XOANI: eXtensible Open-framework for Animal NeuroImaging
Poster No:
2269
Submission Type:
Abstract Submission
Authors:
Sung-Ho Lee1,2,3, Woomi Ban1,2,3, Yen-Yu Shih1,2,3
Institutions:
1Center for Animal MRI, University of North Carolina, Chapel Hill, NC, 2Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, 3Department of Neurology, University of North Carolina, Chapel Hill, NC
First Author:
Sung-Ho Lee
Center for Animal MRI, University of North Carolina|Biomedical Research Imaging Center, University of North Carolina|Department of Neurology, University of North Carolina
Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC
Center for Animal MRI, University of North Carolina|Biomedical Research Imaging Center, University of North Carolina|Department of Neurology, University of North Carolina
Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC
Co-Author(s):
Woomi Ban
Center for Animal MRI, University of North Carolina|Biomedical Research Imaging Center, University of North Carolina|Department of Neurology, University of North Carolina
Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC
Center for Animal MRI, University of North Carolina|Biomedical Research Imaging Center, University of North Carolina|Department of Neurology, University of North Carolina
Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC
Yen-Yu Shih
Center for Animal MRI, University of North Carolina|Biomedical Research Imaging Center, University of North Carolina|Department of Neurology, University of North Carolina
Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC
Center for Animal MRI, University of North Carolina|Biomedical Research Imaging Center, University of North Carolina|Department of Neurology, University of North Carolina
Chapel Hill, NC|Chapel Hill, NC|Chapel Hill, NC
Introduction:
Neuroimaging is a rapidly evolving field that generates massive amounts of data from various sources and modalities. To harness the full potential of this data, researchers need to share and collaborate across different laboratories and institutions (Passerat-Palmbach et al., 2017; Poldrack et al., 2019). However, the lack of tools for data management and analysis are often insufficient, or unavailable, especially for animal studies. This contrasts with the growing interest in collaborative data harmonization for animal neuroimaging (Grandjean et al., 2020; Deruelle et al., 2022). To address this gap, we present XOANI (eXtensible Open-source Animal NeuroImaging Framework), a novel platform that enables standardized, reproducible, and collaborative neuroimaging research for animal models.
XOANI is based on the Brain Imaging Data Structure (BIDS) standard (Gorgolewski et al., 2016), which provides a common framework for organizing and describing neuroimaging data. By adopting BIDS, XOANI ensures that datasets are consistent, predictable, and interoperable, which facilitates data sharing and reuse among researchers.
In addition to data organization, XOANI also provides a solution for seamless data conversion and processing. XOANI leverages Docker Swarm technology (Cerin et al., 2017), which allows researchers to create and deploy reproducible workflows and analysis environments. This feature is particularly useful for animal neuroimaging, where novel and specialized methods are often required. By using Docker Swarm, researchers can ensure that their analysis pipelines are consistent, transparent, and portable, which enhances the reliability and reproducibility of their results (Gorgolewski et al., 2017).
XOANI is based on the Brain Imaging Data Structure (BIDS) standard (Gorgolewski et al., 2016), which provides a common framework for organizing and describing neuroimaging data. By adopting BIDS, XOANI ensures that datasets are consistent, predictable, and interoperable, which facilitates data sharing and reuse among researchers.
In addition to data organization, XOANI also provides a solution for seamless data conversion and processing. XOANI leverages Docker Swarm technology (Cerin et al., 2017), which allows researchers to create and deploy reproducible workflows and analysis environments. This feature is particularly useful for animal neuroimaging, where novel and specialized methods are often required. By using Docker Swarm, researchers can ensure that their analysis pipelines are consistent, transparent, and portable, which enhances the reliability and reproducibility of their results (Gorgolewski et al., 2017).
Methods:
XOANI introduces a structured project hierarchy that simplifies dataset organization into subject and session levels. Docker Swarm's parallelization is harnessed for distributed computing, optimizing resource usage across cluster environments (Figure 1). The proposed framework includes:
- A project structure that organizes comprehensive project data into a hierarchical manner, including BIDS data, subject-level derived data, group-summarized data, and code bases.
- Data Conversion/Orgernizer Module: We developed a Python module to effortlessly transform raw data into the BIDS format, ensuring seamless BIDS-compliant data management.
- DataFlow Module: Another Python module was created to simplify data retrieval and processing within the XOANI framework.
- App Scheduler Module: To enhance task management and deployment, we utilized Docker Swarm. This feature proved invaluable for the development and testing of new pipelines in our preclinical research context.
- A project structure that organizes comprehensive project data into a hierarchical manner, including BIDS data, subject-level derived data, group-summarized data, and code bases.
- Data Conversion/Orgernizer Module: We developed a Python module to effortlessly transform raw data into the BIDS format, ensuring seamless BIDS-compliant data management.
- DataFlow Module: Another Python module was created to simplify data retrieval and processing within the XOANI framework.
- App Scheduler Module: To enhance task management and deployment, we utilized Docker Swarm. This feature proved invaluable for the development and testing of new pipelines in our preclinical research context.
Results:
XOANI's BIDS-Workflow enhances data preprocessing by offering a robust dataset parser, flexible output file configuration, and folder structure compliant with the extended BIDS format (Figure 2). The Docker Swarm integration enables efficient parallel processing, making XOANI suitable for laboratories aiming to leverage existing resources for cluster computing. This scalable solution aims to improve accessibility and efficiency in neuroimaging data analysis, particularly in resource-constrained research environments. The framework offers a multi-tiered organization system that neatly packages BIDS datasets, processed data, segmented images, and research outputs, enhancing clarity and accessibility. The Python module streamlines workflow from data conversion to analysis, reducing the complexity of dependencies and environment configurations. Docker Swarm's deployment aids in scalable and distributed computing, providing a user-friendly interface for managing imaging tasks.
Conclusions:
XOANI presents a transformative open-source platform for preclinical neuroimaging, emphasizing standardized, scalable, and community-driven research. The integration of BIDS standards and Docker Swarm technology positions XOANI as a pivotal tool in advancing neuroimaging studies.
Modeling and Analysis Methods:
Exploratory Modeling and Artifact Removal
Neuroinformatics and Data Sharing:
Workflows 1
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Multi-Modal Imaging
Keywords:
ANIMAL STUDIES
Data Organization
Design and Analysis
Workflows
1|2Indicates the priority used for review
Provide references using author date format
Deruelle T. et al., (2022), ‘A Multicenter Preclinical MRI Study: Definition of Rat Brain Relaxometry Reference Maps’, Frontiers in Neuroinformatics, 14, 00022.
Grandjean J. et al., (2020), ‘Common functional network in the mouse brain revealed by multi-centre resting-state fMRI analysis’, NeuroImage, 205, 116278.
Gorgolewski K.J. et al., (2016), ‘The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments’, Scientific Data, 3, 160044
Gorgolewski K.J. et al., (2017), ‘BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods’, PLOS Computational Biology, 13(3), e1005209.
Passerat-Palmbach J. et al., (2017), ‘Reproducible Large-Scale Neuroimaging Studies with the OpenMOLE Workflow Management System’, Frontiers in Neuroinformatics, 11, 21
Poldrack R.A. et al., (2019), ‘Computational and Informatic Advances for Reproducible Data Analysis in Neuroimaging’, Annual Review of Biomedical Data Science, 2, 119-38