Hans P. A. Van Dongen

Sleep as a fundamental property of neuronal assemblies

Sleep is vital to cognitive performance, productivity, health and well-being. Earlier theories of sleep presumed that it occurred at the level of the whole organism and that it was governed by central control mechanisms. However, evidence now indicates that sleep might be regulated at a more local level in the brain: it seems to be a fundamental property of neuronal networks and is dependent on prior activity in each network. Such local-network sleep might be initiated by metabolically driven changes in the production of sleep-regulatory substances. We discuss a mathematical model which illustrates that the sleep-like states of individual cortical columns can be synchronized through humoral and electrical connections, and that whole-organism sleep occurs as an emergent property of local-network interactions.

Trait interindividual differences in the sleep physiology of healthy young adults

Despite decades of sleep research by means of polysomnography (PSG), systematic interindividual differences in PSG‐assessed sleep parameters have been scarcely investigated. The present study is the first to quantify interindividual variability in standard PSG‐assessed variables of sleep structure in terms of stability and robustness as well as magnitude. Twenty‐one carefully screened healthy young adults were studied continuously in a strictly controlled laboratory environment, where their PSGs were recorded for eight nights interspersed with three separate 36 h sleep deprivation periods. All PSG records were scored blind to subject and condition, using conventional criteria, and delta power in the non‐REM sleep EEG was computed for four electrode derivations. Interindividual differences in sleep variables were examined for stability and robustness, respectively, by comparing results across equivalent nights (e.g. baseline nights) and across experimentally differentiated nights (baseline nights versus recovery nights following sleep deprivation). Among 18 sleep variables analyzed, all except slow‐wave sleep (SWS) latency were found to exhibit significantly stable and robust – i.e. trait‐like – interindividual differences. This was quantified by means of intraclass correlation coefficients (ICCs), which ranged from 36% to 89% across physiologic variables, and were highest for SWS (73%) and delta power in the non‐REM sleep EEG (78–89%). The magnitude of the trait interindividual differences was considerable, consistently exceeding the magnitude of the group‐average effect on sleep structure of 36 h total sleep deprivation. Notably, for non‐REM delta power – a putative marker of sleep homeostasis – the interindividual differences were from 9.9 to 12.8 times greater than the group‐average increase following sleep deprivation relative to baseline. Physiologic sleep variables did not vary among subjects in a completely independent manner – 61.1% of their combined variance clustered in three trait dimensions, which appeared to represent sleep duration, sleep intensity, and sleep discontinuity. Any independent functional significance of these sleep physiologic phenotypes remains to be determined.

Investigating the interaction between the homeostatic and circadian processes of sleep-wake regulation for the prediction of waking neurobehavioural performance

The two‐process model of sleep regulation has been applied successfully to describe, predict, and understand sleep–wake regulation in a variety of experimental protocols such as sleep deprivation and forced desynchrony. A non‐linear interaction between the homeostatic and circadian processes was reported when the model was applied to describe alertness and performance data obtained during forced desynchrony. This non‐linear interaction could also be due to intrinsic non‐linearity in the metrics used to measure alertness and performance, however. Distinguishing these possibilities would be of theoretical interest, but could also have important implications for the design and interpretation of experiments placing sleep at different circadian phases or varying the duration of sleep and/or wakefulness. Although to date no resolution to this controversy has been found, here we show that the issue can be addressed with existing data sets. The interaction between the homeostatic and circadian processes of sleep–wake regulation was investigated using neurobehavioural performance data from a laboratory experiment involving total sleep deprivation. The results provided evidence of an actual non‐linear interaction between the homeostatic and circadian processes of sleep–wake regulation for the prediction of waking neurobehavioural performance.

Circadian Rhythm Profiles in Women with Night Eating Syndrome

Night eating syndrome (NES) is characterized by evening hyperphagia and frequent awakenings accompanied by food intake. Patients with NES display a delayed circadian pattern of food intake but retain a normal sleep-wake cycle. These characteristics initiated the current study, in which the phase and amplitude of behavioral and neuroendocrine circadian rhythms in patients with NES were evaluated. Fifteen women with NES (mean age ± SD, 40.8 ± 8.7 y) and 14 control subjects (38.6 ± 9.5 y) were studied in the laboratory for 3 nights, with food intake measured daily. Blood also was collected for 25 h (every 2 h from 0800 to 2000 h, and then hourly from 2100 to 0900 h) and assayed for glucose and 7 hormones (insulin, ghrelin, leptin, melatonin, cortisol, thyroid-stimulating hormone [TSH] and prolactin). Statistical analyses utilized linear mixed-effects cosinor analysis. Control subjects displayed normal phases and amplitudes for all circadian rhythms. In contrast, patients with NES showed a phase delay in the timing of meals, and delayed circadian rhythms for total caloric, fat, and carbohydrate intake. In addition, phase delays of 1.0 to 2.8 h were found in 2 food-regulatory rhythms—leptin and insulin—and in the circadian melatonin rhythm (with a trend for a delay in the circadian cortisol rhythm). In contrast, circulating levels of ghrelin, the primary hormone that stimulates food intake, were phase advanced by 5.2 h. The glucose rhythm showed an inverted circadian pattern. Patients with NES also showed reduced amplitudes in the circadian rhythms of food intake, cortisol, ghrelin, and insulin, but increased TSH amplitude. Thus, patients with NES demonstrated significant changes in the timing and amplitude of various behavioral and physiological circadian markers involved in appetite and neuroendocrine regulation. As such, NES may result from dissociations between central (suprachiasmatic nucleus) timing mechanisms and putative oscillators elsewhere in the central nervous system or periphery, such as the stomach or liver. Considering these results, chronobiologic treatments for NES such as bright light therapy may be useful. Indeed, bright light therapy has shown efficacy in reducing night eating in case studies and should be evaluated in controlled clinical trials.

Shift Work and Inter‐Individual Differences in Sleep and Sleepiness

Inter‐individual differences in tolerance for shift work have been studied primarily in terms of external factors affecting alertness on the job or the ability to rest and sleep while at home. However, there is increasing evidence that neurobiological factors play a role as well, particularly the major processes involved in the regulation of sleep and wakefulness. These include a sleep homeostatic process seeking to balance wakefulness and sleep and a circadian process seeking to promote wakefulness during the day and sleep during the night. Shift work is associated with a temporal misalignment of these two endogenous processes. During nightwork, this misalignment makes it difficult to stay awake during the nightshift and sleep during the day. However, inter‐individual variability in the processes involved in sleep/wake regulation is substantial. Recent studies have demonstrated the existence of inter‐individual differences in vulnerability to cognitive deficits from sleep loss. Moreover, these inter‐indi...Inter‐individual differences in tolerance for shift work have been studied primarily in terms of external factors affecting alertness on the job or the ability to rest and sleep while at home. However, there is increasing evidence that neurobiological factors play a role as well, particularly the major processes involved in the regulation of sleep and wakefulness. These include a sleep homeostatic process seeking to balance wakefulness and sleep and a circadian process seeking to promote wakefulness during the day and sleep during the night. Shift work is associated with a temporal misalignment of these two endogenous processes. During nightwork, this misalignment makes it difficult to stay awake during the nightshift and sleep during the day. However, inter‐individual variability in the processes involved in sleep/wake regulation is substantial. Recent studies have demonstrated the existence of inter‐individual differences in vulnerability to cognitive deficits from sleep loss. Moreover, these inter‐individual differences were shown to constitute a trait. Interestingly, self‐evaluations of sleepiness did not correspond well with the trait inter‐individual variability in objective levels of performance impairment during sleep deprivation. Perhaps because of this discrepancy, in operational settings, the inter‐individual differences in vulnerability to sleep loss do not appear to be limited due to self‐selection mechanisms. Indeed, even among a highly select group of active‐duty jet fighter pilots flying a series of simulated night missions, systematic inter‐individual differences in performance impairment from sleep loss were still observed. There are significant personal and economic consequences to human error and accidents caused by performance deficits due to sleep loss. It is important, therefore, to study the inter‐individual differences in the regulation of sleep and wakefulness in the work environment so that cognitive impairment during shift work may be better anticipated and prevented.

Sleep deprivation affects multiple distinct cognitive processes.

Sleep deprivation adversely affects the ability to perform cognitive tasks, but theories range from predicting an overall decline in cognitive functioning (because of reduced stability in attentional networks) to claiming specific deficits in executive functions. In the present study, we measured the effects of sleep deprivation on a two-choice numerosity discrimination task. A diffusion model was used to decompose accuracy and response time distributions in order to produce estimates of distinct components of cognitive processing. The model assumes that, over time, noisy evidence from the task stimulus is accumulated to one of two decision criteria and that parameters governing this process can be extracted and interpreted in terms of distinct cognitive processes. The results showed that sleep deprivation affects multiple components of cognitive processing, ranging from stimulus processing to peripheral nondecision processes. Thus, sleep deprivation appears to have wide-ranging effects: Reduced attentional arousal and impaired central processing combine to produce an overall decline in cognitive functioning.

Impact of Five Nights of Sleep Restriction on Glucose Metabolism, Leptin and Testosterone in Young Adult Men

Background Sleep restriction is associated with development of metabolic ill-health, and hormonal mechanisms may underlie these effects. The aim of this study was to determine the impact of short term sleep restriction on male health, particularly glucose metabolism, by examining adrenocorticotropic hormone (ACTH), cortisol, glucose, insulin, triglycerides, leptin, testosterone, and sex hormone binding globulin (SHBG). Methodology/Principal Findings N = 14 healthy men (aged 27.4±3.8, BMI 23.5±2.9) underwent a laboratory-based sleep restriction protocol consisting of 2 baseline nights of 10 h time in bed (TIB) (B1, B2; 22:00–08:00), followed by 5 nights of 4 h TIB (SR1–SR5; 04:00–08:00) and a recovery night of 10 h TIB (R1; 22:00–08:00). Subjects were allowed to move freely inside the laboratory; no strenuous activity was permitted during the study. Food intake was controlled, with subjects consuming an average 2000 kcal/day. Blood was sampled through an indwelling catheter on B1 and SR5, at 09:00 (fasting) and then every 2 hours from 10:00–20:00. On SR5 relative to B1, glucose (F 1,168 = 25.3, p<0.001) and insulin (F 1,168 = 12.2, p<0.001) were increased, triglycerides (F 1,168 = 7.5, p = 0.007) fell and there was no significant change in fasting homeostatic model assessment (HOMA) determined insulin resistance (F 1,168 = 1.3, p = 0.18). Also, cortisol (F 1,168 = 10.2, p = 0.002) and leptin (F 1,168 = 10.7, p = 0.001) increased, sex hormone binding globulin (F 1,167 = 12.1, p<0.001) fell and there were no significant changes in ACTH (F 1,168 = 0.3, p = 0.59) or total testosterone (F 1,168 = 2.8, p = 0.089). Conclusions/Significance Sleep restriction impaired glucose, but improved lipid metabolism. This was associated with an increase in afternoon cortisol, without significant changes in ACTH, suggesting enhanced adrenal reactivity. Increased cortisol and reduced sex hormone binding globulin (SHBG) are both consistent with development of insulin resistance, although hepatic insulin resistance calculated from fasting HOMA did not change significantly. Short term sleep curtailment leads to changes in glucose metabolism and adrenal reactivity, which when experienced repeatedly may increase the risk for type 2 diabetes.

