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.. _general_examples:

General examples
================

Introductory examples.



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    <div class="sphx-glr-thumbcontainer" tooltip="This example loads AFQ data from the Healthy Brain Network (HBN) preprocessed diffusion derivatives [1]_. The HBN is a landmark pediatric mental health study. Over the course of the study, it will collect diffusion MRI data from approximately 5,000 children and adolescents. We recently processed the available data from over 2,000 of these subjects, and provide the tract profiles from this dataset, which can be downloaded from AWS thanks to [INDI](http://fcon_1000.projects.nitrc.org/).">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_hbn_site_profiles_thumb.png
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  :ref:`sphx_glr_auto_examples_plot_hbn_site_profiles.py`

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      <div class="sphx-glr-thumbnail-title">Harmonize HBN data using ComBat</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example loads AFQ data from CSV files and manipulates that data using scikit-learn transformers and estimators. First we fetch the Weston-Havens dataset described in Yeatman et al [1]_. This dataset contains tractometry features from 77 subjects ages 6-50.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_demo_afq_dataset_thumb.png
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  :ref:`sphx_glr_auto_examples_demo_afq_dataset.py`

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      <div class="sphx-glr-thumbnail-title">Load and interact with an AFQ dataset</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Predict subject age from white matter features. This example fetches the Weston-Havens dataset described in Yeatman et al [1]_. This dataset contains tractometry features from 77 subjects ages 6-50. The plots display the absolute value of the mean regression coefficients (averaged across cross-validation splits) for the mean diffusivity (MD) features.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_age_regression_thumb.png
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  :ref:`sphx_glr_auto_examples_plot_age_regression.py`

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      <div class="sphx-glr-thumbnail-title">Predict age from white matter features</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="As a contrast to the approach presented in other examples, we also present a more &quot;standard&quot; statistical approach to analyze tract profile data. Here, we will conduct a point-by-point group comparison using a linear model fit using the standard ordinary least squares (OLS) method.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_als_comparison_thumb.png
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  :ref:`sphx_glr_auto_examples_plot_als_comparison.py`

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      <div class="sphx-glr-thumbnail-title">Use parametric statistics for group comparison</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Predict ALS diagnosis from white matter features. This example fetches the ALS classification dataset from Sarica et al [1]_. This dataset contains tractometry features from 24 patients with ALS and 24 demographically matched control subjects. The plots display the absolute value of the mean regression coefficients (averaged across cross-validation splits) for the fractional anisotropy (FA) features.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_als_classification_thumb.png
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  :ref:`sphx_glr_auto_examples_plot_als_classification.py`

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      <div class="sphx-glr-thumbnail-title">Classify ALS diagnosis from white matter features</div>
    </div>


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    </div>


.. toctree::
   :hidden:

   /auto_examples/plot_hbn_site_profiles
   /auto_examples/demo_afq_dataset
   /auto_examples/plot_age_regression
   /auto_examples/plot_als_comparison
   /auto_examples/plot_als_classification


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  .. container:: sphx-glr-footer sphx-glr-footer-gallery

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download all examples in Python source code: auto_examples_python.zip </auto_examples/auto_examples_python.zip>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip </auto_examples/auto_examples_jupyter.zip>`


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 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_