Alzheimer’s disease remains a major public health concern, with early detection being one of the most critical challenges. Mild cognitive impairment often precedes Alzheimer’s, but predicting which individuals will go on to develop the disease has been difficult. Traditional diagnostic methods rely on spinal fluid and blood biomarkers that identify beta amyloid plaques and tau tangles, but these do not provide direct insights into neuronal function. A more immediate assessment of how neurons are affected by disease-related toxicity could significantly improve early detection. In a promising development, researchers have now identified a pattern in brain electrical signals that can predict which individuals with mild cognitive impairment are likely to develop Alzheimer’s within two and a half years.

The research was conducted by Brown University’s Carney Institute for Brain Science (Providence, RI, USA) in collaboration with the Complutense University of Madrid (Madrid, Spain). The team used a custom-built computational tool called the Spectral Events Toolbox to analyze brain activity recorded through magnetoencephalography (MEG), a noninvasive technique that captures electrical activity while patients rest with their eyes closed. Unlike most MEG analysis methods that average and compress signals, this tool captures discrete neuronal events, measuring when they occur, their duration, frequency, and intensity. The study examined brain recordings from 85 individuals diagnosed with mild cognitive impairment, tracking their progression over several years. The researchers focused on activity in the beta frequency band, which is implicated in memory processing, and found distinct differences in this activity among individuals who later developed Alzheimer’s.

Published in Imaging Neuroscience, the study found that those who progressed to Alzheimer’s produced beta events at a lower rate, shorter duration, and weaker power compared to those who did not. These early signals represent a novel biomarker of Alzheimer’s disease progression. The findings suggest that brain-based electrical biomarkers can offer a more direct and timely method for diagnosing Alzheimer’s, potentially enabling clinicians to intervene earlier and monitor treatment effectiveness. Moving forward, the team plans to explore the mechanisms generating these beta event signals using computational neural modeling, to test therapeutics that could restore healthy brain activity.

"The signal we’ve discovered can aid early detection. Once our finding is replicated, clinicians could use our toolkit for early diagnosis and also to check whether their interventions are working," said Stephanie Jones, co-leader of the study. “Now that we’ve uncovered beta event features that predict Alzheimer’s disease progression, our next step is to study the mechanisms of generation using computational neural modeling tools. If we can recreate what’s going wrong in the brain to generate that signal, then we can work with our collaborators to test therapeutics that might be able to correct the problem.”

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