A Comprehensive, FAIR File Format for Neuroanatomical Structure Modeling

Sullivan, A. E.; Tappan, Susan; Angstman, P. J.; Rodríguez, Alfredo; Thomas, G. C.; Hoppes, D. M.; Abdul-Karim, M. A.; Heal, Maci; Glaser, Jack R.

doi:10.1007/s12021-021-09530-x

Cited by 8 publications

(3 citation statements)

References 43 publications

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“…AxoDetect is capable of both the image segmentation and model construction portions of the AS FEM modeling approach in one workflow. By combining this flexible design with open-source standards and file formats like those described by [19], these platforms and tools can have even farther reach in the development of next generation of neuromodulation predictive models. We also used AD to evaluate the importance of having an accurate anatomical composition of the nerve tissue, inclusive of blood vessels and fat, in the hybrid models for calculating nerve activation thresholds.…”

Section: Discussionmentioning

confidence: 99%

AxoDetect: an automated nerve image segmentation and quantification workflow for computational nerve modeling

Lloyd,

Alejandra Gonzalez-Gonzalez,

Romero-Ortega

2024

J. Neural Eng.

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Objective: Bioelectronic treatments targeting near-organ innervation have unprecedented clinical applications. Particularly in the spleen, the inhibition of the cholinergic inflammatory response by near-organ nerve stimulation has potential to replace pharmacological treatments in chronic and autoimmune diseases. A caveat is that the optimization of therapeutic stimulation parameters relies on in vivo experimentation, which becomes challenging due to the small nerve diameters (2μm), complex anatomy, and mixed axon type composition of the autonomic nerves. Effective development of in silico models requires tools which allow for fast and efficient quantification of axonal composition of specific nerves. Current approaches to generate such information rely on manual image segmentation and quantification. Approach: We developed a combined image-segmentation and model-generation software called AxoDetect: a target- and format-agnostic computer vision algorithm which can segment myelin, endo/epineurium, and both myelinated and unmyelinated fibers from a nerve image without training. Main results: AxoDetect is over 10 times faster on average when compared with current automatic methods while maintaining flexibility through the use of tunable pixel threshold filters to detect different types of tissue. When compared to a distribution-based and a manually segmented model of the splenic nerve terminal branch 1, the model generated with AxoDetect had comparable threshold prediction and was able to accurately detect an increase in activation threshold caused by the addition of surrounding fat tissue to the modeled nerve. Significance: AxoDetect contributes to the acceleration of neuromodulation treatment development through faster model design and iteration without requiring training. Furthermore, the computer vision approach and tunable nature of the filters in our method allow for its use in a variety of histological applications. Our approach will impact not only the study of nerves but also the design of implantable neural interfaces to enhance bioelectronic therapeutic options.

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Section: Discussionmentioning

confidence: 99%

AxoDetect: an automated nerve image segmentation and quantification workflow for computational nerve modeling

Lloyd,

Alejandra Gonzalez-Gonzalez,

Romero-Ortega

2024

J. Neural Eng.

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“…Fitting scaffold models to the experimental data is done by identifying a common set of fiducial points within these images and demarcating concordant points in the segmentation data and the scaffold model. The segmentation data, stored according to MBF Bioscience’s XML neuromorphological file specification ( Angstman et al, 2020 ; Sullivan et al, 2021 ), is then ingested into ABI’s ScaffoldFitter software ( RRID:SCR_019002 ). Each dataset is registered to an individual scaffold, transforming the data into a common coordinate space, making it possible to integrate or average multiple datasets in this space.…”

Section: Methodsmentioning

confidence: 99%

Extending and using anatomical vocabularies in the stimulating peripheral activity to relieve conditions project

Surles-Zeigler

Sincomb

Gillespie

et al. 2022

Front. Neuroinform.

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The stimulating peripheral activity to relieve conditions (SPARC) program is a US National Institutes of Health-funded effort to improve our understanding of the neural circuitry of the autonomic nervous system (ANS) in support of bioelectronic medicine. As part of this effort, the SPARC project is generating multi-species, multimodal data, models, simulations, and anatomical maps supported by a comprehensive knowledge base of autonomic circuitry. To facilitate the organization of and integration across multi-faceted SPARC data and models, SPARC is implementing the findable, accessible, interoperable, and reusable (FAIR) data principles to ensure that all SPARC products are findable, accessible, interoperable, and reusable. We are therefore annotating and describing all products with a common FAIR vocabulary. The SPARC Vocabulary is built from a set of community ontologies covering major domains relevant to SPARC, including anatomy, physiology, experimental techniques, and molecules. The SPARC Vocabulary is incorporated into tools researchers use to segment and annotate their data, facilitating the application of these ontologies for annotation of research data. However, since investigators perform deep annotations on experimental data, not all terms and relationships are available in community ontologies. We therefore implemented a term management and vocabulary extension pipeline where SPARC researchers may extend the SPARC Vocabulary using InterLex, an online vocabulary management system. To ensure the quality of contributed terms, we have set up a curated term request and review pipeline specifically for anatomical terms involving expert review. Accepted terms are added to the SPARC Vocabulary and, when appropriate, contributed back to community ontologies to enhance ANS coverage. Here, we provide an overview of the SPARC Vocabulary, the infrastructure and process for implementing the term management and review pipeline. In an analysis of >300 anatomical contributed terms, the majority represented composite terms that necessitated combining terms within and across existing ontologies. Although these terms are not good candidates for community ontologies, they can be linked to structures contained within these ontologies. We conclude that the term request pipeline serves as a useful adjunct to community ontologies for annotating experimental data and increases the FAIRness of SPARC data.

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“…The neuroscience community increasingly recognizes the bene ts of Findable, Accessible, Interoperable, and Reusable (FAIR 17 ) resources 18 . Possible FAIR le format candidates for digital reconstructions of neural morphology include XML (Extensible Markup Language) by MBF Bioscience 19 , NML by NeuroML 20,21 , and SWC (named after its initial designers Ed Stockley, Howard Wheal, and Robert Cannon), which was introduced in the rst public online archive of traced neuronal arbors 22 .…”

Section: Introductionmentioning

confidence: 99%

Online conversion of reconstructed neural morphologies into standardized SWC format

Ascoli

Mehta

Ljungquist

et al. 2023

Preprint

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Digital reconstructions provide an accurate and reliable way to store, share, model, quantify, and analyze neural morphology. Continuous advances in cellular labeling, tissue processing, microscopic imaging, and automated tracing catalyzed a proliferation of software applications to reconstruct neural morphology. These computer programs typically encode the data in custom file formats. The resulting format heterogeneity severely hampers the interoperability and reusability of these valuable data. Among these many alternatives, the SWC file format has emerged as a popular community choice, coalescing a rich ecosystem of related neuroinformatics resources for tracing, visualization, analysis, and simulation. This report presents a standardized specification of the SWC file format. Additionally, we introduce xyz2swc, a free online service that converts all 23 reconstruction formats (and 68 variations) described in the scientific literature into the SWC standard. The xyz2swc service is available open source through a user-friendly browser interface (https://neuromorpho.org/xyz2swc/ui/) and an Application Programming Interface (API).

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A Comprehensive, FAIR File Format for Neuroanatomical Structure Modeling

Cited by 8 publications

References 43 publications

AxoDetect: an automated nerve image segmentation and quantification workflow for computational nerve modeling

AxoDetect: an automated nerve image segmentation and quantification workflow for computational nerve modeling

Extending and using anatomical vocabularies in the stimulating peripheral activity to relieve conditions project

Online conversion of reconstructed neural morphologies into standardized SWC format

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