Over the last several decades, implantable neuromodulation devices have been increasingly used to treat the symptoms of movement disorders, chronic pain, and some psychiatric disorders. These devices, typically comprised of stimulation circuitry, a battery, and a relatively simple micro-controller, have until recently lacked the ability to sense and respond to signals from the patient. The advent of sensing-enabled implantable devices heralds an inflection point for the implantable neuromodulation industry, bringing the potential of a new ecosystem of data-derived applications, services, and control systems within reach. However, deriving research, business, and patient-centric value from these data sources requires a flexible and scalable architecture to store, search, and compute relevant quantities to answer the diverse questions posed by these highly-diverse stakeholders. This talk briefly introduces four major therapy areas for our team, then describes development concepts, features, and design decisions of such a data science architecture and its use. The system's data collection, data management, and data processing capabilities will be discussed, focusing on examples from deep brain stimulation applications based on the Activa PC+S and Summit RC+S, fully implantable deep-brain stimulators for investigational use capable of simultaneously stimulating and recording local field potential data from inside the brain. Examples of applications to data collected as part of NIH-funded BRAIN Initiative research projects will be used to illustrate some of these concepts.

Biosketch:
Jadin Jackson is currently the Senior Engineering Manager of the Data Science and Algorithm team in the Research and Technology group of Medtronic's Restorative Therapies business division for implantable devices. A major focus of his team at Medtronic is to provide data management tools and computational analyses that feed research and engineering insights and algorithms to drive new and innovative features that solve clinical and patient needs and improve clinical workflows associate managing implantable medical devices for neuromodulation. To achieve that goal, his team works with internal and external investigators to combine neuroscience research with advanced data science tools that facilitate the discovery of new neuroscience applications and novel algorithms for driving therapies based on electrical stimulation of the brain, spinal cord, and peripheral nervous system and pharmacological modulation of spinal circuitry. One area of particular focus for his presentation will cover the development of a scalable data science infrastructure for collecting and processing data from implantable, LFP sensing, investigational deep brain stimulation research systems being used in a number of NIH-funded research projects across the BRAIN initiative.