Using brain scanning to communicate when all else fails
Photo by Richard Catabay on Unsplash
Beep.
Beep.
Beep.
The sound of a heart rate monitor drumming to your sibling’s heartbeat. Slow and steady – a sound that can change in an instant. Turbulent and violent.
Being in the Intensive Care Unit was not always the norm. You gossiped about your crushes, debated over your favourite Marvel characters, and enjoyed food together. There was a lot of hope for the future, but that all seems distant now.
A few hours ago, your sibling’s livelihood was taken away by a major stroke. Typically, strokes can be caused by blocked or burst blood vessels in the brain resulting in immediate brain damage. Right now, your sibling is unconscious and intubated. It almost looks like they’re asleep. Their eyes move like they are reaching out to you but each time you try to reach back, there is no response.
Your family is suddenly left with a difficult choice, one that you never thought you would have to make. Do you want to sustain your sibling’s life and risk a life of disability, or do you want to withdraw life support? Medical doctors are often uncertain of what recovery could look like for such patients. Currently, there are no good tools that detect how functional recovery might look. The lack of information from medical doctors makes end-of-life decisions for families all the more painful. In some cases, a decision to withdraw life support could result in weeks until the patient passes away, which is agonizing for families to witness. In other cases, patients originally deemed to have little chance of improvement could make an unexpected recovery.
The difficulty of these types of decisions are compounded by families often not knowing beforehand what their unconscious family member would want for themselves. But what if families could find out the answer directly from the source? This is the problem that researchers in The Owen Lab are trying to solve.
Imagine playing a game of tennis
Researchers and medical professionals consider brain damage that affects responsiveness and awareness of patients to be disorders of consciousness. Some of these illnesses include coma, vegetative state (now better known as unresponsive wakefulness), minimally conscious state, and locked-in syndrome. What these disorders all have in common is an immense difficulty to communicate in any conventional sense.
In 2006, Dr. Adrian Owen (OBE) and his colleagues found a way to communicate with a disorders of consciousness patient (watch video). He did so by asking the patient to imagine playing a game of tennis while they were hooked up to a large scanner that measures brain activity. What they found shocked the world – because this completely behaviourally unresponsive patient was able to imagine the game of tennis and do so time and time again with just their brain. Like this patient, up to 20% of behaviourally unresponsive patients show brain activity when asked to do certain tasks.
Innovations in brain sciences and machine learning in the past five years have allowed researchers to get much closer to communicating with disorders of consciousness patients. The challenge is that most of the technology currently being researched can be highly invasive, requiring cutting through the skull and attaching a chip to the brain. As you can imagine, a highly invasive procedure could scar the brain tissue and cause infections. Other researchers use less invasive methods such as using brain scanners, but these tend to be much less accurate and include non-portable fixtures that cannot be brought to the bedside.
A powerful brain imaging technique
What if there was some other technology that could help these patients communicate without implanting chips, while also having great accuracy? Post-doctoral fellow, Dr. Androu Abdalmalak, PhD, from The Owen Lab is working to solve this problem using a brain imaging technique named functional near-infrared spectroscopy (fNIRS). This technique is based on the idea that when a brain region is actively working on a task, more blood will rush there. As more blood flows, more oxygen will follow. fNIRS can detect changes in blood oxygen levels by projecting beams of light through the scalp into the surface of brain tissue. Imagine clicking an elevator button and noticing the light pass through your finger. The same principle works here with fNIRS. As light passes through and is reflected by the brain tissue, fNIRS picks up the amount of oxygen present at that brain region. This tells researchers how active a brain region is. What is particularly innovative about fNIRS is that it is portable. It can be moved from place to place because it is simply a cap attached to wires.
In Dr. Abdalmalak’s study, healthy adult participants answered questions like, “Do you have any sisters?” Participants answered by imagining playing tennis when they wanted to say “yes” and imagined doing nothing at all when they wanted to say “no”. Using machine learning, a way of making predictions, the researchers accurately predicted responses 76% of the time. This was an incredible feat, especially with a device that measures brain activity without touching brain tissue. To put into perspective, chance-level would be at 50%, meaning that the prediction was 52% better than chance!
The present and the future
In the last two years since the research was published, Dr. Abdalmalak and his colleagues at The Owen Lab have applied their work using fNIRS to critically ill unresponsive patients in the Intensive Care Unit. Ultimately, the goal is to support families and medical professionals during the informed decision-making process and to predict outcomes more accurately for patients experiencing disorders of consciousness. Dr. Abdalmalak believes that the future of non-invasive brain communication will combine multiple brain imaging techniques, so that accuracy can continue to improve. Although the current state of accuracy is not nearly as high as is necessary to make end-of-life decisions, it is one step closer to helping families communicate with their loved ones.
And maybe – when all other forms of communication fail – we can communicate with the help of fNIRS.
Original article: Abdalmalak, A., Milej, D., Yip, L. C. M., Khan, A. R., Diop, M., Owen, A. M., & St. Lawrence, K. (2020). Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication. Frontiers in Neuroscience, 14.
https://www.frontiersin.org/article/10.3389/fnins.2020.00105