Actions constitute the way we interact with the world, making motor disabilities such as Parkinson’s disease and stroke devastating. The neurological correlates of the injured brain are challenging to study and correct given the adaptation, redundancy, and distributed nature of our motor system. However, recent studies have used increasingly sophisticated technology to sample from this distributed system, improving our understanding of neural patterns that support movement in healthy brains, or compromise movement in injured brains. One approach to translating these findings to into therapies to restore healthy brain patterns is with closed-loop brain-machine interfaces (BMIs). While closed-loop BMIs have been discussed primarily as assistive technologies the underlying techniques may also be useful for rehabilitation.
In this talk, I will describe two preliminary studies that test the use of BMIs as rehabilitative tools. First, in Parkinson’s patients, occurrence of specific oscillations in the basal ganglia have been correlated with severity of rigidity and bradykinesia motor symptoms. These basal ganglia oscillations are thought to be driven by synchronized motor cortical activity. In previous work, we developed a combined BMI and arm reaching task to test the dependence of upper arm motor tasks on synchronized motor cortical patterns in non-parkinsonian subjects. Indeed, we found that on short timescales, reduced synchronization in motor cortex resulted in faster-onset arm movements. We now test this BMI paradigm in Parkinsonian subjects to determine if the same changes in cortical synchronization can alleviate pathological basal ganglia oscillations and accelerate motor behavior.
Second, we are preparing to investigate the use of a BMI constituting a neurally controlled upper limb robotic exoskeleton for the rehabilitation of a hemipeligic chronic stroke patient. Stroke recovery has been correlated with the enlargement of motor cortical maps corresponding to recovering limbs. We hypothesize that daily training with a closed-loop neurally driven robotic exoskeleton in a patient with intact sensory systems will leverage the motor learning and neuroplasticity machinery of the endogenous nervous system and give rise to an expanded cortical map for the trained arm. Further, we hypothesize this expanded cortical map gained from BMI training will support greater control of the trained arm in functional tasks performed without the BMI.
As the motor systems neuroscience field can increasingly record simultaneous neural signals from the distributed motor system, hypotheses about how motor system activity support healthy movement will continue to emerge. Using this knowledge along with closed-loop BMIs may give rise rehabilitative tools allowing for the restoration of healthy brain patterns to re-mobilize individuals suffering from motor disorders.
Biography: I am a postdoctoral fellow at the University of California, Berkeley with Professor Jose M. Carmena. I graduated from the UC Berkeley - UC San Francisco program in BioEngineering in December, 2017 from the Brain-Machine Interface lab, where I studied motor cortical activity patterns in healthy subjects and those with neurological damage. Prior to graduate school in Berkeley, I was an undergraduate at the University of Pennsylvania and spent a summer working in Professor Katherine Kuchenbecker’s Haptics lab on a technology called StrokeSleeve. Working with Dr. Kuchenbecker was my first introduction to robotics, human-machine interaction, and motor learning - themes which I continue to study today!