Phenotyping of Neurobehavioral Vulnerability to Circadian Phase During Sleep Loss

Phenotyping of Neurobehavioral Vulnerability to Circadian Phase During Sleep Loss

CHAPTER THIRTEEN Phenotyping of Neurobehavioral Vulnerability to Circadian Phase During Sleep Loss Namni Goel1, Mathias Basner, David F. Dinges Divis...

981KB Sizes 0 Downloads 3 Views

CHAPTER THIRTEEN

Phenotyping of Neurobehavioral Vulnerability to Circadian Phase During Sleep Loss Namni Goel1, Mathias Basner, David F. Dinges Division of Sleep and Chronobiology, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA 1 Corresponding author: e-mail address: [email protected]

Contents 1. Prevalence and Consequences of Sleep Loss 2. Sleep–Wake and Circadian Regulation: Two-Process Model 3. Subjective and Objective Measures for Circadian Variation in Performance 4. Circadian Variation Assessment in Neurobehavioral Functions 5. Sleep Deprivation and Performance 6. Cumulative Effects on Performance from Chronic Sleep Restriction 7. Phenotypic Individual Differences in Response to Sleep Deprivation 8. The PVT: Example of a Behavioral Assay for Phenotyping Responses to Sleep Loss 9. Conclusions Acknowledgments References

286 286 291 292 294 295 297 300 301 303 303

Abstract The two-process model of sleep–wake regulation posits a neurobiological drive for sleep that varies homeostatically (increasing as a saturating exponential during wakefulness and decreasing in a like manner during sleep) and a circadian process that neurobiologically modulates both the homeostatic drive for sleep and waking alertness and performance. Endogenous circadian rhythms in neurobehavioral functions, including physiological alertness and cognitive performance, have been demonstrated using laboratory protocols that reveal the interaction of the biological clock with the sleep homeostatic drive. Acute total sleep deprivation and chronic sleep restriction increase homeostatic sleep drive and degrade waking neurobehavioral functions as reflected in sleepiness, attention, cognitive speed, and memory. Notably, there is a high degree of stability in neurobehavioral responses to sleep loss, suggesting that these individual differences are trait-like and phenotypic and are not explained by subjects’ baseline functioning or a number of other potential predictors. The Psychomotor Vigilance Test is an important tool for phenotyping as it is sensitive to acute total sleep deprivation and chronic sleep restriction, is affected by the circadian and sleep homeostatic drives, shows large intersubject variability in the response to sleep loss, and tracks recovery Methods in Enzymology, Volume 552 ISSN 0076-6879 http://dx.doi.org/10.1016/bs.mie.2014.10.024

#

2015 Elsevier Inc. All rights reserved.

285

286

Namni Goel et al.

from sleep restriction. Careful phenotyping is critical to accurately predict human performance (and individual differences) in situations in which the circadian and sleep homeostatic systems are perturbed such as acute total sleep loss, chronic sleep restriction, intermittent sleep loss, shift work, and jet lag.

1. PREVALENCE AND CONSEQUENCES OF SLEEP LOSS Studies estimate that 20–40% of the adult US population sleeps less than 7 h per night (Banks & Dinges, 2007)—the minimum sleep duration necessary to prevent cumulative deterioration in performance on a range of cognitive tasks (Belenky et al., 2003; Van Dongen, Maislin, Mullington, & Dinges, 2003). The proportion of people curtailing their sleep due to lifestyle factors is increasing (Banks & Dinges, 2007) and is higher than surveys indicate, since physiological sleep duration is typically at least 1 h less than self-reported sleep duration (Lauderdale, Knutson, Yan, Liu, & Rathouz, 2008; Silva et al., 2007). Moreover, sleep loss has become a significant public health concern—population studies have found that reduced sleep duration (less than 7 h) is associated with increased risks of obesity, morbidity, and mortality (Cappuccio et al., 2008; Ferrie et al., 2007; Krueger & Friedman, 2009). Sleep loss, including chronic sleep restriction—a condition experienced by millions of people on a consecutive and daily basis—can result from medical conditions, sleep disorders, work demands, stress/emotional distress, and social/domestic responsibilities (Banks & Dinges, 2007). In addition, for the majority of people, sleep loss directly causes significant risks via increased fatigue and sleep propensity, and via deficits in mood and neurocognitive functions including vigilant and executive attention, cognitive speed and working memory, and executive functions (Banks & Dinges, 2007; Goel, Rao, Durmer, & Dinges, 2009; Lim & Dinges, 2010).

2. SLEEP–WAKE AND CIRCADIAN REGULATION: TWO-PROCESS MODEL The two-process model of sleep–wake regulation has been applied to the temporal profiles of sleep (Borbe´ly, 1982; Daan, Beersma, & Borbe´ly, 1984) and daytime vigilance (Achermann & Borbe´ly, 1994). The model consists of a homeostatic process (S) and a circadian process (C), which

Phenotyping Neurobehavioral Responses to Sleep Loss

287

combine to determine the timing of sleep onset and offset. The homeostatic process represents the drive for sleep that increases as a saturating exponential during wakefulness (as can be observed when wakefulness is maintained beyond habitual bedtime into the night and subsequent day) and decreases as a saturating exponential during sleep (which represents recuperation obtained from sleep). When the homeostat increases above a certain threshold, sleep is triggered; when it decreases below a different threshold, wakefulness occurs. The circadian process represents daily oscillatory modulation of these threshold levels. It has been suggested that the circadian system actively promotes wakefulness more than sleep (Edgar, Dement, & Fuller, 1993). The circadian drive for wakefulness may be manifested as spontaneously enhanced alertness and better cognitive performance in the early evening after one night or multiple nights without sleep (Doran, Van Dongen, & Dinges, 2001; Lim & Dinges, 2008; Figs. 1 and 2). The endogenous circadian regulating system (i.e., biological clock) that modulates the timing of both sleep and wakefulness is located in the suprachiasmatic nuclei (SCN) of the anterior hypothalamus. Beyond gating the timing of sleep onset and offset, the SCN modulates waking behavior in a circadian manner, as reflected in subjective and physiological sleepiness, behavioral alertness, and a number of fundamental cognitive functions, including vigilant attention, psychomotor and perceptual cognitive speed, and working memory. Alertness and performance, and sleep and sleeplessness are neurobehavioral outputs that involve dynamic circadian variation. Forced desynchrony protocols, which serve to experimentally reveal the variance in neurobehavioral functions attributable primarily to endogenous circadian control and the variance attributable primarily to the sleep homeostatic drive, have revealed that circadian dynamics can expose large neurobehavioral vulnerability during chronic sleep restriction (Cohen et al., 2010; Zhou et al., 2011). These studies demonstrated that sleep restriction induced decreased vigilant attention, as measured by the Psychomotor Vigilance Test (PVT; Lim & Dinges, 2008) most prominently during circadian night, even with short prior wake duration. Another study found that time of day modulated the effects of chronic sleep restriction whereby the buildup rate of cumulative neurobehavioral deficits across days was largest at 0800 h and became progressively smaller across the hours of the day, especially between 1600 h and 2000 h, indicating a late afternoon/early evening period of relatively protected alertness (Mollicone, Van Dongen, Rogers, & Dinges, 2008).

288

Core body temperature (ⴗC)

PVT fastest RTs (ms)

DSST number correct

Subjective sleepiness (VAS)

Namni Goel et al.

