Scientists Develop AI-Based Framework to Show How Distinct Parts of Brain Age
The model trained on MRI scans from nearly 15,000 adults and found regional aging patterns linked to cognitive decline and Alzheimer’s disease.
- On Monday, University of Southern California researchers published a study describing an AI-based model that maps local brain aging across the lifespan using detailed 3D maps at the voxel level.
- Traditional neuroimaging algorithms collapse entire brain scans into single age figures, masking critical regional variations. Associate Professor Andrei Irimia led the team training this deep learning model on MRI scans from nearly 15,000 cognitively healthy adults.
- Findings reveal frontal and temporal lobes appear biologically older than other regions; the researchers identified accelerated aging in the hippocampus and amygdala among participants with mild cognitive impairment and Alzheimer's disease.
- Irimia emphasized the model remains a research tool requiring further validation before clinical adoption, though the framework demonstrates potential to track disease progression and evaluate experimental therapy effectiveness in targeted brain regions.
- Understanding these regional aging pathways could eventually allow doctors to identify at-risk individuals earlier and develop personalized approaches to preserving brain health by monitoring how structural anatomy correlates with cognitive function.
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The Economic Times
Times of IndiaScientists develop AI-based framework to show how distinct parts of brain age
New AI maps brain aging differences across distinct regions. This model reveals how brain changes correlate with cognitive function over time. Frontal and temporal lobes show more advanced aging than other brain areas. The right hemisphere also tends to age slightly faster than the left. These localized aging patterns link directly to cognitive performance and neurodegeneration.
New AI Tool Maps How Different Brain Regions Age
Researchers have developed an AI-based approach to generate detailed maps highlighting differences in how distinct parts of the brain age. This new model reveals that not all brain regions age at the same rate, with frontal and temporal lobes appearing biologically older. The study also links these regional ageing patterns to cognitive function and neurodegeneration, particularly in Alzheimer's disease.
AI-enabled measurements of 'local brain aging' offer detailed insights on dementia and more
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.
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