title: Advancing neuromodulation using a dynamic control framework.
abstract: The current state of neuromodulation can be cast in a classical dynamic control framework such that the nervous system is the classical "plant", the neural stimulator is the controller, tools to collect clinical data are the sensors, and the physician's judgment is the state estimator. This framework characterizes the types of opportunities available to advance neuromodulation. In particular, technology can potentially address two dominant factors limiting the performance of the control system: "observability," the ability to observe the state of the system from output measurements, and "controllability," the ability to drive the system to a desired state using control actuation. Improving sensors and actuation methods are necessary to address these factors. Equally important is improving state estimation by understanding the neural processes underlying diseases. Development of enabling technology to utilize control theory principles facilitates investigations into improving intervention as well as research into the dynamic properties of the nervous system and mechanisms of action of therapies. In this paper, we provide an overview of the control system framework for neuromodulation, its practical challenges, and investigational devices applying this framework for limited applications. To help motivate future efforts, we describe our chronically implantable, low-power neural stimulation system, which integrates sensing, actuation, and state estimation. This research system has been implanted and used in an ovine to address novel research questions.
authors.affiliation: Neuroengineering Group of Medtronic Neuromodulation Technology.
authors.forename: Pedram
authors.initials: P
authors.lastname: Afshar
authors.forename: Xuan
authors.initials: X
authors.lastname: Wei
authors.forename: Maciej
authors.initials: M
authors.lastname: Lazarewicz
authors.forename: Rahul
authors.initials: R
authors.lastname: Gupta
authors.forename: Greg
authors.initials: G
authors.lastname: Molnar
authors.forename: Timothy
authors.initials: T
authors.lastname: Denison
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D004558:Electric Stimulation / Q000379:methods*; D025461:Feedback, Physiological / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D012756:Sheep