Data-driven Modeling and Ultrasound-based Control of Afferent Nerve Stimulation for Tremor Suppression

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PROJECT SUMMARY Over 11 million people in the United States are affected by unintentional and uncontrollable rhythmic movements due to parkinsonian tremor, essential tremor, and cerebellar tremor. Individuals experiencing tremors in the hands and arms face difficulty performing activities of daily living. Electrical stimulation that works by stimulating motor nerves of antagonistic muscles is a potential wearable option for tremor suppression when medication is ineffective, but prior to pursuit of effective yet invasive (and costly) brain surgery. However, the two significant drawbacks of motor nerve stimulation are stimulation-induced muscle fatigue and discomfort due to high stimulation intensity. An intriguing new method of stimulation targets afferent nerve fibers to inhibit tremor in antagonistic muscles. These fibers relay sensory information on muscle position and velocity back to the spinal cord. By stimulating these fibers, spinal neural circuitry can be modulated that in turn inhibits muscle activity due to the descending tremor inputs from the brain. The new method uses low stimulation intensity, which is comfortable and fatigue resistant. However, modulating its stimulation parameters to continually disrupt tremors remains challenging, largely due to the numerous interactions that can occur between stimulated afferent nerves and the descending tremorgenic neural inputs. These interactions occur through a complex neural circuitry whose modeling is difficult and computationally intensive for determining stimulation parameters in real-time. Direct measurements of muscle velocity and length changes with ultrasound can help create a data-driven model of afferent stimulation and help design individual- specific afferent stimulation parameters. However, ultrasound has never been used for tremor suppression control. Real-time algorithms and models that map ultrasound-derived muscle activity to oscillating limb displacement are yet unestablished. These algorithms and models are needed to automate individual-specific stimulation parameters for tremor suppression. Lastly, for future clinical translation wearable ultrasound arrays that monitor multiple muscles need to be developed. The following two specific aims will address this work. SA 1: To model afferent feedback during tremor activity and design and validate a model-informed afferent stimulation strategy. SA2: To develop a wearable ultrasound array. If successful, a data-driven model of afferent stimulation will automate an individualized tremor suppression intervention. A future clinical translation of data-driven modeling, afferent stimulation technology, along with the wearable ultrasound array will improve the quality of life by assisting in suppressing prominent distal tremors.