Scientists have built an AI tool that reads a brain MRI and maps how quickly different regions are aging — a more detailed picture than a single “brain age” number, and one that could help study dementia.
Researchers at the University of Southern California’s Leonard Davis School of Gerontology, led by associate professor Andrei Irimia, trained a deep-learning neural network on MRI scans from 14,748 cognitively normal adults aged 19 to 100, drawn from six public datasets including the UK Biobank. The work was published in PNAS.
What the map shows
Rather than assigning one number to the whole brain, the model measures local brain age down to the voxel (a 3D pixel) level, revealing which regions look biologically older than others. Across participants, the frontal and temporal lobes appeared older than the parietal and occipital regions, and the right hemisphere aged slightly faster than the left.
The dementia link
In people with mild cognitive impairment and Alzheimer’s disease, the model found significantly older local brain ages in structures that neurodegeneration hits early — including the hippocampus and amygdala and other deep memory-related regions. The researchers say the approach could eventually help identify dementia earlier, track its progression, and test whether experimental therapies slow aging in specific brain areas. They caution it needs validation on diverse clinical datasets and long-term follow-up studies before it could be used in routine care.