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Sleep Brainwave "Fingerprints" May Improve Understanding of Schizophrenia, Other Disorders
A new approach for characterizing the activity of the brain during sleep has revealed potential biomarkers in the brain activity of people with schizophrenia, according to study results published online ahead of print in Sleep.
“This work expands the way we can look at brain activity during sleep,” said corresponding author Michael Prerau, PhD, of the Brigham and Women’s Hospital Division of Sleep and Circadian Disorders, Boston, Massachusetts. “By moving beyond traditional notions that break up the complex continuum of sleep into specific categories and waveform classes, we can reveal new types of signals and dynamics that may be important for understanding brain health and disease.”
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The new approach uses a computational tool to extract tens of thousands of short sleep spindle-like waveform events from electroencephalogram (EEG) data throughout the night. Instead of focusing on waveforms in terms of fixed sleep stages, the approach spans a full continuum of gradual changes that occur during sleep. The data is subsequently transformed into slow oscillation power and phase histograms, graphical representations of all waveform activity as a function of continuous sleep depth and synchronized activity in the cortex.
As part of the study, researchers looked at 2 nights of sleep recordings for a group of healthy participants and found patterns that functioned similarly to fingerprints: they were highly specific to each person, with strong consistency across nights. The results, they said, suggest brain activity differs from person to person, even within groups of healthy people.
“We are just starting to understand the scope of neurodiversity that exists within the general population,” said Dr Prerau. “If we can more accurately characterize the individual differences observed in both neurological health and disease, we can work towards improved diagnostics and treatments.”
Researchers also compared sleep recordings between healthy people and a group of patients with schizophrenia. In doing so, they observed not only previously known differences in reduced spindle activity but also differences in other spindle-like waveforms occurring at other frequencies in the brain.
The findings, they explained, suggest potential new EEG biomarkers that may be useful for better understanding the mechanisms of schizophrenia and developing more targeted treatments.
“This approach is really exciting,” said coauthor Dara Manoach, PhD, of the Massachusetts General Hospital Department of Psychiatry in Boston. “We look forward to seeing how we can enhance our understanding, not only of schizophrenia but also of other neurodevelopmental disorders characterized by differences in sleep, such as autism and pediatric epilepsy.”
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