Sleep electroencephalographic characteristics of the Cynomolgus monkey measured by telemetry

Abstract

SUMMARYCynomolgus monkeys are widely used as models of diseases and in preclinical studies to assess the impact of new pharmacotherapies on brain function and behaviour. However, the time course of electroencephalographic delta activity during sleep, which represents the main marker of sleep intensity associated with recovery during sleep, has never been described in this non-human primate. In this study, telemetry implants were used to record one spontaneous 24-h sleep-wake cycle in four freely-moving Cynomolgus monkeys, and to quantify the time course of electroencephalographic activity during sleep using spectral analysis. Animals presented a diurnal activity pattern interrupted by short naps. During the dark period, most of the time was spent in sleep with non-rapid eye movement sleep/rapid eye movement sleep alternations and sleep consolidation profiles intermediate between rodents and humans. Deep non-rapid eye movement sleep showed a typical predominance at the beginning of the night with decreased propensity in the course of the night, which was accompanied by a progressive increase in rapid eye movement sleep duration. Spectral profiles showed characteristic changes between vigilance states as reported in other mammalian species. Importantly, delta activity also followed the expected time course of variation, showing a build-up with wakefulness duration and dissipation across the night. Thus, Cynomolgus monkeys present typical characteristics of sleep architecture and spectral structure as those observed in other mammalian species including humans, validating the use of telemetry in this non-human primate model for translational sleep studies.

Waking State: Rapid Variations Modulate Neural and Behavioral Responses

“…In fact, the mouse may be especially well suited for investigations into the full range of states spanning sleep and waking. Although many mammalian species have periods of exploratory inactivity accompanied by drowsiness or naps punctuating their waking periods (Campbell and Tobler, 1984; Rachalski et al, 2014), head-fixed mice may be particularly prone to this phenomenon. In general, free ranging mice exhibit over 100 transitions between sleep and wakefulness per 24 hour period, with the average sleep or waking period lasting <10 minutes (Van Twyver, 1969).…”

Daily variations in sleep: associated genes and effects on affect

“…Other papers in the issue describe the baseline characteristics of sleep in a diurnal monkey (Rachalski et al, 2014), highlight the risk of sleepiness and sleep apnea in relation to driving accidents (Karimi et al, 2014) and the association between overall sleep status and high-sensitivity C-reactive protein in Japanese factory workers (Nakamura et al, 2014).…”

A surface metric and software toolbox for EEG electrode grids in the macaque

“…Recent years have seen a growing interest in non-invasive EEG-recordings in nonhuman primates (NHP) as a tool for translational research (e.g., Woodman et al, 2007;Sander et al, 2010;Godlove et al, 2011;Honing et al, 2012;Gil-da Costa et al, 2013;Purcell et al, 2013;Phillips et al, 2014;Rachalski et al, 2014;Gindrat et al, 2015;5 Itoh et al, 2015;Teichert, 2016;Teichert et al, 2016;Teichert, 2017;Holliday et al, 2017;Teichert et al, 2019;Itoh et al, 2019). Despite the availability of intra-cranial measures of neural activity that are more spatially-specific, NHP EEG has a number of appealing properties.…”

“…(1) It has the ability to serve as a bridge method that can be used in both humans and NHPs, thus increasing the translatability of invasive intracranial 10 recordings that can only be performed in NHPs (Arthur & Starr, 1984;Paller et al, 1988Paller et al, , 1992Sander et al, 2010;Purcell et al, 2013;Teichert et al, 2019). (2) In some cases, a more global measure of brain activity that sums over activity in many different brain regions may actually be more useful than highly localized recordings-for example, when determining either sleep state (Rachalski et al, 2014), or distributed signals such 15 as mismatch negativity or the P3 (Gil-da Costa et al, 2013;Teichert et al, 2019). (3) The use of sophisticated analysis tools in combination with detailed anatomical models of the head can provide accurate source localization to track neural reorganization over time (Gindrat et al, 2015), or to guide subsequent invasive intracranial recordings or microinjections.…”