New study results may help improve automated methods for detecting these spindles and correlating them with brain function, investigators say.
Certain brain wave patterns that occur while individuals sleep may be assessed by physicians to help them diagnose dementia and other conditions related to language, memory, and thinking, according to the results of a study published in Sleep.
The study was led by investigators at Beth Israel Deaconess Medical Center and Massachusetts General Hospital (MGH) and could help improve automated methods for detecting these brain wave patterns, called sleep spindles, and for correlating them with cognitive function.
Sleep spindles are bursts of brain activity that occur during non-REM sleep and can be assessed through electroencephalograms (EEGs), which are non-invasive electrodes placed on the scalp.
Spindles are considered a “fingerprint” for individuals, because they are inherited and tend to be consistent from night to night.
“With the rising burden of neurodegenerative disease, there is a pressing need for a sensitive biomarker of cognition. This has led to a surge of research examining sleep spindles, an oscillatory pattern of brain activity observed during sleep, and their role in various neuropsychiatric conditions and cognitive performance,” Noor Adra, a clinical research coordinator at MGH, said in a statement.
Although sleep spindles and other brain features are promising potential electrophysiologic markers of neurodegenerative and psychiatric diseases, identifying and assessing them might not always be straightforward.
“People have already known that these transient high frequency events during sleep in the brain are closely linked to cognition, especially to learning and memory. But when you try to detect spindles among more than 100 sleep recordings, things become less clear, such as what is the best threshold, what is the best minimum duration, etc.,” Haoqi Sun, PhD, an investigator in the department of Neurology at MGH, said in the statement.
Sleep spindles are typically analyzed through visual inspection of EEGs, but automated methods can offer more consistent results. There is no consensus for the parameters for these automated methods.
Investigators aimed to address these issues in the study. They designed sleep-related experiments involving 167 adults to characterize how spindle detection parameter settings influence the association between cognition and the spindle features. Then, they identified parameters that best correlated with cognitive performance.
Investigators also found that sleep spindles were most strongly linked with what is known as fluid intelligence, which relies on abstract thinking and problem-solving skills during early stages of dementia.
The findings support sleep spindles as sleep-based biomarkers of fluid cognition, Adra said.
Optimizing the detection of the proposed biomarkers of cognition could help guide future studies that examine the sensitivity of these biomarkers.
“Sleep spindles are 1 among many important measurable features of brain activity during sleep that provide a window into the brain’s current state of health and individuals’ risk for developing brain disease or cognitive decline. Now that we better understand how to measure sleep spindles, we can add these into a growing arsenal of brain health indicators that can be measured during sleep,” M. Brandon Westover, MD, PhD, an investigator in the department of Neurology at MGH and director of Data Science at the MGH McCance Center for Brain Health, said in the statement.
Study may help clinicians use sleep brain wave patterns to diagnose dementia and other forms of cognitive impairment. EurekAlert, News release. January 12, 2022. Accessed January 13, 2022. https://www.eurekalert.org/news-releases/939909