
Machine learning algorithms can now learn features from MRI images of healthy people’s brains that can predict the age of an individual’s brain. By feeding a large number of her MRIs of healthy brains into a machine learning algorithm, along with the actual age of each of those brains, the algorithm can learn how to estimate the age of an individual’s brain based on the MRI. Using this framework, Kounios and his colleagues developed a method to use his EEG instead of MRI.
According to Konios, you can think of this as a measure of general brain health. If your brain looks younger than the brains of other healthy people of the same age, there is no need to worry. But if your brain looks older than the brains of your healthy peers at the same age, premature brain aging, or the “brain age gap,” may exist. Kunios explained that this kind of brain-age gap can be caused by a medical history of disease, toxins, malnutrition, injury, etc., and can make you more susceptible to age-related neurological disorders. .
Despite being an important health marker, brain age estimates are not widely used in medicine.
“Brain MRI is expensive, and until now, brain age estimation has only been done in neuroscience laboratories,” Kunios says. “However, his colleagues and I have developed a machine learning technique that uses low-cost EEG systems to estimate a person’s brain age.”
Electroencephalography (EEG) is the recording of a person’s brain waves. It’s a cheaper and less invasive procedure than an MRI, and patients only need to wear a headset for a few minutes. Therefore, a machine learning program that can estimate brain age using EEG scans rather than MRI could become a more accessible tool for diagnosing brain health, Kounios said.
“This can be used as a relatively inexpensive way to test large numbers of people for age-related vulnerabilities. The low cost also makes it easier to get tested regularly to check for changes over time. We can,” Konios said. “This can help test the effectiveness of drugs and other interventions. And healthy people can take advantage of this technology as part of an overall strategy to optimize brain performance. You can test the effects of lifestyle changes.”
Drexel University has licensed this brain age estimation technology to Canadian healthcare company DiagnaMed Holdings for inclusion in a new digital health platform.
In addition to Kounios, contributing to this study were Dr. Fengqing Zhang and Dr. Yongtaek Oh of Drexel University, and Dr. Jessica Fleck of Stockton University.
Read the full paper Frontiers of neuroergonomics.