Biomechanics Characterization of Autonomic and Somatic Nerves by High Dynamic Closed-Loop MEMS force sensing

The biomechanics of peripheral nerves are determined by the blood-nerve barrier (BNB), together with the epineural barrier, extracellular matrix, and axonal composition, which maintain structural and functional stability. These elements are often ignored in the fabrication of penetrating devices, and the implant process is traumatic due to the mechanical distress, compromising the function of neuroprosthesis for sensory-motor restoration in amputees. Miniaturization of penetrating interfaces offers the unique opportunity of decoding individual nerve fibers associated to specific functions, however, a main issue for their implant is the lack of high-precision standardization of insertion forces. Current automatized electromechanical force sensors are available; however, their sensitivity and range amplitude are limited (i.e. mN), and have been tested only in-vitro. We previously developed a high-precision bi-directional micro-electromechanical force sensor, with a closed-loop mechanism (MEMS-CLFS), that while measuring with high-precision (-211.7N to 211.5N with a resolution of 4.74nN), can be used in alive animal. Our technology has an on-chip electrothermal displacement sensor with a shuttle beam displacement amplification mechanism, for large range and high-frequency resolution (dynamic range of 92.9 dB), which eliminates the adverse effect of flexural nonlinearity measurements, observed with other systems, and reduces the mechanical impact on delicate biological tissue. In this work, we use the MEMS-CLFS for in-vivo bidirectional measurement of biomechanics in somatic and autonomic nerves. Furthermore we define the mechanical implications of irrigation and collagen VI in the BNB, which is different for both autonomic and somatic nerves ([~] 8.5-8.6 fold density of collagen VI and vasculature CD31+ in the VN vs ScN). This study allowed us to create a mathematical approach to predict insertion forces. Our data highlights the necessity of nerve-customization forces to prevent injury when implanting interfaces, and describes a high precision MEMS technology and mathematical model for their measurements.