Preliminary fMRI findings in experimentally sleep-restricted adolescents engaged in a working memory task.

Abstract

Here we report preliminary findings from a small-sample functional magnetic resonance imaging (fMRI) study of healthy adolescents who completed a working memory task in the context of a chronic sleep restriction experiment. Findings were consistent with those previously obtained on acutely sleep-deprived adults. Our data suggest that, when asked to maintain attention and burdened by chronic sleep restriction, the adolescent brain responds via compensatory mechanisms that accentuate the typical activation patterns of attention-relevant brain regions. Specifically, it appeared that regions that are normally active during an attention-demanding working memory task in the well-rested brain became even more active to maintain performance after chronic sleep restriction. In contrast, regions in which activity is normally suppressed during such a task in the well-rested brain showed even greater suppression to maintain performance after chronic sleep restriction. Although limited by the small sample, study results provide important evidence of feasibility, as well as guidance for future research into the functional neurological effects of chronic sleep restriction in general, the effects of sleep restriction in children and adolescents, and the neuroscience of attention and its disorders in children.

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BioMed Central
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Behavioral and Brain Functions
Open Access
Short paper
Preliminary fMRI findings in experimentally sleep-restricted
adolescents engaged in a working memory task
Dean W Beebe*1,2, Mark W DiFrancesco2,3, Sarah J Tlustos4, Kelly A McNally4
and Scott K Holland2,3
Address: 1Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue,
Cincinnati, Ohio, USA, 2Department of Pediatrics, University of Cincinnati College of Medicine, 231 Albert Sabin Way, Cincinnati, Ohio, USA,
3Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, Ohio, USA and 4Psychology
Department, University of Cincinnati College of Arts and Sciences, 2600 Clifton Avenue, Cincinnati, Ohio, USA
Email: Dean W Beebe* - dean.beebe@cchmc.org; Mark W DiFrancesco - mark.difrancesco@cchmc.org; Sarah J Tlustos - tlustossj@gmail.com;
Kelly A McNally - kelly.mcnally@gmail.com; Scott K Holland - scott.holland@cchmc.org
* Corresponding author
Abstract
Here we report preliminary findings from a small-sample functional magnetic resonance imaging
(fMRI) study of healthy adolescents who completed a working memory task in the context of a
chronic sleep restriction experiment. Findings were consistent with those previously obtained on
acutely sleep-deprived adults. Our data suggest that, when asked to maintain attention and
burdened by chronic sleep restriction, the adolescent brain responds via compensatory
mechanisms that accentuate the typical activation patterns of attention-relevant brain regions.
Specifically, it appeared that regions that are normally active during an attention-demanding
working memory task in the well-rested brain became even more active to maintain performance
after chronic sleep restriction. In contrast, regions in which activity is normally suppressed during
such a task in the well-rested brain showed even greater suppression to maintain performance after
chronic sleep restriction. Although limited by the small sample, study results provide important
evidence of feasibility, as well as guidance for future research into the functional neurological effects
of chronic sleep restriction in general, the effects of sleep restriction in children and adolescents,
and the neuroscience of attention and its disorders in children.
Background
Inadequate sleep is endemic among adolescents in devel-
oped countries. Whereas clinical recommendations call
for about 9 hours of nightly sleep in healthy adolescents
[1], nearly half sleep less than 7 hours each school night
[2]. Adolescent sleep restriction results in daytime inatten-
tion [3], but the neural mechanisms that underlie this
effect remain unknown.
In adults, functional magnetic resonance imaging (fMRI)
studies have shown that acute sleep deprivation can alter
the activation patterns of two attention networks. In well-
rested adults, the "task positive" network, which includes
portions of the prefrontal cortex and posterior parietal
lobes, shows more activity during a variety of tasks that
require active allocation of attentional resources than dur-
ing control tasks that require less mental engagement
(e.g., "rest" periods or a simple perception task) [4,5]. In
contrast, the "task negative" network, which includes the
Published: 19 February 2009
Behavioral and Brain Functions 2009, 5:9doi:10.1186/1744-9081-5-9
Received: 1 October 2008
Accepted: 19 February 2009
This article is available from: http://www.behavioralandbrainfunctions.com/content/5/1/9
© 2009 Beebe et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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medial frontal lobes and posterior cingulate, is typically
less active during attention-demanding tasks than control
tasks [4,5]. Although some findings appear task-specific,
when sleep-deprived adults perform normally on atten-
tion-demanding tasks, the divergent activation and deac-
