Doctor of Philosophy, Case Western Reserve University, 0, Biomedical Engineering

Brain-computer interfaces (BCIs) bridge the gap between dysfunctional central nervous system circuitry and prosthetic devices for individuals with somatic function loss. Intracortical microelectrodes (IMEs) record action potentials and transmit signals through BCI systems to enable external device functionality for neurorehabilitation. Although neural interfaces address the clinical need for restoring neurological function, IMEs face significant biological challenges. The initial rupturing of the blood-brain barrier (BBB) and chronic neuroinflammatory response can lead to IME failure. One strategy to mitigate biological response involves using anti-inflammatory drugs like dexamethasone (DEX), which suppress pro-inflammatory genes. However, systemically administered pharmaceuticals lack targeting functionality, limiting their therapeutic potential. This dissertation explores improving IME longevity by mitigating neuroinflammation and resealing the BBB through the use of a drug-loaded bio-inspired nanoparticle. Our approach employs synthetic platelet-inspired nanoparticles (SPINs) designed to leverage platelet physiology for vascular wound healing. Using IHC, we evaluated SPINs' ability to target IME sites, reduce BBB permeability, and decrease the presence of activated glial cells. Within the next chapter, we conducted the first omics analysis of SPINs as a drug delivery vehicle for DEX to target chronic inflammation at IME sites. This analysis revealed that encapsulating DEX alters its biodistribution, providing insights not observed with IHC. Multiple doses of DEX-loaded SPINs (SPIN-DEX) demonstrated potential to extend IME longevity by modulating gene expression to reduce neuroinflammation and promote BBB healing. Nanoparticle-based therapies enable adaptable dosing that can be informed by transcriptomic data, offering a promising solution for enhancing IME performance. By addressing chronic inflammation and BBB repair, SPIN-DEX provides a comprehensive approach t (open full item for complete abstract)

Committee: Andrew Shoffstall (Advisor); Efstathios Karathanasis (Committee Chair); Jeffrey Capadona (Committee Member); Andrew Crofton (Committee Member) Subjects: Biomedical Engineering; Biomedical Research; Genetics; Neurosciences

Doctor of Philosophy, Case Western Reserve University, 2024, Biomedical Engineering

Intracortical microelectrodes (IMEs) are a type of brain-computer interface that allows for the recording of neural signals to communicate between the brain and computers. IMEs can be used to restore motor function in people with spinal cord injury, treatment of neurological disorders, and are a strong basic science tool for understanding the brain. Unfortunately, implanted IMEs consistently see a steady decline in recording ability over time, leading to failure of the device. Damage to the blood-brain barrier (BBB) from IME implantation is a key contributor to device failure. After BBB breach, neurotoxic molecules invade the brain and cause a downstream cascade of neuroinflammation and oxidative stress that further damages the BBB, brain tissue, and the IME itself. Attempts to minimize BBB damage to improve neuroinflammation and IME longevity have shown limited success. Given the lack of solutions to the chronic stability of neural recordings, further investigation into understanding and minimizing the effects of BBB damage is warranted. In my dissertation, I investigate multiple strategies to mitigate and expand our understanding of how BBB damage can impact IME performance. Thermal damage to underlying vasculature because of cranial drilling has been shown to impact BBB permeability. To combat this, I developed a standardized surgical approach to limit surgeon variability and reduce thermal damage on the BBB. Next, I utilized the antioxidant dimethyl fumarate to promote BBB healing and reduce oxidative stress, resulting in acute improvements to IME function without long-term stability. Lastly, I investigated what unknown molecules enter the brain through the permeable BBB and contribute to neuroinflammation. I was the first to discover that gut-derived bacteria invade the site of implantation through the damaged BBB, which can be modulated with antibiotics to alter neuroinflammation and IME performance. New therapeutics can be developed utilizing this connecti (open full item for complete abstract)

