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Cai LT, Moon J, Camacho PB, Anderson AT, Chwa WJ, Sutton BP, Markowitz AJ, Palacios EM, Rodriguez A, Manley GT, Shankar S, Bremer PT, Mukherjee P, Madduri RK. MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline. Neuroinformatics 2024; 22:177-191. [PMID: 38446357 DOI: 10.1007/s12021-024-09650-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/29/2024] [Indexed: 03/07/2024]
Abstract
Large-scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that requires many software packages with complex dependencies and high computational costs. We developed MaPPeRTrac, an edge-centric tractography pipeline that simplifies and accelerates this process in a wide range of high-performance computing (HPC) environments. It fully automates either probabilistic or deterministic tractography, starting from a subject's magnetic resonance imaging (MRI) data, including structural and diffusion MRI images, to the edge density image (EDI) of their structural connectomes. Dependencies are containerized with Singularity (now called Apptainer) and decoupled from code to enable rapid prototyping and modification. Data derivatives are organized with the Brain Imaging Data Structure (BIDS) to ensure that they are findable, accessible, interoperable, and reusable following FAIR principles. The pipeline takes full advantage of HPC resources using the Parsl parallel programming framework, resulting in the creation of connectome datasets of unprecedented size. MaPPeRTrac is publicly available and tested on commercial and scientific hardware, so it can accelerate brain connectome research for a broader user community. MaPPeRTrac is available at: https://github.com/LLNL/mappertrac .
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Affiliation(s)
- Lanya T Cai
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., San Francisco, CA, 94107, USA
| | - Joseph Moon
- Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA, 94550, USA
| | - Paul B Camacho
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N Mathews Ave, Urbana, IL, 61801, USA
| | - Aaron T Anderson
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N Mathews Ave, Urbana, IL, 61801, USA
| | - Won Jong Chwa
- Department of Radiology, Washington University in St. Louis, 510 S Kingshighway Blvd, St. Louis, MO, 63110, USA
| | - Bradley P Sutton
- Bioengineering Department, University of Illinois at Urbana-Champaign, 506 S Mathews Ave, Urbana, IL, 61801, USA
| | - Amy J Markowitz
- Department of Neurosurgery, University of California, San Francisco, 400 Parnassus Ave, San Francisco, CA, 94143, USA
| | - Eva M Palacios
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., San Francisco, CA, 94107, USA
| | - Alexis Rodriguez
- Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL, 60439, USA
| | - Geoffrey T Manley
- Department of Neurosurgery, University of California, San Francisco, 400 Parnassus Ave, San Francisco, CA, 94143, USA
| | - Shivsundaram Shankar
- Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA, 94550, USA
| | - Peer-Timo Bremer
- Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA, 94550, USA
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., San Francisco, CA, 94107, USA.
| | - Ravi K Madduri
- Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL, 60439, USA.
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Raji CA, Wang MB, Nguyen N, Owen JP, Palacios EM, Yuh EL, Mukherjee P. Connectome mapping with edge density imaging differentiates pediatric mild traumatic brain injury from typically developing controls: proof of concept. Pediatr Radiol 2020; 50:1594-601. [PMID: 32607611 DOI: 10.1007/s00247-020-04743-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 04/26/2020] [Accepted: 05/24/2020] [Indexed: 01/06/2023]
Abstract
BACKGROUND Although acute neurologic impairment might be transient, other long-term effects can be observed with mild traumatic brain injury. However, when pediatric patients with mild traumatic brain injury present for medical care, conventional imaging with CT and MR imaging often does not reveal abnormalities. OBJECTIVE To determine whether edge density imaging can separate pediatric mild traumatic brain injury from typically developing controls. MATERIALS AND METHODS Subjects were recruited as part of the "Therapeutic Resources for Attention Improvement using Neuroimaging in Traumatic Brain Injury" (TRAIN-TBI) study. We included 24 adolescents (χ=14.1 years of age, σ=1.6 years, range 10-16 years), 14 with mild traumatic brain injury (TBI) and 10 typically developing controls. Neurocognitive assessments included the pediatric version of the California Verbal Learning Test (CVLT) and the Attention Network Task (ANT). Diffusion MR imaging was acquired on a 3-tesla (T) scanner. Edge density images were computed utilizing fiber tractography. Principal component analysis (PCA) and support vector machines (SVM) were used in an exploratory analysis to separate mild TBI and control groups. The diagnostic accuracy of edge density imaging, neurocognitive tests, and fractional anisotropy (FA) from diffusion tensor imaging (DTI) was computed with two-sample t-tests and receiver operating characteristic (ROC) metrics. RESULTS Support vector machine-principal component analysis of edge density imaging maps identified three white matter regions distinguishing pediatric mild TBI from controls. The bilateral tapetum, sagittal stratum, and callosal splenium identified mild TBI subjects with sensitivity of 79% and specificity of 100%. Accuracy from the area under the ROC curve (AUC) was 94%. Neurocognitive testing provided an AUC of 61% (CVLT) and 71% (ANT). Fractional anisotropy yielded an AUC of 48%. CONCLUSION In this proof-of-concept study, we show that edge density imaging is a new form of connectome mapping that provides better diagnostic delineation between pediatric mild TBI and healthy controls than DTI or neurocognitive assessments of memory or attention.
