Currently, machine-learning algorithms can learn from MRI images of healthy people’s brains what features can predict the age of an individual’s brain. By feeding many MRIs of healthy brains into a machine-learning algorithm along with the chronological ages of each of those brains, the algorithm can learn how to estimate the age of an individual’s brain based on his or her MRI. Using this framework, Kounios and his colleagues developed the method for using EEGs instead of MRIs.
This can be thought of as a measure of general brain health, according to Kounios. If a brain looks younger than the brains of other healthy people of the same age, then there is no cause for concern. But if a brain looks older than the brains of similarly aged healthy peers, there could be premature brain aging – a “brain-age gap.” Kounios explained that this kind of brain-age gap can be caused by a history of diseases, toxins, bad nutrition, and/or injuries, and can make a person vulnerable to age-related neurological disorders.
Despite brain-age estimates being a critical health marker, they have not been widely used in health care.
“Brain MRIs are expensive and, until now, brain-age estimation has been done only in neuroscience research laboratories,” said Kounios. “But my colleagues and I have developed a machine-learning technology to estimate a person’s brain age using a low-cost EEG system.”
Electroencephalography, or EEG, is a recording of a person’s brain waves. It’s a less expensive and less invasive procedure than an MRI — the patient simply wears a headset for a few minutes. So, a machine learning program that can estimate brain age using EEG scans, rather than MRIs, could be a more accessible screening tool for brain health, according to Kounios.
“It can be used as a relatively inexpensive way to screen large numbers of people for vulnerability to age-related. And because of its low cost, a person can be screened at regular intervals to check for changes over time,” Kounios said. “This can help to test the effectiveness of medications and other interventions. And healthy people could use this technique to test the effects of lifestyle changes as part of an overall strategy for optimizing brain performance.”
Drexel University has licensed this brain-age estimation technology to Canadian health care company DiagnaMed Holdings for incorporation into a new digital health platform.
In addition to Kounios, Fengqing Zhang, PhD, and Yongtaek Oh, PhD, of Drexel University and Jessica Fleck, PhD, of Stockton University contributed to this research.
Read the full paper in Frontiers in Neuroergonomics.