Stanford scientists discover hidden brain signals that predict Alzheimer’s disease years before symptoms appear

The study, published November 4 in Scientific advancesindicates a potentially powerful new way to detect warning signs of cognitive decline. Unlike conventional biomarkers, these brain activity patterns could potentially be modified 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 suffered from Alzheimer’s disease, while the other was made up of super-aged people, older people whose memory remained comparable to that of people several decades younger. Using the differences between these groups, scientists created a scoring system to assess brain activity in cognitively healthy adults between the ages of 55 and 70.

The results revealed a striking link 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 beta-amyloid plaques, and be diagnosed with Alzheimer’s during follow-up periods of up to 12 years. Those whose activity patterns resemble those of super-aged people were more likely to maintain their cognitive abilities and avoid these outcomes.

A new way to detect Alzheimer’s disease before memory loss

The new scoring system predicts 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 field of Alzheimer’s disease today: Most studies focus on well-known biomarkers of Alzheimer’s disease, such as beta-amyloid or tau protein. But these 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 early signs of cognitive decline and potentially help us intervene in a timely manner for people.”

Zhang envisions a future in which treatments could potentially shift a person’s brain activity toward 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 its potential to modify brain activity in people being treated for depression. However, none of these proposed approaches have been tested in this context to prevent 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 could be useful therapeutic targets.

“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.”

What makes the brain different from super-agers?

Depending on how you define them, about 10% of older people can be considered super-aged. For this research, the term referred to people aged at least 75 who performed memory tests at a level typical of a 45-year-old while maintaining normal executive function for their age.

Scientists are increasingly interested in these exceptionally resilient individuals. However, most previous studies compared very old people to other older people who performed normally on cognitive assessments.

Zhang acknowledged that this approach might overlook important biological differences. Two people can score similarly on memory tests despite having very different underlying brain health. One might already be developing beta-amyloid plaques or exhibit other biological changes associated with aging, while the other might not.

“Normal individuals, without cognitive impairment, are quite heterogeneous in terms of biological abnormalities,” he said. “Simply comparing super-elderly people with such a heterogeneous population makes it difficult to define what is essential in terms of resilient or vulnerable brain patterns.”

In other words, comparing super-aged people with a wide range of apparently healthy older people makes it difficult to determine which brain features actually protect against decline.

The Stanford team took a different approach. Rather than using typical older people as a comparison group, they looked at people with Alzheimer’s disease who were roughly the same age as the super-aged people. This created two more clearly defined groups at opposite ends of the cognitive spectrum.

Brain scans reveal hidden motive

The researchers relied on imaging information collected through several major research initiatives, including the Open Access Imaging Study Series, the Harvard Aging Brain Study, and the Alzheimer’s Disease Neuroimaging Initiative.

They looked at resting functional MRI scans of 180 elderly people and 110 people with Alzheimer’s disease. This imaging technique records patterns of brain activity while a person lies quietly inside a scanner.

The comparison revealed distinctive differences in activity in several brain regions. Some areas showed greater activity while others showed reduced activity, producing recognizable patterns that distinguished the two groups. Scientists call the relationships between these regions functional connectivity or the functional connectome.

Using these models, the team developed a measure called the Alzheimer’s resilience-vulnerability connectome, or ARC score. This scoring method was able to distinguish the super-ages of Alzheimer’s patients with approximately 85% accuracy.

The researchers then studied whether the same brain signature could help predict what would happen to people who had not yet developed notable cognitive problems.

Predicting Alzheimer’s disease risk years in advance

The team applied the ARC scoring system to more than 1,300 cognitively healthy adults aged 55 to 70. Their scores fell within a wide range, with lower values ​​reflecting brain activity more similar to that of super-aged people and higher values ​​indicating patterns closer to those seen in Alzheimer’s patients.

At the start of the study, people with high and low ARC scores showed no obvious differences in cognitive performance. However, when researchers looked at follow-up data collected over subsequent years, significant differences became apparent.

“We used the ARC score to predict the likelihood that this patient would progress to mild cognitive impairment or Alzheimer’s disease,” said Kanhao (Alex) Zhao, PhD, a postdoctoral researcher in psychiatry and lead author of the study.

The available follow-up periods differed between datasets. Some participants had records spanning 12 years, although most were followed for about five to six years. Even during these shorter periods, initial ARC scores predicted later changes in cognitive abilities.

People whose scores indicated brain activity closer to that of Alzheimer’s disease experienced more rapid declines on standard tests of memory, attention and executive function. In contrast, those whose patterns were most similar to super-aged people generally experienced the slowest cognitive decline.

“We compared the ARC score with existing biomarkers to predict people’s conversion to Alzheimer’s disease,” Zhao said. “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.”

A more precise measurement of brain aging

The team also studied 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 measures showed some correlation, Zhang pointed out that ARC provides more targeted information about a person’s vulnerability or resistance to cognitive decline during aging.

“I am delighted 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,” he said.

The findings suggest that patterns of communication between brain regions may offer valuable information about future cognitive health that cannot be captured by standard memory tests alone.

They also raise the possibility that combining measures of brain activity with established biomarkers of Alzheimer’s disease could improve the identification of people who would benefit most from earlier monitoring or future preventative treatments.

Could brain training help prevent Alzheimer’s disease?

Despite these encouraging results, researchers caution that ARC scores are not intended to diagnose Alzheimer’s disease. For now, the system should be considered as a potential tool for tracking cognitive health and estimating future risks.

Zhang also emphasizes that a person’s score is not a permanent prediction of their future. Brain activity can change over time, and a person who initially exhibits a pattern of resilience may not maintain it indefinitely.

For example, the proportion of people with super-senior-type ARC scores generally declines with age. Brain injuries and neuropsychiatric disorders can also influence…