Scientists have discovered a potential biomarker in the brain that could forecast the development of Alzheimer’s disease from mild cognitive impairment. The discovery hinges on a specialized tool designed to dissect the electrical activity of neurons.

According to Stephanie Jones,a neuroscience professor at Brown University’s Carney Institute for Brain Science,”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.” Jones 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,” Jones added.

The study’s findings were published in Imaging Neuroscience.

The research team,in collaboration with colleagues from the Complutense University of Madrid,Spain,scrutinized brain activity recordings from 85 individuals diagnosed with mild cognitive impairment. They tracked the progression of the disease over several years. The recordings were obtained using magnetoencephalography, or MEG, a non-invasive method for capturing electrical activity in the brain while patients rested with their eyes closed.

Conventional methods of analyzing MEG recordings often compress and average the detected activity, which complicates interpretation at the neuronal level. Jones and her team at Brown University developed the Spectral Events Toolbox, a computational tool that reveals neuronal activity as distinct events. This tool illustrates the timing, frequency, duration, and intensity of neuronal activity.It has gained widespread use and has been cited in over 300 academic papers.

The team employed the Spectral Events Toolbox to examine brain activity events in patients with mild cognitive impairment, specifically focusing on the beta frequency band. According to Jones, this frequency has been linked to memory processing, making it crucial for Alzheimer’s disease research. The researchers identified notable differences in the beta events of participants who developed Alzheimer’s disease within two and a half years compared to those who did not.

Danylyna Shpakivska, the study’s lead author based in Madrid, stated, “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.”

“To our knowledge, this is the first time scientists have looked at beta events in relation to Alzheimer’s disease,” Shpakivska added.

According to David Zhou,a postdoctoral researcher in Jones’ lab at Brown,spinal fluid and blood biomarkers can detect the presence of toxic beta amyloid plaques and tau tangles,proteins that accumulate in the brain and are believed to contribute to Alzheimer’s disease symptoms. Zhou, who will spearhead the project’s next phase, suggests that a biomarker derived from brain activity itself offers a more direct approach to evaluating how neurons respond to this toxicity.

Jones believes that the Spectral Events Toolbox could be used by clinicians to diagnose Alzheimer’s disease before it advances.

“The signal we’ve discovered can aid early detection,” Jones explains. “Once our finding is replicated, clinicians could use our toolkit for early diagnosis and also to check whether their interventions are working.”

Jones and her team are embarking on a new research phase, 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 says.

“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,” Jones concludes.

The research received funding from the National Institutes of Health, including the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative, as well as from Spanish agencies.

“The signal we’ve discovered can aid early detection. Once our finding is replicated, clinicians could use our toolkit for early diagnosis…”