Supramolecular architectures for neural prostheses
Author(s)
Theogarajan, Luke Satish Kumar
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Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Advisor
Marc A. Baldo.
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Neural prosthetic devices offer a means of restoring function that have been lost due to neural damage. The first part of this thesis investigates the design of a 15-channel, low-power, fully implantable stimulator chip. The chip is powered wirelessly and receives wireless commands. The chip features a CMOS only ASK detector, a single-differential converter based on a novel feedback loop, a low-power adaptive bandwidth DLL and 15 programmable current sources that can be controlled via four commands. Though it is feasible to build an implantable stimulator chip, the amount of power required to stimulate more than 16 channels is prohibitively large. Clearly, there is a need for a fundamentally different approach. The ultimate challenge is to design a self-sufficient neural interface. The ideal device will lend itself to seamless integration with the existing neural architecture. This necessitates that communication with the neural tissue should be performed via chemical rather than electrical messages. However, catastrophic destruction of neural tissue due to the release of large quantities of a neuroactive species, like neurotransmitters, precludes the storage of quantities large enough to suffice for the lifetime of the device. The ideal device then should actively sequester the chemical species from the body and release it upon receiving appropriate triggers in a power efficient manner. This thesis proposes the use of ionic gradients, specifically K+ ions as an alternative chemical stimulation method. The required ions can readily be sequestered from the background extracellular fluid. The parameters of using such a stimulation technique are first established by performing in-vitro experiments on rabbit retinas. The results show that modest increases (~~10mM) of K+ ions are sufficient to elicit a neural response. (cont.) The first building block of making such a stimulation technique possible is the development of a potassium selective membrane. To achieve low-power the membranes must be ultrathin to allow for efficient operation in the diffusive transport limited regime. One method of achieving this is to use lyotropic self-assembly; unfortunately, conventional lipid bilayers cannot be used since they are not robust enough. Furthermore, the membrane cannot be made potassium selective by simply incorporating ion carriers since they would eventually leach away from the membrane. A single solution that solves all the above issues was then investigated in this thesis. A novel facile synthesis of self-assembling receptor functionalized polymers was achieved. By combining the properties of hydrophobic and hydrophilic interactions of two polymers a triblock co-polymer was synthesized. The middle hydrophobic block was composed of biocompatible polysiloxanes and further derivatized to posses ion recognition capabilities via pendant crown ether chains. The hydrophilic blocks were composed of biocompatible polyoxazolines. The self-assembling properties of the membrane were then studied by electroforming them into vesicular structures. The ion responsive properties of these polymers were then examined. These polymers show emergent behavior such as, spontaneous fusion and shape transformation to ionic stimuli due to the synergy between form and function. The results from the thesis show that it is feasible to build a renewable chemically based neural prosthesis based on supramolecular architectures. However, there remains a lot of fundamental work that needs to be pursued in the future to bring the idea to complete fruition.
Description
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007. Includes bibliographical references (leaves 213-230).
Date issued
2007Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.