Parameters for automatic detection systems are defined by researchers.
Clinicians may examine certain brain wave patterns that occur while a person sleeps to aid in the diagnosis of dementia and other memory, language, and cognitive disorders. Researchers from Massachusetts General Hospital (MGH) and Beth Israel Deaconess Medical Center (BIDMC) conducted a new research published in Sleep that might help enhance automated techniques for identifying these brain wave patterns, known as sleep spindles, and connecting them with cognitive performance.
Sleep spindles are bursts of brain activity that occur during non-REM sleep and may be measured with non-invasive electrodes implanted on the scalp using electroencephalograms (EECs). Spindles are a kind of “fingerprint” that differs from person to person, is highly heritable, and is stable from night to night.
“The growing prevalence of neurodegenerative illness necessitates the development of a sensitive cognitive biomarker. This has sparked a wave of study into sleep spindles, an oscillating pattern of brain activity seen during sleep, and their function in neuropsychiatric disorders and cognitive performance “Noor Adra, a clinical research coordinator at MGH, is the primary author.
Sleep spindles and other brain properties are interesting prospective electrophysiologic indicators of neurodegenerative and mental illnesses, but detecting and interpreting them is difficult. “People have long suspected that these brief high frequency events in the brain during sleep are connected to cognition, particularly learning and memory. However, when attempting to find spindles among more than 100 sleep recordings, questions such as what is the optimal threshold, what is the ideal minimum length, and so on become less obvious “Haoqi Sun, PhD, a researcher at MGH’s Department of Neurology, is a co-author.
EEGs are normally examined visually for sleep spindles, however automated approaches may provide more reliable findings. However, there is no agreement on the parameters for such automated approaches.
The researchers used 167 people in sleep-related trials to describe how spindle detection parameter settings impact the link between spindle characteristics and cognition, and to identify parameters that best correspond with cognitive function.
The researchers also discovered that sleep spindles were most closely associated to fluid intelligence, a kind of intelligence that depends on abstract thinking and problem-solving abilities that diminishes during dementia’s early stages. As a result, Adra explains, “our results support the use of sleep spindles as a sleep-based biomarker of fluid cognition.” “We intend to direct future investigations that explore the sensitivity of this hypothesized sleep-based biomarker of cognition by maximizing the detection of this biomarker in neurodegenerative populations.”
“Sleep spindles are one of several significant quantifiable aspects of brain activity during sleep that give insight into the brain’s present state of health and the risk of developing brain illness or cognitive decline in people. Now that we know how to measure sleep spindles, we can add them to the growing list of brain health indicators that can be monitored while sleeping “M. Brandon Westover, MD, PhD, a researcher in the department of Neurology at MGH and director of Data Science at the MGH McCance Center for Brain Health, adds as a co-senior author. “These indications will be critical in our effort to find medicines that might protect and improve brain health,” says the researcher.
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 are among the co-authors of the paper.
The Glenn Foundation for Medical Research, the American Federation for Aging Research, the National Institutes of Health, and the American Academy of Sleep Medicine all contributed to the study’s success.