Could changes in brain activity help predict Alzheimer’s disease years before memory problems begin? A new study led by researchers at Stanford Medicine suggests that specific patterns of brain activity may reveal an individual’s risk of developing Alzheimer’s disease, even when they show no signs of cognitive decline.
Published on November 4 in Science Advances, the study found that brain activity patterns could identify individuals more likely to experience cognitive decline, develop amyloid-beta plaques and receive an Alzheimer’s diagnosis over the following years. Interestingly, researchers also identified distinct brain patterns associated with better memory and resistance to age-related cognitive decline, commonly seen in people known as super agers.
Brain activity reveals early warning signs
The researchers examined resting-state brain activity in adults aged 75 and older, comparing 180 super agers with 110 individuals diagnosed with Alzheimer’s disease. Using functional magnetic resonance imaging (fMRI), which measures brain activity while a person rests, they identified differences in activity and connectivity across various brain regions.
Based on these findings, the team developed a scoring system called the Alzheimer’s Resilience-Vulnerability Connectome (ARC) score. This system distinguishes brain activity patterns associated with Alzheimer’s vulnerability from those linked to cognitive resilience, achieving approximately 85% accuracy in differentiating the two groups.
The researchers then applied the scoring system to more than 1,300 cognitively healthy adults aged 55 to 70. Although these individuals showed no noticeable differences in cognitive ability at the time of brain imaging, their ARC scores revealed varying levels of vulnerability and resilience.
Brain patterns may predict cognitive decline
During follow-up periods extending up to 12 years, researchers observed that individuals with more Alzheimer’s-like brain activity patterns experienced faster declines in memory, attention and executive function. In contrast, those with super-ager-like patterns showed slower cognitive decline and were more likely to maintain their cognitive abilities.
The ARC scoring system also demonstrated stronger predictive potential than several existing measures, including demographic factors, amyloid-beta and tau biomarkers, and genetic information. Furthermore, combining the ARC score with established biomarkers improved prediction accuracy.
According to Yu Zhang, Ph.D., assistant professor of psychiatry and behavioural sciences at Stanford Medicine and senior author of the study, existing Alzheimer’s biomarkers often become detectable at relatively late stages of the disease. Therefore, identifying earlier indicators of cognitive decline could help researchers recognise at-risk individuals and explore timely interventions.
Could brain activity patterns be changed
Beyond predicting Alzheimer’s risk, the findings could help researchers identify new approaches to protecting brain health. The team hopes that interventions such as transcranial magnetic stimulation and cognitive training may eventually help shift brain activity towards patterns associated with greater cognitive resilience.
However, researchers have not yet tested these approaches for preventing cognitive decline by changing ARC scores. It also remains unclear whether modifying brain activity patterns can actually alter the progression of Alzheimer’s disease.
To investigate this possibility, Zhang and his colleagues are planning a trial to examine ARC scores before and after cognitive training.
Early detection remains the priority
The researchers emphasised that the ARC score is not a diagnostic tool and cannot determine whether someone will develop Alzheimer’s disease. Instead, it could serve as an early indicator to help monitor brain health and identify individuals who may benefit from further assessment.
The findings highlight the potential of brain activity patterns as an early biomarker of Alzheimer’s disease, possibly offering a new direction for identifying cognitive vulnerability before conventional symptoms or established disease markers appear.
As reported by Medical Xpress, the research could open new avenues for early Alzheimer’s risk assessment and future interventions aimed at preserving cognitive health.