Scientists at Stanford Medicine in California say they’ve developed a way to spot warning signs of Alzheimer’s disease years before a person shows any memory problems, using a brain-scan measurement they call the Alzheimer’s resilience-vulnerability connectome score, or ARC score. The findings were published in the journal Science Advances.
Rather than hunting for the usual physical markers of the disease, such as amyloid plaque buildup or brain shrinkage, the research team looked at how different regions of the brain talk to one another. They compared resting brain scans from two very different groups of older adults: people diagnosed with Alzheimer’s disease, and so-called “super agers” whose memory performance rivals people decades younger.
By comparing activity patterns between those two groups, the researchers built a model that could tell super agers apart from Alzheimer’s patients with roughly 85% accuracy.
“I’m excited we established a signature that can help us better understand the difference between people who can maintain excellent cognitive performance and people who show cognitive decline,” said Yu Zhang, the study’s senior author and an assistant professor of psychiatry and behavioral sciences at Stanford Medicine.
Testing the Model on More Than 1,300 Adults
The researchers then applied their scoring system to more than 1,300 cognitively healthy adults between the ages of 55 and 70. All of them scored normally on standard memory tests at the outset, but their brain scans told a different story — some showed activity patterns resembling the super agers, while others looked more like the Alzheimer’s patients.
Over follow-up periods stretching up to 12 years, with most participants tracked for five to six years, the adults whose brain scans resembled the Alzheimer’s pattern experienced faster mental decline, developed more amyloid plaques, and were more likely to eventually be diagnosed with the disease.
The ARC score matched or outperformed existing tools used to predict Alzheimer’s risk, including amyloid and tau biomarker tests and genetic screening, according to the study.
“We compared the ARC score with existing biomarkers in predicting people converting to Alzheimer’s disease,” said Kanhao Zhao, the study’s lead author and a postdoctoral scholar in psychiatry at Stanford Medicine. “Our model shows comparable or better prediction ability, and it can do so at an earlier time point. And if we combine our score with the existing biomarkers, the prediction performance improves.”
Why Early Detection Matters
Alzheimer’s disease affects millions of older adults worldwide. One of the biggest obstacles to treating it is that the underlying brain damage typically begins decades before patients or their families notice any memory lapses — by which point available treatments do far less good.
The Stanford team cautioned that the brain-scan method is not ready to be used on its own by doctors to diagnose Alzheimer’s. More research is needed to confirm how accurate it is and how it compares with other emerging tests.
Still, the researchers say that understanding the difference between healthy aging and the early stages of neurodegeneration could help guide the testing of future treatments, including non-surgical brain stimulation techniques.
“There are two main components: One is to know which part of the brain to target. The other is how we should change it,” Zhang said. “That’s why identifying the super ager brain resilience pattern is a critical component — so we know what a good pattern should be.”
