Researchers define parameters for automated detection methods. — ScienceDaily

Certain brainwave patterns that occur while a person is asleep can be evaluated by doctors to help them diagnose dementia and other disorders related to memory, language, and thinking. A new study published in sleep led by researchers at Massachusetts General Hospital (MGH) and Beth Israel Deaconess Medical Center (BIDMC) could help improve automated methods for detecting these brain wave patterns, or sleep spindles, and correlating them with cognitive function.

Sleep spindles are bursts of brain activity that occur during non-REM sleep and can be assessed by electroencephalograms (EECs) using non-invasive electrodes placed on the scalp. Spindles are considered a “fingerprint” that varies from person to person, is highly heritable, and is consistent from night to night.

“With the increasing burden of neurodegenerative diseases, there is an urgent need for a sensitive biomarker of cognition. This has fueled a wave of research examining sleep spindles, an oscillating pattern of brain activity observed during sleep, and their role in various neuropsychiatric disorders and cognitive performance,” says lead author Noor Adra, clinical research coordinator at MGH.

Although sleep spindles and other brain features represent promising potential electrophysiological markers for neurodegenerative and psychiatric diseases, recognizing and assessing sleep spindles is not straightforward. “People already know that these transient high-frequency events during sleep in the brain are closely related to cognition, especially learning and memory what is the best threshold, what is the best minimum duration, etc.” says co-author Haoqi Sun, PhD, a researcher in the Department of Neurology at MGH.

Sleep spindles are typically analyzed by visual inspection of EEGs, but automated methods can provide more consistent results. However, there is no consensus on parameters for such automated procedures.

To address these issues, researchers designed sleep-related experiments in 167 adults to characterize how spindle recognition parameter settings affect the association between spindle traits and cognition, and identified parameters that best correlate with cognitive performance.

The team also found that sleep spindles are most strongly associated with what is known as fluid intelligence, which relies on abstract thinking and problem-solving skills and declines in early stages of dementia. “Hence, our results support sleep spindles as a sleep-based biomarker of fluid perception,” says Adra. “By optimizing the detection of this proposed sleep-based biomarker of cognition, we hope to guide future studies examining the sensitivity of this biomarker in neurodegenerative populations.”

“Sleep spindles are one of many important measurable characteristics of brain activity during sleep that provide insight into the current state of brain health and an individual’s risk of developing brain disease or cognitive decline. Now that we better understand how sleep spindles are measured, we can add them to a growing arsenal of brain health indicators that can be measured during sleep,” adds co-senior author M. Brandon Westover, MD, PhD , a researcher in the Department of Neurology at MGH and Director of Data Science at MGH, adds McCance Center for Brain Health, “These indicators will be essential tools in our quest to develop treatments that can maintain and improve brain health.”

Co-authors include Wolfgang Ganglberger, Elissa M. Ye, Lisa W. Dümmer, Ryan A. Tesh, Mike Westmeijer, Madalena Da Silva Cardoso, Erin Kitchener, An Ouyang, Joel Salinas, Jonathan Rosand, Sydney S. Cash, and Robert J. Thomas.

The study was funded by the Glenn Foundation for Medical Research, the American Federation for Aging Research, the National Institutes of Health, and the American Academy of Sleep Medicine.

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