.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "howto/howto_examples/plot_acoustic_radiations.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_howto_howto_examples_plot_acoustic_radiations.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_howto_howto_examples_plot_acoustic_radiations.py:


===============================================================
How to add new bundles into pyAFQ (Acoustic Radiations Example)
===============================================================

pyAFQ is designed to be customizable and extensible. This example shows how you
can customize it to define a new bundle based on a definition of waypoint and
endpoint ROIs of your design. In this case, we add the acoustic radiations.

We start by importing some of the components that we need for this example and
fixing the random seed for reproducibility

.. GENERATED FROM PYTHON SOURCE LINES 14-26

.. code-block:: Python


    import os.path as op
    import plotly
    import numpy as np

    from AFQ.api.group import GroupAFQ
    import AFQ.api.bundle_dict as abd
    import AFQ.data.fetch as afd
    from AFQ.definitions.image import ImageFile, RoiImage
    np.random.seed(1234)









.. GENERATED FROM PYTHON SOURCE LINES 27-34

Get dMRI data
---------------
We will analyze one subject from the Healthy Brain Network Processed Open
Diffusion Derivatives dataset (HBN-POD2) [1]_, [2]_. We'll use a fetcher to
get preprocessed dMRI data for one of the >2,000 subjects in that study. The
data gets organized into a BIDS-compatible format in the `~/AFQ_data/HBN`
folder:

.. GENERATED FROM PYTHON SOURCE LINES 34-37

.. code-block:: Python


    study_dir = afd.fetch_hbn_preproc(["NDARAA948VFH"])[1]





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


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.. GENERATED FROM PYTHON SOURCE LINES 38-48

Define custom `BundleDict` object
--------------------------------
The `BundleDict` object holds information about "include" and "exclude" ROIs,
as well as endpoint ROIS, and whether the bundle crosses the midline. In this
case, the ROIs are all defined in the MNI template space that is used as the
default template space in pyAFQ, but, in principle, other template spaces
could be used.

The ROIs for the case can be downloaded using a custom fetcher which saves
the ROIs to a folder and creates a dictionary of paths to the ROIs:

.. GENERATED FROM PYTHON SOURCE LINES 48-65

.. code-block:: Python


    ar_rois = afd.read_ar_templates()

    bundles = abd.BundleDict({
        "Left Acoustic Radiation": {
            "start": ar_rois["AAL_Thal_L"],
            "end": ar_rois["AAL_TempSup_L"],
            "cross_midline": False,
        },
        "Right Acoustic Radiation": {
            "start": ar_rois["AAL_Thal_R"],
            "end": ar_rois["AAL_TempSup_R"],
            "cross_midline": False
        }
    })






.. rst-class:: sphx-glr-script-out

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.. GENERATED FROM PYTHON SOURCE LINES 66-81

Define GroupAFQ object
----------------------
HBN POD2 have been processed with qsiprep [3]_. This means that a brain mask
has already been computer for them. As you can see in other examples, these
data also have a mapping calculated for them, which can also be incorporated
into processing. However, in this case, we will let pyAFQ calculate its own
SyN-based mapping so that the `combine_bundle` method can be used below to
create a montage visualization.

For tractography, we use CSD-based probabilistic tractography seeding
extensively (`n_seeds=4` means 81 seeds per voxel!), but only within the ROIs
and not throughout the white matter. This is controlled by passing
`"seed_mask": RoiImage()` in the `tracking_params` dict. The custom bundles
are passed as `bundle_info=bundles`. The call to `my_afq.export_all()`
initiates the pipeline.

.. GENERATED FROM PYTHON SOURCE LINES 81-102

.. code-block:: Python


    brain_mask_definition = ImageFile(
        suffix="mask",
        filters={'desc': 'brain',
                 'space': 'T1w',
                 'scope': 'qsiprep'})

    my_afq = GroupAFQ(
        bids_path=study_dir,
        preproc_pipeline="qsiprep",
        participant_labels=["NDARAA948VFH"],
        output_dir=op.join(study_dir, "derivatives", "afq_ar"),
        brain_mask_definition=brain_mask_definition,
        tracking_params={"n_seeds": 4,
                         "directions": "prob",
                         "odf_model": "CSD",
                         "seed_mask": RoiImage(use_endpoints=True)},
        bundle_info=bundles)

    my_afq.export_all()





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


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    Downloading https://templateflow.s3.amazonaws.com/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_res-01_T1w.nii.gz

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    Optimizing level 2 [max iter: 10000]
    Optimizing level 1 [max iter: 1000]
    Optimizing level 0 [max iter: 100]
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    Optimizing level 0 [max iter: 100]
    Optimizing level 2 [max iter: 10000]
    Optimizing level 1 [max iter: 1000]
    Optimizing level 0 [max iter: 100]

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.. GENERATED FROM PYTHON SOURCE LINES 103-109

Interactive bundle visualization
--------------------------------
Another way to examine the outputs is to export the individual bundle
figures, which show the streamlines, as well as the ROIs used to define the
bundle. This is an html file, which contains an interactive figure that can
be navigated, zoomed, rotated, etc.

.. GENERATED FROM PYTHON SOURCE LINES 109-113

.. code-block:: Python


    bundle_html = my_afq.export("indiv_bundles_figures")
    plotly.io.show(bundle_html["NDARAA948VFH"]["Left Acoustic Radiation"])




.. raw:: html
    :file: images/sphx_glr_plot_acoustic_radiations_001.html





.. GENERATED FROM PYTHON SOURCE LINES 114-127

References
----------
.. [1] Alexander LM, Escalera J, Ai L, et al. An open resource for
    transdiagnostic research in pediatric mental health and learning
    disorders. Sci Data. 2017;4:170181.

.. [2] Richie-Halford A, Cieslak M, Ai L, et al. An analysis-ready and quality
    controlled resource for pediatric brain white-matter research. Scientific
    Data. 2022;9(1):1-27.

.. [3] Cieslak M, Cook PA, He X, et al. QSIPrep: an integrative platform for
    preprocessing and reconstructing diffusion MRI data. Nat Methods.
    2021;18(7):775-778.


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (34 minutes 28.893 seconds)

**Estimated memory usage:**  4178 MB


.. _sphx_glr_download_howto_howto_examples_plot_acoustic_radiations.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

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

      :download:`Download Jupyter notebook: plot_acoustic_radiations.ipynb <plot_acoustic_radiations.ipynb>`

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

      :download:`Download Python source code: plot_acoustic_radiations.py <plot_acoustic_radiations.py>`


.. only:: html

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

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