02/11/2025
By Danielle Fretwell

The Francis College of Engineering, Department of Mechanical Engineering, invites you to attend a Doctoral Dissertation Proposal defense by Dongyang Yi on: "Accurate and Buckling-Free Implantation of Microelectrode Arrays as Brain-Machine Interfaces"

Candidate Name: Dongyang Yi
Degree: Doctoral
Defense Date: Tuesday, February 25, 2025
Time: 2 - 4 p.m.
Location: Perry Hall, Room 215

Committee:
Advisor: Lei Chen, Ph.D., Assistant Professor, Mechanical and Industrial Engineering, University of Massachusetts Lowell

Committee Members:
Lei Chen, Ph.D., Assistant Professor, Mechanical and Industrial Engineering, University of Massachusetts Lowell
Scott Stapleton, Ph.D., Associate Professor, Mechanical and Industrial Engineering, University of Massachusetts Lowell
Walfre Franco, Ph.D., Department Chair, Associate Professor, Biomedical Engineering, University of Massachusetts Lowell
Brendon O. Watson, M.D., Ph.D./ Assistant Professor, Department of Psychiatry, University of Michigan

Brief Abstract:

1: Introduction
High temporal resolution electrophysiological recording with implantable microelectrode arrays (MEAs) is a powerful tool for understanding brain function. Brain-wide signal recording requires the implantation of ultra-small, flexible MEAs, but this process faces two key challenges: (1) the labor-intensive, skill-dependent manual implantation process and (2) the buckling of MEAs against the brain’s meningeal layers during insertion. This dissertation addresses these challenges by (a) developing surgical platforms to streamline multi-region implantation and (b) conducting experimental and theoretical studies of microelectrode insertion mechanics to reduce buckling risk.

2: 3D-Printed Headcap Systems for Custom Multi-Region Implantations
To address the inefficiencies of manual implantation, we developed a 3D-printed skull cap system tailored to pre-determined MEA implantation locations, enabling custom neuroscience experiments. A prototyped 32-channel microwire MEA system successfully recorded spiking activity for five months through the skull cap. Additionally, we designed a cost-efficient 3D-printed headcap with an embedded microdrive (THEM) system to streamline precise multi-region neural probe implantations. By shifting stereotaxic alignment to pre-surgical preparation and incorporating a preassembled framework for packaging and customization, our system reduces surgical time, simplifies multi-implant procedures, and improves accuracy and repeatability.

3: Investigation of the Static Buckling Behavior of Microwire Electrodes
Microwire electrodes are often modeled as columns to evaluate buckling behavior using Euler’s buckling equation. Strategies to increase the critical buckling load, such as stiffening with rigid supports or reducing unsupported length (e.g., our published inchworm insertion mechanism), have been explored. However, the relationship between microwire configuration (size and tip geometry) and the buckling load constant remains poorly understood. To address this, we developed a multi-layer brain-mimicking phantom that replicates dura-pia-brain tissue mechanics for implantation experiments. Protocols were established to fabricate diverse tip profiles and measure critical buckling loads. Future work will integrate insights on tip geometry, wire diameter, and material properties to refine theoretical predictions of buckling loads for brain-machine interface development.

4: Dynamic Effects of Ultrasonic Vibration-Assisted Insertion of Microwire Electrodes
Ultrasonic vibration-assisted insertion has shown promise for penetrating rodent pia mater, but the underlying mechanisms remain unclear, limiting its application to tougher membranes like dura mater or thinner neural interfaces. Using our experimental setup for monitoring ultrasonic-assisted insertion, preliminary findings suggest that ultrasonic vibrations influence insertion by (1) increasing the wire’s critical buckling load through enhanced stiffness and stability (tested against a rigid metal plate) and (2) reducing the penetration force required for membrane rupture (tested with a multi-layer brain phantom). This chapter aims to correlate vibration parameters (frequency and magnitude) with buckling resistance and insertion performance metrics, such as rupture force and membrane dimpling depth.