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7,863 changes: 7,863 additions & 0 deletions source/_static/001-pyafq.html

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# Introduction

<div>
<iframe src="../_static/001-pyafq.html" width="100%" height="500px"></iframe>
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# Visualizing AFQ derivatives

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# BabyAFQ

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<iframe src="../_static/003-BabyAFQ.html" width="100%" height="500px"></iframe>
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# RecoBundles

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<iframe src="../_static/004-RecoBundles.html" width="100%" height="500px"></iframe>
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# Optic Radiations

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<iframe src="../_static/005-Optic-Radiations.html" width="100%" height="500px"></iframe>
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# Acoustic Radiations

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# SLF Subdivisions

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<iframe src="../_static/007-SLF-Subdivisions.html" width="100%" height="500px"></iframe>
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# GPU Tractography

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<iframe src="../_static/008-GPU-Tractography.html" width="100%" height="500px"></iframe>
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# Ray MSMT

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<iframe src="../_static/009-Ray-MSMT.html" width="100%" height="500px"></iframe>
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# Parametric Statistics for Group Comparison

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# Regression: Predict Age from White Matter

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# Classification: ALS Diagnosis

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# Fitting GAMs

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# Examples

```{eval-rst}

.. toctree::
:maxdepth: 1

001.md
002.md
003.md
004.md
005.md
006.md
007.md
008.md
009.md
010.md
011.md
012.md
013.md
```
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# Tractometry
Tractometry uses diffusion-weighted MRI (dMRI) to extract microstructural tissue properties of major white matter pathways. Here, we maintain a suite of integrated, open-source software that performs all analysis stages:

## Projects
[**pyAFQ**](https://tractometry.org/pyAFQ): Automated Fiber Quantification in Python:
&emsp;• post-processing of dMRI data
&emsp;• delineation of major white matter pathways
&emsp;• modeling of the tissue properties within them
&emsp;• expects pre-processed data. Data can be pre-processed with [*QSIprep*](https://qsiprep.readthedocs.io/en/latest/)

[*pyAFQ*](https://tractometry.org/pyAFQ): Automated Fiber Quantification in Python
[**AFQ-Insight**](https://tractometry.org/AFQ-Insight): Machine learning and statistics for tractomtery:
&emsp;• novel machine learning models such as convolutional neural networks (CNNs) and recurrent neural network (RNNs)
&emsp;• more standard approaches, such as ordinary least squares (OLS) and principal component analysis (PCA)
&emsp;• integrated with [*scikit-learn*](https://scikit-learn.org/stable/) and adapted for tract data, bridging these two worlds

[*AFQ-Insight*](https://tractometry.org/AFQ-Insight): Machine learning and statistics for tractomtery.
[**Tractable**](https://tractometry.org/tractable): R-based statistical analysis of tractometry:
&emsp;• focuses on generalized additive models (GAMs)

[*Tractable*](https://tractometry.org/tractable): R-based statistical analysis of tractometry.
[**AFQ-Browser**](https://tractometry.org/AFQ-Browser): Interactive exploratory visualization and sharing of tractometry studies:
&emsp;• allows researchers to interactively query the data to explore patterns

[*AFQ-Browser*](https://tractometry.org/AFQ-Browser): Interactive exploratory visualization and sharing of tractometry studies.
[**Tractoscope**](https://nrdg.github.io/tractoscope): Visualization of large openly-available tractometry studies.

## [Examples](examples/index.md)

**pyAFQ**

- *Basics*

• [Introduction](examples/001.md)
• [Visualizing AFQ derivatives](examples/002.md)

- *Extensions*

• [BabyAFQ: tractometry for infant dMRI data](examples/003.md)
• [RecoBundles for tract delineation](examples/004.md)

- *Adding New Bundles*

• [Optic Radiations](examples/005.md)
• [Acoustic Radiations](examples/006.md)
• [SLF 1/2/3 Subdivisions](examples/007.md)

- *Acceleration*

• [GPU Tractography](examples/008.md)
• [Multiprocessing for Model Fitting (Ray)](examples/009.md)

**AFQ-Insight**

- [Parametric Statistics for Group Comparison](examples/010.md)
- [Regression: Predict Age from White Matter](examples/011.md)
- [Classification: ALS Diagnosis](examples/012.md)

**Tractable**

- [Fitting Generalized Additive Models (GAMs)](examples/013.md)

## How to get help

We encourage you to seek help and share your questions with the community. Here's how to get support for Tractometry-related projects:

1. **Check [NeuroStars](https://neurostars.org/)**
NeuroStars is a community forum for neuroimaging questions. Search for existing answers or post your question using the `pyafq`, `afq-insight`, or other relevant tags.

2. **Browse or open issues on the respective GitHub repositories**
Many questions may already be answered in the project's issue tracker. If not, you can open a new issue:

- [**pyAFQ**](https://github.com/tractometry/pyAFQ/issues)
- [**AFQ-Insight**](https://github.com/tractometry/AFQ-Insight/issues)
- [**Tractable**](https://github.com/tractometry/tractable/issues)
- [**AFQ-Browser**](https://github.com/tractometry/AFQ-Browser/issues)
- [**Tractoscope**](https://github.com/tractometry/tractoscope/issues)

3. **Include details when asking for help**
When posting, please include:
- The software version you're using
- Relevant code or command-line calls
- Error messages (if any)
- Expected vs. actual behavior

[*Tractoscope*](https://tractometry.org/tractoscope): Visualization of large openly-available tractometry studies.

```{eval-rst}

.. toctree::
:maxdepth: 2
:caption: Contents:
:maxdepth: 1
:hidden:

examples/index.md
```
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