A new AI model by USC researchers offers a powerful non-invasive method to measure the pace of brain aging, potentially transforming dementia and cognitive decline treatments.
An artificial intelligence model developed by researchers at the University of Southern California (USC) has made significant strides in measuring the speed at which the brain ages. This new tool offers hope for enhanced understanding, prevention and treatment of cognitive decline and dementia.
“This is a novel measurement that could change the way we track brain health both in the research lab and in the clinic,” senior author Andrei Irimia, an associate professor at the USC Leonard Davis School of Gerontology, said in a news release. “Knowing how fast one’s brain is aging can be powerful.”
Published in the Proceedings of the National Academy of Sciences, the study details how this first-of-its-kind AI model analyzes magnetic resonance imaging (MRI) scans to non-invasively track brain changes over time.
Irimia emphasizes the importance of this development, noting that faster brain aging is closely linked to a higher risk of cognitive impairment.
Unlike traditional methods that rely on blood samples to measure biological age, this AI tool leverages advanced deep learning techniques to provide a more precise measure of brain aging. A key limitation of existing models is their inability to track changes over time, whereas this new model employs a longitudinal approach, comparing baseline and follow-up MRI scans from the same individual.
The AI model, a three-dimensional convolutional neural network (3D-CNN), was developed in collaboration with Paul Bogdan, associate professor at the USC Viterbi School of Engineering.
The researchers trained and validated the model using over 3,000 MRI scans of cognitively normal adults. By applying the model to a group of 104 cognitively healthy adults and 140 Alzheimer’s patients, they found a close correlation between brain aging speed and cognitive test results, underlining the model’s potential as a diagnostic tool.
This model holds significant promise for characterizing both healthy aging and disease trajectories, and may one day enable personalized treatment plans based on individual aging rates, according to Bogdan.
“Rates of brain aging are correlated significantly with changes in cognitive function,” Irimia added. “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.”
Further studies indicated that brain aging rates differed between brains of men and women, which may offer insights into varying risks for neurodegenerative disorders like Alzheimer’s between the sexes.
Irimia noted that this model also has the potential to identify individuals with accelerated brain aging before symptoms of cognitive impairment arise, providing early intervention opportunities.
“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,’” Irimia added. “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.”