Inside the brains of some cognitively normal adults, firing patterns were able to foretell Alzheimer's disease years before the onset of memory loss or the accumulation of plaques and tangles, according to new Stanford Medicine-led research. For others, different firing patterns indicated the opposite — brains unusually resilient to cognitive decline and on track for "super ager" status.
The findings , published Nov. 4 in Science Advances, suggest a new type of early biomarker — perhaps a modifiable one — for cognitive decline in old age.
In the new study, researchers first analyzed the resting brain activity of adults 75 and older on two ends of the cognitive spectrum: those diagnosed with Alzheimer's disease and so-called super agers, who have the memory capacity of people decades younger. Extrapolating from these two extremes, the researchers developed a scoring system that could place slightly younger adults, aged 55 to 70, who had no signs of cognitive decline, along the spectrum based on their brain activity.
The more Alzheimer's-like someone's brain activity, the more likely they would experience mental decline, develop amyloid-beta plaques and be diagnosed with Alzheimer's within the next 12 years. The more super-ager-like someone's brain activity, the more likely they were to avoid these outcomes.
The new scoring system was better at predicting cognitive decline than demographics, amyloid-beta and tau biomarkers , and genetics. It could provide some of the earliest warning signs yet of Alzheimer's disease risk in people with no apparent cognitive impairment.
"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's hope is that interventions such as transcranial magnetic stimulation or cognitive training could one day nudge a person's brain patterns closer to the resilient end of the spectrum. Although transcranial magnetic stimulation has shown promise in altering brain activity to treat depression, these interventions have not been tested for cognitive decline, and it's not known whether shifting brain patterns will redirect disease trajectories. But the new findings are the first step in defining potential 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."
Two ends of the spectrum
About 10% of the elderly may qualify as super agers, depending on the specific criteria applied. In the new study, super agers were defined as people 75 or older who have memory capacity that's typical of a 45-year-old as well as normal executive function for their age.
Super agers are increasingly the subject of research, but until now, studies have generally compared them with their cognitively typical peers.
Zhang realized that such comparisons have a fundamental flaw — older adults who appear typical on standard cognitive tests may harbor divergent neurobiological traits under the surface. Some may already have the beginnings of amyloid-beta plaques or other signs of brain aging.
"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."
It's akin to comparing apples to a fruit basket.
Instead, Zhang's team contrasted super agers with a more well-defined group on the other extreme — similarly aged adults diagnosed with Alzheimer's.
They used neuroimaging data from several large-scale projects, including the Open Access Series of Imaging Studies, the Harvard Aging Brain Study and the Alzheimer's Disease Neuroimaging Initiative.
Comparing resting-state functional MRI, which measures brain activity as participants lie still in a scanner, in 180 super agers and 110 people with Alzheimer's disease, the researchers found distinct patterns of underactivity and overactivity in various brain regions that separated the two groups. These patterns are known as functional connectivity or the functional connectome.
They derived a scoring system, known as the Alzheimer's resilience-vulnerability connectome or ARC score, which could distinguish super agers from people with Alzheimer's with roughly 85% accuracy.
Predicting decline
The researchers then applied their scoring system to more than 1,300 people aged 55 to 70 who had no signs of cognitive impairment. Indeed, they found a range of intermediate scores, with lower scores indicating more super-ager-like brain activity and higher scores indicating more Alzheimer's-like brain activity.
At the time of imaging, there were no clear cognitive differences between people who scored higher or lower, but a split emerged in the long-term data.
"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 datasets varied in the extent of longitudinal data with the longest up to 12 years, but most around five to six years, Zhao said. Even in this relatively brief period, however, the baseline ARC scores showed predictive value.
Those with more Alzheimer's-like scores exhibited faster cognitive decline, as measured by standard tests of memory, attention and executive function. Those with the most super-ager-like scores showed the slowest declines.
"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 some correlation between ARC scores and a broader brain health measure known as brain age, which was developed from functional MRI studies of a healthy population.
The ARC score is a more specific gauge of resilience and vulnerability to mental decline in an older population, Zhang said.
"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.
Not destiny
For now, the ARC score should not be considered diagnostic, but rather a helpful guide for monitoring cognitive status.
Zhang noted that an ARC score, whether good or bad, is only a snapshot, not destiny. The proportion of people with super-ager-like ARC scores tends to decrease with age, for example, as people suffer brain injuries or develop neuropsychiatric conditions.
By the same token, Zhang hopes that intentional interventions will be able to shift brain activity in the other direction — toward resilience. His team is planning a trial to investigate ARC scores before and after cognitive training.
"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."
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 contributed to the work.
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.
