Postdoctoral Researcher, Neurosurgery Department Stanford University
(SNI Jr. Faculty Candidate)
Abstract: To date, the scope of brain-machine interfaces (BMIs) has largely been to restore lost function to people with paralysis stemming from conditions such as neurodegenerative disease and spinal cord injury. These systems interface with the brain using neurosurgically implanted electrodes, measure the voltage of individual and groups of neurons, and translate these measurements via a decoding algorithm to control an end effector such as a computer cursor. I will discuss work performed in preclinical rhesus models that led to the highest performing communication BMI demonstrated to date, as well as recent results of an ongoing clinical trial where these preclinical algorithmic innovations have been successfully translated to a human participant, again yielding the highest communication rates of any known clinical BMI. The example of prosthetics is just one important application leveraging intracortical BMIs as a platform for accurately assessing and acting on the neural state. However, these measurements could play a crucial role in the diagnosis and management of a wide range of neurological and psychiatric diseases and disorders, ranging from stroke and epilepsy to depression and unconsciousness. Just as EEG recordings help localize seizures both temporally and spatially, and MRI imaging provides morphological and gross functional evaluations of the brain, BMI measurements may reveal previously unrecognized disease-specific adulterations in the neural state. Not only could this aid in forming better prognoses, but may also lead to interventions to prevent or alleviate undesirable symptoms and improve rehabilitation. In this manner, the utility of BMIs could extend far beyond communication or motor prosthetics to become an indispensable clinical tool in the treatment of brain disorders. I will discuss the emerging potential and key initial steps of this new class of medical system.