Central Sleep Spindle Rates Follow a Predictable Developmental Trajectory over Early Childhood
Abstract number :
1.196
Submission category :
3. Neurophysiology / 3G. Computational Analysis & Modeling of EEG
Year :
2021
Submission ID :
1826121
Source :
www.aesnet.org
Presentation date :
12/9/2021 12:00:00 PM
Published date :
Nov 22, 2021, 06:52 AM
Authors :
Katherine Walsh, BS - Massachusetts General Hospital; Hunki Kwon – Massachusetts General Hospital; Erin Berja – Massachusetts General Hospital; Mark Kramer – Massachusetts General Hospital; Catherine Chu – Massachusetts General Hospital
Rationale: Early development is marked by pronounced changes in brain rhythms and neuronal networks. Evaluating developing cortical rhythms using objective approaches is essential to characterize these age-specific alterations. Sleep spindles, brief bursts of 9-16 Hz oscillations, are a hallmark of electroencephalogram (EEG) recordings during non-rapid eye movement (NREM) sleep. Sleep spindles are generated in and elaborated by thalamocortical circuits and have been linked to a number of cognitive processes, including cognitive performance, learning, and memory consolidation. Here we characterize sleep spindle rates in scalp EEG recordings from a large cohort of developmentally normal children.
Methods: Normal scalp EEG recordings from a cohort of 301 subjects aged 0-6 years (141F) were obtained from the Massachusetts General Hospital EEG database between 2/2002 and 5/2012. EEG recordings were included from subjects with normal neurodevelopment and diagnosed with non-epileptic events not expected to alter baseline EEG rhythms. Epochs containing ≥ NREM stage 2 sleep were identified manually from EEG recordings and included in the analysis. An automated latent state model (LSM) spindle detector was applied to the EEG recordings to detect sleep spindles. The LSM detector was trained and validated on 15161 total manual spindle markings from 75 subjects from 3 different control and epilepsy infant and pediatric EEG data sets. In each case, the detector was found to have excellent performance compared to manual markings using leave-one-out cross validation. Spindle rates (spindles/min) were computed in all 19 cerebral 10-20 EEG channels. Spindle rate in the CZ channel was modeled using linear regression with age as a predictor and in the C3/C4 channels together using a mixed effect generalized linear model with age as a predictor and a subject-specific intercept. In each case, models were fit separately for subjects < 1 year old and ≥ 1 year old.
Results: Topoplots confirm that spindle rate is high in the central channels at each age (Fig1). In the midline Rolandic region (CZ), we find a strong positive correlation between spindle rate and age in the first year of life (β=2.2763, p~0) and a weak negative correlation between spindle rate and age between 1-6 years (β=-0.2541, p=0.0006; Fig2A). In the lateral Rolandic regions (C3/C4), we find a strong positive relationship between spindle rate and age in the first year of life (β=2.1242, p~0), but no evidence of a relationship with age between 1-6 years (p=0.27; Fig2B).
Conclusions: These results demonstrate that sleep spindle rates exhibit distinct age-specific and regional-specific patterns over early development. Characterizing these changes is essential to expand our current knowledge of normal physiological brain development and to detect deviations from the expected trajectory in at-risk populations.
Funding: Please list any funding that was received in support of this abstract.: NIH NINDS R01NS115868.
Neurophysiology