New AI model generates detailed maps of aging in brain regions

USC researchers have developed an approach that uses artificial intelligence to generate detailed maps that highlight differences in how distinct parts of the brain age. The new model also sheds light on how patterns of brain changes correlate with changes in cognitive function across the lifespan, according to a new USC study published in the journal Proceedings of the National Academy of Sciences.

The researchers, led by Associate Professor Andrei Irimia of the USC Leonard Davis School of Gerontology, used magnetic resonance imaging from nearly 15,000 cognitively healthy individuals to train a deep learning AI model. The data provided a baseline against which the model could measure local brain age, or how old specific regions of the brain appear. When the AI model was then used to analyze MRI images from people with mild cognitive impairment and Alzheimer's disease, it revealed distinct patterns of accelerated aging in brain regions known to be affected early in neurodegeneration.

While most studies of brain age measure this phenomenon using a single number, the new model provides a much richer picture of typical aging and neurodegeneration. Rather than assigning a single "brain age" to an individual, the approach generates a detailed map showing how old different parts of the brain appear relative to what is typical for someone of the same chronological age.

Not all brain regions age at the same rate. Some areas appear to be more resilient, while others are more vulnerable to aging and disease. By measuring local brain aging, we can identify where the brain is aging faster than expected and how those changes relate to cognitive function."