Investigates nerve cuff designs, enhancing anodal block in myelinated fibers, suggesting improved nerve block efficacy.
Anodal block is a common mechanism used to generate unidirectional activation of myelinated nerve fibers. However, nerve block is often inconsistent and difficult to achieve. In this study, we used a computational model to investigate electrode cuff designs that can be exploited to enhance anodal block. Asymmetric bipolar and tripolar nerve cuff electrodes were initially tested and validated in a rodent sciatic nerve model. A finite element model was implemented, with physical dimensions approximating a rat sciatic nerve implanted with a flat-interface nerve electrode. The McIntyre–Richardson–Grill model was used to predict the activation, block, and reactivation thresholds of myelinated fibers (diameter = 5.7 to 16 μm) as the stimulation amplitude was increased. In vivo experiments were performed using identical nerve cuff electrodes. The asymmetric electrode designs were subsequently investigated using a human-sized model of a nerve implanted with a cylindrical cuff electrode. Key design parameters included the electrode edge length (EEL), anodic contact width, inter-electrode distance, and current division between primary and secondary anodes. The rodent model reproduced blocking properties in both computational and in vivo studies, where maximum anodic block reached 88% and 75% of activated fibers, respectively. Simulations involving the human nerve model identified two critical design parameters that can markedly enhance anodal block. An optimal distal EEL (≤ 1 mm) enhanced the performance of bipolar stimulation (anodic block = 91%), whereas, optimizing the current ratio between the primary:secondary anodes (e.g., 6:4) enhanced nerve block in asymmetric tripolar electrodes (anodic block = 90%). This study presents a set of instructions for optimizing the anodal blocking properties of nerve cuff electrodes, where an asymmetric tripolar design with variable anodic current offers a novel method that can adjust for variability at the nerve electrode interface.
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Tovbis et al. (2026) studied this question.
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