Resting-state functional MRI can predict Alzheimer’s disease up to 12 years before memory loss or brain plaques appear. A new scoring system does this by analyzing brain connectivity patterns, according to research published November 4 in Science Advances.
Stanford Researchers Create ARC Score
Stanford Medicine researchers developed the Alzheimer’s resilience-vulnerability connectome, or ARC, score by mapping neural firing networks. The team analyzed data from 110 adults with clinically diagnosed Alzheimer’s disease and 180 individuals identified as “super agers”—people aged 75 and older who retain the memory performance and executive function of 45-year-olds.
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Senior author Dr Yu Zhang observes that conventional Alzheimer’s research is limited because it compares cognitively healthy seniors to average individuals. This strategy is problematic since the cognitively normal
baseline harbours wide neurobiological heterogeneity. To overcome this limitation, the researchers contrasted two extreme biological cohorts aged 75 and older.
By identifying specific regions of underactivity and overactivity that distinguish these two groups, the researchers established a model that differentiates super agers from Alzheimer’s patients with approximately 85 percent accuracy.
Model Predicts Mid-Life Decline
The researchers tested the scoring system on more than 1,300 cognitively intact adults aged 55 to 70. While participants showed no cognitive differences at the start of the assessment, the ARC scores successfully predicted long-term outcomes. Higher scores indicate brain activity similar to that of Alzheimer’s patients. These scores correlated with faster declines in memory, attention, and executive function.
We used the ARC score to make a prediction of how likely this patient will convert into mild cognitive impairment or Alzheimer’s disease,
Kanhao (Alex) Zhao, postdoctoral scholar in psychiatry
The longitudinal data for this group spanned up to 12 years. Most participants were tracked for five to six years. The study found that the ARC score outperformed traditional metrics, including demographic data, genetic markers, and existing fluid/PET biomarkers for amyloid-beta and tau.
Intervention Targets Remain Unclear
The researchers identified functional connectivity as a target for early intervention. Unlike fixed genetic traits, these neural patterns may be influenced by advanced technologies. Future studies will investigate whether non-surgical methods—such as cognitive training and targeted transcranial magnetic stimulation (TMS)—can successfully shift vulnerable brain activity to match the resilient “super-ager” profile.
If we can precisely identify such as early indicator, I’m optimistic that we can reverse or prevent this process through advanced intervention technologies,
Yu Zhang
There are two main components: One is to know which part of the brain to target, the other is how we should change it,
Yu Zhang, senior author
Financial support for the study was provided by the National Institutes of Health. The grants are R21AG080425, R01MH129694 and R01AG081768. Additional funding came from an Alzheimer’s Association grant, a Lehigh University CORE grant, and a collaborative program between the Rosenkranz Foundation and the Knight Initiative.
Can non-surgical methods successfully shift vulnerable brain activity to match the resilient super-ager profile?