BabyAFQ : tractometry for infant dMRI data

The following is an example of tractometry for infant bundles. This example and resulting pyAFQ support for pediatric bundles was inspired by and largely due to the work of Grotheer et al. [Grotheer2022], as implemented in [Grotheer2023].

Note

Because it is time and disk-space consuming, this example is not run when the pyAFQ documentation is built. To run this example yourself, you can download the contents of this file as an executable .py file or as a Jupyter notebook from the links at the bottom of the page.

import os.path as op
import plotly
import wget
import zipfile

from AFQ.api.group import GroupAFQ
import AFQ.api.bundle_dict as abd
import AFQ.data.fetch as afd

Baby dMRI data

Infant MRI data are quite different from data acquired in grownup participants, and even from children that are just a few years older. First, there is the rather obvious difference in size. Baby brains are approximately 25% the size of grownup brains at birth. But there are also often less known differences in brain tissue properties. For example, the myelin content of white matter is much lower in infants than in grownups. This is important because the diffusion signal that we measure with dMRI is sensitive to the myelin content, and it means that the dMRI signal differs quite a bit in newborn infants. For the purpose of delineating the major white matter pathways, it is also important to know that their shape, location and curvature is different in infants than in grownups. For example, the arcuate fasciculus is much more curved in infants than in grownups. Because of this, we use a different set of templates for infant brains than for grownup brains. These templates were created and validated by Mareike Grotheer and colleagues in [Grotheer2022]. They are available for download as part of the pyAFQ software, as we will show below.

In this example, we will demonstrate the use of pyAFQ on data from one infant. The data, provided by Kalanit Grill Spector’s Stanford Vision and Perception Neuroscience Lab <http://vpnl.stanford.edu/>, and was previously published in [Grotheer2022]. The data is available to download on Figshare <https://figshare.com/articles/dataset/Example_babyAFQ_BIDS_subject/21440739>. You can download it from there and unzip it into ~/AFQ_Data/baby_example/ (Note that this is 2.69GB of data, so it can take a while to download). Or you can download it and unzip it using the following block of code.

data_folder = op.join(op.expanduser('~'), "AFQ_data/")
baby_zip = op.join(data_folder, "baby_example.zip")
if not op.exists(baby_zip):
    print("Downloading processed pediatric data; this could take a while...")
    wget.download(
        "https://figshare.com/ndownloader/files/38053692",
        baby_zip)

with zipfile.ZipFile(baby_zip, 'r') as zip_ref:
    zip_ref.extractall(op.join(data_folder, "baby_example"))

Initialize a GroupAFQ object:

Now that the data is downloaded and organized in a BIDS-compliant structure, we can start running pyAFQ on it. We start by initializing a GroupAFQ object. This object manages all of the data transformations and computations conducted by the software, based on its initial configuration, which we set up below.

A few special things to note here:

1. The data were preprocessed using the vistasoft pipeline, so we set preproc_pipeline = “vistasoft”.

2. We use the UNC neonatal template, which can be read on a call to the read_pediatric_templates function in AFQ.data.fetch.

3. We use the baby_bd to define the bundles that we want to segment. This dictionary is different from the default behavior in that it uses the waypoint ROIs from [Grotheer2022].

4. In this case, tractography has already been run using MRTRIX <https://www.mrtrix.org/>, and is accessed using the import_tract key-word argument.

5. We set filter_by_endpoints = False in the segmentation_params because endpoint ROIs are not defined from newborn bundles.

myafq = GroupAFQ(
    bids_path=op.join(op.expanduser('~'),
                      "AFQ_data/baby_example/example_bids_subject"),
    preproc_pipeline="vistasoft",
    reg_template_spec=afd.read_pediatric_templates(
    )["UNCNeo-withCerebellum-for-babyAFQ"],
    reg_subject_spec="b0",
    bundle_info=abd.baby_bd(),
    import_tract={
        "suffix": "tractography", "scope": "mrtrix"},
    segmentation_params={
        "filter_by_endpoints": False},
)

Running the pipeline

A call to the export function will trigger the pyAFQ pipeline. This will run tractography, bundle segmentation, and bundle cleaning. The results will be saved in the ~/AFQ_data/baby_example/derivatives/afq folder. This can take a while to run, depending on your computer. In this case, we call export with the all_bundles_figure option. This is because visualizations are created after most other parts of the pipeline have been run. This means that when this call is done, you should have many of the derivative results in the output folder, including the tractography, segmentation, and tract profile results, as well as the visualizations.

viz = myafq.export("all_bundles_figure")

Viewing the results

One way to view the results is to open the file named sub-01_ses-01_dwi_space-RASMM_model-probCSD_algo-AFQ_desc-viz_dwi.html in your browser. This is a visualization of the tractography and segmentation results for all of the bundles. You can navigate this visualization by clicking on the different bundles in the legend on the right side of the screen. You can also zoom in and out using the mouse wheel, and rotate the view by clicking and dragging with the mouse. You can also view the FA tract profiles in a plot on the left side of the page.

If the baby bundles appear dark in the html visualization due to low FA values, you can reduce the upper limit of the range in the sbv_lims_bundles option when building your GroupAFQ object (e.g. GroupAFQ(…, sbv_lims_bundles=[0, 0.5])).

References:

Grotheer2021

Grotheer, Mareike, Mona Rosenke, Hua Wu, Holly Kular, Francesca R. Querdasi, Vaidehi S. Natu, Jason D. Yeatman, and Kalanit Grill-Spector. “White matter myelination during early infancy is linked to spatial gradients and myelin content at birth.” Nature communications 13: 997.

Grotheer2023

Grotheer, Mareike, David Bloom, John Kruper, Adam Richie-Halford, Stephanie Zika, Vicente A. Aguilera González, Jason D. Yeatman, Kalanit Grill-Spector, and Ariel Rokem. “Human white matter myelinates faster in utero than ex utero.” Proceedings of the National Academy of Sciences 120: e2303491120.

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