Acta Astronautica 60 (2007) 354 – 361
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# Optimizing sleep/wake schedules in space: Sleep during chronic nocturnal sleep restriction with and without diurnal naps

Daniel J. Mollicone a , b 

## , Hans P.A. Van Dongenc 

## , David F. Dinges a , b

, ∗

a Division of Sleep and Chronobiology, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104-6021, USA b Center for Sleep and Respiratory Neurobiology, University of Pennsylvania School of Medicine, 1013 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021, USA

c Sleep and Performance Research Center, Washington State University, Spokane, WA 99210-1495, USA

Available online 18 October 2006

Abstract

Effective sleep/wake schedules for space operations must balance severe time constraints with allocating sufficient time for sleep in order to sustain high levels of neurobehavioral performance. Developing such schedules requires knowledge about the relationship between scheduled “time in bed” (TIB) and actual physiological sleep obtained. A ground-based laboratory study in
N = 93 healthy adult subjects was conducted to investigate physiological sleep obtained in a range of restricted sleep schedules. Eighteen different conditions with restricted nocturnal anchor sleep, with and without diurnal naps, were examined in a response surface mapping paradigm. Sleep efficiency was found to be a function of total TIB per 24 h regardless of how the sleep was divided among nocturnal anchor sleep and diurnal nap sleep periods. The amounts of sleep stages 1+2 and REM showed more complex relationships with the durations of the anchor and nap sleep periods, while slow-wave sleep was essentially preserved among the different conditions of the experiment. The results of the study indicated that when sleep was chronically restricted, sleep duration was largely unaffected by whether the sleep was placed nocturnally or split between nocturnal anchor sleep periods and daytime naps. Having thus assessed that split-sleep schedules are feasible in terms of obtaining physiological sleep, further research will reveal whether these schedules and the associated variations in the distribution of sleep stages may be advantageous in mitigating neurobehavioral performance impairment in the face of limited time for sleep. © 2006 Elsevier Ltd. All rights reserved.

Keywords: Chronic sleep restriction; Split-sleep schedules; Space flight; Anchor sleep; Naps; Sleep efficiency; Physiology; Response surface mapping; Mixed-effects regression models; Ground-based; Healthy adult humans

1. Introduction

Space operations, in which high-level sustained neurobehavioral performance demands are coupled with time constraints, necessitate precise scheduling of

∗ Corresponding author. Division of Sleep and Chronobiology, Department of Psychiatry, University of Pennsylvania School of Medicine, 1013 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021, USA. Tel.: +1 215 8989949; fax: +1 215 5736410.
E-mail address: dinges@mail.med.upenn.edu (D.F. Dinges).

0094-5765/$ - see front matter © 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.actaastro.2006.09.022 sleep opportunities in order to best preserve optimal performance. There is extensive objective evidencethat astronaut sleep is restricted in space flight, averaging between 4–6.5 h/day, as a result of endogenous disturbances of sleep (e.g., motion sickness, circadian desynchrony), environmental disruptions of sleep, andfrequently, curtailment of sleep due to the work demands [1] . This is of concern, since ground-based experiments have shown that cognitive performance deficitsprogressively worsen (i.e., accumulate) over consecutive days when sleep is restricted to levels experiencedD.J. Mollicone et al. / Acta Astronautica 60 (2007) 354 – 361 355

by astronauts [1,2] . In as little as 1 week of restricted sleep, performance deficits develop in a range of criticalfunctions including increased lapses of attention, degradation of response times, deficits in complex problem solving, reduced learning, increased negative mood, anddisruption of essential endocrine, metabolic, and immune responses [2,3]

. Scheduling time for sleep during space flight is largely determined by the operational needs of a mission. However, sleep is regulated by a complex neurobiology involving homeostatic and circadian mechanisms that interact to determine sleep timing, duration, and structure [4–6] . As a consequence, total sleep time (TST) in space flight can be considerablyless than the scheduled time in bed (TIB). The percentage of time actually spent asleep is referred to as sleep efficiency (SE). Reduced SE in space flight—regardless of the reason—poses a risk to performance of critical mission activities. Consequently, sleep–wake schedules need to be identified that optimize the recovery benefits of sleep while minimizing the time required for sleep.