12

18

12

18

0

6

12

18

0

0

6

12

18

0

10 20 30

64 60 56 52

190 200 210 220

37.5 37.3 37.1 36.9 36.7

Time of day (h)

Figure 1 Circadian variation across a 40-h period of wakefulness in measures of subjective sleepiness as assessed by visual analogue scale (VAS, note reversed scale direction); in cognitive performance speed as assessed by the digit symbol substitution task (DSST); in psychomotor speed as reflected in the 10% fastest reaction times (RTs) assessed by the Psychomotor Vigilance Test (PVT); and in core body temperature (CBT) as assessed by a rectal thermistor. Data shown are the mean values from five

Phenotyping Neurobehavioral Responses to Sleep Loss

289

Thus, while the two-process model has been very successful in explaining changes in neurobehavioral performance in acute total sleep deprivation paradigms, it fails to adequately predict the escalating declines in vigilant attention observed under chronic sleep restriction conditions. The two-process model has proven to be most useful for generating mathematical predictions of the dynamics of human alertness and performance under varying conditions of sleep loss and circadian misalignment (Mallis, Mejdal, Nguyen, & Dinges, 2004). When these mathematical models are compared to experimental data on performance relative to sleep–wake dynamics, they often reveal new deficiencies in the two-process model (Van Dongen, 2004). An improvement on the predictions of the two-process model can be found in a mathematical modeling paper by McCauley et al. (2009), which showed that the two-process model belongs to a broader class of models formulated in terms of coupled nonhomogeneous first-order ordinary differential equations. This new model includes an additional component modulating the homeostatic process across days and weeks and better reflects the neurobehavioral changes observed under both acute total sleep loss and chronic sleep restriction than the original two-process model. Importantly, the model predicts a critical amount of daily wake duration of 20.2 h. If daily wake duration is above ca. 15.8 h (Van Dongen et al., 2003) but below 20.2 h (corresponding to a total sleep time of 3.8–8.2 h), the model, over a period of weeks, converges to an asymptotically stable equilibrium (i.e., performance deficits will stabilize at a certain level). If daily wake duration is above 20.2 h, the model diverges and, similar to acute total sleep deprivation, performance impairments escalate (McCauley et al., 2009). This model also predicts that a single subjects who remained awake in dim light, in bed, in a constant routine protocol, for 36 h consecutively (a distance-weighted least-squares function was fitted to each variable). The circadian trough is evident in each variable (marked by vertical broken lines). A phase difference is also apparent, such that all three neurobehavioral variables had their average minimum between 3.0 and 4.5 h after the time of the body temperature minimum. This phase delay in neurobehavioral functions relative to CBT has been consistently observed. Although body temperature reflects predominantly the endogenous circadian clock, neurobehavioral functions are also affected by the homeostatic pressure for sleep, which escalates with time awake and which may contribute to the phase delay through interaction with the circadian clock. Neurobehavioral functions usually show a circadian decline at night as is observed in CBT, but they continue their decline after CBT begins to rise, making the subsequent 2–6 h period (clock time approximately 0600–1000 h) a zone of maximum vulnerability to loss of alertness and to performance failure. Reprinted with permission from Goel, Van Dongen, and Dinges (2011).

290

Namni Goel et al.

Figure 2 PVT performance parameters of healthy adults during an 88-h period of limited to no sleep in the laboratory. The open circles represent 13 subjects undergoing 88 h of total sleep deprivation, and the filled squares represent 15 control subjects given a 2-h time in bed nap opportunity once every 12 h (0245–0445 h and 1445–1645 h) throughout the 88-h period (nap times are not shown in the figure). Graph A: mean (SEM) PVT reaction times (RTs), which as RTs > 500 ms are frank errors of omission

Phenotyping Neurobehavioral Responses to Sleep Loss

291

night of recovery sleep is inadequate to recover from a prolonged period of sleep restriction, a finding we have experimentally confirmed (Banks, Van Dongen, Maislin, & Dinges, 2010). The authors acknowledge further model development is needed to integrate more comprehensive mathematical models of the circadian component and to account for sleep inertia and trait-like individual differences in vulnerability to sleep loss (McCauley et al., 2009). Indeed, a recent paper by this group incorporates time dependence in the amplitude of the circadian modulation of performance into a revised model (McCauley et al., 2013).

3. SUBJECTIVE AND OBJECTIVE MEASURES FOR CIRCADIAN VARIATION IN PERFORMANCE Subjective measures of sleepiness and alertness can reflect circadian variation, if the scale employed requests ratings on the near immediate state of the subject. These include visual analogue scales (Monk, 1989), Likerttype rating scales such as the Stanford Sleepiness Scale (Hoddes, Zarcone, Smythe, Phillips, & Dement, 1973) and the Karolinska Sleepiness Scale (A˚kerstedt & Gillberg, 1990), and certain fatigue-related subscales of standard adjective checklists such as the Profile of Mood States (McNair, Lorr, & Druppleman, 1971). Despite structural differences among these scales, all self-reports of sleepiness are highly intercorrelated and because they are relative psychometrics, they are subject to a number of sources of variance, including varying uses of the scale response range by different subjects. The effects of cognitive performance testing on subsequent posttest subjective alertness ratings are evident only when sleep loss has commenced and this effect is modulated by circadian variation (Mollicone et al., 2008). Many studies rely on objective performance measures to track the temporal dynamics of endogenous circadian rhythmicity. Circadian variation in performance is most evident when sleep loss is present, and sleep loss has its largest effects on attention, working memory, and cognitive throughput and referred to as lapses of attention (i.e., responding too slowly). Graph B: mean (SEM) PVT errors of commission, which result from premature responses and reflect impulsiveness (i.e., responding too fast). Graph C: mean (SEM) of PVT RT standard deviations for each test bout, reflecting the magnitude of interindividual differences in performance. The subjects who underwent 88 h without sleep showed clear circadian variation in both lapses of attention (A) and premature responses (B), as well as interindividual differences in these effects (C). Reprinted with permission from Goel, Basner, Rao, and Dinges (2013).

292

Namni Goel et al.

(Lim & Dinges, 2010). Typically, response speed and accuracy to a series of repetitive stimuli are analyzed, although the sensitivity of the performance metric used to track circadian variation depends on whether the task is work-paced versus subject-paced, on speed versus accuracy trade-offs in performance metrics (Osman et al., 2000), on the rate and number of responses acquired during the task, on whether the task metrics reflect performance variability or mean performance, and on the overall technical precision of the measurement. Even short-duration, work-paced tasks that precisely measure variability in performance can be used to demonstrate circadian variation (Dorrian, Rogers, & Dinges, 2005). It is likely that the modulatory effects of the circadian system on speed and accuracy render many tasks sensitive to process C, more so than any aspect of task demand. Under strictly controlled laboratory conditions, intertask differences in circadian variation disappear ( Johnson et al., 1992; Monk et al., 1997). Under controlled sleep deprivation conditions, the circadian rhythms of neurobehavioral performance variables covary with each other and with subjective sleepiness (Fig. 1). Importantly, these rhythms mimic the circadian profile of core body temperature, a conventional marker of the biological clock (Baehr, Revelle, & Eastman, 2000; Kerkhof & Van Dongen, 1996). Under entrained conditions, higher and lower core body temperature values typically correspond to good and poor performance, respectively (Blake, 1967; Kleitman & Jackson, 1950; Monk et al., 1997).

