\\$\rightarrow$Provisional reject};
% ----- node: 11
\node(11)[decision, left of=chrej10,label={180:$node\ 11$},xshift=-3cm]{Partial F test for\\Motion Regressors\\$p_{Full} \leq 0.05$\\$R^2_{Full} \geq 0.5$\\$p_{Motion} \leq 0.05$};
\node(chtag11)[changeclass, below of=11, xshift=0cm, yshift=-2cm, align=center]{Tag:\\Fits motion\\external regressors};
% ----- node: 12
\node(12)[decision, below of=chrej10,label={150:$node\ 12$},yshift=-2cm]{Partial F test for\\CSF Regressors\\$p_{Full} \leq 0.05$\\$R^2_{Full} \geq 0.5$\\$p_{CSF} \leq 0.05$};
\node(chtag12)[changeclass, below of=12, xshift=0cm, yshift=-2cm, align=center]{Tag:\\Fits CSF\\external regressors};
% ----- node: 13
\node(prej13)[changeclass, below of=chtag11, xshift=0cm, yshift=-0.5cm]{Provisional reject};
\node(13)[decision, below of=prej13,label={180:$node\ 13$},label={[align=center] 335: If fits task and\\contains T2*, accept\\even if other criteria\\would have rejected},yshift=-2cm]{F test for\\Task Regressors\\$p_{Task} \leq 0.05$\\$R^2_{Task} \geq 0.5$\\$\kappa \geq \kappa$ elbow};
\node(chacc13)[accept, right of=13,xshift=3cm, align=center]{Fits task\\$\rightarrow$Accept};
% ----- node: 14
\node(14)[decision, below of=13,label={180:$node\ 14$},label={[align=left] 335: Will accept the lowest\\variance components until\\1\% of total variance is\\accepted this way}, yshift=-3.5cm]{$if$ component variance $<0.1$};%--check in kundu
\node(acc14)[accept, right of=14, xshift=2.5cm, align=center]{Low variance\\$\rightarrow$ Accept};
% ----- node: 15
\node(15)[accept, below of=14,label={180:$node\ 15$},yshift=-1.5cm, align=center]{Likely BOLD\\Change provisional accept\\$\rightarrow$Accept};
% ----- node: 16
\node(16)[reject, below of=15,label={180:$node\ 16$}, yshift=0cm, align=center]{Unlikely BOLD\\Change provisional reject\\$\rightarrow$Reject};

% ------------------------- connections -------------------------
% draw[x](origin)--node[anchor=position]{text}(destination);
\draw[arrow](0)--(1);
\draw[arrow](1)--node[anchor=south, right=0] {no} (2);
\draw[arrow](1)--node[anchor=south] {yes} (rej1);
\draw[arrow](2)--node[anchor=south, right=0] {no} (3);
\draw[arrow](2)--node[anchor=south] {yes} (rej2);
\draw[arrow](3)--(4);
\draw[arrow](4)--node[anchor=south, right=0] {no} (5);
\draw[arrow](4)--node[anchor=south] {yes} (rej4);
\draw[arrow](5)--node[anchor=south, right=0] {no} (6);
\draw[arrow](5)--node[anchor=south] {yes} (rej5);
\draw[arrow](6)--(7);
\draw[arrow](7)--(8);
\draw[arrow](8)--node[anchor=south] {yes} (chacc8);
\draw[arrow](8)--node[anchor=south, right=0] {no} (chrej8);
\draw[arrow](chrej8)--(9);
\draw[arrow](9)--node[anchor=south] {yes} (chacc9);
\draw[arrow](9)--node[anchor=south, right=0] {no} (chrej9);
\draw[arrow](chacc9)--(10);
\draw[arrow](chrej9)--(10);
\draw[arrow](10)--node[anchor=south, right=0] {yes} (chrej10);
\draw[arrow](chrej10)--(11);
\draw[arrow](11)--node[anchor=south, right=0] {yes} (chtag11);
\draw[arrow](chrej10)--(12);
\draw[arrow](12)--node[anchor=south, right=0] {yes} (chtag12);
\draw[arrow](prej13)--(13);
\draw[arrow](13)--node[anchor=south] {yes} (chacc13);
\draw[arrow](13)--node[anchor=south, right=0] {no} (14);
\draw[arrow](14)--node[anchor=south] {yes} (acc14);
\end{tikzpicture}
\end{document}
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109 changes: 109 additions & 0 deletions docs/_static/decision_tree_demo_external_regressors_single_model.tex
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@@ -0,0 +1,109 @@
\documentclass[border=2pt]{standalone}
\usepackage[utf8]{inputenc} % Required for inserting images
\usepackage{tikz}
\usepackage{helvet}
\usetikzlibrary{shapes.geometric, arrows}
\pagecolor{white}

