Subtle differences in brain activity may reveal who is most vulnerable to Alzheimer’s disease and who could remain mentally sharp well into old age.
Distinct patterns of brain activity may predict Alzheimer’s disease years before memory problems or hallmark brain changes appear. Stanford Medicine researchers have developed a scoring system that identifies people at greater risk of cognitive decline while also recognizing those whose brains show unusual resilience to aging.
The study, published November 4 in Science Advances, found that these patterns could predict future cognitive decline more effectively than several established risk indicators, including genetics and Alzheimer’s biomarkers.
Identifying the Brain’s Hidden Patterns
Researchers analyzed resting-state functional MRI scans from 180 “super agers,” adults aged 75 and older with memory abilities comparable to those of typical 45-year-olds, and 110 similarly aged people diagnosed with Alzheimer’s disease.
“Normal, cognitively unimpaired individuals are pretty heterogeneous in terms of their biological abnormalities,” said Yu Zhang, PhD, assistant professor of psychiatry and behavioral sciences at Stanford Medicine. “Simply comparing super agers with such a heterogeneous population makes it challenging to define what is critical in terms of resilient or vulnerable brain patterns.”
The team identified distinct patterns of activity across brain networks and developed the Alzheimer’s resilience-vulnerability connectome (ARC) score. The system distinguished super agers from people with Alzheimer’s with approximately 85% accuracy.
Predicting Alzheimer’s Before Symptoms Appear
The researchers tested the ARC score in more than 1,300 cognitively healthy adults aged 55 to 70. Although participants initially showed no clear differences in cognitive performance, their brain activity patterns predicted different outcomes over follow-up periods extending as long as 12 years.
People with more Alzheimer’s-like activity experienced faster cognitive decline and were more likely to develop amyloid-beta plaques or receive an Alzheimer’s diagnosis. Those with super-ager-like patterns generally maintained stronger cognitive abilities.
“We used the ARC score to make a prediction of how likely this patient will convert into mild cognitive impairment or Alzheimer’s disease,” said Kanhao (Alex) Zhao, PhD, a Stanford postdoctoral scholar and lead author.
Going Beyond Traditional Biomarkers
“We want to address a major challenge in the Alzheimer’s field nowadays — most studies are looking at well-known Alzheimer’s disease biomarkers, such as amyloid-beta or tau. But those are observable at relatively late stages of the disease,” Zhang said. “What we really care about is defining early markers that can detect earlier signs of cognitive decline and potentially help us deliver timely interventions to people.”
The ARC score performed as well as or better than existing biomarkers at predicting progression toward Alzheimer’s, while offering information at an earlier stage. Combining the score with traditional biomarkers improved predictions further.
“We compared the ARC score with existing biomarkers in predicting people converting to Alzheimer’s disease,” Zhao said. “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.”
The researchers also found a relationship between ARC scores and brain age, a broader measure of aging derived from functional MRI data.
“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,” Zhang said.
Could Brain Activity Be Changed to Prevent Decline?
ARC scores are not diagnostic, and they do not determine a person’s future. Brain activity can change with aging, injuries, and neuropsychiatric conditions. Researchers hope that targeted interventions might eventually shift vulnerable brain patterns toward greater resilience.
Potential approaches include cognitive training and transcranial magnetic stimulation, which uses magnetic pulses to influence brain activity. However, these interventions have not been shown to prevent cognitive decline by changing ARC patterns.
“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.”
Zhang’s team is planning a trial to examine whether cognitive training can alter ARC scores.
“But the critical part is that we have to intervene at an early stage,” Zhang said. “That’s why we focus on the cognitively unimpaired population to identify super-ager-like versus Alzheimer’s-like individuals. If we can precisely identify such as early indicator, I’m optimistic that we can reverse or prevent this process through advanced intervention technologies.”
Reference: “A functional connectome identifies vulnerability to cognitive decline and Alzheimer’s disease progression” by Kanhao Zhao, Hua Xie, Gregory A. Fonzo, Nancy B. Carlisle, Tovia Jacobs, Ricardo S. Osorio, Arpana Church, Feng Vankee Lin, Yu Zhang and Alzheimer’s Disease Neuroimaging Initiative, 4 November 2026, Alzheimer’s Disease Neuroimaging Initiative.
DOI: 10.1126/sciadv.aed9351
The study received funding from the National Institutes of Health (grants R21AG080425, R01MH129694 and R01AG081768), a joint Rosenkranz Foundation-Knight Initiative program, an Alzheimer’s Association grant and a Lehigh University CORE grant.
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