A hidden brain signal may reveal Alzheimer’s long before diagnosis
Using a specially designed analysis tool, scientists at Brown University have identified a brain-based biomarker that may help predict
Using a specially designed analysis tool, scientists at Brown University have identified a brain-based biomarker that may help predict whether mild cognitive impairment will progress into Alzheimer’s disease. The approach focuses on measuring electrical activity produced by neurons, offering a new way to spot early signs of the disease directly in the brain.
“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,” said Stephanie Jones, a professor of neuroscience affiliated with Brown’s Carney Institute for Brain Science who co-led the research. “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.”
The results were published in the journal Imaging Neuroscience.
Tracking Brain Activity in People With Mild Cognitive Impairment
In collaboration with researchers at the Complutense University of Madrid in Spain, the team studied brain activity recordings from 85 people diagnosed with mild cognitive impairment. The researchers followed these participants for several years to see how their conditions changed over time.
Brain activity was recorded using magnetoencephalography, or MEG — a noninvasive method that captures electrical signals from the brain. During the recordings, participants were resting quietly with their eyes closed.
A New Way to See Neuronal Signals
Traditional approaches to analyzing MEG data often rely on averaging signals, which can blur important details about how individual neurons behave. To overcome this limitation, Jones and her colleagues at Brown developed a computational method known as the Spectral Events Toolbox.
This tool breaks brain activity down into distinct events, revealing when signals occur, how frequently they appear, how long they last, and how strong they are. The Spectral Events Toolbox has gained wide adoption and has been cited in more than 300 academic studies.
Memory-Related Brain Signals Reveal Key Differences
Using this tool, the researchers focused on brain activity in the beta frequency band, which has been linked to memory processes and is especially relevant in Alzheimer’s research, according to Jones. They compared beta activity patterns in people with mild cognitive impairment who later developed Alzheimer’s disease with those who did not.
Clear differences emerged. Participants who went on to develop Alzheimer’s within two and a half years showed noticeable changes in their beta activity compared with those whose condition remained stable.
“Two and a half years prior to their Alzheimer’s disease diagnosis, patients were producing beta events at a lower rate, shorter in duration and at a weaker power,” said Danylyna Shpakivska, the Madrid-based first author of the study. “To our knowledge, this is the first time scientists have looked at beta events in relation to Alzheimer’s disease.”
Why Brain-Based Biomarkers Matter
Current biomarkers found in spinal fluid or blood can detect beta amyloid plaques and tau tangles, proteins that accumulate in the brain and are believed to drive Alzheimer’s symptoms. However, these markers do not directly show how brain cells respond to this damage.
A biomarker based on brain activity itself offers a more direct look at how neurons are functioning under this stress, said David Zhou, a postdoctoral researcher in Jones’ lab at Brown who will lead the next stage of the research.
Toward Earlier Diagnosis and Better Treatments
Jones believes the Spectral Events Toolbox could eventually help clinicians identify Alzheimer’s disease earlier, before significant cognitive decline occurs.
“The signal we’ve discovered can aid early detection,” Jones said. “Once our finding is replicated, clinicians could use our toolkit for early diagnosis and also to check whether their interventions are working.”
The team is now moving into a new phase of the project, supported by a Zimmerman Innovation Award in Brain Science from the Carney Institute.
“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,” Jones said. “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.”
The research was funded by the National Institutes of Health, including the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative, along with support from funding agencies in Spain.
