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.
from paths import afq_home
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)1Get dMRI data¶
We will analyze one subject from the Healthy Brain Network Processed Open Diffusion Derivatives dataset (HBN-POD2) Richie-Halford et al. (2022). We’ll use a fetcher to get preprocessed dMRI data for one of the >2,000 subjects in that study.
study_dir = afd.fetch_hbn_preproc(["NDARAA948VFH"])[1]2Define 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.
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
}
})3Define GroupAFQ object¶
For tractography, we use CSD-based probabilistic tractography seeding
extensively (n_seeds=4 means 81 seeds per voxel!), but only within the ROIs.
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("profiles")INFO:AFQ:No stop mask given, using FA (or first scalar if none are FA)thresholded to 0.2
{'NDARAA948VFH': '/home/jovyan/data/tractometry/tractometry/HBN/derivatives/afq_ar/sub-NDARAA948VFH/ses-HBNsiteRU/dwi/sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_desc-profiles_tractography.csv'}4Interactive bundle visualization¶
Another way to examine the outputs is to export the bundles as interactive HTML files.
bundle_html = my_afq.export("all_bundles_figure")
plotly.io.show(bundle_html["NDARAA948VFH"][0])5References¶
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. Richie-Halford et al. (2022)
Cieslak M, Cook PA, He X, et al. QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data. Nat Methods. 2021. Cieslak et al. (2021)
- Richie-Halford, A., Cieslak, M., Ai, L., Caffarra, S., Covitz, S., Franco, A. R., Karipidis, I. I., Kruper, J., Milham, M., Avelar-Pereira, B., Roy, E., Sydnor, V. J., Yeatman, J. D., Abbott, N. J., Anderson, J. A. E., Gagana, B., Bleile, M., Bloomfield, P. S., Bottom, V., … Rokem, A. (2022). An analysis-ready and quality controlled resource for pediatric brain white-matter research. Scientific Data, 9(1). 10.1038/s41597-022-01695-7
- Cieslak, M., Cook, P. A., He, X., Yeh, F.-C., Dhollander, T., Adebimpe, A., Aguirre, G. K., Bassett, D. S., Betzel, R. F., Bourque, J., Cabral, L. M., Davatzikos, C., Detre, J. A., Earl, E., Elliott, M. A., Fadnavis, S., Fair, D. A., Foran, W., Fotiadis, P., … Satterthwaite, T. D. (2021). QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data. Nature Methods, 18(7), 775–778. 10.1038/s41592-021-01185-5