AI can detect if your brain is aging too quickly

Aging affects every part of the body, but the brain holds the key to how well we navigate our later years. Some people maintain sharp cognition well into their 80s and 90s, while others experience rapid mental decline much earlier. Understanding why this happens has long been a challenge for scientists.

A remarkable study has now introduced a revolutionary way to measure how fast the brain is aging with the help of artificial intelligence (AI). Unlike previous methods that provided a single estimate of brain age, this approach calculates the rate of decline over time.

By tracking how quickly the brain changes between two MRI scans, researchers can determine an individual’s “pace of aging.”

“This is a novel measurement that could change the way we track brain health both in the research lab and in the clinic. Knowing how fast one’s brain is aging can be powerful,” said Andrei Irimia, an associate professor at the USC Leonard Davis School of Gerontology.

Biological age vs. chronological age

Chronological age measures time since birth, but biological age reflects how fast an individual’s body and brain are aging.

Some people experience aging at a slower or faster rate than their peers. Earlier methods only provided a static snapshot of brain age, missing important information about ongoing changes.

The new technique addresses this gap by analyzing two MRI scans from the same person at different times. By comparing these scans, researchers can calculate the rate of change and determine whether someone’s brain is aging faster or slower than expected.

“The pace of brain aging conveys the rate of aging-related alteration in neurobiological system integrity,” wrote the researchers. “For example, faster pace reflects faster adverse cognitive changes contributing to morbidity and mortality.”

Limits of blood-based aging tests

Some studies have attempted to measure biological aging through blood samples. However, this method does not provide an accurate picture of brain aging.

“The barrier between the brain and the bloodstream prevents blood cells from crossing into the brain, such that a blood sample from one’s arm does not directly reflect methylation and other aging-related processes in the brain,” explained Professor Irimia.

To overcome this challenge, researchers at the University of Southern California trained their AI model using MRI scans from thousands of cognitively normal adults. The model was then tested on independent groups, including patients with Alzheimer’s disease.

Measuring the pace of brain aging

This new approach examines changes between two MRI scans over time. Instead of estimating brain age at a single point, the AI system determines how much aging has occurred between scans and calculates the rate of decline.

The AI model achieved a mean absolute error of just 0.16 years when predicting brain aging in cognitively normal adults. In comparison, the best traditional model had an error of 1.85 years – more than ten times worse.

AI reveals differences in brain aging

Beyond tracking the pace of aging, the researchers identified key differences between sexes, age groups, and cognitive conditions.

“The 3D-CNN also generates interpretable ‘saliency maps,’ which indicate the specific brain regions that are most important for determining the pace of aging,” said Paul Bogdan, associate professor at the USC Viterbi School of Engineering.

For women, brain aging was most prominent in regions such as the right precentral gyrus, superior parietal lobules, and precunei. Men, however, exhibited more aging-related changes in the left transverse frontopolar gyrus and right supramarginal gyrus.

People in their 50s showed aging in different regions compared to those in their 70s. Younger individuals had more decline in the left lateral temporal lobe and right medial occipital lobe. Meanwhile, those in their 70s experienced faster aging in the right central and postcentral gyri.

Linking brain aging to cognitive decline

The study also found that individuals with faster brain aging had greater declines in cognitive function over time.

“Rates of brain aging are correlated significantly with changes in cognitive function,” Irimia said. “So, if you have a high rate of brain aging, you’re more likely to have a high rate of degradation in cognitive function, including memory, executive speed, executive function, and processing speed.”

This was particularly evident in scores from the Alzheimer’s Disease Assessment Scale (ADAS13). People whose brains were aging more quickly showed greater errors in cognitive tests, indicating a strong link between brain structure and cognitive function.

Identifying at-risk individuals

Perhaps the most significant finding was that the pace of brain aging could predict future cognitive impairment. Among the study participants, those who later developed cognitive decline had a significantly higher rate of brain aging than those who remained cognitively normal.

“The alignment of these measures with cognitive test results indicates that the framework may serve as an early biomarker of neurocognitive decline,” Bogdan said. “Moreover, it demonstrates its applicability in both cognitively normal individuals and those with cognitive impairment.”

Early identification of at-risk individuals could allow for preventive measures, such as lifestyle changes or medical interventions, before symptoms appear.

Future of brain health monitoring

Irimia believes this model could help detect brain aging before cognitive symptoms emerge. With new Alzheimer’s treatments available, early intervention may be crucial in improving outcomes.

“One thing that my lab is very interested in is estimating risk for Alzheimer’s; we’d like to one day be able to say, ‘Right now, it looks like this person has a 30% risk for Alzheimer’s.’ We’re not there yet, but we’re working on it,” Irimia said.

“I think this kind of measure will be very helpful to produce variables that are prognostic and can help to forecast Alzheimer’s risk. That would be really powerful, especially as we start developing potential drugs for prevention.”

Since men and women showed different patterns of brain aging, this model could also help researchers understand why they face different risks for neurodegenerative diseases.

Future research directions

The study had some limitations. Because it calculates an average pace of aging over a set period, it may not capture recent changes in brain health.

Additionally, the model performed better for cognitively normal individuals than for those with Alzheimer’s, likely because it was trained on healthy adults.

Future studies with larger and more diverse populations could improve the model’s accuracy. However, even with its limitations, this research represents a major step forward in understanding how brain aging progresses over time.

By focusing on how fast the brain is changing rather than just its current state, this research could lead to more personalized medical interventions. Maintaining brain health isn’t just about age – it’s about how quickly aging is taking place.

The study is published in the journal Proceedings of the National Academy of Sciences.

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