Zachary Danziger, PhD - US grants

DESCRIPTION (provided by applicant): Urinary retention is a disease of the urinary tract that prevents hundreds of thousands of people from properly emptying the contents of their bladder. Urinary retention can be caused by many things such as natural aging, acute trauma (especially during surgical procedures), diabetes, multiple sclerosis and others. Despite the range of causes, a feature common to most cases of urinary retention is a reduced neural sensitivity for detecting fluid flow in the urethra. This reduction in sensitivity, in turn, limits the effectivenes of reflexes that naturally control bladder voiding. We hypothesize that enhancing urethral sensitivity will mitigate many of the symptoms associated with retention by allowing the reflexes to once again function properly. The work in this proposal will demonstrate that it is possible to enhance urethral sensitivity. To accomplish this enhancement we will use a process called stochastic resonance, where we inject sub-threshold levels of electrical noise (amounts small enough that they cannot be detected by the sensory neurons) directly into the urethra. This will be done in a rat model of the urinary tract. Stochastic resonance has been successful in other biological sensory systems and we hypothesize that these undetectable perturbations will lower the activation threshold of the urethral sensory neurons, effectively increasing their sensitivity o flow. We will demonstrate our findings by recording from the pudendal nerve, which carries sensory information from the urethra to the spinal cord. We will compare the pudendal nerve's response to fluid flow in the urethra both with and without the stimulation at a range of flowrates We can conclude that sensitivity was enhanced if more neural activity is observed in the presence of the stimulation than without it, and if slower flowrates can be detected with the stimulation it will also serve as evidence of enhanced sensitivity. Finally, a mathematical model of stochastic resonance in the lower urinary tract will be developed in tandem with the animal experiments. This model will allow us to simulate many different types of stimulation and flowrates to better guide the animal work. Using the model we can investigate a wide range of experiment parameters, many more than we could test using the biological system. We will use the model estimates of the optimal stimulation parameters and flowrates to give ourselves the best chance of success for enhancing urethral sensitivity.

An intracortical brain-computer interface (iBCI) is used to record electrical signals directly from a person's brain, predict their intention from those signals, then control an assistive device (e.g., a computer cursor, prosthetic limb, or powered wheelchair) according to those intentions. This technology enables severely paralyzed people to interact with the world. However, designing robust algorithms to extract intent from recordings of single neurons is extremely challenging, in large part because of the very limited access to humans, or even monkeys, from whom these invasive recordings can be made. In this project, we will develop a model iBCI system that generates real-time biomimetic neural data by capturing the high-degree-of-freedom finger movements of able-bodied human subjects. To accomplish this, we will construct a modular recurrent neural network (RNN). The RNN will be trained to predict the motor cortex activity of a monkey from the monkey's own finger kinematics. Small modules of the RNN will be interchanged according the particular animal or recording session to model the high inter-session variability present in motor cortex. Once the modular RNN is trained, its weights will be fixed and human finger kinematics will be used as the RNN inputs, which will generate subject-controlled emulated neural activity. The emulated neural activity can be passed to iBCI decoding algorithms that control computer cursors or other physical devices, allowing human subjects to interact directly with decoders in real time, closed-loop conditions. We call this model system the jaBCI. The jaBCI is low cost and noninvasive, making it possible to rapidly test and design novel iBCI decoders using statistically rigorous sample sizes. The project will be executed in close collaboration with intracortical microelectrode array data expert Dr. Lee Miller at Northwestern University. Dr. Miller's lab, with the help of our consultant Dr. Mathis, will obtain simultaneous finger kinematics and neural activity of monkey subjects that will serve as the training data for the RNN component of the iBCI model. We will validate the emulated neural data generated by the jaBCI across many measures to ensure the model captures as many features of intracortical data as possible. These include comparing the model and actual iBCI in subject performance, learning rates, control strategies, neural variation across days, neural firing rate distributions, and low-dimensional neural dynamics. With the validated model, we will undertake a study to rigorously evaluate the highest performing, current state-of-the-art iBCI decoders. This will yield useful insight into the features of decoders that yield the greatest performance gains, overcoming the current impossibility to compare iBCI decoders in well-controlled studies using more than two or three naïve human subjects. We will also use the iBCI model to evaluate novel decoder designs, and to determine the features of neural dynamics that are consistent across common iBCI tasks to help focus decoder development on those features.