4. CIRCADIAN VARIATION ASSESSMENT IN NEUROBEHAVIORAL FUNCTIONS Considerable research has been devoted to unmasking (or eliminating sources of extraneous variance) circadian rhythms to expose the endogenous circadian rhythms of variables of interest, including alertness and cognitive performance. Two such experimental approaches include constant routine and forced desynchrony protocols. The constant routine procedure (Mills, Minors, & Waterhouse, 1978) is generally regarded as the gold standard for measuring circadian rhythmicity. By keeping subjects awake with a fixed posture in a constant laboratory environment for at least 24 h, circadian rhythms in a variety of physiologic and neurobehavioral variables can be recorded without biases (Fig. 1). Indeed, the circadian rhythm of body temperature is believed to be largely free of masking effects when derived under a constant routine.

Phenotyping Neurobehavioral Responses to Sleep Loss

293

However, when neurobehavioral variables are considered, sleep deprivation and the stimuli used to sustain wakefulness can constitute masking factors. In constant routine experiments, these masking effects are evident in subjective measures of sleepiness and alertness (Kerkhof & Van Dongen, 1996; Monk & Carrier, 1997). Figure 1 shows the somewhat reduced values for subjective alertness as well as cognitive and psychomotor performance after 30 h awake in a constant routine, compared with the values of these variables 24 h earlier (i.e., at the same circadian phase but without sleep deprivation). A progressive change associated with the time spent awake is superimposed on the circadian rhythm of neurobehavioral variables (A˚kerstedt, Gillberg, & Wetterberg, 1982; Van Dongen & Dinges, 2005). When total sleep deprivation is continued for several days (whether in a constant routine procedure or in an experimental design involving ambulation), the detrimental effects on alertness and performance increase, and although the circadian process can be exposed (Babkoff, Mikulincer, Caspy, Carasso, & Sing, 1989), it is overlaid on a continuing (nearly linear) change reflecting increasing homeostatic pressure for sleep (Bohlin & Kjellberg, 1973). This is illustrated in Fig. 2 for PVT lapses—perhaps the most sensitive waking measure of homeostatic sleep drive and circadian phase, and the least masked by aptitude and learning (Basner & Dinges, 2011; Lim & Dinges, 2008). Notably, decreased alertness during the circadian trough is associated with increased intraindividual variability in performance. This is evidenced by intermittent lapsing (Williams, Lubin, & Goodnow, 1959) which reflects wake state instability (Doran et al., 2001; Lim & Dinges, 2008). The wake state instability hypothesis posits that sleep-initiating mechanisms may interfere with wakefulness, making sustained performance unstable and dependent on compensatory mechanisms (Lim & Dinges, 2008). The forced desynchrony protocol (Dijk & Czeisler, 1994; Kleitman & Kleitman, 1953) conducted in temporally and environmentally isolated conditions is an experimental procedure particularly suitable for studying the interaction of the circadian and homeostatic processes (Dijk, Duffy, & Czeisler, 1992; Johnson et al., 1992; Monk, Moline, Fookson, & Peetz, 1989). In this protocol, a subject’s imposed timing and duration of wake and sleep (typically maintained in a 2:1 ratio) deviates from the normal 24-h day (e.g., 20- or 28-h days), such that the subject’s biological clock is unable to entrain to this schedule. The subject experiences two distinct influences simultaneously—the schedule of predetermined sleep and waking times representing the homeostatic system and the rhythm of the subject’s

294

Namni Goel et al.

unsynchronized (i.e., free-running) circadian system. Neurobehavioral functions are assayed during the waking periods. By folding the data over either the free-running circadian rhythm or the imposed sleep–wake cycle, the other component can be balanced out. Thus, the separate effects of circadian rhythms and wake duration (i.e., homeostatic drive for sleep) on neurobehavioral variables can be assessed. Forced desynchrony studies have found that both the circadian and homeostatic processes influence sleepiness and performance (Cohen et al., 2010; Zhou et al., 2011). The interaction of the two systems is oppositional during diurnal wake periods (from approximately 0700 h until 2300 h), such that a relatively stable level of alertness and performance can be maintained throughout the day (Dijk & Czeisler, 1994; Dijk et al., 1992). This explains why in many studies of alertness and performance, very little temporal variation is observed during the waking portion of a normal day, especially when there is no sleep deprivation (Doran et al., 2001; Fig. 2). The interaction of the homeostatic and circadian processes is believed to be nonlinear (i.e., nonadditive; Dijk et al., 1992; Van Dongen & Dinges, 2003). Therefore, the separation of circadian and homeostatic influences on neurobehavioral variables presents a conceptual and mathematical challenge, and it is difficult, if not impossible, to quantify the relative importance of the two influences on neurobehavioral functions. Moreover, their relative contributions may vary across different experimental conditions (Dijk et al., 1992; Johnson et al., 1992) and among subjects (Lenne´, Triggs, & Redman, 1998).

5. SLEEP DEPRIVATION AND PERFORMANCE Sleep deprivation induces a variety of physiological and neurobehavioral changes (Goel et al., 2009). Both objective and subjective measures of sleep propensity increase with sleep deprivation. Sleep deprivation affects a wide range of cognitive domains (including attention, working memory, abstraction, and decision making) and results in decreases in the encoding of new information and memory consolidation (Diekelmann & Born, 2010). Vigilant attention performance and psychomotor speed, as assessed with the PVT, are affected early and progressively more severely by sleep deprivation (Basner & Dinges, 2011; Basner, Mollicone, & Dinges, 2011; Lim & Dinges, 2008). There is an overall slowing of response times (RTs), a steady increase in the number of errors of omission (i.e., lapses of attention, historically defined as RTs  twice the mean RT or 500 ms),

Phenotyping Neurobehavioral Responses to Sleep Loss

295

and a more modest increase in errors of commission (i.e., responses without a stimulus or false starts) (Fig. 2; Van Dongen et al., 2003). Although sustained attention seems to be a prerequisite for high levels of performance on more complex cognitive tasks, several studies have shown that the latter are less affected by sleep loss than attention, probably because they are more challenging and engaging than sustained attention tasks that unmask fatigue by their limited evocation of additional neural processing areas (Lim & Dinges, 2010; Lo et al., 2012). In addition, some of the differences among tasks in sensitivity to sleep deprivation may be explained by practice effects confounding the effects of sleep deprivation on more complex tasks.

6. CUMULATIVE EFFECTS ON PERFORMANCE FROM CHRONIC SLEEP RESTRICTION Chronic reductions of sleep time related to compensated work hours is common among Americans (Basner et al., 2007). Increased sleep propensity, degradation of behavioral alertness, psychomotor vigilance lapses, and cognitive slowing can be detected even when sleep deprivation is relatively modest but chronic (Banks et al., 2010; Carskadon & Dement, 1981; Dinges et al., 1997; Lim & Dinges, 2008), and especially when sleep is restricted in duration below 7 h a night (Belenky et al., 2003; Van Dongen et al., 2003). Two seminal experimental studies documented precise dose-related effects of chronic sleep restriction on neurobehavioral performance measures in healthy adults (Belenky et al., 2003; Van Dongen et al., 2003). In both experiments, performance deficits increased steadily across consecutive days of sleep restriction, and the less sleep provided each night below 7 h, the more rapidly the performance deficits increased across days of restriction. Within 5–6 days of sleep restricted to less than 7 h, decrements in behavioral alertness increased to levels equivalent to 24–48 h of no sleep (Fig. 3). Beyond cumulative effects, another consistent finding from chronic sleep restriction experiments is that subjects overestimate their subjective alertness and underestimate the severity of their reduced behavioral alertness and the likelihood of having performance lapses or sudden sleep onsets. Although people often struggle with sleepiness while performing safetysensitive tasks, such as driving (Horne & Baulk, 2004; Reyner & Horne, 1998b), they are not able to accurately judge when they will experience an involuntary lapse, a microsleep or full-blown sleep attack—especially

296

Namni Goel et al.

