Acta Astronautica 60 (2007) 354 – 361

www.elsevier.com/locate/actaastro

Optimizing sleep/wake schedules in space: Sleep during chronic

nocturnal sleep restriction with and without diurnal naps

Daniel J. Molliconea,b, Hans P.A. Van Dongenc, David F. Dingesa,b,∗

aDivision of Sleep and Chronobiology, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA

19104-6021, USA

bCenter for Sleep and Respiratory Neurobiology, University of Pennsylvania School of Medicine, 1013 Blockley Hall, 423 Guardian Drive,

Philadelphia, PA 19104-6021, USA cSleep 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 evidence

that 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, and

frequently, curtailment of sleep due to the work demands[1]. This is of concern, since ground-based experiments have shown that cognitive performance deficits

progressively worsen (i.e., accumulate) over consecutive days when sleep is restricted to levels experienced
D.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 critical

functions including increased lapses of attention, degradation of response times, deficits in complex problem

solving, reduced learning, increased negative mood, and

disruption 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 considerably

less 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 using

split-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 experiments

have 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-scale

laboratory experiment of 18 different split-sleep schedules maintained chronically for 10 days was undertaken

using 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 screened

to 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 time

awake. 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 a

specific sleep regime that was maintained for 10 consecutive days. The final day of the study consisted of a

recovery 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 nap

sleep 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 four

anchor-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 were

kept 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 laboratory

staff 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 turned
356 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

aCondition not studied.

off (< 1 lx). Polysomnography (PSG) recordings (EEG

C3/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 PSG

records 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, all

sleep variables were calculated three ways: for nocturnal anchor sleep only, for diurnal nap sleep only, and

for overall sleep during the total TIB of the 24-h period. Since some subjects slept much less on the 10th

and 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-effects

models have the advantage of accounting for systematic inter-individual differences in the sleep variables

[12] and being robust to missing data. Sleep variables

were first analyzed with mixed-effects regression models that assumed condition-specific linear changes over

days (“full model”). The condition-specific intercepts

for 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 (“xaxis”) and nap sleep TIB (“y-axis”) assignments (i.e.,

the intention-to-treat dosages of sleep).

Following estimation of the full models with

condition-specific linear changes over days and covariates for age and gender, various reduced versions of

the 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 the

day, 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 models

including only condition-specific intercepts (i.e., collapsed across days). Fig. 1 shows the resulting RSM for

overall 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. 2a), 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 TIB

described 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. 3a), diurnal nap sleep periods (Fig. 3b), and

total sleep per 24 h (Fig. 3c). 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 every

additional 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 for

SWS identified five conditions with statistically significant (t[92]−0.21, p0.047) 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, p0.028) 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 could

not 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 that

assumed changes over days to be negligible—while acknowledging that this constituted a simplification of the

study 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. 4a), 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 range

examined 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 as

an 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 short

nap 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 must

be interpreted only for conditions of the current experiment, where anchor sleeps occurred in the nocturnal

portion 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 which

anchor and nap sleep placements were reversed in circadian time is being completed and will provide needed

information on the extent to which circadian placement

of 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 performance

across 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. The

former 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 their

sixth, 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 adults

are 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 sleep

per 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 schedules

involving 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 the

anchor 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 time

for 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 the
D.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 Greg

Maislin 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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