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Abstract Dorsal root ganglia (DRG), which contain the somata of primary sensory neurons, have increasingly been considered as novel targets for clinical neural interfaces, both for neuroprosthetic and pain applications. Effective use of either neural recording or stimulation technologies requires an appropriate spatial position relative to the target neural element, whether axon or cell body. However, the internal three‐dimensional spatial organization of human DRG neural fibers and somata has not been quantitatively described. In this study, we analyzed 202 cross‐sectional images across the length of 31 human L4 and L5 DRG from 10 donors. We used a custom semi‐automated graphical user interface to identify the locations of neural elements in the images and normalize the output to a consistent spatial reference for direct comparison by spinal level. By applying a recursive partitioning algorithm, we found that the highest density of cell bodies at both spinal levels could be found in the inner 85% of DRG length, the outer‐most 25–30% radially, and the dorsal‐most 69–76%. While axonal density was fairly homogeneous across the DRG length, there was a distinct low density region in the outer 7–11% radially. These findings are consistent with previous qualitative reports of neural distribution in DRG. The quantitative measurements we provide will enable improved targeting of future neural interface technologies and DRG‐focused pharmaceutical therapies, and provide a rigorous anatomical description of the bridge between the central and peripheral nervous systems.
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This content will become publicly available on January 1, 2026
Physiological Computing for All: Exploring Neural Interface Education
In 2019, Meta purchased CTRLLabs, a neural interface start-up, for more than $500 million. A recent report by Morgan Stanley analysts valued the total addressable market of brain-computer interfaces (BCIs) at around $400 billion in the U.S. alone. Headlines discussing the exploration of novel neural interface technologies by companies such as Blackrock Neurotech, Synchron, and Neuralink have become increasingly common. Although the media often piques our curiosity about this technology, few people truly comprehend its underlying mechanics. Over the past decade, I have dedicated my career to addressing this gap. This article shares experiences and insights obtained from introducing students to physiological computing through the Neuroblock software.
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- Award ID(s):
- 2045561
- PAR ID:
- 10585680
- Publisher / Repository:
- ACM interactions
- Date Published:
- Journal Name:
- Interactions
- Volume:
- 32
- Issue:
- 1
- ISSN:
- 1072-5520
- Page Range / eLocation ID:
- 40 to 43
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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