Focus Area 1 - VA Providence Healthcare System
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VA Providence Healthcare System

 

Focus Area 1

Focus Area 2

Focus Area 3







Restoring Communication and Mobility

Director
 John Simeral, PhD

Core Investigators
Leigh Hochberg, MD, PhD
David Borton, PhD
W. Curt LaFrance, MD, PhD
David Lin, MD

Associate Investigators
Michael Paradiso, PhD
Carlos Vargas-Irwin, PhD
Farah Laiwalla, MD, PhD
Susan Fasoli, ScD, OTR/L


Research in this CfNN Focus Area aims to restore function for Veterans with severe communication or movement impairment resulting from amyotrophic lateral sclerosis (ALS), spinal cord injury (SCI), stroke, seizure disorders or disorders of consciousness.

With the direct research participation of Veterans and others, this research aims to improve the independence and well-being of those with severe motor disability through the development of novel neurotechnologies and innovative medical approaches based on fundamentally new views of the neural processes underlying these conditions.

One area of research is BrainGate*, an investigational neural interface system that uses a small sensor implanted in a motor-related area of the brain to record neural activity associated with intended arm and hand movements of an individual with paralysis and translate these into commands for assistive devices. Using the BrainGate system in a pilot clinical trial, Veterans and others with longstanding tetraplegia resulting from stroke, ALS or SCI have achieved highly efficient point-and-click typing for digital access and communication through on-screen computer keyboards1,2. Recently, two individuals with ALS used the BrainGate real-time neural decoding platform with Android tablet computers and unmodified commercial software “apps” to browse the web, chat online, compose and send email, control a home television and more3.

Two individuals in the BrainGate trial were the first people to use an implanted neural sensor to command robotic and prosthetic arms to reach for and grasp objects – years after the onset of tetraplegia4. In an ongoing collaboration with Case Western/Louis Stokes Cleveland VA Medical Center, this work is being extended to enable an individual with upper limb paralysis to command their own paralyzed arm to reach for and grasp objects. For one man with paralysis, this was achieved by linking his BrainGate Neural Interface System to a Functional Electrical Stimulation (FES) system that delivered sequences of patterned stimulation directly to his paralyzed arm and hand muscles in response to his own imagined movements5. This exciting progress demonstrates the potential of enabling Veterans and others with spinal cord injury or stroke to control their own arm directly from the intuitive movement-related signals generated by the brain.

Ongoing work is improving the reliability and portability of the BrainGate system to enable in-home, independent use of the BCI over extended periods without technical oversight.

A new research thrust, led by investigator Dr. LaFrance, is investigating innovative biomarkers for better diagnosis, management and amelioration of epileptic and psychogenic non-epileptic seizures, particularly in Veterans with chronic seizures. The approach is based on the combined use of biosensor monitoring using a wearable device recording autonomic nervous system signals (in particular sympathetic activity) and behavioral signals (details of movement, for example). The analyses of these biometric signals is used to inform novel therapeutic approaches based on Cognitive Behavioral Therapy and Present Centered Therapy to be used to address brain-behavior disorders.

*Caution. Investigational Device. Limited by Federal Law to Investigational Use.

1 Gilja et al., Nature Medicine, 2015.
2 Jarosiewicz et al., Science Translational Medicine, 2015.
3 Tablet scientific manuscript submitted for peer review.
4 Hochberg et al., Nature, 2012.
5 Ajiboye et al., Lancet, 2017.
6 Truccolo et al., Nature Neuroscience, 2011.
7 Wagner et al., NeuroImage, 2015.
8 Park et al., IEEE EMBC, 2014.
9 Aghagolzadeh et al., IEEE EMBC, 2016.


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