New Brain Activity Patterns May Predict Alzheimer’s Risk Years Before Symptoms Appear
Key Clinical Takeaways:
- Researchers discovered that resting-state functional MRI scans can identify brain activity patterns linked to Alzheimer’s risk up to 12 years before clinical symptoms emerge.
- The new scoring system, called the Alzheimer’s resilience-vulnerability connectome (ARC score), distinguishes super agers from Alzheimer’s patients with approximately 85% accuracy.
- Published November 4 in Science Advances, the study points to potential future interventions like cognitive training and transcranial magnetic stimulation to shift brain activity toward resilience.
The results revealed a striking connection between brain activity and future cognitive health.
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.
Zhang said.
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.
The new findings provide a starting point for identifying which brain regions might be useful treatment targets.
Zhang said.
Depending on how they are defined, approximately 10% of older adults may qualify as super agers. 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.
“Normal, cognitively unimpaired individuals are pretty heterogeneous in terms of their biological abnormalities.”
Zhang said.
The Stanford team took a different approach by examining people with Alzheimer’s disease who were approximately the same age as the super agers.
The comparison uncovered distinctive differences in activity across multiple brain regions, producing recognizable patterns that distinguished the two groups. This scoring method distinguished super agers from Alzheimer’s patients with approximately 85% accuracy.
said Kanhao (Alex) Zhao, PhD, a postdoctoral scholar in psychiatry and lead author of the study.
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. And if we combine our score with the existing biomarkers, the prediction performance improves.”
Zhao said.
Disclaimer: The information provided in this article is for educational and scientific communication purposes only and does not constitute medical advice. Always consult with a qualified healthcare provider regarding any medical condition, diagnosis, or treatment plan.
Related reading
- Out-of-Pocket Costs Rival Vomiting in GLP-1 Discontinuation
- Lino Banfi and Experts Discuss Alzheimer’s Detection at Al Tempo della Salute
- Measles Kills 1 to 3 in 1,000 Children; New York's First Death in 30 Years (daybreakwire.com)
- Curtin University Researchers Find Plate Tectonics May Have Begun 2.5 Billion Years Ago (time.news)