Transient oscillation dynamics during sleep provide a robust basis for electroencephalographic phenotyping and biomarker identification

Patrick A Stokes; Preetish Rath; Thomas Possidente; Mingjian He; Shaun Purcell; Dara S Manoach; Robert Stickgold; Michael J Prerau

doi:10.1093/sleep/zsac223

Transient oscillation dynamics during sleep provide a robust basis for electroencephalographic phenotyping and biomarker identification

Sleep. 2023 Jan 11;46(1):zsac223. doi: 10.1093/sleep/zsac223.

Authors

Patrick A Stokes¹, Preetish Rath^{1

2}, Thomas Possidente¹, Mingjian He^{1

3

4}, Shaun Purcell⁵, Dara S Manoach⁶, Robert Stickgold⁷, Michael J Prerau¹

Affiliations

¹ Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.
² Department of Computer Science, Tufts University, Medford, MA, USA.
³ Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁴ Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁵ Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA.
⁶ Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
⁷ Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA.

Abstract

Transient oscillatory events in the sleep electroencephalogram represent short-term coordinated network activity. Of particular importance, sleep spindles are transient oscillatory events associated with memory consolidation, which are altered in aging and in several psychiatric and neurodegenerative disorders. Spindle identification, however, currently contains implicit assumptions derived from what waveforms were historically easiest to discern by eye, and has recently been shown to select only a high-amplitude subset of transient events. Moreover, spindle activity is typically averaged across a sleep stage, collapsing continuous dynamics into discrete states. What information can be gained by expanding our view of transient oscillatory events and their dynamics? In this paper, we develop a novel approach to electroencephalographic phenotyping, characterizing a generalized class of transient time-frequency events across a wide frequency range using continuous dynamics. We demonstrate that the complex temporal evolution of transient events during sleep is highly stereotyped when viewed as a function of slow oscillation power (an objective, continuous metric of depth-of-sleep) and phase (a correlate of cortical up/down states). This two-fold power-phase representation has large intersubject variability-even within healthy controls-yet strong night-to-night stability for individuals, suggesting a robust basis for phenotyping. As a clinical application, we then analyze patients with schizophrenia, confirming established spindle (12-15 Hz) deficits as well as identifying novel differences in transient non-rapid eye movement events in low-alpha (7-10 Hz) and theta (4-6 Hz) ranges. Overall, these results offer an expanded view of transient activity, describing a broad class of events with properties varying continuously across spatial, temporal, and phase-coupling dimensions.

Keywords: EEG; NREM; TF-peaks; phase coupling; slow oscillation; spindles; transient oscillations.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Electroencephalography / methods
Humans
Memory Consolidation*
Schizophrenia*
Sleep
Sleep Stages

Abstract

Publication types

MeSH terms

Grants and funding