0437 Characterizing the Impact of EEG Referencing on Sleep Spindle and Slow Oscillation Analyses. (27th May 2020)

• Record Type: Journal Article
• Authors: He, M; Prerau, M J; Dimitrov, T S
• Is Part Of: Sleep. Volume 43(2020)Supplement 1
• Page Start: A167
• Page End: A168
• Publication Date: 2020-05-27
• Subjects: Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsaa056.434
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Introduction: The impact of EEG referencing on sleep oscillations, such as spindles and slow oscillations, is largely overlooked across studies. While it is recognized that a topographic head plot of EEG activity does not reflect the true location of the underlying cortical activity, spatial distributions, as well as spectral properties and morphology of EEG oscillations can change dramatically as a function of referencing scheme. It is therefore vital to understand the impact of referencing when drawing inferences about the nature of EEG sleep oscillations. In this study, we use MRI structural data to construct subject-specific forward models of EEG signals. Using these models, we can simulate cortical activity and observe its true representation on the scalp. In particular, we simulate spindles and slow wave oscillations and examine how referencing affects topography, spectral power, and phase of oscillations. Methods: High-density EEG (Brain Vision, 64-channel) polysomnography was performed on 9 healthy young subjects. 3T structural MRI scans were acquired and forward models were built in MNE-Python using 3-shell Boundary Element Models (BEM) based on individual anatomical details processed with Freesurfer. Simulations of various sleep spindle and slow oscillation dynamics were projected to the sensor space. Different referencing schemes (common average, Laplacian, linked-mastoid) were then applied to the experimental and simulated data and analyzed for effects on time-frequency characteristics of sleep oscillations. Results: Analyses of experimental data showed distinct reference-based differences in topographical distribution of spectral power and phase of oscillations. Simulated data revealed many scenarios in which the spatial distribution of activity the EEG sensor space poorly represented the true location of the underlying source activity. Moreover, there were alterations to the spatial spread and envelope form of sleep spindle events under different referencing schemes despite from identical source activities. Conclusion: This study shows that spindle and slow oscillation activity is highly variable across referencing schemes and that EEG topographical plots on the scalp may poorly represent cortical activity locations. It is thus vital to consider the choice of referencing when quantifying characteristics of sleep EEG oscillations. Support: This work was supported by R01 NS-096177.