Abstract
Sleep spindles are thalamocortical oscillations associated with several behavioural and clinical phenomena. In clinical populations, spindle activity has been shown to be reduced in schizophrenia, as well as after thalamic stroke. Automatic spindle detection algorithms present the only feasible way to systematically examine individual spindle characteristics. We took an established algorithm for spindle detection, and adapted it to high-density EEG sleep recordings. To illustrate the detection and analysis procedure, we examined how spindle characteristics changed across the night and introduced a linear mixed model approach applied to individual spindles in adults (n = 9). Next we examined spindle characteristics between a group of paramedian thalamic stroke patients (n = 9) and matched controls. We found a high spindle incidence rate and that, from early to late in the night, individual spindle power increased with the duration and globality of spindles; despite decreases in spindle incidence and peak-to-peak amplitude. In stroke patients, we found that only left-sided damage reduced individual spindle power. Furthermore, reduction was specific to posterior/fast spindles. Altogether, we demonstrate how state-of-the-art spindle detection techniques, applied to high-density recordings, and analysed using advanced statistical approaches can yield novel insights into how both normal and pathological circumstances affect sleep.
Highlights
Sleep spindles are among the most recognisable electrophysiological features occurring in non-rapid-eye-movement (NREM) sleep
We used this opportunity to outline and demonstrate the tools required for automatic spindle detection and analysis, with specific focus on how previous tools can be extended for multi-channel EEG recordings, by examining how individual spindle properties change through the course of a night of sleep
Linear mixed models (LMM) are ideally suited for this sort of data structure and here we introduce the concept to perform the statistical analysis at the individual spindle level
Summary
Sleep spindles are among the most recognisable electrophysiological features occurring in non-rapid-eye-movement (NREM) sleep They can be readily seen in continuous electroencephalography (EEG) traces as a series of stereotypical sinusoidal waves waxing and waning at frequencies between 11–15 Hz for approximately a second[1]. Spectral power has been a useful tool in capturing aspects of spindles which differ between experimental conditions and/or distinct populations. We used this opportunity to outline and demonstrate the tools required for automatic spindle detection and analysis, with specific focus on how previous tools can be extended for multi-channel EEG recordings, by examining how individual spindle properties change through the course of a night of sleep. The second is to show how spindles can, and should, be analysed at the individual level using a mixed model approach in both a healthy population, as well as a clinically interesting, set of patients following thalamic stroke
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