Intel pushes AI to bridge spinal gap in paralyzed patients

“Recent studies have demonstrated that we can use electrical pulses to write into the spinal cord for motor control, and we are starting to learn how to write into the spinal cord for sensory restoration, to some degree,” explains David Borton, assistant professor of engineering at Brown University School of Engineering and the Carney Institute for Brain Science.

“We are now attempting to address the question that’s always been there tantalizing us: can we ‘bridge the gap’ in a spinal cord injury?”

As far back as 2006, the BrainGate project demonstrated that the acquired signals from implanted brain-recording electrodes could be used to drive a cursor on a computer screen, enabling paralyzed patients to type an email or play a virtual piano using their minds. Borton’s thesis work focused on the development of next-generation neural recording systems that were fully implanted, using wireless power and data telemetry.

The upcoming intelligent spine interface project builds on Borton’s 2016 postdoctoral work with Gregoire Courtine at the Center for Neuroprosthetic and Brain Mind Institute at the Swiss Federal Institute of Technology Lausanne, where they developed a brain-spine interface. In that study, they were able to record from the brain and use those signals to stimulate the spinal cord, bypassing a spinal injury. Here the system receives data from the leg area of the motor cortex and, similar to the computer-in-the-loop BrainGate system, relays that signal to a computer, which interprets whether the person wants to lift up the leg or put it down, and then relays that information to a Medtronic spinal cord stimulator that stimulates the nonfunctional limb.

But the new project aims to bypass the invasive brain surgery, limiting the medical intervention to the spine. An intelligent spinal interface would sit above and below the spinal cord lesion and stimulate across that gap, utilizing an AI-trained computer chip to decode signals recorded from the spinal cord, retrain the remaining biological networks, and initiate the correct intended behaviors.

The chips are created by Intel, one of the project’s commercial partners. The team is also using Intel’s open-source compilation library, nGraph, which allows neural network models to be compiled to different hardware targets. This means that the same network code can be run on a server with a lot of power or compiled down to run on something with much less processing power, like a phone or, perhaps one day, an implanted device.

“nGraph gives us a unique pathway to pilot possible future therapeutic models across a wide range of hardware targets. The hope is we can compile our algorithms down to an implantable system in the future, without significant redesign,” says Borton.

In the upcoming project, the initial research will focus on exploring what signals remain to be recorded in the spinal cord post-injury, and how those signals could be used to control the legs for walking, standing, and signals related to bladder control, which is a primary concern reported by people with spinal cord injuries. The project aims to demonstrate that the spine-to-spine device can properly target the neural circuits that influence these activities.

During the initial phase, an external computer will connect the upper and lower spine and decode the signals, similar to the computer-in-the-loop systems utilized in Borton’s earlier neural device work. But the researchers hope this study will lay the groundwork for a future fully implantable device. The first human studies are expected to start in spring 2020.

“The project is a big step from a scientific perspective. We simply don’t know everything about what the spinal cord does, particularly post-injury,” says Borton. He compares the limited knowledge of the spinal cord to the state of our knowledge of the brain 20 years ago. Thanks to increased funding and interest in brain research, that knowledge has grown substantially in recent years.

Intel – www.intel.com

Brown University – www.brown.edu

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