AI can tell if your brain is aging faster than you are

A new machine-learning approach that examines brain activity during sleep may help identify people with an elevated risk of developing dementia. The research was led by scientists at UC San Francisco and Beth Israel Deaconess Medical Center in Boston.

The system estimates a person's "brain age" by analyzing electrical signals collected through electroencephalography, or EEG, while they sleep. Researchers found that dementia risk increased when the brain appeared older than the person's actual chronological age.

For every 10-year increase between estimated brain age and actual age, the likelihood of developing dementia rose by nearly 40%. People whose estimated brain age was younger than their actual age had a lower risk.

The findings were published in JAMA Network Open.

AI Estimates Brain Age From Sleep Signals

The researchers developed a machine-learning model that combines 13 microscopic features found within EEG brain wave recordings. They applied the model to information from approximately 7,000 people who had participated in five separate studies.

Participants ranged in age from 40 to 94, and none had dementia when their respective studies began. Researchers monitored them for periods ranging from 3.5 to 17 years. During that time, about 1,000 participants developed dementia.

The analysis showed that small and highly detailed patterns in sleeping brain waves may provide information that standard sleep measurements fail to detect.

Previous pooled analyses involving several groups of participants found no meaningful association between dementia risk and common sleep measurements. These traditional measures include how much time someone spends in different stages of sleep and how efficiently they remain asleep during the night.

"Broad sleep metrics don't fully capture the complex multidimensional nature of sleep physiology," said senior author Yue Leng, MBBS, PhD, associate professor of psychiatry at the UCSF School of Medicine.

Brain Wave Patterns Connected to Memory

Several of the EEG patterns used to calculate brain age are already known to support memory and cognitive health.

One example is delta waves, the slow and rolling electrical patterns commonly associated with deep sleep. Another is sleep spindles, brief bursts of rapid brain activity that are believed to help the brain strengthen and store memories.

One of the study's most notable findings involved large, sudden spikes in EEG signals. This feature, called kurtosis, was associated with a lower risk of developing dementia.

The connection between an older estimated brain age and greater dementia risk remained significant even after researchers accounted for education, smoking, body mass index, physical activity, other medical conditions, and genetic risk factors.

A Possible Tool for Earlier Dementia Detection

Because EEG readings can be collected without invasive medical procedures, the researchers believe sleep-based brain age measurements could eventually help assess dementia risk outside traditional clinics. Future wearable technologies, for example, might be able to record the necessary brain signals during sleep.

"Brain age is calculated from sleep brain waves," said Leng. "We know that brain activity during sleep provides a measurable window into how well the brain is aging."

The results also suggest that improving sleep health might affect the way the brain ages. Leng noted that previous research has shown that treating sleep disorders can alter brain wave activity recorded during sleep.

"Better body management, such as lowering body mass index and increasing exercise to reduce the likelihood of apnea, may have an impact," said first author Haoqi Sun, PhD, assistant professor of neurology at Beth Israel Deaconess Medical Center, who developed the model with two co-authors. "But there's no magic pill to improve brain health."

Study Authors and Funding

Co-authors: Robert J. Thomas, MD, and M. Brandon Westover, MD, PhD, of Beth Israel Deaconess Medical Center, developed the machine-learning model with Sun. For other authors, please see the paper.

Funding: National Institutes of Health (R01NS102190, R01NS102574, R01NS107291, RF1AG064312, RF1NS120947, R01AG073410, RF1AG064312, R01NS102190, R01AG062531); National Institute on Aging (R21AG085495 and R01AG083836); National Science Foundation (2014431); National Health and Medical Research Council (GTN2009264); American Academy of Sleep Medicine.

Reporting from ScienceDaily contributed to this story.