A 16

B 2.5 SSS sleepiness score

14

PVT lapses

12 10 8 6 4 2 0 BL

2.0 1.5 1.0 0.5 0.0

2

4

6

8

10 12

Days of sleep restriction

14

BL

2

4

6

8

10 12 14

Days of sleep restriction

Figure 3 The effects of chronic sleep restriction on the occurrence of behavioral lapses of attention via the PVT shown in (A), and on subjective ratings of sleepiness via the Stanford Sleepiness Scale (SSS), shown in (B). Healthy adults were randomized to 4 h (N ¼ 13; ○), 6 h (N ¼ 13; □), or 8 h (N ¼ 9; ◊) time in bed for sleep each night for 14 days, and compared to subjects given no sleep (N ¼ 13; ■) for 3 consecutive days. Higher values correspond to poorer performance and greater sleepiness. Curves represent nonlinear mixed-effects models of responses to sleep dose. Mean  SEM ranges for PVT and SSS for 1 and 2 days of 0 h sleep are shown as light and dark gray bands. PVT lapses increased steadily across days of sleep restriction (A), but ratings of subjective sleepiness changed little after an initial increase (B). In the 4- and 8-h sleep conditions, performance lapses reached levels equivalent to those observed after two nights without sleep by days 9 and 14, respectively. Reprinted with permission from Van Dongen et al. (2003).

during periods of chronic sleep restriction (Van Dongen et al., 2003). That is, subjects believe they can overcome sleepiness either by force of will or by engaging in certain behaviors (e.g., listening to music, etc.), but these alerting stimuli have only negligible and short-lived effects (Reyner & Horne, 1998a; Schwarz et al., 2012). The neurobehavioral effects of chronic sleep restriction are less severe than those observed after acute total sleep deprivation (Fig. 3), but the former can reach levels of deficit equivalent to total sleep loss when the sleep restriction is severe enough (i.e., the consecutive days of restricted sleep continue long enough; Belenky et al., 2003; Van Dongen et al., 2003). Chronic sleep restriction experiments suggest that the neurobiology underlying the neurobehavioral deficits can continue to undergo long-term changes. This is supported by a study investigating recovery after chronic sleep restriction that suggests a single recovery night of up to 10 h time in bed is insufficient for some behavioral functions to return to prerestriction

Phenotyping Neurobehavioral Responses to Sleep Loss

297

levels (Banks et al., 2010). Evidence of longer time constants in homeostatic sleep pressure manifesting in waking neurobehavioral functions has been reported by Rupp, Wesensten, Bliese, and Balkin (2009) who varied the amount of baseline nightly sleep prior to chronic sleep restriction and found that it affected the rate at which alertness was degraded and the rate at which deficits were reversed by repeated nights of recovery sleep.

7. PHENOTYPIC INDIVIDUAL DIFFERENCES IN RESPONSE TO SLEEP DEPRIVATION Although sleep experts often refer to the effects of sleep loss as occurring equally in all individuals, it has been known for decades this is categorically not the case (Dinges & Kribbs, 1991; Doran et al., 2001; Van Dongen, Baynard, Maislin, & Dinges, 2004). As a group of otherwise healthy individuals undergo either acute total sleep deprivation for a night or repeated nights of chronic sleep restriction, the changes in sleepiness and other sensitive neurobehavioral measures reveal not only a mean change over time but also an ever greater standard deviation over time (Doran et al., 2001; Van Dongen, Baynard, et al., 2004). This proportionality between the mean and standard deviation indicates that although the average (and median) neurobehavioral response to sleep loss is increasing over time, some individuals are experiencing much more sleepiness and neurobehavioral instability than others. The long-standing assumption that this intersubject variance in response to sleep loss was primarily random error variance was called into question by the results of the first systematic experimental effort to replicate these individual responses to sleep deprivation (Dijkman et al., 1997; Van Dongen, Dijkman, Maislin, & Dinges, 1999). Our laboratory was the first to experimentally establish that subjects undergoing acute total sleep deprivation—in which no sleep is obtained—show differential vulnerability to sleep loss, demonstrating robust interindividual (trait-like, phenotypic) differences in response to the same laboratory conditions, as measured by various physiological and subjective sleep measures and neurobehavioral tasks sensitive to sleep loss (e.g., Van Dongen, Baynard, et al., 2004; Van Dongen, Maislin, & Dinges, 2004). The intraclass correlation coefficients (ICCs)—which express the proportion of variance in the data explained by systematic interindividual variability—revealed that stable (trait-like) responses accounted for 58% and 68% of the overall variance in PVT lapses (greater than 500 ms reaction times) between multiple sleep-deprivation exposures in the same subjects

298

Namni Goel et al.

(Dijkman et al., 1997; Van Dongen et al., 1999, 2003; Van Dongen, Maislin et al., 2004). Thus, individuals who showed high PVT lapse rates during total sleep deprivation after one exposure also showed high lapse rates during a second exposure; similarly, those with low PVT lapse rates during one exposure showed low PVT lapse rates during a second exposure. Most importantly, because these high ICCs were found when the subjects were exposed to total sleep deprivation two to three times under markedly different conditions [e.g., high versus low stimulation (Dijkman et al., 1997); 6 h versus 12 h sleep time per night (Van Dongen, Baynard, et al., 2004)], the differences in neurobehavioral vulnerability to sleep deprivation are considered phenotypic. [A phenotype has the following characteristics: (1) it is an observed trait or characteristic of an organism (e.g., morphology, development, and behavior); (2) it is a characteristic that can be made visible by some technical procedure; and (3) it is a product of genotypes, but is also influenced by extragenetic or environmental factors. In addition, phenotypic variation (due to underlying heritable genetic variation) is a fundamental prerequisite for evolution by natural selection (Goel & Dinges, 2011).] Figure 4 shows data demonstrating the stability of the trait-like (phenotypic) differences in PVT lapses of attention to one night of acute total sleep deprivation in these protocols and highlights that fact that while some individuals are highly vulnerable to cognitive performance deficits when sleep deprived (Type 3), others show remarkable levels of cognitive resistance to sleep loss (Type 1) and others show intermediate responses (Type 2; Van Dongen, Maislin, et al., 2004). Other laboratories have confirmed our findings of large, stable (trait-like) differences in neurobehavioral responses to acute total sleep deprivation (e.g., Frey, Badia, & Wright, 2004; Kuna et al., 2012; Leproult et al., 2003). Notably, such differences have not been accounted for by demographic factors (age, sex, and IQ), by baseline functioning, by circadian chronotype, or by sleep need; moreover, psychometric scales have not reliably identified neurobehaviorally vulnerable individuals (Van Dongen, Baynard, et al., 2004; Van Dongen et al., 1999, 2003). We and other groups have found similar differential vulnerability to chronic sleep restriction, in which sleep is reduced to 3–7 h time in bed per night (Bliese, Wesensten, & Balkin, 2006; Goel, Banks, Lin, Mignot, & Dinges, 2011; Goel, Banks, Mignot, & Dinges, 2009, 2010; Van Dongen et al., 2003). It remains unclear, however, whether the same individuals vulnerable to the adverse neurobehavioral effects of chronic sleep restriction are also vulnerable to acute total sleep deprivation. Some studies have reported