Limited evidence [7,8] suggests that performance could be maintained on reduced amounts of sleep usingsplit-sleep schedules made up of a daily nocturnal period for anchor sleep (from 4–6 h) and a daily diurnal nap period (from 0.4 to 2.4 h). Earth-based experimentshave demonstrated that prophylactic napping is a useful strategy to reduce performance impairment during subsequent total sleep deprivation [9] , but the effectiveness of split-sleep schedules with nocturnal “anchor”sleep and diurnal napping to mitigate performance impairment across days of chronic sleep restriction has not yet been systematically investigated. A large-scalelaboratory experiment of 18 different split-sleep schedules maintained chronically for 10 days was undertakenusing response surface modeling to assess the relationship between split-sleep schedule SE, duration, and structure, and what role these factors had in preventing cumulative performance impairments over time. The present report focuses on the efficiency, quantity, and type of physiological sleep obtained across 18 different split-sleep schedules.

2. Methods

2.1. Subjects

A total N = 93 healthy adult subjects (54 males, 39 females; mean age 29.3 years, age range 21–49 years) participated in the experiment. Subjects were screenedto ensure they had no medical, psychiatric, or sleeprelated disorders and were drug-free. This was determined by history, physical examination, and psychological questionnaires, and by blood and urine laboratory tests and toxicological screening. Subjects could not have worked regular or rotating shift work within the past 2 years, and they could not have traveled across time zones in the 3 months prior to the experiments. They were required to be in bed for approximately 8 h daily during the week preceding the study, as verified by wrist actigraphy combined with daily diary reports and time-stamped phone records for time to bed and timeawake. The Institutional Review Board of the University of Pennsylvania reviewed and approved the study, and each subject gave written informed consent.

2.2. Experimental design

Subjects participated in a 14-day ground-based laboratory study involving strict schedules for TIB. The laboratory experiment began with two baseline nights (8.2-h TIB). Subjects were then randomly assigned to one of 18 sleep-restriction conditions, each involving aspecific sleep regime that was maintained for 10 consecutive days. The final day of the study consisted of arecovery sleep period of 14-h TIB. The 10-day sleeprestriction assignments involved one of four nocturnal anchor sleep conditions: 4.2, 5.2, 6.2, or 8.2-h TIB centered around 02:00—and one of six diurnal napsleep conditions: 0.4, 0.8, 1.2, 1.6, 2.0, 2.4-h TIB centered around 14:00. These different anchor and nap sleep durations were crossed to yield a total of fouranchor-sleep-only conditions and 14 anchor-plus-napsleep or split-sleep conditions (see Table 1 ). Five to six subjects were assigned to each of the experimental conditions.

Throughout the entire 13-day experimental period, subjects remained in an environmentally controlled laboratory, free of external time cues, and under 24-h behavioral monitoring. During all waking they werekept awake by performance requirements, and behavioral/social interaction. They underwent neurobehavioral assessments of cognitive performance, mood, and symptom complaints every 2 h throughout all waking periods each day. Between test bouts they were allowed to read, watch movies, and interact with laboratorystaff to help them stay awake, but no vigorous activities were permitted. To simulate some of the social confinement, habitat, and environmental conditions of space flight, subjects were studied in small groups of

n = 5; and they slept in separate beds in a common sleeping room (n = 2

.3 to a room). They were in less than 50 lx of light during all scheduled wake times; during scheduled sleep times, all lights were turned356 D.J. Mollicone et al. / Acta Astronautica 60 (2007) 354 – 361

Table 1 Total time in bed (TIB) per 24 h for each of the 18 sleep-schedule conditions studied in the response surface modeling experiment

Diurnal nap sleep TIB (h) Nocturnal anchor sleep TIB (h)

4.2 5.2 6.2 8.2

0.0 4.2 5.2 6.2 8.2
0.4 4.6 5.6 6.6 a
0.8 5.0 6.0 7.0 a
1.2 5.4 6.4 7.4 a
1.6 5.8 6.8 a a
2.0 6.2 7.2 a a
2.4 6.6 aaa

a Condition not studied.

off ( < 1 lx). Polysomnography (PSG) recordings (EEGC3/O2; EOG LOC/ROC; EMG) were made for nocturnal anchor sleep periods and diurnal nap sleep periods during the 10 sleep-restriction days, except for the diurnal nap sleep periods on days 2 and 7 and the subsequent nocturnal anchor sleep periods on days 3 and 8 (these two 24-h periods were without electrodes to permit them to shower and recovery from electrode irritation). PSG recordings were also made during both baseline sleep periods, and during the recovery sleep period that followed the sleep-restriction days.

