Subtle patterns of brain activity may reveal the risk of Alzheimer’s disease years before memory problems develop or harmful proteins begin accumulating in the brain, according to new research led by Stanford Medicine. The same approach can also identify people whose brains show exceptional resistance to cognitive decline, suggesting they may eventually become “super agers.”
The study, published November 4 in Science Advances, points to a potentially powerful new way to detect early warning signs of cognitive decline. Unlike conventional biomarkers, these patterns of brain activity might eventually be possible to change through targeted interventions.
To investigate, the research team examined brain activity in adults aged 75 and older who represented opposite ends of the cognitive aging spectrum. One group had Alzheimer’s disease, while the other consisted of super agers, older individuals whose memories remained comparable to those of people decades younger. Using the differences between these groups, the scientists created a scoring system to evaluate brain activity in cognitively healthy adults between 55 and 70.
The results revealed a striking connection between brain activity and future cognitive health. People whose brain patterns more closely resembled those of Alzheimer’s patients were more likely to experience cognitive decline, accumulate amyloid-beta plaques, and receive an Alzheimer’s diagnosis during follow-up periods extending up to 12 years. Those with activity patterns resembling super agers were more likely to maintain their cognitive abilities and avoid these outcomes.
A New Way to Detect Alzheimer’s Before Memory Loss
The new scoring system predicted future cognitive decline more effectively than several established indicators, including demographic characteristics, genetic risk factors, and amyloid-beta and tau biomarkers. This suggests that examining how different regions of the brain communicate could reveal Alzheimer’s risk earlier than existing approaches.
“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,” said Yu Zhang, PhD, assistant professor of psychiatry and behavioral sciences and senior author of the study. “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.”
Zhang envisions a future in which treatments could potentially move someone’s brain activity toward the patterns associated with greater cognitive resilience. Possible approaches include cognitive training and transcranial magnetic stimulation, which uses magnetic pulses to influence brain activity.
Transcranial magnetic stimulation has already demonstrated potential for changing brain activity in people being treated for depression. However, neither of these proposed approaches has been tested in this context for preventing cognitive decline, and researchers do not yet know whether changing brain activity would alter the course of Alzheimer’s disease. The new findings provide a starting point for identifying which brain regions might be useful treatment targets.
“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.”
What Makes Super Agers’ Brains Different?
Depending on how they are defined, approximately 10% of older adults may qualify as super agers. For this research, the term referred to people at least 75 years old who performed on memory tests at a level typical of a 45-year-old while maintaining normal executive function for their age.
Scientists have become increasingly interested in these unusually resilient individuals. Most earlier studies, however, compared super-agers with other older adults who performed normally on cognitive assessments.
Zhang recognized that this approach could overlook important biological differences. Two people might receive similar scores on memory tests despite having very different underlying brain health. One might already be developing amyloid-beta plaques or showing other biological changes associated with aging, while the other might not.
“Normal, cognitively unimpaired individuals are pretty heterogeneous in terms of their biological abnormalities,” he said. “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.”
In other words, comparing super agers with a broad mixture of seemingly healthy older adults makes it difficult to determine which brain characteristics actually protect against decline.
The Stanford team took a different approach. Rather than using typical older adults as the comparison group, they examined people with Alzheimer’s disease who were approximately the same age as the super agers. This created two more clearly defined groups at opposite ends of the cognitive spectrum.
Brain Scans Reveal a Hidden Pattern
The researchers drew on imaging information collected through several major research initiatives, including the Open Access Series of Imaging Studies, the Harvard Aging Brain Study, and the Alzheimer’s Disease Neuroimaging Initiative.
They examined resting-state functional MRI scans from 180 super agers and 110 people diagnosed with Alzheimer’s disease. This imaging technique records patterns of brain activity while a person lies quietly inside a scanner.
The comparison uncovered distinctive differences in activity across multiple brain regions. Some areas showed greater activity while others showed reduced activity, producing recognizable patterns that distinguished the two groups. Scientists refer to the relationships among these regions as functional connectivity, or the functional connectome.
Using these patterns, the team developed a measurement called the Alzheimer’s resilience-vulnerability connectome, or ARC score. This scoring method distinguished super agers from Alzheimer’s patients with approximately 85% accuracy.
The researchers then investigated whether the same brain signature could help predict what would happen to people who had not yet developed any noticeable cognitive problems.
Predicting Alzheimer’s Risk Years in Advance
The team applied the ARC scoring system to more than 1,300 cognitively healthy adults between the ages of 55 and 70. Their scores fell across a broad range, with lower values reflecting brain activity more similar to super agers and higher values indicating patterns closer to those observed in Alzheimer’s patients.
At the beginning of the study, people with high and low ARC scores showed no obvious differences in cognitive performance. As researchers examined follow-up data collected over subsequent years, however, important differences became apparent.
“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 postdoctoral scholar in psychiatry and lead author of the study.
The available follow-up periods differed across the datasets. Some participants had records extending as long as 12 years, although most were tracked for approximately five to six years. Even within these shorter periods, the initial ARC scores helped predict subsequent changes in cognitive abilities.
People whose scores indicated more Alzheimer’s-like brain activity experienced more rapid declines on standard tests of memory, attention, and executive function. In contrast, those with patterns most similar to super agers generally experienced the slowest cognitive decline.
“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.”
A More Precise Measure of Brain Aging
The team also investigated how ARC scores related to another measure of neurological health called brain age. This measure estimates brain aging using functional MRI information collected from healthy individuals.
Although the two measurements showed some correlation, Zhang emphasized that ARC provides more targeted information about a person’s vulnerability or resistance to cognitive decline during aging.
“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,” he said.
The findings suggest that patterns of communication between brain regions may offer valuable information about future cognitive health that cannot be captured through standard memory testing alone.
They also raise the possibility that combining brain activity measurements with established Alzheimer’s biomarkers could improve the identification of people who would benefit most from earlier monitoring or future preventive treatments.
Could Brain Training Help Prevent Alzheimer’s?
Despite the encouraging results, researchers caution that ARC scores are not intended to diagnose Alzheimer’s disease. For now, the system should be viewed as a potential tool for tracking cognitive health and estimating future risk.
Zhang also stresses that a person’s score is not a permanent prediction of their future. Brain activity can change over time, and an individual who initially shows a resilient pattern may not maintain it indefinitely.
For example, the proportion of people with super-ager-like ARC scores generally declines with advancing age. Brain injuries and neuropsychiatric conditions can also influence the patterns associated with cognitive resilience.
Importantly, this potential for change may work in both directions. If brain activity can shift toward patterns associated with vulnerability, Zhang hopes future treatments might also encourage it to move toward greater resilience.
His research team is preparing a trial that will measure ARC scores before and after cognitive training. The goal is to explore whether targeted training can influence the brain activity patterns associated with healthier cognitive aging.
“But the critical part is that we have to intervene at an early stage,” he 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.”
Although it remains unknown whether modifying these brain patterns can prevent or delay Alzheimer’s, the discovery offers a potentially important new direction for research. By identifying differences in brain activity before symptoms emerge, scientists may eventually gain a valuable opportunity to intervene while cognitive function is still intact.
Researchers from Children’s National Hospital; the University of Texas at Austin; Lehigh University; Penn State College of Medicine; New York University Grossman School of Medicine; and the University of California, Los Angeles also participated in the research.
Funding was provided by 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.