Committee: Jeffrey Capadona (Advisor); Anirban Sen Gupta (Committee Chair); A. Bolu Ajiboye (Committee Member); Andrew Shoffstall (Committee Member); Gary Wnek (Committee Member) Subjects: Biomedical Engineering; Engineering

Doctor of Philosophy, Case Western Reserve University, 2023, Biomedical Engineering

Functional electrical stimulation (FES) of arm and hand muscles combined with decoded motor intentions from intracortical recordings can restore reaching ability to individuals with spinal cord injury. This research aims to improve FES generated arm movements by incorporating real-time modulation of limb stiffness in addition to limb kinematics. During normal arm movements people actively adjust the amount of cocontraction, or the degree to which opposing muscles are simultaneously active. Increasing cocontraction stiffens our limbs making them more resistant to external perturbations while decreasing cocontraction minimizes energy use and facilitates rapid movement. Optimizing the degree of cocontraction is particularly important for FES because excess cocontraction can lead to muscle fatigue and faster battery drain. Conversely, too little cocontraction can lead to poorly controlled movements that are prone to perturbations from the environment. First, we incorporated automated stiffness modulation into FES systems using a virtual arm model. After running many simulations, we found the optimal control method to automatically modulate limb stiffness based on the intended limb velocity command. Next, we built upon that method by addressing two key practical issues: 1) balancing stimulation across multiple redundant muscles, and 2) optimizing stimulation of biarticular muscles. With these advances, we demonstrate how to generate an upper limb FES control algorithm that is clinically practical to implement and allows one to control limb stiffness in addition to kinematics. Finally, with the goal of finding a neurally encoded volitional stiffness signal, we trained macaques with intracortical microelectrodes to perform an EMG controlled cursor task in which independent activation of antagonist muscles controlled one dimension and a separate dimension of movement was controlled by the cocontraction of the same muscles. Results revealed some neural signal (open full item for complete abstract)

Committee: Dawn Taylor (Advisor); Roger Quinn (Committee Member); A. Bolu Ajiboye (Committee Member); Robert Kirsch (Committee Chair) Subjects: Biomedical Engineering; Neurosciences

Doctor of Philosophy, Case Western Reserve University, 2011, Biomedical Engineering

People with severe paralysis have limited options for commanding assistive device movements. Accessing movement commands directly from the brain will increase the options available to this population and could enable them to control complex device movements. The studies discussed here focus on improving decoding algorithms and strategies commonly used in brain machine interfaces (BMIs) for continuous device movements. These studies focus on improving micro‐electrocorticography (μECoG) based BMIs that decode naturalistic arm movements, but many of the results shown here can be applied to all forms of continuous‐movement BMIs. In the first phase of the project, we evaluated how various spatial filtering methods affect decoding quality in μECoG‐based BMIs. Spatial filtering is the process of creating new signals from linear combinations of the raw signals in order to remove common noise and maximize the detection of unique information from the entire set of electrodes. Many novel variants of the common spatial pattern spatial filter were compared to three standard methods to determine which methods are most effective at improving decoding performance. Our results suggest that some novel variants of common spatial patterns developed here can dramatically improve field potential decoding. In the second phase of this project, we determined which movement parameters should be decoded from the brain to maximize BMI performance. Furthermore, in situations where the ideal parameter cannot be decoded, various options for using one decoded movement parameter to control another aspect of device movement may improve BMI performance. The final phase of the project evaluated how different characteristics of decoders vary as a consequence of using increased amounts of past data to predict the current arm state. We also quantified how subtle changes in offline decoders affect BMI performance during online use. This knowledge will enable people to develop BMIs that will be effecti (open full item for complete abstract)

Committee: Robert Kirsch (Committee Chair); Dawn Taylor (Advisor); Kenneth Gustafson (Committee Member); Cameron McIntyre (Committee Member); Lynn Landmesser (Committee Member) Subjects: Biomedical Engineering