This model highlights the distinction between biological age, which reflects the brain's functional state, and chronological age, as individuals may experience different rates of brain aging.

Researchers at USC have developed a groundbreaking AI model that measures the speed of brain aging, providing valuable insights into cognitive decline and dementia.

The research aims to provide prognostic measures for Alzheimer's risk, which could lead to the development of tailored prevention drugs and strategies.

Traditional methods of assessing biological age, such as blood samples, are ineffective for measuring brain aging due to the brain-blood barrier, necessitating a non-invasive approach like MRI.

By validating the model with extensive MRI data, researchers can better identify neuroanatomic changes associated with aging.

The study, published in the Proceedings of the National Academy of Sciences on February 24, 2025, utilized over 3,000 MRI scans from cognitively healthy adults to train a three-dimensional convolutional neural network (3D-CNN).

Previous AI models relied on single MRI scans to estimate brain age, which limited their ability to track changes over time or indicate the pace of aging.

Lead researcher Irimia is optimistic that this model can identify individuals at risk for accelerated brain aging before cognitive impairments manifest, potentially improving the efficacy of Alzheimer's treatments.

The study emphasizes the critical need to understand biological brain age in contrast to chronological age, which can significantly impact cognitive health.