Researchers at the University of Southern California have developed an approach that uses artificial intelligence to generate detailed maps that highlight differences in how distinct parts of the brain age. The researchers, led by associate professor Andrei Irimia, PhD, at the USC Leonard Davis School of Gerontology, used magnetic resonance imaging (MRI) 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 (LBA), or how old specific regions of the brain appear.

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” (BA) 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.

When the AI model was then used to analyze MRI images from people with mild cognitive impairment and Alzheimer’s disease (AD), it revealed distinct patterns of accelerated aging in brain regions known to be affected early in neurodegeneration.

“Not all brain regions age at the same rate,” Irimia said. “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.”

In their in paper in PNAS, titled “Deep learning maps local brain aging in relation to cognition across human adulthood,” senior author Irimia and colleagues stated, “By providing spatially resolved measures of brain aging, this work enables more precise investigation of how neuroanatomic alterations and cognitive impairment affect brain anatomy, above and beyond global brain age measures.”

Aging is a prominent risk factor for the onset of brain diseases, including Alzheimer’s disease and related dementias, the authors wrote. “One of the most prominent biological features of brain aging is atrophy, i.e., brain volume decrease that often involves loss of brain cells and neural connectivity.”

The newly reported research builds on previous efforts to estimate BA, an emerging neuroimaging biomarker that compares a person’s brain structure to patterns seen in healthy people across the lifespan. But while human brain aging is not uniform across cortical regions, traditional methods typically reduce the brain to a single age estimate, which can obscure important regional differences. The new approach instead measures local brain age at the voxel level—the three-dimensional units that make up an MRI scan—producing a much more detailed picture of structural aging throughout the brain.

“This more nuanced understanding of how the brain ages could pave the way for earlier identification of dementia, a better understanding of what factors affect risk and new ideas for treatment approaches,” Irimia said.

To develop the model, the researchers trained a deep-learning neural network using MRI scans from 14,748 cognitively normal adults ages 19 years to 100 years, drawn from six large public datasets, including the UK Biobank, the Human Connectome Project and the Alzheimer’s Disease Neuroimaging Initiative. The team then tested the model using MRI scans from more than 1,900 additional participants in the Alzheimer’s Disease Neuroimaging Initiative, including cognitively normal adults, people with mild cognitive impairment and people with Alzheimer’s disease.

Across healthy adults, the model consistently found that the frontal and temporal lobes—regions involved in decision-making, memory and other higher cognitive functions—appeared biologically older than the parietal and occipital regions, which are involved in spatial awareness and sensory processing functions. “Our approach consistently reveals spatial patterns of aging, including relatively advanced aging in frontal and temporal regions, across both typical aging and Alzheimer’s disease,” the investigators noted. The researchers also found that the brain’s right hemisphere tended to show slightly more advanced aging than the left, a pattern that persisted regardless of whether participants were right- or left-handed.

As cognitive impairment progressed, the differences became even more pronounced. Compared with cognitively normal adults, participants with mild cognitive impairment (MCI) or Alzheimer’s disease showed significantly older local brain ages in structures that are among the first affected by Alzheimer’s pathology, including the hippocampus, amygdala and several deep brain regions involved in memory and cognitive processing.

The researchers also found that older local brain age was associated with poorer performance on cognitive assessments, strengthening the link between structural brain changes and real-world function. “Deviations from normative regional aging are significantly associated with cognitive performance supported by neural processes linked to those regions … thereby relating anatomic aging to functional outcomes,” they stated. The strongest relationships appeared in people with Alzheimer’s disease, suggesting that regional brain aging may become increasingly informative as neurodegeneration advances.

Because the model produces anatomically detailed maps, it could eventually help scientists better understand why some people experience faster decline in specific cognitive abilities than others. The approach may also prove useful for tracking disease progression or evaluating whether experimental therapies are slowing degeneration in targeted brain regions. “By providing spatially resolved measures of brain aging, this work enables more precise investigation of how neuroanatomic alterations and cognitive impairment affect brain anatomy, above and beyond global brain age measures,” they commented.

Although the findings are promising, Irimia emphasized that the method remains a research tool. The model was trained primarily on research-quality MRI data and will require additional validation using more diverse clinical datasets before it can be adopted in routine patient care. The study also relied largely on cross-sectional data, meaning that future longitudinal studies will be needed to determine whether local brain aging can reliably predict who will progress from healthy aging to mild cognitive impairment or Alzheimer’s disease.

Still, the researchers believe that moving beyond a single measure of brain age represents an important advance for neuroscience. “By quantifying the anatomy of brain aging and aligning it with cognition and disease stage, this work establishes a foundation for mechanistic inquiry and personalized intervention in neurodegeneration,” the authors stated. “This scalable framework paves the way for monitoring a broad spectrum of neurodegenerative and aging related disorders,” Irimia added, “Brain aging isn’t uniform. “By understanding how individual regions age, as well as how those patterns differ from person to person, we’re moving toward a much more precise understanding of healthy aging and neurodegenerative disease. Ultimately, that could help us identify people at risk earlier and develop more personalized approaches to preserving brain health.”

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