Stanford Medicine study uses brain scans to predict early-stage Alzheimer’s disease

Small changes in the way parts of the brain communicate with each other can help predict a person’s likelihood of developing Alzheimer’s disease years before memory lapses appear, according to a study published in Science Advances.

Stanford Medicine researchers used brain scans and a computer model to look for early clues about why some older adults develop problems with memory and thinking while others remain alert.

Investigators looked at resting brain scans of two contrasting groups: patients diagnosed with Alzheimer’s disease and older people known as “super-aged,” whose memory abilities match those of people decades younger.

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Instead of looking for physical characteristics such as amyloid plaque buildup or brain shrinkage, researchers looked at how different parts of the brain communicate with each other.

By comparing the two groups’ brain activity patterns, the researchers calculated a measure called the Alzheimer’s Resilience-Vulnerability Connectome Score, or ARC score, which distinguishes super-aged people from people with Alzheimer’s disease with about 85 percent accuracy.

Researchers have developed a brain activity scoring model, the ARC score, that can predict Alzheimer’s disease risk years before notable memory loss occurs. (iStock)

“I am pleased that we have established a signature that can help us better understand the difference between people who are able to maintain excellent cognitive performance and those who show cognitive decline,” Yu Zhang, lead author of the study and assistant professor of psychiatry and behavioral sciences at Stanford Medicine, said in a press release.

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Next, the scientists applied the scoring model to more than 1,300 adults aged 55 to 70, without cognitive impairment.

Although all of the adults performed normally on memory tests, their brain scans showed differences: Some had activity patterns similar to those of super-aged people, while others had patterns closer to those seen in people with Alzheimer’s disease.

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Over follow-up periods of up to 12 years, with most participants followed for five to six years, those whose brain activity more closely resembled Alzheimer’s patterns showed faster declines in memory and thinking, developed more amyloid plaques and were more likely to be diagnosed with the disease, the researchers found.

The model was able to differentiate between the two extreme cognitive groups with approximately 85% accuracy. (Saul Loeb/AFP/Getty Images)

The team found that the ARC score matched or outperformed standard risk measures, including amyloid and tau biomarkers as well as genetic testing, in detecting early decline.

“We compared the ARC score with existing biomarkers to predict people’s conversion to Alzheimer’s disease,” lead author Kanhao Zhao, a postdoctoral researcher in psychiatry at Stanford Medicine, said in the release.

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“Our model shows comparable or better prediction ability, and it can do it at an earlier time point. And if we combine our score with existing biomarkers, the prediction performance improves.”

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Alzheimer’s disease affects millions of older people around the world, according to leading health institutions.

One of the main challenges in treating the disease is that physical damage often begins decades before patients notice memory loss, in which case treatments offer only limited benefit.

During follow-up periods of up to 12 years, people with Alzheimer’s-like brain patterns experienced more rapid cognitive decline and higher diagnosis rates. (iStock)

The researchers stressed that the brain scan method is not yet ready for doctors to use to diagnose Alzheimer’s disease. Further studies are needed to confirm its accuracy and compare it with other new tests.

However, understanding how brain activity differs between healthy aging and neurodegeneration could help researchers test future treatments, including techniques that stimulate the brain without surgery.

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“There are two main things: One is knowing which part of the brain to target. The other is knowing how to modify it,” Zhang said.

“This is why identifying the super-aging model of brain resilience is a critical part – so we know what a good model should be.”