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Research at Stanford

Department of Neurology and Neurosurgery

Central Thalamic Deep Brain Stimulation for the Treatment of Traumatic Brain Injury

Principal Investigator: Nicholas D. Schiff, MD (Weill-Cornell)

Stanford PI: Jaimie Henderson, MD
Sponsor: Weill-Cornell Medical College

Clinical Research Coordinator (Sep. 2017 – Aug. 2018)

People who experience a traumatic brain injury often have few options for regaining brain function after the physical wounds have healed. Problems include difficulty concentrating, chronic fatigue, and memory deficits. This research was the first of its kind to use deep-brain stimulation (DBS) for the treatment of traumatic brain injuries.

Giving Voice to Neuroethics

Principal Investigator: Joseph Fins (Weill-Cornell)

Stanford PI: Jaimie Henderson, MD
Sponsor: Weill Cornell Medical College

Clinical Research Coordinator (Sep. 2017 – Aug. 2018)

What do we need to consider as a society when we have the ability to augment brain functioning after injury? What does a patient consider when thinking of undergoing brain surgery? How do close friends and family feel about the patient's condition and the possibilities of surgery? These and many other topics are covered in this interview-based research.

Consortium for the Advanced Study of Brain Injury at Weill-Cornell

BrainGate2: Feasibility Study of an Intracortical Neural System for Persons with Tetraplegia

Principal Investigator: Leigh R. Hochberg, MD, PhD (MGH)

Stanford PI: Jaimie Henderson, MD
Sponsor: Massachusetts General Hospital

Clinical Research Coordinator (Sep. 2017 – Aug. 2018)

When a patient suffers a spinal cord injury or has a neurodegenerative disease such as ALS, the part of the brain responsible for moving the body typically still functions well. Only the signals to the body are disrupted. This clinical trial involved implanting an electrode array onto the surface of the motor cortex of patients who no longer have the ability to move. The array allowed the neural signals to be wirelessly broadcasted to a computer. Machine learning was used to decode the brain signals, and after many training sessions, the patient could move a cursor on a computer screen through the thought of moving their hand or arm alone. With enough training, patients could advance to controlling a robotic arm.

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