2.3. Data analyses

Analyses focused on the anchor and nap sleep periods of the sleep-restriction days. A total of 461 complete PSG records were available for these days. These PSGrecords were scored according to conventional sleepstage scoring criteria for the physiological records [10]

, and the following sleep variables were assessed: TST, wakefulness after sleep onset (WASO), durations of non-rapid eye movement (NREM) sleep stages 1 and 2 (S1+2), NREM stages 3 and 4 combined as slow-wave sleep (SWS), and rapid eye movement sleep (REM). In addition, SE was calculated as SE = 100% × TST / TIB. For every sleep-restriction day with PSG recordings, allsleep variables were calculated three ways: for nocturnal anchor sleep only, for diurnal nap sleep only, andfor overall sleep during the total TIB of the 24-h period. Since some subjects slept much less on the 10thand final day of sleep restriction—due to an “end-ofstudy effect” (i.e., anticipating recovery sleep and laboratory departure)—data analyses included only sleeprestriction days 1–9.In order to analyze sleep variables over sleeprestriction days and across the different conditions, mixed-effects regression models [11] were implemented using SAS statistical software. Mixed-effectsmodels have the advantage of accounting for systematic inter-individual differences in the sleep variables [12] and being robust to missing data. Sleep variableswere first analyzed with mixed-effects regression models that assumed condition-specific linear changes over days (“full model”). The condition-specific interceptsfor these linear changes (which was centered on sleeprestriction day 5) were modulated by covariates for age and gender, as well as a log-normally distributed random effect over subjects. These full models were estimated in a single regression step, but they were in essence equivalent to a two-stage procedure with the first stage being a linear regression over days and the second stage being a two-way analysis of variance (ANOVA) over the different anchor sleep and nap sleep conditions. This approach allowed for response surface mapping (RSM) of results—that is, the construction of three-dimensional graphs of the intercepts or slopes of the condition-specific linear changes over days (on the “z-axis”) versus the different anchor sleep TIB (“x

axis”) and nap sleep TIB (“y-axis”) assignments (i.e., the intention-to-treat dosages of sleep).

Following estimation of the full models withcondition-specific linear changes over days and covariates for age and gender, various reduced versions ofthe model were fitted in order to investigate the statistical significance of the covariates and to establish systematic patterns among the different conditions.These reduced model versions included models assuming that there were no changes over days in the various sleep variables (i.e., only condition-specific intercepts). In addition, further reduced models were assessed in which the specific anchor and nap sleep TIBs were substituted by the combined TIB during the 24 h of theday, and the two-way ANOVA structure was substituted by a linear regression against the combined TIB.D.J. Mollicone et al. / Acta Astronautica 60 (2007) 354 – 361 357

These latter models also allowed for replacement of the three-dimensional RSMs (with both anchor sleep TIB and nap sleep TIB as independent variables) by more traditional two-dimensional graphs of the results (with the combined TIB as the only independent variable). Likelihood ratio tests for nested models were used to compare the different model versions statistically.

3. Results

3.1. Sleep efficiency and total sleep time

TIB for sleep can be considered as the “intentionto-treat” dosage of sleep, while TST would be the “astreated” dosage. SE is produced by these two variables and is frequently used to describe the effectiveness of sleep interventions. Analyses of the observed SEs and TSTs among all conditions revealed that there were no significant linear changes across days (SE: 2 [ 18 ] =
22

. 9, p = 0

. 19 ; TST : 2 [ 18 ] = 24

. 9, p = 0

. 13). Therefore, analyses were continued with regression modelsincluding only condition-specific intercepts (i.e., collapsed across days). Fig. 1 shows the resulting RSM foroverall SE, as defined by TST over the total TIB during the 24 h of the day, plotted against anchor sleep TIB and nap sleep TIB.

