Date of Award
2023
Document Type
Thesis (Ph.D.)
Department or Program
Engineering Sciences
First Advisor
Hui Fang
Abstract
The primary goal of neural interface technology is to establish a connection between the nervous system and the external world by recording and modulating neural signals. Over the past few decades, microelectrode array (MEA) technology has played an essential role in the acquisition of electrophysiological signals, with impressive progress being made towards developing high-density, large-throughput, and ultra-flexible MEAs. However, despite these advancements, there remain long-standing limitations in current neural interfaces. For example, electrophysiology is limited in its ability to distinguish cell types and has limited spatial resolution due to its electrical recording nature. Furthermore, conventional neural MEAs are limited to interfacing with the brain in two dimensions (2D), which is incompatible with the three-dimensional (3D) nature of neural circuits. Besides the design of MEAs, the connecting strategy is also a crucial factor in successful neural recording, with no reliable approach currently available to efficiently connect large-scale, soft arrays to recording electronics. To address these limitations, we present two next-generation neural interfaces, namely transparent microelectrode arrays and 3D neural probes, along with detailed device characterizations, such as impedance, crosstalk, and device stability. In addition, we introduce a novel device bonding strategy called the MagMatrix interface to achieve a robust and repeatable bonding between soft MEAs and the data acquisition system (DAQ). Furthermore, we provide comprehensive in vivo validations in rodents and non-human primates (NHP) brains to evaluate the performance of these advanced neural interfaces. Collectively, we believe that these next-generation neural interfaces will be widely utilized in neuroscience research, significantly advancing our understanding of the nervous system.
Original Citation
Transparent electrophysiology microelectrodes and interconnects from metal nanomesh[J]. ACS nano, 2017, 11(4): 4365-4372
Bilayer nanomesh structures for transparent recording and stimulating microelectrodes[J]. Advanced Functional Materials, 2017, 27(48): 1704117.
Transparent arrays of bilayer-nanomesh microelectrodes for simultaneous electrophysiology and two-photon imaging in the brain[J]. Science advances, 2018, 4(9): eaat0626.
Crosstalk in polymer microelectrode arrays. Nano research, 14, 3240-3247.
Recommended Citation
Qiang, Yi, "Design of Next-Generation Neural Interfaces for Decoding the Brain" (2023). Dartmouth College Ph.D Dissertations. 215.
https://digitalcommons.dartmouth.edu/dissertations/215