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Payabvash S, Palacios EM, Owen JP, Wang MB, Tavassoli T, Gerdes M, Brandes-Aitken A, Marco EJ, Mukherjee P. Diffusion tensor tractography in children with sensory processing disorder: Potentials for devising machine learning classifiers. Neuroimage Clin 2019; 23:101831. [PMID: 31035231 PMCID: PMC6488562 DOI: 10.1016/j.nicl.2019.101831] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Revised: 03/22/2019] [Accepted: 04/18/2019] [Indexed: 12/11/2022]
Abstract
The "sensory processing disorder" (SPD) refers to brain's inability to organize sensory input for appropriate use. In this study, we determined the diffusion tensor imaging (DTI) microstructural and connectivity correlates of SPD, and apply machine learning algorithms for identification of children with SPD based on DTI/tractography metrics. A total of 44 children with SPD and 41 typically developing children (TDC) were prospectively recruited and scanned. In addition to fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD), we applied probabilistic tractography to generate edge density (ED) and track density (TD) from DTI maps. For identification of children with SPD, accurate classification rates from a combination of DTI microstructural (FA, MD, AD, and RD), connectivity (TD) and connectomic (ED) metrics with different machine learning algorithms - including naïve Bayes, random forest, support vector machine, and neural networks - were determined. In voxel-wise analysis, children with SPD had lower FA, ED, and TD but higher MD and RD compared to TDC - predominantly in posterior white matter tracts including posterior corona radiata, posterior thalamic radiation, and posterior body and splenium of corpus callosum. In stepwise penalized logistic regression, the only independent variable distinguishing children with SPD from TDC was the average TD in the splenium (p < 0.001). Among different combinations of machine learning algorithms and DTI/connectivity metrics, random forest models using tract-based TD yielded the highest accuracy in classification of SPD - 77.5% accuracy, 73.8% sensitivity, and 81.6% specificity. Our findings demonstrate impaired microstructural and connectivity/connectomic integrity in children with SPD, predominantly in posterior white matter tracts, and with reduced TD of the splenium of corpus callosum as the most distinctive pattern. Applying machine learning algorithms, these connectivity metrics can be used to devise novel imaging biomarkers for neurodevelopmental disorders.
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Affiliation(s)
- Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States of America; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States of America
| | - Eva M Palacios
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States of America
| | - Julia P Owen
- Department of Radiology, University of Washington, Seattle, WA, United States of America
| | - Maxwell B Wang
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States of America
| | - Teresa Tavassoli
- Department of Psychology and Clinical Sciences, University of Reading, Reading, United Kingdom
| | - Molly Gerdes
- Department of Neurology, University of California, San Francisco, CA, United States of America
| | - Anne Brandes-Aitken
- Department of Applied Psychology, New York University, New York, NY, United States of America
| | - Elysa J Marco
- Department of Neurology, University of California, San Francisco, CA, United States of America; Department of Pediatric Neurology, Cortica Healthcare, San Rafael, CA, United States of America
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States of America; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, United States of America.
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