It was found that without significant loss of information ( 2 [ 16 ] = 19

. 0, p = 0

. 27), overall SE could be described simply as a function of the total TIB during the

Fig. 1. Response surface map of sleep efficiency (SE, indicated on
the z-axis), plotted against anchor sleep TIB (x-axis) and nap sleep TIB (y-axis). Red dots indicate SE estimates for each of the different
conditions. 24 h of the day, with greater total TIB per 24 h resulting in lesser overall SE. Fig. 2 shows the relationships between SE and TIB for variation in anchor sleep TIB (Fig. 2 a), for variation in nap sleep TIB (Fig. 2b), and for total TIB per 24 h (Fig. 2c). For every hour of additional TIB per 24 h, SE dropped by 1.6% (sem

= 0.4%) on average (Fig. 2c). However, this drop in SE was not evenly distributed over anchor and nap sleep. There was a reciprocal relationship between SE in the anchor sleep periods and SE in the nap sleep periods. An additional hour of nap sleep resulted in an average reduction of anchor sleep SE by 1.5% (sem = 0.6%,

t [ 92 ]=− 2

. 51, p = 0

. 014). Likewise, an additional hour of anchor sleep resulted in an average reduction of nap sleep SE by 6.7% (sem = 1

. 6%, t [71 ]=− 4

. 11,

p = 0

. 001). However, this reciprocal relationship did not extend to a negative correlation between anchor and nap sleep SE at the level of individual subjects and days ( = 0

. 068, p = 0

. 18).Similar to results for SE, overall TST could be described simply as a function of total TIB during the 24 h of the day, regardless of how that TIB was achieved ( 2 [ 16 ] = 18

. 0, p = 0

. 32). This simple relationship between TST and TIB explained 84.8% of the variance, indicating that the decrease of SE with increasing TIBdescribed above had a negligible effect on TST in absolute terms. Fig. 3 shows the linear relationship between TST and TIB for nocturnal anchor sleep periods (Fig. 3 a), diurnal nap sleep periods (Fig. 3 b), and total sleep per 24 h (Fig. 3 c). For every hour of additional TIB per 24 h, overall TST increased by 0.76 (sem = 0.03 h) on average, regardless of whether the additional TIB came from anchor sleep or nap sleep ( t [ 92 ]=− 1

. 00, p = 0

. 32). The results for time wake after sleep onset (WASO) were complementary to those for TST, with WASO also being a function solely of total TIB ( 2 [ 16 ] = 20

. 2, p = 0

. 21). For every hour of additional TIB per 24 h, time awake after sleep onset increased by 0.12 h (sem = 0.02 h) on average, regardless of whether the additional TIB came from anchor or nap sleep ( t [ 92 ] = 6

. 01, p < 0

. 001).

Age was statistically significant as a covariate both for overall SE and for overall TST (SE: 2 [ 1 ] = 9

. 8,

p = 0

. 002; TST: 2 [ 1 ] = 13

. 1, p < 0

. 001). Across the age range of the study (21–49 years), overall SE was reduced by 0.24% (sem = 0.07%) on average for every additional year of age, and accordingly overall TST was reduced by 0.02 h (sem = 0.01 h) on average for everyadditional year of age. Gender was also statistically significant as a covariate both for overall SE and for overall TST (SE: 2 [ 1 ] = 8

. 5, p = 0

. 004; TST: 2 [ 1 ] = 9

. 1,

p = 0

. 003). On average, females achieved 2.76%358 D.J. Mollicone et al. / Acta Astronautica 60 (2007) 354 – 361

Fig. 2. Sleep efficiency (SE) versus TIB for nocturnal anchor sleep (graph a), for diurnal nap sleep (graph b) and for anchor and nap sleep combined (graph c). Squares indicate condition estimates for SE, triangles indicate subject averages for SE. The trend line marks the linear relationship between TIB and SE. In graph b five subjects had SEs below 50% (i.e., below the horizon on the plot) and hence these points are not shown. However, they were included in the average for condition (squares). These outlier points were as follows: 0.4 nap, SE = 40%; 0.8 nap, SE = 30%; 0.8 nap, SE = 23%; 1.2 nap, SE = 48%; 1.6 nap, SE = 37%. Fig. 3. Total sleep time (TST) versus TIB for nocturnal anchor sleep (graph a), for diurnal nap sleep (graph b) and for anchor and nap sleep combined (graph c). Squares indicated condition estimates for TST; triangles indicate subject averages for TST. The trend line marks the linear relationship between TIB and TST.

