Styx and NiWrap: A Modern Interface Generator for Command-Line Neuroimaging Tools
Poster No:
1858
Submission Type:
Abstract Submission
Authors:
Florian Rupprecht1, Jason Kai1, Biraj Shrestha1, Connor Lane1, Steven Giavasis1, Tristan Glatard2, Michael Milham1, Gregory Kiar1
Institutions:
1Child Mind Institute, New York, NY, 2Krembil Centre for Neuroinformatics, Toronto, Ontario
First Author:
Co-Author(s):
Introduction:
Command-line tools remain essential in neuroimaging research but can be challenging to integrate into modern data science workflows. Integrating these tools typically requires complex shell scripting or manually written wrappers, creating barriers to reproducibility and efficient pipeline development. We present Styx, a compiler for generating language-native wrapper functions from static tool metadata, and NiWrap, a comprehensive collection of brain imaging tool descriptions that together enable seamless integration of neuroimaging tools within the data science ecosystem.
Methods:
Styx employs a three-phase compiler architecture: (1) A frontend that processes tool descriptions in formats such as Boutiques, (2) an intermediate representation for optimization, and (3) backends that generate native interfaces in Python, R, and TypeScript. We extended the Boutiques descriptor format to capture complex command-line patterns using concepts from formal language theory, including hierarchy, alternation, and repetition. For NiWrap, we developed two approaches for generating tool descriptions at scale: direct source code extraction for well-structured packages (e.g., MRTrix3, Workbench), and an LLM-assisted approach for packages with traditional documentation. The generated wrappers provide type safety, IDE integration with autocompletion, and seamless integration with container technologies for reproducible execution.
·Architectural overview of the Styx compiler. The compiler follows a three-phase design consisting of frontends, an intermediate representation (IR), and backends.
Results:
We achieved extensive coverage across major neuroimaging packages including AFNI, ANTs, Convert3D, FSL, FreeSurfer, MRTrix3, NiftyReg, and Workbench. The compiler-based approach ensures maintainability, as improvements propagate automatically to all wrapped tools across supported languages. Integration with container technologies (Docker/Singularity) ensures reproducible execution across computing environments. Unlike workflow engines that impose specific execution patterns, Styx wrappers can be used with any parallel processing solution or workflow framework.
Conclusions:
Styx and NiWrap bridge the gap between legacy command-line tools and modern scientific computing practices. By providing type-safe, documented interfaces across multiple programming languages, they significantly reduce the complexity of writing and maintaining neuroimaging pipelines. Our solution offers a sustainable path forward for modernizing access to crucial neuroimaging tools while preserving their battle-tested implementations.
Modeling and Analysis Methods:
Methods Development 2
Other Methods
Neuroinformatics and Data Sharing:
Workflows 1
Informatics Other
Keywords:
Computational Neuroscience
Computing
Data analysis
Data Organization
Development
Informatics
Open-Source Code
Open-Source Software
Workflows
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Köster, J., & Rahmann, S. (2018). Snakemake-a scalable bioinformatics workflow engine. Bioinformatics, 34(20), 3600.
Gorgolewski, K., Burns, C. D., Madison, C., Clark, D., Halchenko, Y. O., Waskom, M. L., & Ghosh, S. S. (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front. Neuroinform., 5, 13.
Glatard, T., Kiar, G., Aumentado-Armstrong, T., Beck, N., Bellec, P., Bernard, R., … Evans, A. C. (2018). Boutiques: a flexible framework to integrate command-line applications in computing platforms. Gigascience, 7(5).
Hopcroft, J. E. (2001). Introduction to Automata Theory, Languages, and Computation. Addison-Wesley.
Chapman, B., Chilton, J., Heuer, M., Kartashov, A., Leehr, D., Ménager, H., … Stojanovic, L. (2016). Common Workflow Language, v1.0 (P. Amstutz, M. R. Crusoe, & N. Tijanić, Eds.). doi:10.6084/m9.figshare.3115156.v2