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Spinal Nerves: Plexus I

Spinal Nerves: Plexus I

Nerve plexuses are networks of interlacing nerves that serve as communication hubs to distribute and organize nerve action across various body regions. The nerve plexuses are organized into the cervical plexus located in the neck region, brachial plexus in the shoulder area, lumbar plexus found in the lower back, sacral plexus situated in the pelvis, and coccygeal plexus located in the coccygeal region.
The Cervical Plexus
The cervical plexus, formed by the anterior rami of the first four...

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Related Experiment Video

Updated: Sep 11, 2025

Three-dimensional Imaging of Nociceptive Intraepidermal Nerve Fibers in Human Skin Biopsies

Three-dimensional Imaging of Nociceptive Intraepidermal Nerve Fibers in Human Skin Biopsies

Published on: April 29, 2013

Self-supervised representation learning for nerve fiber distribution patterns in 3D-PLI.

Alexander Oberstrass^1,2, Sascha E A Muenzing¹, Meiqi Niu¹

¹Institute of Neuroscience and Medicine (INM- 1), Research Centre Jülich, Jülich, Germany.

Imaging Neuroscience (Cambridge, Mass.)

|August 13, 2025

Summary

This summary is machine-generated.

We developed a data-driven method using self-supervised learning to analyze nerve fiber architecture in 3D-PLI brain images. This approach provides observer-independent characterization for better understanding brain organization.

Keywords:

contrastive learning deep learning fiber architecture occipital lobe polarized light imaging vervet monkey brain

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Last Updated: Sep 11, 2025

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Published on: April 29, 2013

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Stereo-Imaging System DLT Calibration to Capture 3D In Situ Displacements of Stretched Peripheral Nerves

Published on: January 12, 2024

Area of Science:

Neuroscience
Biomedical Imaging
Computational Biology

Background:

Understanding human brain organization requires quantifiable descriptors of nerve fiber architecture.
Three-dimensional polarized light imaging (3D-PLI) offers high-resolution insights into myelinated nerve fiber organization.
Current methods lack observer-independent characterization of fiber architecture in 3D-PLI.

Purpose of the Study:

To propose a fully data-driven approach for observer-independent characterization of nerve fiber architecture in 3D-PLI images.
To introduce a novel self-supervised representation learning objective, 3D-Context Contrastive Learning (CL-3D).
To enable downstream analysis tasks like multimodal correlation, clustering, and mapping of brain structures.

Main Methods:

Application of self-supervised representation learning using a 3D-Context Contrastive Learning (CL-3D) objective.
Utilizing the spatial neighborhood of texture examples across histological sections for sampling positive pairs.
Employing specifically designed image augmentations for robustness against variations in 3D-PLI parameter maps.

Main Results:

Extracted features demonstrate high sensitivity to nerve fiber configurations.
Features exhibit robustness to variations between consecutive histological brain sections.
Demonstrated practical applicability for clustering, classification, and retrieval of fiber architecture components.

Conclusions:

The CL-3D approach provides a robust and data-driven method for characterizing nerve fiber architecture in 3D-PLI.
This method facilitates observer-independent analysis, crucial for advancing brain organization studies.
The approach has significant potential for various neuroimaging analysis tasks, including U-fiber identification.