J Vis Exp. 2022 Jul 13;(185). doi: 10.3791/63878.
Peripheral nerve ultrasound is a well-established imaging technique to evaluate certain peripheral nerve pathologies. However, there is a poor correlation between ultrasound abnormalities of peripheral nerves and electrodiagnostic or clinical evidence of axonal loss. This is a significant limitation of peripheral nerve ultrasound, as many peripheral nerve diseases encountered in clinical settings are related to axonal loss. Furthermore, clinical and electrodiagnostic evidence of axonal loss directly correlates with disability in all peripheral nerve diseases. However, due to the floor effects often encountered in electrodiagnostic studies, these correlations, as well as definitive diagnoses, are often challenging. Thus, imaging techniques that correlate with axonal loss are essential for expanding the utility of peripheral nerve ultrasound as a potential biomarker for peripheral nerve diseases. With new technological advancements and the ever-increasing imaging capabilities of high-frequency ultrasound, the palmar and digital nerve branches of the hand can be imaged with exceptionally high resolution even using point-of-care ultrasound devices. Their superficial and distal-most anatomic locations are ideal for evaluating polyneuropathies, as these branches degenerate earliest during axonal loss. However, no studies have systematically evaluated these nerve branches to determine if they can be reproducibly measured with ultrasound. The current protocol was adapted for the systematic assessment of cross-sectional areas of the median and ulnar nerves in the palmar surface and digits of the hand. This protocol provides reference data for a subset of nerves that demonstrate high intraclass correlation coefficients between three separate ultrasonographers. Finally, as a proof of concept and to demonstrate the clinical applications of this protocol, representative data from individuals with genetically confirmed inherited polyneuropathies are compared with established normative data to examine cross-sectional area differences.
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