(sem = 0.81%) greater SE than males, which was equivalent to 0.22 h (sem = 0.01 h) more TST per day. Based on these results, analyses were also performed with age and gender as covariates.D.J. Mollicone et al. / Acta Astronautica 60 (2007) 354 – 361 359

3.2. Physiological sleep stages

Analysis of S1+2 sleep revealed no significant linear change across days ( 2 [ 18 ]= 15

. 9, p = 0

. 60). SWS and REM sleep, however, did show linear changes across days for certain conditions (SWS: 2 [ 18 ] = 45

. 6, p = 0

. 001; REM: 2 [ 18 ]= 29

. 7, p = 0

. 040). Post hoc tests forSWS identified five conditions with statistically significant ( t [ 92 ] − 0

. 21, p 0

. 047) linear decreases across days by 0.040 h (sem = 0.020 h) per day in the 4.2-h anchor plus 1.2-h nap condition; 0.055 h (sem = 0.020 h) per day in the 4.2-h anchor plus 2.0-h nap condition; 0.038 h (sem = 0.018 h) per day in the 4.2-h anchor plus 2.4-h nap condition; 0.060 h (sem = 0.018 h) per day in the 5.2-h anchor plus 1.6-h nap condition, and 0.047 h (sem = 0.018 h) per day in the 5.2-h anchor plus 2.0-h nap condition. Post hoc tests for REM sleep identified three conditions with statistically significant ( t [ 92 ]  2

. 23, p 0

. 028) linear increases across days by 0.053 h (sem = 0.021 h) per day in the 4.2-h anchor plus 1.2-h nap condition; 0.055 h (sem = 0.020 h) per day in the 4.2-h anchor plus 2.0-h nap condition, and 0.048 h (sem = 0.022 h) per day in the 6.2-h anchor plus 1.6-h nap condition. When SWS and REM sleep were analyzed together, the changes across days did not completely cancel each other out ( 2 [ 18 ] = 28

. 6,

p = 0

. 053), suggesting that the observed changes couldnot be explained entirely by reciprocal interactions between SWS and REM. In order to investigate the overall effects of the different experimental conditions on sleep physiology, despite the added complexities of changes over days, analyses were continued using regression models thatassumed changes over days to be negligible—while acknowledging that this constituted a simplification of thestudy findings for SWS and REM sleep in the conditions mentioned above. This approach resulted a model that described overall REM sleep solely as a function of the total TIB per 24-h day, with no significant further loss of information; for every hour of additional TIB per 24 h, the overall amount of REM sleep increased by 0.19 h (sem = 0.03 h) on average (2 [ 16 ] = 24

. 5,

p = 0

. 08). However, a borderline p value of 0.08 suggested a more complex relationship between the amount of REM sleep and the time allotted for anchor and nap sleep ( 2 [ 16 ] = 24

. 5, p = 0

. 08). Similarly, S1+2 sleep was not merely a function of total TIB, but showed a more complex dependence on condition ( 2 [ 16 ]= 35

. 4,

p = 0

. 004). Fig. 4 shows the resulting RSMs for overall REM sleep (Fig. 4 a), SWS (Fig. 4b), and S1+2 sleep (Fig. 4c) over the total TIB during the 24 h of the day, plotted against anchor sleep TIB and nap sleep TIB. Fig. 4. Response surface maps of REM sleep (z-axis; graph a), SWS (z-axis; graph b), and stage 1+2 sleep (z-axis; graph c), plotted against anchor sleep TIB (x-axis) and nap sleep TIB (y-axis). Red
dots indicate SE estimates for each of the different conditions.360 D.J. Mollicone et al. / Acta Astronautica 60 (2007) 354 – 361

A notable finding was that SWS was essentially preserved over all conditions ( 2 [ 17 ] = 15

. 8, p = 0

. 54), showing no relationship with daily TIB in the rangeexamined in this study. When condition-specific dayby-day mathematical predictions for the homeostatic process of sleep regulation [13] , for which SWS is a surrogate marker, were put in the regression model asan explanatory variable, there was a statistically significant improvement ( 2 [ 1 ] = 25

. 6, p < 0

. 001) in how well the model described the variations in SWS across the different conditions. The homeostatic process of sleep regulation predicted that extra time in the shortnap sleep periods would lead to greater marginal increases in SWS than extra time in the longer anchor sleep periods, and the expression of SWS was indeed consistent with this prediction.

