Carnegie Mellon University
Browse

File(s) under embargo

4

month(s)

16

day(s)

until file(s) become available

Microfabricated Stainless Steel Neural Probes and Electro-optic Multiplexing for Ultra-High-density Neural Recording

thesis
posted on 2024-04-12, 18:44 authored by Zabir AhmedZabir Ahmed

 Neural interfaces, especially implantable neural probes, are tools for understanding brain function and its role in mediating cognition and physical skills. Directly inserted into the brain tissue, implantable probes can record neuronal activity. Recent advances have focused on increasing electrode density and improving biocompatibility of these neural probes to minimize tissue damage. Still scalability, cost-effectiveness, and reusability of neural probes need to be significantly improved, while maintaining stringent biocompatibility standards.

This thesis addresses two critical needs in neural interface technology design: (i) the need for robust, high-density and high aspect-ratio neural probes for large animals and (ii) the demand for massively multiplexed recording from a large number of channels. 

To address the first need, I have introduced “Steeltrode”, a microfabricated, high aspect ratio stainless steel neural probe with lithographically-defined features. Steeltrode architecture addresses the limitations of conventional silicon-based neural probes for use in non-human primates (NHP). Silicon probes, while effective for rodents, are too brittle, especially for long shanks required to reach deep brain regions in NHPs, often breaking due to low fracture toughness. I developed a novel fabrication process to implement robust, scalable, high aspect ratio steeltrodes for both small and large animal brains, by leveraging the mechanical properties and biocompatibility of stainless steel. I demonstrated steeltrodes with long shanks up to 12 cm, featuring a slim cross-section of 140 µm x 280 µm, equipped with 16-102 densely packed electrodes, validated through high-fidelity neural recordings in macaques as well as rodent models to detect spontaneous and evoked single units, multi-units and local field potentials (LFPs) in auditory and motor cortex. 

In my thesis, I addressed another major need for simultaneous recording from a large ensemble of neurons. Currently, in passive probes each electrode is connected to a dedicated wire. Therefore, adding more channels, while keeping the probe shank slim is hindered by fabrication difficulties and signal crosstalk. Active CMOS electronic neural probes use time division multiplexing (TDM) for accessing multiple electrodes with fewer wires, but they face bandwidth and power limitations, restricting the number of simultaneously addressable channels. To overcome these challenges, a paradigm shift in neural probe design is required. We designed an innovative neural probe architecture based on truly parallel optical wavelength division multiplexing (WDM) of neural recording to massively scale up the number of simultaneously recorded channels, while transmitting the aggregate data through a single optical waveguide out of the brain. The core of this technology is a novel electro-optic sensor based on 2D graphene integrated with high quality factor (high-Q) photonic resonators to convert electrophysiology signals to optical modulation. I have experimentally demonstrated that a low noise floor of ~14 µV can be achieved in the spikes band and also sub-mV LFP signals can be detected from mouse brain tissue in cortical layers and hippocampus. This novel design for optically multiplexed neural recording offers massive scalability, potentially enabling simultaneous recording from thousands of channels. 

In my thesis, I discuss the design and experimental characterization of steeltrodes and electro-optic sensors for high resolution electrophysiology recording. 

History

Date

2024-02-20

Degree Type

  • Dissertation

Department

  • Electrical and Computer Engineering

Degree Name

  • Doctor of Philosophy (PhD)

Advisor(s)

Chamanzar Maysamreza

Usage metrics

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC