Contrasting Existence and Robustness of REM/Non-REM Cycling in Physiologically Based Models of REM Sleep Regulatory Networks

Abstract

Typical human sleep throughout the night consists of alternating periods of rapid eye movement (REM) sleep and non-REM (NREM) sleep. This ultradian rhythm of NREM/REM cycling is thought to be produced by the state-dependent activity of "REM-on" and "REM-off" brainstem and hypothalamic neuronal populations that, respectively, promote or suppress REM sleep. Synaptic interactions among these populations define REM sleep regulatory networks; however, the identity of the key neuronal populations in these networks and the dynamics of interactions among them are disputed and cannot be addressed comprehensively with current experimental techniques. The purpose of this study is to use physiologically based mathematical models to explore the dynamic implications associated with competing hypotheses for network-based REM sleep regulation. Generally, putative REM sleep regulatory networks fall into two categories: a reciprocal interaction network consisting of an excitatory REM-on population interacting with an inhibitory REM-off population, and a mutual inhibition network where both REM-on and REM-off populations are inhibitory. We focus on the generation of regular periodic cycling solutions, which would generate the NREM/REM ultradian rhythm, in these networks. By applying our understanding of network mechanisms, we develop efficient numerical criteria for the existence of stable cycling solutions in each network structure. To investigate the robustness of cycling, we systematically analyze the response of model dynamics to manipulation of the components governing network interactions. By establishing the implications of network structure for the mechanisms and dynamics of NREM/REM state transitions, this comparative analysis identifies key targets for future experimental work to distinguish the structure of the proposed physiological REM sleep regulatory network. Introduction.Physiologically based mathematical modeling of sleep-wake behavior offers a novel technique for probing the putative neural mechanisms generating transitions between wake and sleep states. Human sleep is composed of both rapid eye movement (REM) sleep and non-REM (NREM) sleep, and the transitions among states of wake, NREM sleep, and REM sleep have distinct, stereotyped properties. The structure of a typical consolidated nighttime sleep period begins with NREM sleep and reveals a robust cyclic alternation between NREM and REM sleep over the course of the night. Known as the ultradian rhythm, these

Regulation of REM and Non-REM Sleep by Periaqueductal GABAergic Neurons

“…6 ). These firing rate modulations mirror the accumulation and dissipation of REM sleep pressure 33 , 34 , and implementation of such slow modulations of REM-off neurons in circuit models can account for the temporal dynamics of REM/NREM alternations 38 , 39 . Previous studies have shown that REM sleep deprivation for several hours decreased/increased the activity of REM-off/REM-on neurons 40 and increased the expression of brain-derived neurotrophic factor in the pons 41 , 42 .…”

“…Subsequent studies have highlighted the importance of GABAergic inhibition between REM-on and REM-off neurons 7 , 9 , 11 , 35 . Although fast GABAergic interactions can account for rapid transitions between brain states, they are insufficient to explain the temporal dynamics of NREM/REM alternation on a timescale of minutes (rodents) to hours (humans); a separate, slow-varying process is required 38 , 39 . Here, in addition to confirming the correlation between the REM episode duration and subsequent inter-REM interval in mice (Fig.…”

Pathway-Dependent Regulation of Sleep Dynamics in a Network Model of the Sleep–Wake Cycle

“…In previous studies, the performances of network models have been explored (Diniz Behn and Booth, 2012;Diniz Behn et al, 2013;Weber, 2017) and these models can replicate sleep dynamics (Diniz-Behn and Booth, 2010) as well as statedependent neural firing (Tamakawa et al, 2006). However, few studies have reported how the strength of synaptic connections between wake-and sleep-promoting populations contribute to the sleep architectures.…”

“…Reciprocal excitatoryinhibitory connections (McCarley and Hobson, 1975;Diniz Behn et al, 2007;Diniz-Behn and Booth, 2010;Diniz Behn and Booth, 2012;Booth et al, 2017) and mutual inhibitory interactions (Saper et al, 2001) can be recognized as key network motifs within sleep-wake regulating circuits. Although their dynamics have been explored (Diniz Behn and Booth, 2012;Diniz Behn et al, 2013;Weber, 2017), and those models can replicate sleep architecture of humans and animals (Diniz-Behn and Booth, 2010) as well as state-dependent neural firing (Tamakawa et al, 2006), few studies have investigated how the strength of synaptic connections between wake-and sleep-promoting populations contribute to sleep dynamics. As controlling neural activity at high spatiotemporal resolution in vivo becomes feasible experimentally, computational approaches can be considered as complementary approaches for investigating the role of specific neural pathways in sleep-wake regulation.…”

Pathway-dependent regulation of sleep dynamics in a network model of the sleep-wake cycle

“…The activity of these populations was characterized by differential equations describing the population firing rates which defined the state of the network (see Methods). These equations have been proved to be components of suitable sleep/wake regulatory computational models in previous studies [22, 23, 26, 27, 35]. The pathways from one population to the other were either excitatory or inhibitory.…”