%-------------------------defining colorblind friendly colors
% Using pale color scheme in Figure 6
% by Paul Tol https://personal.sron.nl/~pault/
\definecolor{cbblue}{HTML}{BBCCEE}
\definecolor{cbcyan}{HTML}{CCEEFF}
\definecolor{cbgreen}{HTML}{CCDDAA}
\definecolor{cbyellow}{HTML}{EEEEBB}
\definecolor{cbred}{HTML}{FFCCCC}
\definecolor{cbgrey}{HTML}{DDDDDD}

% -------------------------defining nodes
\tikzstyle{input} = [trapezium, trapezium left angle =80, trapezium right angle = 100,
minimum width= 3cm, minimum height=0.5cm, text centered, draw=black, fill=cbblue]
\tikzstyle{process} = [rectangle, minimum width = 3cm, minimum height = 1cm,
text centered, , text width=4cm,draw=black, fill=cbgrey]
\tikzstyle{decision} = [diamond, minimum width = 3cm, minimum height = 1cm,
text centered, , text width=3cm, draw=black, fill=cbcyan]
\tikzstyle{changeclass} = [rectangle, rounded corners, minimum width=3cm, minimum height=1cm,
text centered, draw = black, fill=cbyellow]
\tikzstyle{reject} = [trapezium, trapezium left angle =80, trapezium right angle = 100,
minimum width= 1cm, minimum height=0.5cm, text centered, draw=black, fill=cbred]
\tikzstyle{accept} = [trapezium, trapezium left angle =80, trapezium right angle = 100,
minimum width= 1cm, minimum height=0.5cm, text centered, draw=black, fill=cbgreen]

% -------------------------defining connectors
\tikzstyle{arrow} = [thick,->, >=stealth]
\tikzstyle{line} = [thick,-,>=stealth]
\begin{document}

% ------------------------- tikz image (flow chart)
\begin{tikzpicture}[node distance = 2cm]

% ------------------------- nodes -------------------------
% ----- node: 0
\node(0)[input,label={90:\textbf{Demo Decision Tree. Single model with external regressors}}, label={180:$node\ 0$}]{Set all components to unclassified};
% ----- node: 1
\node(1)[decision, below of=0,label={180:$node\ 1$}, yshift=-1cm]{$\rho$ $>$ $\kappa$};
\node(rej1)[reject, right of=1, xshift=2cm, align=center]{Unlikely BOLD\\$\rightarrow$ Reject};
% ----- node: 2
\node(2)[decision, below of=1,label={180:$node\ 2$}, label={[align=center] 315: voxel counts for signif fit\\of multi-echo data\\to $T_2$ or $S_0$ decay models}, yshift=-3.0cm]{$n \, FS_0 \, > \, n \, FT_2$ \& $n \,FT_2$ $>$ 0};
\node(rej2)[reject, right of=2, xshift=2cm, align=center]{Unlikely BOLD\\$\rightarrow$ Reject};
% ----- node: 3
\node(3)[process, below of=2, label={180:$node\ 3$}, label={[align=center] 315: varex: variance explained\\by each component}, yshift=-1.5cm]{Calculate median(varex) across all components};
% ----- node: 4
\node(4)[decision, below of=3,label={180:$node\ 4$},label={[align=center] 315:DICE overlap between $T_2$ or $S_0$\\decay models and ICA component\\peak clusters}, yshift=-1.5cm]{dice $FS_0$ $>$ dice $FT_2$ \& varex $>$ median(varex)
};
\node(rej4)[reject, right of=4, xshift=2.5cm, align=center]{Unlikely BOLD\\$\rightarrow$ Reject};
% ----- node: 5
\node(5)[decision, below of=4,label={180:$node\ 5$}, label={[align=center] 315: $t-statistic$ of $FT_2$ values\\in component peak clusters vs\\peak voxels outside of clusters}, yshift=-4.0cm]{ $0 \, >$ signal-noise \& varex $>$ median(varex)};
\node(rej5)[reject, right of=5, xshift=2.5cm, align=center]{Unlikely BOLD\\$\rightarrow$ Reject};
% ----- node: 6
\node(6)[process, below of=5, label={180:$node\ 6$}, label={0: Uses all components}, yshift=-2.0cm]{Calculate $\kappa$ elbow};
% ----- node: 7
\node(7)[process, below of=6, label={180:$node\ 7$}, label={[align=center] 0: Uses all components and subset\\of unclassified components}]{Calculate $\rho$ elbow\\(liberal method)};
% ----- node: 7
\node(8)[decision, below of=7,label={180:$node\ 8$}, yshift=-1.5cm]{$\kappa \geq \kappa$ elbow};
\node(chrej8)[changeclass, below of=8, yshift=-1.5cm]{Provisional reject};
\node(chacc8)[changeclass, right of=8, xshift=3cm, yshift=0cm]{Provisional accept};
% ----- node: 8
\node(9)[decision, below of=chacc8,label={170:$node\ 9$}, yshift=-1.5cm]{ $\rho$ $>$ $\rho$ elbow};
\node(chrej9)[changeclass, below of=9, yshift=-1.5cm]{Provisional reject};
% ----- node: 9
\node(10)[decision, left of=chrej9,label={180:$node\ 10$},label={235: Accept even if $\rho < \rho\ elbow$},xshift=-3.5cm]{$\kappa \geq \kappa$ elbow\\$\kappa > 2\rho$ };
\node(chacc10)[changeclass, below of=10, xshift=0cm, yshift=-1.5cm, align=center]{Provisional accept};
% ----- node: 10
\node(11)[decision, below of=chacc10,label={180:$node\ 11$}, xshift=0cm, yshift=-2cm]{External regressor\\nuisance model\\$p<0.05$\\$R^2>0.5$};%--check in kundu
\node(chrej11)[changeclass, below of=11, xshift=0cm, yshift=-1.5cm, align=center]{Tag: External Regressors\\Provisional reject};
% ----- node: 11
\node(12)[decision, below of=chrej11,label={180:$node\ 11$},label={[align=left] 335: Will accept the lowest\\variance components until\\1\% of total variance is\\accepted this way}, yshift=-1.5cm]{$if$ component variance $<0.1$};%--check in kundu
\node(acc12)[accept, right of=12, xshift=3cm, align=center]{Low variance\\$\rightarrow$ Accept};
% ----- node: 12
\node(13)[accept, below of=12,label={180:$node\ 12$},yshift=-1.5cm, align=center]{Likely BOLD\\Change provisional accept\\$\rightarrow$Accept};
% ----- node: 13
\node(14)[reject, below of=13,label={180:$node\ 13$}, yshift=0cm, align=center]{Unlikely BOLD\\Change provisional reject\\$\rightarrow$Reject};