4. Discussion

SE and consequently TST were found to be a function of total TIB per 24 h regardless of whether the sleep was obtained in one episode or split-sleep episodes per 24 h, and independent of how sleep was divided among nocturnal anchor sleep and diurnal nap sleep periods. This suggests that flexibility in sleep timing, including the use of split-sleep schedules, is possible without sacrificing sleep quality or quantity. This finding mustbe interpreted only for conditions of the current experiment, where anchor sleeps occurred in the nocturnalportion of the circadian cycle and nap sleep in the diurnal portions, since circadian phase at which sleep is taken has a profound effect on both the efficiency and structure of sleep [4] . A separate experiment in whichanchor and nap sleep placements were reversed in circadian time is being completed and will provide needed information on the extent to which circadian placementof split sleeps affects total sleep obtained and its recovery potential. It remains to be determined whether the split-sleep schedules in the current experiment afford any advantages in maintaining performanceacross days of limited sleep time, relative to sleep schedules in which TST is obtained in a single sleep episode. Both age and gender were found to affect SE. Theformer is somewhat surprising given that the vast majority of subjects were between 21 and 39 years of age (i.e., in the young adult age range). On the other hand, the effect of age on SE was consistent with what is known about sleep across a wider adult age range—namely that SE declines with age as people progress through theirsixth, seventh, and eighth decades of life. We found a reduction of 0.24% per year that translates into an average difference in SE of 6.72% between a healthy 21-year old and a healthy 49-year old, which is a decrease of approximately half an hour during an 8-h TIB for sleep. Thirty minutes less sleep a day can accumulate to 6.5 h less sleep a week, which can mean that middle-aged adultsare at greater risk for cumulative sleep debt if sleep periods are of inadequate duration in space flight. Consequently, if long duration space flight missions are to involve crew members in their fifth decade—as appears to be the case—sleep scheduling should take into account their reduced ability to use sleep time efficiently.

The effect of gender observed on SE was also unexpected, although there have been few systematic studies of sleep and gender among healthy young adults.Women had higher SEs than men—there was a reduction of 2.76% in males compared to females, which translates to difference of approximately 15 min over an 8-h period scheduled TIB. This would not be expected to make much difference in sleep scheduling, however, since unlike age, the difference is not amplified each year. On the other hand, it amounts to 1.75 h more sleepper week on average, which can be important in an environment in which recovery sleep opportunities are rare or limited.

In terms of sleep structure, we were particularly interested in SWS, which is increasingly regarded as critical for the recovery of waking neurobehavioral functions [7,13] . As expected, SWS was found not to be a function of TIB but instead a function of the level of homeostatic process S as predicted by the two-process model of sleep regulation [13] . It will be important to establish in subsequent analyses of the performance data from this experiment whether split-sleep schedulesinvolving reduced amounts of sleep prevented performance deficits from accumulating across days (i.e., prevented sleep debt), and what role SWS played in that response. If split-sleep schedules provide no protection against cumulative performance impairments—even if they maintain SWS—then it will suggest SWS is not the sole determinant of cognitive capability, as another study has also recently suggested [2]

.

The amounts of sleep stages 1+2 and REM showed more complex relationships with the durations of theanchor and nap sleep periods. Based on recent experiments suggesting that specific sleep stages have unique cognitive benefits [14,15] , especially memory functions, analyses of cognitive performance data may confirm that the durations of specific sleep stages make critical contributions to cognitive recovery when timefor sleep is restricted. Having thus assessed that splitsleep schedules are feasible in terms of obtaining physiological sleep, further research will reveal whether these schedules and the associated variations in theD.J. Mollicone et al. / Acta Astronautica 60 (2007) 354 – 361 361

distribution of sleep stages may be advantageous in mitigating neurobehavioral performance impairment in the face of limited time for sleep.

Acknowledgments

This work was supported by the National Space Biomedical Research Institute through NASA NCC 9-58 by National Institutes of Health Grant M01-RR00040, and in part by the Institute for Experimental Psychiatry Research Foundation. We thank GregMaislin for contributing the response surface modeling statistical approach and analytic techniques; Janet Mullington, Naomi L. Rogers, J.B. Crabbe and Scott Doran for participating in data acquisition; and Michele Carlin, Claire Fox, Adrian Ecker, John W. Powell, IV, Marty Szuba, and Nick Price for making the project technically feasible.

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