Brain Waves Offer Early Alzheimer’s Clues
New Biomarker May Predict Disease Progression Years in Advance
A groundbreaking discovery from Brown University researchers could offer a vital tool for predicting Alzheimer’s disease progression in individuals with mild cognitive impairment (MCI). The study identifies specific patterns in brain electrical activity that appear up to two and a half years before a formal diagnosis.
Detecting Subtle Brain Activity Shifts
Researchers at Brown University’s Carney Institute for Brain Science have pinpointed distinct alterations in brain electrical signals that may serve as an early warning for Alzheimer’s disease (AD). Their work, published in Imaging Neuroscience, analyzed brain activity in 85 individuals diagnosed with MCI using magnetoencephalography (MEG).
“We’ve detected a pattern in electrical signals of brain activity that predicts which patients are most likely to develop the disease within two and a half years,” explained Stephanie Jones, PhD, a professor of neuroscience at Brown and co-lead investigator. “Being able to noninvasively observe a new early marker of Alzheimer’s disease progression in the brain for the first time is a very exciting step.”
Novel Tool Uncovers Neural Event Signatures
The research team utilized a new analytical method developed at Brown, the Spectral Events Toolbox. This computational tool precisely analyzes MEG data to identify discrete neuronal events, including their timing, frequency, power, and duration. The toolbox is increasingly employed to study neuronal dynamics across various neurological conditions.
Typically, individuals with AD exhibit a slowing of brain oscillatory activity, characterized by increased delta and theta rhythms and decreased alpha and beta activity. This pattern often emerges early in MCI, and potentially even earlier during subjective cognitive decline, progressing from frontal to posterior brain regions.
The study focused specifically on activity within the beta frequency band, which is known to be involved in memory processing. Lead author Danylyna Shpakivska noted that individuals whose MCI progressed to AD showed a reduced rate, shorter duration, and weaker power of beta events up to two and a half years before diagnosis, compared to those whose condition did not worsen.
New research from Brown University identifies a potential noninvasive biomarker for Alzheimer's progression using brain activity patterns. https://t.co/n62E1d3j0w #Alzheimers #Neuroscience #Biomarker
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While current biomarkers detect amyloid and tau protein buildup, this brain activity-based signal offers a more direct view of neuronal response to neurodegeneration. “A biomarker from brain activity itself represents a more direct method of assessing how neurons respond to this toxicity,” stated David Zhou, PhD, a postdoctoral researcher involved in the project.
Potential for Early Diagnosis and Treatment Monitoring
The findings suggest that this brain activity signature could significantly aid in the early detection of Alzheimer’s. “The signal we’ve discovered can aid early detection,” Jones added. “Once our finding is replicated, clinicians could use our toolkit for early diagnosis and also to check whether their interventions are working.”
The next phase of research will involve computational neural modeling to understand the mechanisms generating these specific beta event features. This could pave the way for developing therapeutics aimed at correcting the underlying issues. The US National Institutes of Health and Spanish funding agencies support this research.
Early detection is crucial, as studies indicate that timely interventions can make a difference. For instance, lifestyle changes and early medication have shown potential to slow cognitive decline in some individuals, according to the Alzheimer’s Association (Alzheimer’s Association).
