New Brain Signal May Predict Alzheimer’s Progression Years Before Diagnosis

Researchers at Brown University have identified a distinct pattern in brain electrical activity that may predict the progression of Alzheimer’s disease years before a formal diagnosis. Published in Imaging Neuroscience, the study shows that specific changes in neural signals can differentiate people with mild cognitive impairment (MCI) who later develop Alzheimer’s from those whose condition remains stable, with detectable differences emerging as early as two and a half years before diagnosis.

Led by scientists at Brown’s Carney Institute for Brain Science in collaboration with the Complutense University of Madrid, the research followed 85 individuals with MCI over several years. Brain activity was measured using magnetoencephalography (MEG), a noninvasive method that captures the electrical signals produced by neurons while participants rested with their eyes closed. To analyze the data, the team used Brown’s Spectral Events Toolbox, a computational approach that breaks neural activity into discrete events rather than averaging signals, allowing precise assessment of timing, frequency, duration, and strength.

The researchers focused on beta-frequency brain activity, which plays a key role in memory and cognition. They found that patients who later developed Alzheimer’s showed beta signals that occurred less frequently, lasted for shorter periods, and were weaker in strength compared with those who did not progress. These differences were present well before clinical diagnosis, suggesting that changes in brain activity may serve as an early biomarker of disease progression.

The findings point to neural electrical signals as a potential complement to existing biomarkers such as blood and spinal fluid tests for amyloid and tau, offering a more direct measure of how neurons are affected by disease. The team plans to validate the results in larger studies, explore the biological mechanisms using computational modeling, and assess whether these signals could be used to monitor treatment response or guide early interventions. The work was supported by the U.S. National Institutes of Health, including the BRAIN Initiative, and research agencies in Spain.