Mechano-to-Neural Transduction of the Pacinian Corpuscle

Abstract

Cutaneous mechanoreceptors are responsible for our ability to distinguish between different touch modalities and experience the physical world around us. Mechanoreceptors are innervated by afferent mechanosensitive neurons that transduce mechanical stimuli into action potentials and terminate in specialized end organs. The Pacinian corpuscle (PC) has been studied more than any of our other mechanoreceptors due to its large size and ease of identification during dissection. The PC, which is found primarily within the dermis of glabrous skin, responds to low-amplitude, high-frequency vibrations in the 20-1000 Hz range. The PC functions as a bandpass filter to vibrations, an effect attributed to the structural and mechanical complexity of its end organ. The PC contains a central mechanosensitive nerve fiber (neurite) that is encapsulated by alternating layers of flat, epithelial-type cells (lamellae) and fluid. The overarching goal of this thesis was to unify the anatomical and electrophysiological observations of the PC via a detailed mechanistic model of PC response to mechanical stimulation, requiring a multiphysics, multiscale approach. First, we developed a multiscale finite-element mechanical model to simulate the equilibrium response of the PC to indentation while accounting for the layered, anisotropic structure of the PC and its deep location within the skin. Next, we developed a three-stage finite-element model of the PC’s mechanical and neural responses to a vibratory input that accounted for the lamellar mechanics and neurite electrochemistry. This mechano-neural model was able to simulate the PC’s band-pass filtration of vibratory stimuli and rapid adaptation to sustained mechanical stimuli. We then used this model to evaluate the relationship between the PC’s material and geometric parameters and its response to vibration and developed dimensionless expressions for the relationship between these parameters and peak frequency or bandwidth. We then embedded multiple mechano-neural PC models within a finite-element model of human skin to simulate the mechanical and neural behavior of a PC cluster in vivo. We then performed a literature search to compile the structural parameters of PCs from various species and used our mechano-neural model to simulate the frequency response across species. Finally, we isolated PCs from human cadaveric hands and performed micropipette aspiration experiments to determine an apparent Young’s modulus of the PC. The computational and experimental work performed in this thesis contribute to the understanding of the fundamental behavior of mechanoreceptors, which is a necessary first step towards the development of haptic feedback-enabled devices.