Cognitive decline tracked by new AI tool

Biological age is distinct from an individual’s chronological age, and faster brain ageing closely correlates with a higher risk of cognitive impairment. Using more than 3,000 MRI scans of cognitively normal adults, the researchers trained a three-dimensional convolutional neural network (3D-CNN) that provides a precise way to measure how the brain ages over time. The work, carried out by engineers at the University of Southern California (USC), is published in the Proceedings of the National Academy of Sciences.

“This is a novel measurement that could change the way we track brain health both in the research lab and in the clinic,” said senior author Andrei Irimia, associate professor of gerontology, biomedical engineering, quantitative & computational biology and neuroscience at USC. “

“Knowing how fast one’s brain is ageing can be powerful.”

When applied to a group of 104 cognitively healthy adults and 140 Alzheimer’s disease patients, the new model’s calculations of brain ageing speed closely correlated with changes in cognitive function tests. According to the USC team, the model has the potential to better characterise both healthy ageing and disease trajectories, and its predictive power could one day be applied to assessing which treatments would be more effective based on individual characteristics.