What is happening in your brain as you are scrolling through this page? In other words, which areas of your brain are active, which neurons are talking to which others, and what signals are they sending to your muscles?

Mapping neural activity to corresponding behaviors is a major goal for neuroscientists developing brain-machine interfaces (BMIs): devices that read and interpret brain activity and transmit instructions to a computer or machine. Though this may seem like science fiction, existing BMIs can, for example, connect a paralyzed person with a robotic arm; the device interprets the person’s neural activity and intentions and moves the robotic arm correspondingly.

A major limitation for the development of BMIs is that the devices require invasive brain surgery to read out neural activity. But now, a team at Caltech has developed a new type of minimally invasive BMI to read out brain activity corresponding to the planning of movement. Using functional ultrasound (fUS) technology, it can accurately map brain activity from precise regions deep within the brain at a resolution of 100 micrometers (the size of a single neuron is approximately 10 micrometers).

The new fUS technology is a major step in creating less invasive, yet still highly capable, BMIs. “Invasive forms of brain-machine interfaces can already give movement back to those who have lost it due to neurological injury or disease,” says Sumner Norman, postdoctoral fellow in the Andersen lab and co-first author on the new study.

“Unfortunately, only a select few with the most severe paralysis are eligible and willing to have electrodes implanted into their brain,” Norman adds. “Functional ultrasound is an incredibly exciting new method to record detailed brain activity without damaging brain tissue. We pushed the limits of ultrasound neuroimaging and were thrilled that it could predict movement. What’s most exciting is that fUS is a young technique with huge potential—this is just our first step in bringing high performance, less invasive BMI to more people.”

The technology was developed with the aid of non-human primates, who were taught to do simple tasks that involved moving their eyes or arms in certain directions when presented with certain cues. As the primates completed the tasks, the fUS measured brain activity in the posterior parietal cortex (PPC), a region of the brain involved in planning movement. Caltech’s Andersen lab has studied the PPC for decades and has previously created maps of brain activity in the region using electrophysiology. To validate the accuracy of fUS, the researchers compared brain imaging activity from fUS to previously obtained detailed electrophysiology data.

Next, through the support of the T&C Chen Brain-Machine Interface Center at Caltech, the team aimed to see if the activity-dependent changes in the fUS images could be used to decode the intentions of the non-human primate, even before it initiated a movement. The ultrasound imaging data and the corresponding tasks were then processed by a machine-learning algorithm, which learned what patterns of brain activity correlated with which tasks. Once the algorithm was trained, it was presented with ultrasound data collected in real time from the non-human primates.

The algorithm predicted, within a few seconds, what behavior the non-human primate was going to carry out (eye movement or reach), direction of the movement (left or right), and when they planned to make the movement.

“The first milestone was to show that ultrasound could capture brain signals related to the thought of planning a physical movement,” says study collaborator and ultrasound expert David Maresca, PhD. “Functional ultrasound imaging manages to record these signals with 10 times more sensitivity and better resolution than functional MRI. This finding is at the core of the success of brain-machine interfacing based on functional ultrasound.”

“Current high-resolution brain-machine interfaces use electrode arrays that require brain surgery, which includes opening the dura, the strong fibrous membrane between the skull and the brain—and implanting the electrodes directly into the brain. But ultrasound signals can pass through the dura and brain non-invasively. Only a small, ultrasound-transparent window needs to be implanted in the skull; this surgery is significantly less invasive than that required for implanting electrodes,” adds collaborator Richard Andersen, PhD, the James G. Boswell Professor of Neuroscience and Leadership Chair and director of the Tianqiao and Chrissy Chen Brain-Machine Interface Center in the Tianqiao and Chrissy Chen Institute for Neuroscience at Caltech.

Though this research was carried out in non-human primates, a collaboration is in the works with Dr. Charles Liu, a neurosurgeon at the University of Southern California, to study the technology with human volunteers who, because of traumatic brain injuries, have had a piece of skull removed. Because ultrasound waves can pass unaffected through these “acoustic windows,” it will be possible to study how well functional ultrasound can measure and decode brain activity in these individuals.