299

Phenotyping Neurobehavioral Responses to Sleep Loss

120 Type 3 (n = 3)

PVT performances lapses (greater impairment →)

Type 2 (n = 5)

N = 10 ICC = 58%

PVT performances lapses (greater impairment →)

Type 1 (n = 2)

Type 1

Type 2

Type 3

100 80 60 40 20

N = 19 ICC = 68%

0 1

2

3

4

5

6

Subjects

7

8

9 10

UOQ N F C AGB J HM T K S I R L E Subjects

Figure 4 The left panel shows mean PVT performance lapses per test bout after acute total sleep deprivation from an initial experiment on 10 healthy adults studied during two separate acute total sleep deprivation periods (circles versus squares) that differed by the degree of activity and social stimulation (data from Dijkman et al., 1997; Van Dongen et al., 1999). The intraclass correlation (ICC), which expresses the proportion of variance explained by systematic interindividual variability, was 58% between the two experiments. The right panel shows results of a second experiment from our laboratory in which 19 adults underwent (in randomized order) three nights of acute total sleep deprivation on three separate occasions, two of which followed a week of sleep extension of 12 h time in bed (squares: first exposure to total sleep deprivation following sleep extension; diamonds: second exposure to total sleep deprivation following sleep extension), and one of which (solid circles) involved a prior week of chronic sleep restriction (6 h time in bed). Despite markedly different sleep histories, the ICC revealed that 68% of the variance in PVT lapsing during acute total sleep deprivation was stable (trait-like) among subjects (data from Van Dongen, Baynard, et al., 2004). Thus, both experiments confirmed that differential cognitive vulnerability to acute total sleep deprivation was a major source of variance, and that some subjects consistently had few lapses of attention in response to sleep loss (i.e., Type 1 responders), while others showed large increases in lapsing (Type 3 responders), and some were in the intermediate lapse range (Type 2 responders). Thus, PVT performance deficits from acute total sleep deprivation varied significantly among individuals and were stable within individuals. Importantly, these stable individual differences in responses to sleep deprivation were not merely a result of variations in sleep history or other factors. Rather, they were a result of trait-like differential vulnerability to impairment from sleep loss. Reprinted with permission from Goel and Dinges (2011).

differences in behavioral, sleep homeostatic, and/or physiological responses to both types of deprivation (Drummond, Anderson, Straus, Vogel, & Perez, 2012; Rowland et al., 2005; Van Dongen et al., 2003). Moreover, only a few experiments have systematically examined the same subjects in both types of deprivation (Drake et al., 2001; Lo et al., 2012; Philip et al., 2012; Rupp, Wesensten, & Balkin, 2012; Tassi et al., 2012). These studies have reported

300

Namni Goel et al.

inconsistent results, likely due to small sample sizes, different populations, varying doses of sleep restriction, and different outcome measures.

8. THE PVT: EXAMPLE OF A BEHAVIORAL ASSAY FOR PHENOTYPING RESPONSES TO SLEEP LOSS The PVT (Dinges et al., 1997; Dinges & Powell, 1985; Doran et al., 2001; Lim & Dinges, 2008) has become arguably the most commonly used measure of behavioral alertness due to the combination of its high sensitivity to sleep deprivation (Dorrian et al., 2005; Lim & Dinges, 2008) and its psychometric advantages over other cognitive tests. The standard 10-min PVT is based on simple reaction time and measures sustained or vigilant attention by recording RTs to visual (or auditory) stimuli that occur at random interstimulus intervals (ISIs) (Dinges & Kribbs, 1991; Dinges & Powell, 1985; Dorrian et al., 2005; Warm, Parasuraman, & Matthews, 2008). The PVT has been used since the late nineteenth century in sleep deprivation research (Dinges & Kribbs, 1991; Patrick & Gilbert, 1896) as it offers an easy way to track changes in behavioral alertness caused by insufficient sleep, without the confounding effects of aptitude and learning (Doran et al., 2001; Dorrian et al., 2005; Lim & Dinges, 2008). Moreover, the 10-min PVT has been shown to be highly reliable, with intraclass correlations measuring test–retest reliability above 0.8 (Dorrian et al., 2005). PVT performance also has ecological validity as it likely reflects realworld risks, since deficits in sustained attention and timely reactions adversely affect many tasks, especially those in which prompt responses are essential (e.g., transportation, security-related tasks, and industrial tasks). There is a large body of literature showing attentional deficits have serious consequences in real-world settings (Dinges, 1995; Philip & A˚kerstedt, 2006; Van Dongen & Dinges, 2005). Both sleep loss and time on task contribute to PVT lapses in attention (Dinges & Kribbs, 1991; Lim et al., 2010). Despite its widespread use, there is large variation among published studies in PVT performance outcomes and test durations. Basner and Dinges (2011) determined PVT metrics that optimally discriminated sleep deprived from alert subjects and found that psychomotor speed (i.e., reciprocal RT or 1/RT) and lapses of attention (defined as RTs 500 ms) were superior to other commonly used PVT outcomes (e.g., median or mean RT) both conceptually and in terms of their statistical properties/effect sizes (Basner & Dinges, 2011). The standard duration of the PVT is 10 min with two 10-s ISIs. Test duration is an important aspect of the PVT because even severely sleep-

Phenotyping Neurobehavioral Responses to Sleep Loss

301

deprived subjects may be able to perform normally for a short time by increasing compensatory effort. However, a 10-min test duration is often considered impractical in applied or operational contexts. Accordingly, shorter versions of the PVT have been developed and validated. A 5-min handheld version of the PVT has shown acceptable properties in terms of sensitivity to sleep loss (Lamond, Dawson, & Roach, 2005; Lamond et al., 2008; Loh, Lamond, Dorrian, Roach, & Dawson, 2004; Roach, Dawson, & Lamond, 2006; Thorne et al., 2005). However, both the 2-min (Loh et al., 2004) and 90-s (Roach et al., 2006) versions of the PVT were deemed too insensitive to be used as valid tools for the detection of neurobehavioral effects of fatigue. Basner et al. (2011) validated a modified brief version of the PVT (PVT-B) that lasts 3 min and uses 1–4 s ISIs and a 355 ms threshold instead of the standard 500 ms threshold for lapses of attention. The PVT-B tracks standard 10-min PVT performance throughout both acute total sleep loss (Fig. 5) and chronic sleep restriction and yielded medium to large effect sizes. Relative to the 70% decrease in test duration, the 22.7% average decline in effect sizes of the PVT-B was deemed an acceptable trade-off between test duration and test sensitivity. Finally, Basner and Dinges (2012) developed an adaptive duration PVT (PVT-A) based on the standard 10-min PVT, which stops sampling once it has gathered enough information to correctly classify PVT performance. In the validation data set, 95.7% of test bouts were correctly classified and agreement corrected for chance was excellent (kappa ¼ 0.92). Test duration averaged 6.4 min (SD: 1.7 min), with a minimum of 27 s, increasing practicability of the test in operational and clinical settings. The adaptive duration strategy may be superior to a simple reduction of PVT duration when the predetermined test duration may be too short to identify subjects with moderate impairment (showing deficits only later during the test), but unnecessarily long for those who are either fully alert or severely impaired. In summary, the 10-min PVT is a critical tool for phenotyping as it is sensitive to acute total sleep deprivation and chronic sleep restriction, is affected by circadian and sleep homeostatic drives, shows large intersubject variability in the response to sleep loss, and tracks recovery from sleep restriction.

9. CONCLUSIONS The circadian drive for wakefulness, the homeostatic drive for sleep, and masking factors simultaneously interact to affect neurobehavioral

302

Namni Goel et al.

0.4 0.2 Mean1/RT [1/s]

0 −0.2 −0.4 −0.6 −0.8 −1 −1.2

Number of lapses (500 vs. 355 ms)

−1.4

07 09 11 13 15 17 19 21 23 01 03 05 07 09 11 13 15 17 19

25 20 15 10 5 0 −5

07 09 11 13 15 17 19 21 23 01 03 05 07 09 11 13 15 17 19 Time of day

Figure 5 Between-subject averages (N ¼ 31 subjects) of PVT response speed (upper panel) and lapses of attention (lower panel, 500 ms lapse threshold for 10-min PVT and 355 ms lapse threshold for 3-min PVT) are shown for each of 17 tests performed every 2 h during a 33-h period of total sleep deprivation for both the 10-min (black circles) and the 3-min (open circles) PVT. Error bars represent 95% bootstrap confidence intervals based on 1,000,000 replications. The outcome variables of the 3-min and the 10-min PVT were centered around alert performance (average of test bouts 1–7). Modified from Basner et al. (2011).

functioning. The sleep homeostat and neurobehavioral performance are affected by acute total sleep deprivation and chronic sleep restriction, although the two forms of sleep loss likely differentially affect behavioral responses. Moreover, differential vulnerability to sleep loss also markedly affects neurobehavioral responses. Accurate phenotyping of responses by use of the PVT and other measures in the context of the complex interactions of the sleep homeostatic and circadian systems is of high priority and

Phenotyping Neurobehavioral Responses to Sleep Loss

303

will aid in predicting performance deficits in a variety of situations in which these two processes are dynamically covarying, such as occurs with shift work, jet lag or imposed acute, chronic or intermittent sleep loss.

ACKNOWLEDGMENTS Preparation of this manuscript was supported by ONR N00014-11-1-0361 (N. G.), NASA NNX14AN49G (N. G.), NIH MH102310 (N.G.), NASA National Space Biomedical Research Institute through NASA NCC 9-58 (M. B., D. F. D.), and NIH NR004281 (D. F. D.).

REFERENCES Achermann, P., & Borbe´ly, A. A. (1994). Simulation of daytime vigilance by the additive interaction of a homeostatic and a circadian process. Biological Cybernetics, 71, 115–121. A˚kerstedt, T., & Gillberg, M. (1990). Subjective and objective sleepiness in the active individual. International Journal of Neuroscience, 52, 29–37. ˚ kerstedt, T., Gillberg, M., & Wetterberg, L. (1982). The circadian covariation of fatigue A and urinary melatonin. Biological Psychiatry, 17, 547–554. Babkoff, H., Mikulincer, M., Caspy, T., Carasso, R. L., & Sing, H. C. (1989). The implications of sleep loss for circadian performance accuracy. Work and Stress, 3, 3–14. Baehr, E. K., Revelle, W., & Eastman, C. I. (2000). Individual differences in the phase and amplitude of the human circadian temperature rhythm: With an emphasis on morningness-eveningness. Journal of Sleep Research, 9, 117–127. Banks, S., & Dinges, D. F. (2007). Behavioral and physiological consequences of sleep restriction in humans. Journal of Clinical Sleep Medicine, 3, 519–528. Banks, S., Van Dongen, H. P. A., Maislin, G., & Dinges, D. F. (2010). Neurobehavioral dynamics following chronic sleep restriction: Dose-response effects of one night for recovery. Sleep, 33, 1013–1026. Basner, M., & Dinges, D. F. (2011). Maximizing sensitivity of the psychomotor vigilance test (PVT) to sleep loss. Sleep, 34, 581–591. Basner, M., & Dinges, D. F. (2012). An adaptive duration version of the PVT accurately tracks changes in psychomotor vigilance induced by sleep restriction. Sleep, 35, 193–202. Basner, M., Fomberstein, K., Razavi, F. M., William, J., Simpson, N., Rosa, R., et al. (2007). American time use survey: Sleep time and its relationship to waking activities. Sleep, 30, 1081–1091. Basner, M., Mollicone, D., & Dinges, D. F. (2011). Validity and sensitivity of a brief psychomotor vigilance test (PVT-B) to total and partial sleep deprivation. Acta Astronautica, 69, 949–959. Belenky, G., Wesensten, N. J., Thorne, D. R., Thomas, M. L., Sing, H. C., Redmond, D. P., et al. (2003). Patterns of performance degradation and restoration during sleep restriction and subsequent recovery: A sleep dose-response study. Journal of Sleep Research, 12, 1–12. Blake, M. J. F. (1967). Time of day effects on performance in a range of tasks. Psychonomic Science, 9, 349–350. Bliese, P. D., Wesensten, N. J., & Balkin, T. J. (2006). Age and individual variability in performance during sleep restriction. Journal of Sleep Research, 15, 376–385. Bohlin, G., & Kjellberg, A. (1973). Self-reported arousal during sleep deprivation and its relation to performance and physiological variables. Scandinavian Journal of Psychology, 14, 78–86.

304

Namni Goel et al.

Borbe´ly, A. A. (1982). A two-process model of sleep regulation. Human Neurobiology, 1, 195–204. Cappuccio, F. P., Taggart, F. M., Kandala, N. B., Currie, A., Peile, E., Stranges, S., et al. (2008). Meta-analysis of short duration and obesity in children and adults. Sleep, 31, 619–626. Carskadon, M. A., & Dement, W. C. (1981). Cumulative effects of sleep restriction on daytime sleepiness. Psychophysiology, 18, 107–113. Cohen, D. A., Wang, W., Wyatt, J. K., Kronauer, R. E., Dijk, D. J., Czeisler, C. A., et al. (2010). Uncovering residual effects of chronic sleep loss on human performance. Science Translational Medicine, 2, 14ra13. Daan, S., Beersma, D. G. M., & Borbe´ly, A. A. (1984). Timing of human sleep: Recovery process gated by a circadian pacemaker. American Journal of Physiology, 246, R161–R178. Diekelmann, S., & Born, J. (2010). The memory function of sleep. Nature Reviews Neuroscience, 11, 114–126. Dijk, D. J., & Czeisler, C. A. (1994). Paradoxical timing of the circadian rhythm of sleep propensity serves to consolidate sleep and wakefulness in humans. Neuroscience Letters, 166, 63–68. Dijk, D. J., Duffy, J. F., & Czeisler, C. A. (1992). Circadian and sleep/wake dependent aspects of subjective alertness and cognitive performance. Journal of Sleep Research, 1, 112–117. Dijkman, M., Sachs, N., Levine, E., Mallis, M., Carlin, M. M., Gillen, K. A., et al. (1997). Effects of reduced stimulation on neurobehavioral alertness depend on circadian phase during human sleep deprivation. Sleep Research Online, 26, 265. Dinges, D. F. (1995). An overview of sleepiness and accidents. Journal of Sleep Research, 4, 4–14. Dinges, D. F., & Kribbs, N. B. (1991). Performing while sleepy: Effects of experimentallyinduced sleepiness. In T. H. Monk (Ed.), Sleep, sleepiness and performance (pp. 97–128). Chichester: John Wiley and Sons, Ltd. Dinges, D. F., Pack, F., Williams, K., Gillen, K. A., Powell, J. W., Ott, G. E., et al. (1997). Cumulative sleepiness, mood disturbance, and psychomotor vigilance performance decrements during a week of sleep restricted to 4-5 hours per night. Sleep, 20, 267–277. Dinges, D. F., & Powell, J. W. (1985). Microcomputer analysis of performance on a portable, simple visual RT task during sustained operations. Behavior Research Methods, Instruments, & Computers, 6, 652–655. Doran, S. M., Van Dongen, H. P. A., & Dinges, D. F. (2001). Sustained attention performance during sleep deprivation: Evidence of state instability. Archives Italiennes De Biologie, 139, 253–267. Dorrian, J., Rogers, N. L., & Dinges, D. F. (2005). Psychomotor vigilance performance: Neurocognitive assay sensitive to sleep loss. In C. A. Kushida (Ed.), Sleep deprivation: Clinical issues, pharmacology, and sleep loss effects (pp. 39–70). New York: Marcel Dekker. Drake, C. L., Roehrs, T. A., Burduvali, E., Bonahoom, A., Rosekind, M., & Roth, T. (2001). Effects of rapid versus slow accumulation of eight hours of sleep loss. Psychophysiology, 38, 979–987. Drummond, S. P., Anderson, D. E., Straus, L. D., Vogel, E. K., & Perez, V. B. (2012). The effects of two types of sleep deprivation on visual working memory capacity and filtering efficiency. PLoS One, 7, e35653. Edgar, D. M., Dement, W. C., & Fuller, C. A. (1993). Effect of SCN lesions on sleep in squirrel monkeys: Evidence for opponent processes in sleep-wake regulation. Journal of Neuroscience, 13, 1065–1079. Ferrie, J. E., Shipley, M. J., Cappuccio, F. P., Brunner, E., Miller, M. A., Kumari, M., et al. (2007). A prospective study of change in sleep duration: Associations with mortality in the Whitehall II cohort. Sleep, 30, 1659–1666.

