A multiscale scaffold framework for mapping the human vagus nerve across individuals

The SPARC Phase-2 REVA project has produced high-resolution multimodal imaging and segmentation datasets of vagus nerves from a large cohort of human subjects. All resulting datasets are in the process of being publicly released through the SPARC Portal in compliance with FAIR Data Principles. This study registers data from these published datasets into anatomical scaffolds defining a standardized vagus trunk coordinate system with per-subject radiating branch patterns, which are highly variable. This transforms the data to be independent of subject scale and normalized to anatomical landmarks, required for practical population-level and comparative studies of vagus nerve anatomy. We present techniques and results of the vagus scaffold mapping work, including overviews of the geometric normalization process, plus visualizations and quantitative information about the number, position and orientations of branches innervating selected target organs, across the whole cohort of subjects. Subject-specific vagus scaffolds are derived from ex-vivo specimens that are imaged and digitized in cut segments then digitally re-stitched. Thus, they do not capture the in-body anatomical configuration. We present a process for remapping subject-specific vagus scaffolds into the body and show visualizations of the results. This enables examination of spatial relationships between the vagus and target organs registered in the body. Finally, we describe how these FAIR datasets, together with free and open-source software tools, support the registration of fascicular and immunohistochemical data into the scaffold framework. This integrated, multiscale representation of vagus nerve anatomy provides a foundation for advancing current understanding and informing the development of neuromodulation therapies, particularly the design and placement of nerve stimulation devices. This work is supported by the NIH SPARC program under award numbers OT3OD025347 and 75N98022C00019. This abstract was presented at the American Physiology Summit 2026 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.