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Abstract

Neuroprosthetic devices are engineered to study, support or replace impaired functions of the nervous system. The neural interface is an essential element of neuroprosthetic systems as it allows for transduction of signals and stimuli of desired functions (recording, stimulation, neuromodulation). A persistent challenge for translating neuroprosthetics from the laboratory to the clinic is the lack of long-term biointegration of neural interfaces. This thesis aims at improving biointegration of neural interfaces by reducing the mechanical mismatch between implant and neural tissue. In this thesis, the design, fabrication and characterization of soft surface neural interfaces is described. These soft neural interfaces, termed electronic dura mater or e-dura, were designed to mimic the mechanical properties of dura mater. In contrast with conventional neural technologies, e-dura neural interfaces were made of soft and compliant materials. They conform to the circumvolutions of the brain and spinal cord and follow their dynamic deformation without damaging the surrounding neural tissues. These soft multimodal neural interfaces were fabricated on silicone substrates using techniques imported from the microfabrication industry and incorporate compliant electrodes, stretchable electrical interconnects and a micro-catheter for drug delivery. Evaluation of the e-dura biointegration with spinal tissues demonstrated reduced foreign body reaction, compared to stiff polyimide based implants. Additionally, mechanical tests on an in-vitro spinal surrogate provided insights on the complex biomechanical coupling between implants and neural tissue. E-dura interfaces, implanted in rodents, maintained their functionality over extended periods and provided high-resolution neuronal recordings and concurrent delivery of electrical and chemical neuromodulation. Eventually, the use of gallium thin films was explored to create highly conductive and stretchable interconnects for integration of active electronic components in e-dura neural interfaces.

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