A Docker Pipeline for High-throughput, Reproducible and Comprehensive Mapping of White Matter Fibers
Poster No:
1789
Submission Type:
Abstract Submission
Authors:
Runjia Lin1,2, Minhui Ouyang1,3, Xin Fan2, Hao Huang4,3
Institutions:
1Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 2School of Software, Dalian University of Technology, Dalian, Liaoning, 3Department of Radiology, University of Pennsylvania, Philadelphia, PA, 4Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA
First Author:
Runjia Lin
Department of Radiology, Children's Hospital of Philadelphia|School of Software, Dalian University of Technology
Philadelphia, PA|Dalian, Liaoning
Department of Radiology, Children's Hospital of Philadelphia|School of Software, Dalian University of Technology
Philadelphia, PA|Dalian, Liaoning
Co-Author(s):
Minhui Ouyang
Department of Radiology, Children's Hospital of Philadelphia|Department of Radiology, University of Pennsylvania
Philadelphia, PA|Philadelphia, PA
Department of Radiology, Children's Hospital of Philadelphia|Department of Radiology, University of Pennsylvania
Philadelphia, PA|Philadelphia, PA
Hao Huang
Department of Radiology, Children’s Hospital of Philadelphia|Department of Radiology, University of Pennsylvania
Philadelphia, PA|Philadelphia, PA
Department of Radiology, Children’s Hospital of Philadelphia|Department of Radiology, University of Pennsylvania
Philadelphia, PA|Philadelphia, PA
Introduction:
The complex whole brain connectome is underlined by white matter (WM) fibers featuring both long and short-range connections (Ouyang et al., 2017). Most existing protocols are tailored to trace long-range fibers (Huang et al., 2004, Wakana et al., 2007), incapable of comprehensively tracing all fibers including both long and short-range fibers. To achieve comprehensive mapping of whole-brain WM fibers in a unified framework including specifically high-fidelity tracing of short-range fibers, we developed a Docker-based Ensemble Pipeline for Tractogram with High-throughput, Reproducible and Comprehensive Mapping of White Matter Fibers (DEPTH). The Docker-based containerization ensures cross-platform usability and versatility without complex setup and configuration.
Methods:
Data used in this study include fourty subjects from four dataset (ten from each), including Human Connectome Project (HCP), Autism Brain Imaging Data Exchange (ABIDE), Adolescent Brain Cognitive Development (ABCD) and Healthy Brain Network (HBN).
DEPTH is containerized using Docker (Gorgolewski et al., 2017), packaging the extended STTAR (Zhao et al., 2020) protocol and other necessary dependencies such as FreeSurfer (Fischl et al., 2012), FSL (Jenkinson et al., 2012) and Dipy (Garyfallidis et al., 2014) to form an all-in-one platform (Fig. 1, left panel), compatible with most operating systems.
Overview of DEPTH protocol is shown in Fig. 1, right panel. Raw data is first preprocessed (a, b). STTAR tracing is conducted (c), short-/long-range fibers are extracted (d), followed by HDBSCAN (McInnes et al., 2017) for delineating clusters (e). To delineate deep tracts, the whole brain tractogram is registered to a streamline atlas (Yeh et al., 2018) and pruned using RecoBundles implemented in Dipy (f). Details are explained below.
1.dMRI and anatomical preprocessing: Raw diffusion weighted images (DWI) are denoised using Patch2Self and distortion-corrected using FSL's topup and eddy. The T1w images are corrected for intensity nonuniformity and skull-stripped with mri_synthstrip. Parcellation is performed using mri_synthseg. The parcellated labels are then transferred to DWI space using FSL.
2.Extended STTAR tracing: Whole brain mask is used as seed ROI. Bedpostx in FSL is applied for local fiber orientation estimation with two fibers per voxel. Probtrackx is used for streamline tracing with 10 seeds per voxel.
3.ROI filtering and clustering: Streamlines that terminate in adjacent regions are extracted as short-range fibers and filtered with the same setting detailed in STTAR. Streamlines connecting non-adjacent regions are extracted as long-range fibers and filtered with the saming setting. HDBSCAN is then used to delineate SAF clusters.
4.Atlas-based deep tract parcellation: The traced whole brain tractogram is registered to the reference atlas using streamline-based registration in Dipy. Deep white matter tracts are parcellated using RecoBundles.
DEPTH is containerized using Docker (Gorgolewski et al., 2017), packaging the extended STTAR (Zhao et al., 2020) protocol and other necessary dependencies such as FreeSurfer (Fischl et al., 2012), FSL (Jenkinson et al., 2012) and Dipy (Garyfallidis et al., 2014) to form an all-in-one platform (Fig. 1, left panel), compatible with most operating systems.