A new mathematical model for the homeostatic effects of sleep loss on neurobehavioral performance

The two-process model of sleep regulation makes accurate predictions of sleep timing and duration for a variety of experimental sleep deprivation and nap sleep scenarios. Upon extending its application to waking neurobehavioral performance, however, the model fails to predict the effects of chronic sleep restriction. Here we show that the two-process model belongs to a broader class of models formulated in terms of coupled non-homogeneous first-order ordinary differential equations, which have a dynamic repertoire capturing waking neurobehavioral functions across a wide range of wake/sleep schedules. We examine a specific case of this new model class, and demonstrate the existence of a bifurcation: for daily amounts of wakefulness less than a critical threshold, neurobehavioral performance is predicted to converge to an asymptotically stable state of equilibrium; whereas for daily wakefulness extended beyond the critical threshold, neurobehavioral performance is predicted to diverge from an unstable state of equilibrium. Comparison of model simulations to laboratory observations of lapses of attention on a psychomotor vigilance test (PVT), in experiments on the effects of chronic sleep restriction and acute total sleep deprivation, suggests that this bifurcation is an essential feature of performance impairment due to sleep loss. We present three new predictions that may be experimentally verified to validate the model. These predictions, if confirmed, challenge conventional notions about the effects of sleep and sleep loss on neurobehavioral performance. The new model class implicates a biological system analogous to two connected compartments containing interacting compounds with time-varying concentrations as being a key mechanism for the regulation of psychomotor vigilance as a function of sleep loss. We suggest that the adenosinergic neuromodulator/receptor system may provide the underlying neurobiology.

Diffusion model for one-choice reaction-time tasks and the cognitive effects of sleep deprivation

One-choice reaction-time (RT) tasks are used in many domains, including assessments of motor vehicle driving and assessments of the cognitive/behavioral consequences of sleep deprivation. In such tasks, subjects are asked to respond when they detect the onset of a stimulus; the dependent variable is RT. We present a cognitive model for one-choice RT tasks that uses a one-boundary diffusion process to represent the accumulation of stimulus information. When the accumulated evidence reaches a decision criterion, a response is initiated. This model is distinct in accounting for the RT distributions observed for one-choice RT tasks, which can have long tails that have not been accurately captured by earlier cognitive modeling approaches. We show that the model explains performance on a brightness-detection task (a “simple RT task”) and on a psychomotor vigilance test. The latter is used extensively to examine the clinical and behavioral effects of sleep deprivation. For the brightness-detection task, the model explains the behavior of RT distributions as a function of brightness. For the psychomotor vigilance test, it accounts for lapses in performance under conditions of sleep deprivation and for changes in the shapes of RT distributions over the course of sleep deprivation. The model also successfully maps the rate of accumulation of stimulus information onto independently derived predictions of alertness. The model is a unified, mechanistic account of one-choice RT under conditions of sleep deprivation.

Mixed-model regression analysis and dealing with interindividual differences.

Publisher Summary This chapter considers mixed-model regression analysis, which is a specific technique for analyzing longitudinal data that properly deals with within- and between-subjects variance. The term ‘‘mixed model’’ refers to the inclusion of both fixed effects, which are model components used to define systematic relationships such as overall changes over time and/ or experimentally induced group differences; and random effects, which account for variability among subjects around the systematic relationships captured by the fixed effects. To illustrate how the mixed-model regression approach can help analyze longitudinal data with large inter-individual differences, the psychomotor vigilance data is considered from an experiment involving 88 h of total sleep deprivation, during which subjects received either sustained low-dose caffeine or placebo. The traditional repeated-measures analysis of variance (ANOVA) is applied, and it is shown that that this method is not robust against systematic interindividual variability. The data are then reanalyzed using linear mixed-model regression analysis in order to properly take into account the interindividual differences. The study concludes with an application of nonlinear mixed-model regression analysis of the data at hand, to demonstrate the considerable potential of this relatively novel statistical approach.