Listening to the Internal Representation of Actions Within the Posterior Parietal Cortex

Citation

Griggs, Whitney Scott (2023) Listening to the Internal Representation of Actions Within the Posterior Parietal Cortex. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/0eae-hk18. https://resolver.caltech.edu/CaltechTHESIS:01192023-014456964

Abstract

More than 5.4 million people in the United States live with chronic paralysis and roughly 20 million people worldwide live with spinal cord injuries. Brain-machine interfaces (BMIs) can be transformative for these people, enabling them to control computers, robots, and more with only thought. State-of-the-art BMIs have already made this future a reality in limited clinical trials. However, these state-of-the-art BMIs have shortcomings that limit user adoption; high-performance BMIs currently require highly invasive electrodes above or in the brain; device degradation limits longevity to about 5 years; and their field of view is small, restricting the number, and type, of applications possible. This illustrates the need for a new generation of BMIs with a brain recording modality that is longer lasting, less invasive, and scalable to sense activity from large regions of the brain.

Functional ultrasound imaging (fUSI) is a recently developed technique that meets these criteria. fUSI measures cerebral hemodynamics with exceptional spatiotemporal resolution (<100 µm; ~100 ms) and a large field of view (several cm)—specifications ideally suited to recording detailed activity of entire cortical regions in parallel. In a series of novel results, we work towards developing the first high-performance ultrasonic BMI for human use. We first demonstrate that posterior parietal cortex (PPC), an area important for sensorimotor transformation, contains mesoscopic populations tuned to the intended movement direction. Using offline recorded data from several rhesus macaque monkeys, we can decode intended movement direction, task state, and expected action reward magnitude on a single trial basis. Having demonstrated that we could decode a variety of motor and cognitive variables using offline data, we developed a real-time, closed-loop ultrasonic BMI capable of decoding up to eight directions of intended movement with high accuracy. Finally, we began to translate these results into human applications and demonstrate the ability to measure changes in cerebral hemodynamics with high sensitivity through an acoustically transparent skull replacement in human subjects.

Taken together, our work is a novel characterization of how functional ultrasound neuroimaging may enable a new generation of BMIs. Additionally, this work reinforces the validity of fUSI as a robust and accessible neuroimaging technique for future neuroscience questions about mesoscopic populations and their interrelationships throughout the brain.

Thesis Files