Method to Predict and Diagnose Seizures and Other Neurological Events (Case 2045)

Overview
Though epilepsy is a common neurological disorder, the medical community still struggles to define and understand seizures and related neurological events. Our method uses implanted microelectrode arrays to predict and detect neurological events—a crucial first step toward being able to prevent and control them. 

Market Opportunity
Because epilepsy is incurable, the ability to prevent epileptic seizures (and other neurological events) could profoundly affect the lives of millions of people. Our method enables clinicians to predict and detect seizures before they are underway. 

Innovation and Meaningful Advantages
Current understanding of epilepsy is based mainly on the study of large populations of cortical neurons; the behavior of single neurons remains largely unknown. Our method records and identifies continuous signals generated from single cells, including brain neurons; measures and characterizes the spiking activity of those single cells; and plots the path of the individual spiking activity measurements. Interpreting spatiotemporal patterns in the recorded signals enables the prediction and detection of neurological events. 

Commercial Development: Current State and Next Steps
We have demonstrated the efficacy of our method using neural data recorded from humans and non-human primates. Long-term, our method has the potential to enable clinicians to predict seizures and other neurological events far enough in advance to prevent them. It could also be used to make a diagnosis and prognosis in cases of cortical dysfunction following traumatic brain injury and of incipient ischemia in cerebral vasospasm following subarachnoid hemorrhage, as well as to determine the severity of metabolic encephalopathy in critical medical illnesses, including liver failure.

Collaboration Opportunity
We are interested in exploring 1) startup opportunities with investors in the medical device space; 2) research collaborations with leading medical device companies to develop this tool for patient identification and stratification; and 3) licensing opportunities with medical device companies. 

Principal Investigator
John P. Donoghue, PhD
H.M. Wriston Professor of Neuroscience
Professor of Engineering 
Brown University
Brown Tech ID #2045J
john_donoghue@brown.edu
https://vivo.brown.edu/display/jdonoghu

Contact
Melissa Simon, PhD
Director of Business Development, Brown Technology Innovations
melissa_j_simon@brown.edu

IP Information
Patent Issued US10448877B2; priority date Dec 5, 2010

Publications
Rule ME, Vargas-Irwin CE, Donoghue JP, Truccolo W. Dissociation between sustained single-neuron spiking and transient β-LFP oscillations in primate motor cortex. Journal of Neurophysiology. 2017 Apr 01;117(4):1524-1543. doi.org/10.1152/jn.00651.2016.

Vargas-Irwin CE, Brandman DM, Zimmermann JB, Donoghue JP, Black MJ. Spike Train SIMilarity Space (SSIMS): A Framework for Single Neuron and Ensemble Data Analysis. Neural Computation. 2015 Jan 01;27(1);1-31. doi.org/10.1162/NECO_a_00684.

Patent Information:
For Information, Contact:
Brown Technology Innovations
350 Eddy Street - Box 1949
Providence, RI 02903
tech-innovation@brown.edu
401-863-7499
Inventors:
John Donoghue
Leigh Hochberg
Wilson Truccolo
Keywords:
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