Overview of DEPTH protocol is shown in Fig. 1, right panel. Raw data is first preprocessed (a, b). STTAR tracing is conducted (c), short-/long-range fibers are extracted (d), followed by HDBSCAN (McInnes et al., 2017) for delineating clusters (e). To delineate deep tracts, the whole brain tractogram is registered to a streamline atlas (Yeh et al., 2018) and pruned using RecoBundles implemented in Dipy (f). Details are explained below.
1.dMRI and anatomical preprocessing: Raw diffusion weighted images (DWI) are denoised using Patch2Self and distortion-corrected using FSL's topup and eddy. The T1w images are corrected for intensity nonuniformity and skull-stripped with mri_synthstrip. Parcellation is performed using mri_synthseg. The parcellated labels are then transferred to DWI space using FSL.
2.Extended STTAR tracing: Whole brain mask is used as seed ROI. Bedpostx in FSL is applied for local fiber orientation estimation with two fibers per voxel. Probtrackx is used for streamline tracing with 10 seeds per voxel.
3.ROI filtering and clustering: Streamlines that terminate in adjacent regions are extracted as short-range fibers and filtered with the same setting detailed in STTAR. Streamlines connecting non-adjacent regions are extracted as long-range fibers and filtered with the saming setting. HDBSCAN is then used to delineate SAF clusters.
4.Atlas-based deep tract parcellation: The traced whole brain tractogram is registered to the reference atlas using streamline-based registration in Dipy. Deep white matter tracts are parcellated using RecoBundles.
·Fig. 1. The proposed DEPTH framework. Distributed as Docker container, it integrates advanced protocols and essential dependencies, ensuring its reproducibility and cross-platform usability.
Results:
Seventeen short-range association fiber (SAF) clusters linking eight pairs of cortical regions were traced by DEPTH (Fig. 2a). In addition, two long-range association fiber clusters in two pairs of cortical connetions and four well-defined deep tracts are traced (Fig. 2b). All clusters exhbit high consistency across subjects. We also generated population-averaged probability maps of seven clusters traced from the four datasets (Fig. 2c). In the panel of each dataset, the first row shows merged centroids streamlines and the second row shows the corresponding probability maps (Fig. 2c bottom panel). The high intensity of the probability shows high reproducibility of DEPTH-traced fibers.
·Fig. 2. Visual display of representative (a) short-range fibers from eight pairs of gyri, (b) six long-range clusters and (c) probability maps across datasets derived from DEPTH.
Conclusions:
The proposed DEPTH is versatile and reproducible for comprehensive mapping of long and short-range white matter fibers. It can be potentially used to automatically trace fibers linking any user-customized ROIs to study connectivity in focused brain regions, paving the way for broader neuroscientific and clinical discoveries.
Brain Stimulation:
Non-invasive Magnetic/TMS
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Diffusion MRI Modeling and Analysis 2
Methods Development
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 1
Keywords:
Computing
Data analysis
Modeling
MRI
Tractography
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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2. Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., Van Der Walt, S., Descoteaux, M., ... & Dipy Contributors. (2014). Dipy, a library for the analysis of diffusion MRI data. Frontiers in neuroinformatics, 8, 8.
3. Huang, H., Zhang, J., Van Zijl, P. C., & Mori, S. (2004). Analysis of noise effects on DTI‐based tractography using the brute‐force and multi‐ROI approach. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 52(3), 559-565.
4. Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). Fsl. Neuroimage, 62(2), 782-790.
5. McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. J. Open Source Softw., 2(11), 205.
6. Ouyang, M., Kang, H., Detre, J. A., Roberts, T. P., & Huang, H. (2017). Short-range connections in the developmental connectome during typical and atypical brain maturation. Neuroscience & Biobehavioral Reviews, 83, 109-122.
7. Wakana, S., Caprihan, A., Panzenboeck, M. M., Fallon, J. H., Perry, M., Gollub, R. L., ... & Mori, S. (2007). Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage, 36(3), 630-644.
8. Yeh, F. C., Panesar, S., Fernandes, D., Meola, A., Yoshino, M., Fernandez-Miranda, J. C., ... & Verstynen, T. (2018). Population-averaged atlas of the macroscale human structural connectome and its network topology. Neuroimage, 178, 57-68.
9. Zhao, C., Ouyang, M., Yu, Q., Huang, H. (2020). Short-range Tractography with high Throughput and Reproducibility (STTAR) characterized by FDT tracing and HDBSCAN clustering. Proceeding of ISMRM, 857.