Phenotyping Neurobehavioral Responses to Sleep Loss

305

Frey, D. J., Badia, P., & Wright, K. P., Jr. (2004). Inter- and intra-individual variability in performance near the circadian nadir during sleep deprivation. Journal of Sleep Research, 13, 305–315. Goel, N., Banks, S., Lin, L., Mignot, E., & Dinges, D. F. (2011). Catechol-Omethyltransferase Val158Met polymorphism associates with individual differences in sleep physiologic responses to chronic sleep loss. PLoS One, 6, e29283. Goel, N., Banks, S., Mignot, E., & Dinges, D. F. (2009). PER3 polymorphism predicts cumulative sleep homeostatic but not neurobehavioral changes to chronic partial sleep deprivation. PLoS One, 4, e5874. Goel, N., Banks, S., Mignot, E., & Dinges, D. F. (2010). DQB1*0602 predicts interindividual differences in physiologic sleep, sleepiness and fatigue. Neurology, 75, 1509–1519. Goel, N., Basner, M., Rao, H., & Dinges, D. F. (2013). Circadian rhythms, sleep deprivation and human performance. Progress in Molecular Biology and Translational Science, 119, 155–190. Goel, N., & Dinges, D. F. (2011). Sleep deprivation: Biomarkers for identifying and predicting individual differences in response to sleep loss. In M. J. Thorpy, & M. Billiard (Eds.), Sleepiness: Causes, consequences and treatment (pp. 101–110). Cambridge, UK: Cambridge University Press. Goel, N., Rao, H., Durmer, J. S., & Dinges, D. F. (2009). Neurocognitive consequences of sleep deprivation. Seminars in Neurology, 29, 320–339. Goel, N., Van Dongen, H. P. A., & Dinges, D. F. (2011). Circadian rhythms in sleepiness, alertness, and performance. In M. H. Kryger, T. Roth, & W. C. Dement (Eds.), Principles and practice of sleep medicine (pp. 445–455). Philadelphia: Elsevier. Hoddes, E., Zarcone, V., Smythe, H., Phillips, R., & Dement, W. C. (1973). Quantification of sleepiness: A new approach. Psychophysiology, 10, 431–436. Horne, J. A., & Baulk, S. D. (2004). Awareness of sleepiness when driving. Psychophysiology, 41, 161–165. Johnson, M. P., Duffy, J. F., Dijk, D. J., Ronda, J. M., Dyal, C. M., & Czeisler, C. A. (1992). Short-term memory, alertness and performance: A reappraisal of their relationship to body temperature. Journal of Sleep Research, 1, 24–29. Kerkhof, G. A., & Van Dongen, H. P. A. (1996). Morning-type and evening-type individuals differ in the phase position of their endogenous circadian oscillator. Neuroscience Letters, 218, 153–156. Kleitman, N., & Jackson, D. P. (1950). Body temperature and performance under different routines. Journal of Applied Physiology, 51, 309–328. Kleitman, N., & Kleitman, E. (1953). Effect of non-twenty-four-hour routines of living on oral temperature and heart rate. Journal of Applied Physiology, 6, 283–291. Krueger, P. M., & Friedman, E. M. (2009). Sleep duration in the United States: A crosssectional population-based study. American Journal of Epidemiology, 169, 1052–1063. Kuna, S. T., Maislin, G., Pack, F. M., Staley, B., Hachadoorian, R., Coccaro, E. F., et al. (2012). Heritability of performance deficit accumulation during acute sleep deprivation in twins. Sleep, 35, 1223–1233. Lamond, N., Dawson, D., & Roach, G. D. (2005). Fatigue assessment in the field: Validation of a hand-held electronic psychomotor vigilance task. Aviation, Space, and Environmental Medicine, 76, 486–489. Lamond, N., Jay, S. M., Dorrian, J., Ferguson, S. A., Roach, G. D., & Dawson, D. (2008). The sensitivity of a palm-based psychomotor vigilance task to severe sleep loss. Behavior Research Methods, 40, 347–352. Lauderdale, D. S., Knutson, K. L., Yan, L. L., Liu, K., & Rathouz, P. J. (2008). Self-reported and measured sleep duration: How similar are they? Epidemiology, 19, 838–845.

306

Namni Goel et al.

