For the first time, scientists have mapped the genetics of how the brain ages, region by region

A landmark research paper for the first time maps the genetics of how individual regions of the brain age —and why some of those regions are the very ones most ravaged by Alzheimer’s and dementia. Published in the journal GeroScience, the paper is titled “Deep Neural Networks and Genome-Wide Associations Reveal the Polygenic Architecture of Local Brain Aging.” Where previous studies assigned the brain a single aging score, Kim’s research asked a more precise question: how do genetic factors contribute to aging across different brain regions?

For about a decade, scientists have measured what they call “brain age,” an estimate of how old your brain appears on an MRI scan, which can differ from your actual age. A 40-year-old’s brain might, for instance, look 50, a gap can signal elevated risk for cognitive decline.

“We’ve treated brain age like a single number, almost like a GPA for your brain,” Nicholas Kim the study’s lead author who is a senior in the is senior year at the USC Viterbi School of Engineering Alfred E. Mann Department of Biomedical Engineering, said. “But just like a GPA, that single score hides a lot of nuances.”

What Kim and his team advised by Andrei Irimia, associate professor of gerontology, quantitative & computational biology, biomedical engineering and neuroscience at USC, discovered is that the brain doesn’t age uniformly. Different regions age at different rates, and those differences aren’t random; They are influenced by distributed groups of genetic factors. 

“What I find very exciting about this research,” Irimia says, “is that it highlights how brain aging is not driven by one genetic factor, but rather by a polygenic architecture whose properties differ across brain regions. Combining measures of local brain aging with genetic analysis allows us to begin mapping how distinct inherited factors influence vulnerability in important neural systems. This advances our understanding of human brain aging and helps to explain why some brain regions are more susceptible to Alzheimer’s disease.” 

By analyzing MRI scans from 41,708 adults in the UK Biobank, a large British health database, the researchers divided the brain into 148 distinct regions and separately measured the amount of excessive or delayed aging in each one. They then scanned each participant’s DNA, testing more than 600,000 genetic variants, and identified which variants were linked to excessive aging in which regions. The result: 1,212 significant genetic associations, a detailed genetic map of how and where the brain grows old.

Genes That Age the Brain and Genes That Protect It

The study identified both factors that predict excessive aging and those that protect against it. One particular gene, KCNK2, controls potassium channels that help regulate electrical signaling between neurons. Genetic variance in or near this gene was strongly associated with advanced aging in brain regions that are especially vulnerable in Alzheimer’s disease.
On the other hand, variants in a gene called NUAK1, which helps maintain the structural skeleton of brain cells, were associated with a younger-appearing brain across wide areas of the cortex.

Kim is careful not to overstate what this means for any individual. “Carrying a risky genetic variant is like having a slightly heavier backpack,” he said. “It makes the climb harder, but it doesn’t decide whether you reach the top. Lifestyle, environment, vascular health, cognitive engagement, these all matter enormously.”

One of the study’s most significant findings is that the regions of the brain that exhibit the most excessive amount of aging are most devastated by Alzheimer’s disease and frontotemporal dementia.

The Role of AI
None of this would have happened without artificial intelligence. Each MRI scan is a three-dimensional image made up of more than two million tiny cubes of data. No human could process that volume. The team designed and built a custom AI system, a 3D neural network, that learned to detect the subtle structural signatures of aging across every region of the brain simultaneously.The entire project took about a year and a half and required a computer cluster of four servers running roughly 120 processors simultaneously.

“AI was essential because some aging signals are very subtle,” Kim said. “We trained a neural network to learn the structural patterns associated with age, and that gave us the trait we needed to run the genetic study.” 

Could this research one day help a doctor identify who is at risk for dementia years before symptoms appear, or guide the development of targeted treatments? Possibly. “This is mostly a powerful research tool right now, not a diagnostic test,” he said. “There are a lot of barriers to moving to the clinical side. Maybe in decades.”

Kim’s co-authors include Ayati Mishra, a USC sophomore studying neuroscience; neuroscience Ph.D. students Owen M. Vega, and Nahian F. Chowdhury; Nikhil Chaudhari, a biomedical engineering doctoral student whose adviser is Irimia;  lab manager Samuel D. Anderson; Kenneth H. Buetow, a professor of genetics at Arizona State University; Paul M. Thompson,  director of the USC Imaging Genetics Center and professor of ophthalmology, neurology, psychiatry and the behavioral sciences, radiology, psychiatry, electrical and computer engineering, and biomedical engineering; and Irimia, who supervised the project.