AI measures the speed at which the brain ages

Artificial intelligence is now among the potential tools for determining ageing. A study published in ' PNAS ' presents a new artificial intelligence model that measures how quickly a person's brain ages and could be a new tool for understanding, preventing and treating cognitive decline and dementia.

The tool designed by a team at the University of California, San Francisco, can noninvasively track the pace of brain changes by analyzing magnetic resonance imaging (MRI). Faster brain aging closely correlates with a higher risk of cognitive decline, says Andrei Irimia .

“AI could change the way we monitor brain health, both in the research lab and in the clinic,” he says. “Knowing how fast the brain ages could be a very useful tool.”

Biological age is different from an individual's chronological age. Two people who are the same age by birth date may have very different biological ages due to how well their body functions and how "old" the body's tissues appear to be at the cellular level.

Some tests for assessing biological age use blood samples to measure epigenetic aging and DNA methylation, which influences the role of genes in the cell. However, measuring biological age from blood samples is a poor strategy for measuring brain age, Irimia explained.

Brain aging can be measured more accurately with this new 3D-CNN neural network model, which compares MRI scans of the same individual over time. Unlike traditional methods, this approach identifies neuroanatomical changes associated with accelerated or decelerated aging and generates maps highlighting key brain areas.

When applied to a group of 104 cognitively healthy adults and 140 patients with Alzheimer's disease , the new model's estimates of the rate of brain aging correlated closely with changes in cognitive function tests performed at both time points.

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

The model has the potential to better characterize both healthy aging and disease trajectories, and its predictive power could one day be applied to assess which treatments would be most effective based on individual characteristics.

The new model was able to distinguish different rates of aging in various brain regions. Delving deeper into these differences – including how they vary based on genetics, environment and lifestyle factors – could provide insight into how different pathologies develop in the brain, Irimia said.

The study also showed that the pace of brain aging in certain regions differs between the sexes, which could shed light on why men and women face different risks for neurodegenerative disorders, including Alzheimer's.