Lenne´, M. G., Triggs, T. J., & Redman, J. R. (1998). Interactive effects of sleep deprivation, time of day, and driving experience on a driving task. Sleep, 21, 38–44. Leproult, R., Colecchia, E. F., Berardi, A. M., Stickgold, R., Kosslyn, S. M., & Van Cauter, E. (2003). Individual differences in subjective and objective alertness during sleep deprivation are stable and unrelated. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology, 284, R280–R290. Lim, J., & Dinges, D. F. (2008). Sleep deprivation and vigilant attention. Annals of the New York Academy of Sciences, 1129, 305–322. Lim, J., & Dinges, D. F. (2010). A meta-analysis of the impact of short-term sleep deprivation on cognitive variables. Psychological Bulletin, 136, 375–389. Lim, J., Wu, W. C., Wang, J., Detre, J. A., Dinges, D. F., & Rao, H. (2010). Imaging brain fatigue from sustained mental workload: An ASL perfusion study of the time-on-task effect. NeuroImage, 49, 3426–3435. Lo, J. C., Groeger, J. A., Santhi, N., Arbon, E. L., Lazar, A. S., Hasan, S., et al. (2012). Effects of partial and acute total sleep deprivation on performance across cognitive domains, individuals and circadian phase. PLoS One, 7, e45987. Loh, S., Lamond, N., Dorrian, J., Roach, G., & Dawson, D. (2004). The validity of psychomotor vigilance tasks of less than 10-minute duration. Behavior Research Methods, Instruments, & Computers, 36, 339–346. Mallis, M. M., Mejdal, S., Nguyen, T. T., & Dinges, D. F. (2004). Summary of the key features of seven biomathematical models of human fatigue and performance. Aviation, Space, and Environmental Medicine, 75, A4–A14. McCauley, P., Kalachev, L. V., Mollicone, D. J., Banks, S., Dinges, D. F., & Van Dongen, H. P. (2013). Dynamic circadian modulation in a biomathematical model for the effects of sleep and sleep loss on waking neurobehavioral performance. Sleep, 36, 1987–1997. McCauley, P., Kalachev, L. V., Smith, A. D., Belenky, G., Dinges, D. F., & Van Dongen, H. P. A. (2009). A new mathematical model for the homeostatic effects of sleep loss on neurobehavioral performance. Journal of Theoretical Biology, 256, 227–239. McNair, D. M., Lorr, M., & Druppleman, L. F. (1971). EITS manual for the profile of mood states. San Diego: Educational and Industrial Test Services. Mills, J. N., Minors, D. S., & Waterhouse, J. M. (1978). Adaptation to abrupt time shifts of the oscillator(s) controlling human circadian rhythms. Journal of Physiology, 285, 455–470. Mollicone, D. J., Van Dongen, H. P. A., Rogers, N. L., & Dinges, D. F. (2008). Response surface mapping of neurobehavioral performance: Testing the feasibility of split sleep schedules for space operations. Acta Astronautica, 63, 833–840. Monk, T. H. (1989). A visual analogue scale technique to measure global vigor and affect. Psychiatry Research, 27, 89–99. Monk, T. H., Buysse, D. J., Reynolds, C. F., Berga, S. L., Jarrett, D. B., Begley, A. E., et al. (1997). Circadian rhythms in human performance and mood under constant conditions. Journal of Sleep Research, 6, 9–18. Monk, T. H., & Carrier, J. (1997). Speed of mental processing in the middle of the night. Sleep, 20, 399–401. Monk, T. H., Moline, M. L., Fookson, J. E., & Peetz, S. M. (1989). Circadian determinants of subjective alertness. Journal of Biological Rhythms, 4, 393–404. Osman, A., Lou, L., Muller-Gethmann, H., Rinkenauer, G., Mattes, S., & Ulrich, R. (2000). Mechanisms of speed-accuracy tradeoff: Evidence from covert motor processes. Biological Psychology, 51, 173–199. Patrick, G. T. W., & Gilbert, J. A. (1896). On the effects of sleep loss. Psychological Review, 3, 469–483.

Phenotyping Neurobehavioral Responses to Sleep Loss

307

Philip, P., & A˚kerstedt, T. (2006). Transport and industrial safety, how are they affected by sleepiness and sleep restriction? Sleep Medicine Reviews, 10, 347–356. Philip, P., Sagaspe, P., Prague, M., Tassi, P., Capelli, A., Bioulac, B., et al. (2012). Acute versus chronic partial sleep deprivation in middle-aged people: Differential effect on performance and sleepiness. Sleep, 35, 997–1002. Reyner, L. A., & Horne, J. A. (1998a). Evaluation “in-car” countermeasures to sleepiness: Cold air and radio. Sleep, 21, 46–50. Reyner, L. A., & Horne, J. A. (1998b). Falling asleep whilst driving: Are drivers aware of prior sleepiness? International Journal of Legal Medicine, 111, 120–123. Roach, G. D., Dawson, D., & Lamond, N. (2006). Can a shorter psychomotor vigilance task be used as a reasonable substitute for the ten-minute psychomotor vigilance task? Chronobiology International, 23, 1379–1387. Rowland, L. M., Thomas, M. L., Thorne, D. R., Sing, H. C., Krichmar, J. L., Davis, H. Q., et al. (2005). Oculomotor responses during partial and total sleep deprivation. Aviation, Space, and Environmental Medicine, 76, C104–C113. Rupp, T. L., Wesensten, N. J., & Balkin, T. J. (2012). Trait-like vulnerability to total and partial sleep loss. Sleep, 35, 1163–1172. Rupp, T. L., Wesensten, N. J., Bliese, P. D., & Balkin, T. J. (2009). Banking sleep: Realization of benefits during subsequent sleep restriction and recovery. Sleep, 32, 311–321. Schwarz, J. F. A., Ingre, M., Fors, C., Anund, A., Kecklund, G., & Taillard, J. (2012). In-car countermeasures open window and music revisited on the real road: Popular but hardly effective against driver sleepiness. Journal of Sleep Research, 21, 595–599. Silva, G. E., Goodwin, J. L., Sherrill, D. L., Arnold, J. L., Bootzin, R. R., Smith, T., et al. (2007). Relationship between reported and measured sleep times: The Sleep Heart Health Study (SHHS). Journal of Clinical Sleep Medicine, 3, 622–630. Tassi, P., Schimchowitsch, S., Rohmer, O., Elbaz, M., Bonnefond, A., Sagaspe, P., et al. (2012). Effects of acute and chronic sleep deprivation on daytime alertness and cognitive performance of healthy snorers and non-snorers. Sleep Medicine, 13, 29–35. Thorne, D. R., Johnson, D. E., Redmond, D. P., Sing, H. C., Belenky, G., & Shapiro, J. M. (2005). The Walter Reed palm-held psychomotor vigilance test. Behavior Research Methods, 37, 111–118. Van Dongen, H. P. A. (2004). Comparison of mathematical model predictions to experimental data of fatigue and performance. Aviation, Space, and Environmental Medicine, 75, A15–A36. Van Dongen, H. P. A., Baynard, M. D., Maislin, G., & Dinges, D. F. (2004). Systematic interindividual differences in neurobehavioral impairment from sleep loss: Evidence of trait-like differential vulnerability. Sleep, 27, 423–433. Van Dongen, H. P. A., Dijkman, M. V., Maislin, G., & Dinges, D. F. (1999). Phenotypic aspect of vigilance decrement during sleep deprivation. Physiologist, 42, A-5. Van Dongen, H. P. A., & Dinges, D. F. (2003). Investigating the interaction between the homeostatic and circadian processes of sleep-wake regulation for the prediction of waking neurobehavioural performance. Journal of Sleep Research, 12, 181–187. Van Dongen, H. P. A., & Dinges, D. F. (2005). Sleep, circadian rhythms, and psychomotor vigilance performance. Clinics in Sports Medicine, 24, 237–249. Van Dongen, H. P. A., Maislin, G., & Dinges, D. F. (2004). Dealing with inter-individual differences in the temporal dynamics of fatigue and performance: Importance and techniques. Aviation, Space, and Environmental Medicine, 75, A147–A154. Van Dongen, H. P. A., Maislin, G., Mullington, J. M., & Dinges, D. F. (2003). The cumulative cost of additional wakefulness: Dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total sleep deprivation. Sleep, 26, 117–126.

308

Namni Goel et al.

Warm, J. S., Parasuraman, R., & Matthews, G. (2008). Vigilance requires hard mental work and is stressful. Human Factors, 50, 433–441. Williams, H. L., Lubin, A., & Goodnow, J. J. (1959). Impaired performance with acute sleep loss. Psychological Monographs: General and Applied, 73, 1–26. Zhou, X., Ferguson, S. A., Matthews, R. W., Sargent, C., Darwent, D., Kennaway, D. J., et al. (2011). Sleep, wake and phase dependent changes in neurobehavioral function under forced desynchrony. Sleep, 34, 931–941.