Brain Waves Predict Alzheimer’s Development
- Using a custom-built tool to analyze electrical activity from neurons, researchers have identified a brain-based biomarker that could be used to predict whether mild cognitive impairment will develop...
- "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," says...
- "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."
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New Biomarker Predicts Alzheimer’s Development with High accuracy
Table of Contents
What Happened?
Using a custom-built tool to analyze electrical activity from neurons, researchers have identified a brain-based biomarker that could be used to predict whether mild cognitive impairment will develop into Alzheimer’s disease.
“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,” says Stephanie Jones, a professor of neuroscience affiliated with Brown University’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 findings appear in imaging Neuroscience.
How the Research Was Conducted
Working with collaborators at the Complutense university of Madrid in Spain, the research team analyzed recordings of brain activity from 85 patients diagnosed with mild cognitive impairment and monitored disease progress over the next several years. The recordings were made using magnetoencephalography,or MEG-a noninvasive technique to record electrical activity in the brain-while patients were in a resting state with their eyes closed.
Most methods for studying MEG recordings compress and average the detected activity, making it tough to interpret at the neuronal level. Jones and other researchers at Brown pioneered a computational tool, called the Spectral Events Toolbox, that reveals neuronal activity as discrete events, showing exactly when and how often activity occurs, how long it lasts and how strong or weak it is. The tool has b
The Spectral Events Toolbox: A Key Innovation
The Spectral Events Toolbox is crucial to this discovery. Conventional MEG analysis methods often obscure the subtle neuronal signals needed for early detection. This toolbox allows researchers to analyze MEG data at