Physiological Ripples During Sleep in Scalp Electroencephalogram of Healthy Infants
Abstract number :
3.03
Submission category :
1. Basic Mechanisms / 1C. Electrophysiology/High frequency oscillations
Year :
2022
Submission ID :
2204313
Source :
www.aesnet.org
Presentation date :
12/5/2022 12:00:00 PM
Published date :
Nov 22, 2022, 05:24 AM
Authors :
Kavyakantha Remakanthakurup Sindhu, B.Tech, M.Tech – University of California, Irvine; Christopher Phan, Undergraduate Student – University of California, Irvine; Sara Anis, MS – University of California, Irvine; Aliza Riba, CPNP – Children's Hospital of Orange County (CHOC); Cristal Garner, REEGT, RPSGT – Children's Hospital of Orange County (CHOC); Amber Magers, RRT-NPS, RPSGT – Children's Hospital of Orange County (CHOC); Nhi Tran, RPSGT – Children's Hospital of Orange County (CHOC); Anjalee Galion, MD – Children's Hospital of Orange County (CHOC); Amy Maser, Ph.D – Children's Hospital of Orange County (CHOC); Daniel Shrey, MD – Children's Hospital of Orange County (CHOC); Beth Lopour, Ph.D – University of California, Irvine
Rationale: During sleep, high frequency oscillations (HFOs) occur broadly throughout the human brain as a result of healthy, physiological brain activity. These HFOs are believed to be associated with vision, motor, and memory consolidation processes. While they were initially recorded invasively in hippocampus and neocortex, a few recent studies have demonstrated that they can also be measured using scalp EEG. However, significant challenges associated with detecting these rare events limited prior studies to small amounts of data. In this study, we detect physiological ripples (100-250 Hz) during sleep in long-term scalp EEG of healthy infants and obtain estimates of their spatiotemporal characteristics.
Methods: A total of 184 hours of sleep data were obtained from overnight EEG recordings of 15 healthy infants younger than 12 months of age. We used a previously validated automatic HFO detection algorithm for the initial detection of ripples in the EEG. A five-step artifact rejection algorithm was then used to reject muscle noise, sharp events, and other spurious detections. Events that passed the artifact rejection step were visually validated by two independent reviewers. HFO characteristics, including rate, amplitude, duration, and peak frequency were calculated for the visually validated ripples. These properties were compared across different sleep stages, brain regions, and ages of subjects.
Results: Of the 51,000 HFOs that remained after the automatic artifact rejection step, 11,718 (22.97%) were marked as true HFOs by the two reviewers. The global HFO rates ranged from 0.2 to 1.66 per minute. The median values of amplitude, duration, and peak frequency were found to be 2.46 µV, 32.8 ms, and 99 Hz, respectively. We found that HFO rates were significantly higher in the anterior regions of the brain than the posterior regions for subjects older than four months (p< 0.01), but this difference was not significant in the subjects younger than four months. The frontal, temporal, and prefrontal regions had the highest HFO rates across all sleep stages and subjects. For subjects less than four months of age, the global HFO rate during active sleep was significantly higher than that in quiet sleep (p< 0.05). For subjects older than four months, REM sleep exhibited the highest rates (p< 0.05) followed by N1 sleep. We found no correlation between global ripple rate and subject age.
Basic Mechanisms