System for Closed-Loop Deep Brain Stimulation Using Neural and Behavioral Biomarkers of Specific Motor Features

System for Closed-Loop Deep Brain Stimulation Using Neural and Behavioral Biomarkers of Specific Motor Features

Overview

Parkinson’s Disease (PD) is characterized by distinct motor phenomena that are expressed asynchronously. Understanding the neurophysiological correlates of these different motor states could enable optimal closed-loop neuromodulation. Existing approaches to closed-loop deep brain stimulation (DBS) rely on generic, nonspecific biomarkers to drive stimulation. However, Parkinson’s Disease is characterized by different forms of motor dysfunction and these dimensions of motor pathology are characterized by distinct and even opposing neurophysiological and behavioral features. A DBS system in which more specific and varied neural signals are used to drive the location, type, and timing of stimulation to address specific aspects of PD dysfunction would be substantially impactful to the quality of life of patients as well as enable more advanced research into PD progression and therapeutic efficacy.

Market Opportunity

Parkinson’s disease (PD) is a common and complex neurodegenerative disorder characterized by the dynamic expression of particular motor features such as tremor and bradykinesia. These distinct motor signs are expressed variably across patients and may respond differently to dopamine replacement therapy. Despite this heterogeneity, both of these motor features respond to high-frequency deep brain stimulation (DBS) applied to the subthalamic nucleus (STN). DBS delivered in a closed-loop fashion (i.e., in response to neurophysiological biomarkers) has shown promising therapeutic potential primarily toward alleviating bradykinesia, but current efforts focusing on β frequency oscillations (15–30 Hz) have been shown to inadequately treat or worsen tremor in some cases. Thus, tremor may be better signaled by different components within the local field potential (LFP) spectrum, and closed-loop DBS could benefit from a clearer understanding of the neurophysiological biomarkers that differentiate these motor signs from each other, and from more optimal motor performance in the absence of these impairments.

Innovation and Meaningful Advantages

We propose a system for DBS in which multiple control signals are used to drive the location, type, and timing of stimulation in order to address distinct aspects of disease progression. Multiple control signals derived from electrophysiological biomarkers specific for distinct motor features refine DBS stimulation for improved symptom treatment and control. To achieve this goal neural activity in the basal ganglia and cortex of subjects with PD were examined during a quantitative motor task to decode tremor and bradykinesia — two cardinal motor signs of this disease — and relatively asymptomatic periods of behavior. Patients performed a continuous visual-motor task in which they followed an on-screen target with a cursor controlled by either a joystick or a stylus and tablet – tremor amplitude and cursor speed were calculated to reflect the expression of tremor and bradykinesia. At the same time, STN (micro- and macroelectrode) and cortical (electrocorticography) recordings were acquired. Machine learning models were trained to directly decode tremor or slowness from neural recordings to reveal the spectral and anatomical fingerprints of these cardinal motor features of PD. Support vector regression analysis of microelectrode and electrocorticography recordings demonstrated that tremor, bradykinesia, and symptom-free states were represented by different functional motifs with distinct localization and signal frequency in the STN and sensorimotor cortex. These results can be applied to DBS systems for more refined symptom control and improved patient Quality of Life.

Collaboration Opportunity: We are interested in exploring research collaborations and licensing opportunities.

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