% ------------------------- connections -------------------------
\draw[arrow](0)--(1);
\draw[arrow](1)--node[anchor=south, right=0] {no} (2);
\draw[arrow](1)--node[anchor=south] {yes} (rej1);
\draw[arrow](2)--node[anchor=south, right=0] {no} (3);
\draw[arrow](2)--node[anchor=south] {yes} (rej2);
\draw[arrow](3)--(4);
\draw[arrow](4)--node[anchor=south, right=0] {no} (5);
\draw[arrow](4)--node[anchor=south] {yes} (rej4);
\draw[arrow](5)--node[anchor=south, right=0] {no} (6);
\draw[arrow](5)--node[anchor=south] {yes} (rej5);
\draw[arrow](6)--(7);
\draw[arrow](7)--(8);
\draw[arrow](8)--node[anchor=south] {yes} (chacc8);
\draw[arrow](8)--node[anchor=south, right=0] {no} (chrej8);
\draw[arrow](chacc8)--(9);
\draw[arrow](chrej8)--(9);
\draw[arrow](9)--node[anchor=south, right=0] {yes} (chrej9);
\draw[arrow](chrej9)--(10);
\draw[arrow](10)--node[anchor=south, right=0] {yes} (chacc10);
\draw[arrow](chacc10)--(11);
\draw[arrow](11)--node[anchor=south, right=0] {yes} (chrej11);
\draw[arrow](chrej11)--(12);
\draw[arrow](12)--node[anchor=south] {yes} (acc12);
\end{tikzpicture}
\end{document}
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1 change: 1 addition & 0 deletions docs/api.rst
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Expand Up @@ -105,6 +105,7 @@ API

tedana.metrics.collect
tedana.metrics.dependence
tedana.metrics.external


.. _api_selection_ref:
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5 changes: 4 additions & 1 deletion docs/approach.rst
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Expand Up @@ -311,6 +311,8 @@ Next, ``tedana`` applies TE-dependent independent component analysis (ICA) in
order to identify and remove TE-independent (i.e., non-BOLD noise) components.
The dimensionally reduced optimally combined data are first subjected to ICA in
order to fit a mixing matrix to the whitened data.
``tedana`` can use a single interation of FastICA or multiple interations of robustICA,
with an explanation of those approaches `in our FAQ`_.
This generates a number of independent timeseries (saved as **desc-ICA_mixing.tsv**),
as well as parameter estimate maps which show the spatial loading of these components on the
brain (**desc-ICA_components.nii.gz**).
Expand Down Expand Up @@ -352,10 +354,11 @@ The actual decision tree is dependent on the component selection algorithm emplo
thresholds applied to each of the metrics). `These decision trees are detailed here`_.

Components that are classified as noise are projected out of the optimally combined data,
yielding a denoised timeseries, which is saved as **desc-optcomDenoised_bold.nii.gz**.
yielding a denoised timeseries, which is saved as **desc-denoised_bold.nii.gz**.

.. image:: /_static/a15_denoised_data_timeseries.png

.. _in our FAQ: faq.html#tedana-what-is-the-right-number-of-ica-components-what-options-let-me-get-it
.. _These decision trees are detailed here: included_decision_trees.html

*******************************
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