학술논문

On the Relationship Between Fascicle Diameter and Perineurium Thickness in the Ulnar Nerve of Pigs
Document Type
Conference
Source
2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Neural Engineering (NER), 2023 11th International IEEE/EMBS Conference on. :1-4 Apr, 2023
Subject
Bioengineering
Signal Processing and Analysis
Animals
Neural engineering
Anatomical structure
Conductivity
Mathematical models
Electric fields
Thickness measurement
ulnar nerve
nerve morphology
perineurium thickness
nerve stimulation
Language
ISSN
1948-3554
Abstract
The anatomical structures of peripheral nerves significantly influence the performance and selectivity of neural interfaces. Among these structures, the perineurium thickness, because of its high resistivity, is important in shaping the electric field distribution within a nerve. However, data on the perineurium thickness of somatic nerves in animals is sparse. As animal models are the first step towards developing novel implantable nerve interfaces, this study characterises the perineurium thickness in the ulnar nerve of pigs. Distal and proximal sections of the ulnar nerve $(\mathbf{n}=\boldsymbol{6})$ were extracted, stained with haematoxylin and eosin, and the perineurium thickness and fascicular diameters were measured. In total, 129 fascicles were quantified, and the average fascicle diameter was ${269\pm 73.3}\ \upmu \mathrm{m}$ and $\boldsymbol{277\pm 81.1}\ \upmu \mathrm{m}$ for the distal and proximal nerves, respectively $(\boldsymbol{p}=\boldsymbol{0.59})$. A linear relationship was observed between fascicle diameter and perineurium thickness $(\mathbf{R}^{2}=\boldsymbol{0.69})$. This relationship was affected by the location of the nerve section, with distal sections having a greater perineurium thickness than proximal segments. Finally, equations were provided to estimate the perineurium thickness based on fascicle diameter. This information can be used to develop realistic peripheral nerve models, which can reduce variability and improve the selectivity of neural interfaces.