tivation patterns in task positive and task negative
networks typically become even more pronounced [6-12],
but when they perform poorly, this divergence is attenu-
ated [6,7,12-16]. The former is believed to reflect a com-
pensatory response to the burden of sleep deprivation on
these networks, whereas the latter represents a breakdown
in that response [6,8,11].
Although intriguing, these findings have been limited to
adults, and may not generalize well to adolescents.
Whereas adolescents often experience chronic partial
sleep restriction (i.e., obtaining inadequate sleep across
several consecutive nights), past fMRI studies have used 1-
and 2-night complete sleep deprivation protocols. Moreo-
ver, adolescents are often asked to attend to tasks at a time
much closer to their circadian nadir than adults because of
early school start times and a delayed sleep phase [1].
Developmental shifts in the homeostatic sleep drive [17]
may also affect adolescents' response to chronic sleep
restriction.
We recently documented the feasibility and behavioral
effects of experimental chronic sleep restriction in healthy
adolescents [3]. Here we report pilot data on a subsample
from that group who underwent fMRI examination while
they completed a working memory task known to require
effortful attention and to conform to the task-positive/
task negative functional activation model.
Methods
The experimental protocol and sample used for this study
are detailed in Beebe et al. [3]. Briefly, healthy adolescents
underwent a 3-week protocol which included a baseline
week, followed in random order by a sleep restriction
week (SR) and a healthy duration week (HD). Wake time,
which did not significantly vary across the three weeks,
was established as the time each subject would need to
arise to attend an 8:30 am meeting. In contrast, bedtimes
were systematically varied. Subjects self-selected their bed-
times during the baseline week, which was intended to
stabilize subjects' sleep phase prior to any experimental
manipulation. On the Monday-Friday nights of the SR
week, subjects set a late bedtime that limited them to 6.5
hours in bed; on the Monday-Friday nights of the HD
week, bedtime was set earlier to allow 10 hours in bed
with lights out. Saturday and Sunday nights were consid-
ered "washout" periods, during which bedtime was sub-
ject-selected. Sleep was monitored via objective
actigraphy and self-report. We intended to study 10 of the
20 subjects from the larger study, but obtained complete
fMRI data on 6 due to equipment malfunction for 2 sub-
jects, 1 falling asleep mid-scan, and 1 who misunderstood
task instructions. These 6 subjects (mean age = 15.3 ± 0.7
years; 4 males) averaged 2 1/2 hours more sleep per night
(range 1.7 – 3.0) in the HD condition than the SR condi-
tion (Table 1). On study-specific questionnaires [3], these
subjects also self-reported much quicker sleep onset at
night and greater difficulty waking in the morning, and
parents reported observing greater daytime sleepiness and
inattentive behaviors, during the SR week.
Between 9 and 11:30 a.m. on the Saturday mornings at
the end of each experimental week, subjects completed a
computerized n-back task while undergoing fMRI moni-
toring. We chose an n-back task because such tasks allow
for strong discrimination between basic perceptual/motor
demands versus higher-level demands placed on atten-
tion and working memory, resulting in well-established
functional activation patterns that conform to the task-
positive versus task negative network model [18]. In the n-
back task we used [19], subjects viewed numbers from 1
to 4 that consistently appeared in one of four visual quad-
rants. In the "0-back" control task, subjects were to press a
Table 1: Sleep and behavioral functioning across conditions
Baseline Sleep restriction (SR)Healthy duration (HD)
Actigraphy data
Duration (hr)***
Efficiency*
Sleep diary data
Duration (hr)***
Latency (min)**
Difficulty waking scale*
Behavioral data per parent
Attention problems (raw)†
Daytime sleepiness (raw)*
7.7 ± 1.1
89% ± 8
6.3 ± 0.5
92% ± 6
8.7 ± 0.4
85% ± 8
7.6 ± 1.0
12.6 ± 6.3
5.2 ± 3.3
6.4 ± 0.2
7.2 ± 6.4
7.0 ± 2.8
9.1 ± 0.3
32.5 ± 12.4
2.4 ± 2.1
3.7 ± 2.5
2.3 ± 1.5
5.7 ± 2.0
4.3 ± 2.3
3.0 ± 1.9
1.5 ± 1.6
†p < .10; *p < .05; **p < .005, ***p < .001 comparing the SR vs. HD sleep conditions. These findings are comparable to those shown in the full
sample of the parent study. See Beebe et al. [3] for a full description of the parent study, including sleep and behavioral measures.

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button to match each stimulus as it was presented. In the
"2-back" task, subjects indicated the stimulus that
appeared two steps earlier. Stimuli were presented in a
periodic block-design paradigm in which 30 second inter-
vals of the two conditions were interleaved beginning
with a block of the control task followed by 5 blocks of
each task. Within each block, 17 stimuli appeared (ISI =
1.7 sec, 0.2 second blank screen between stimuli); preced-
ing each block there was a 3 second visual warning of the
change in task (total task duration: 6 min, 3 sec).
Scans were performed on a Bruker Biospec 30/60 3T MRI
scanner (Bruker Biospin, Ettlingen, Germany) (n = 4) and
on a Siemens Trio 3T scanner (Siemens AG, Munich, Ger-
many) (n = 2). Activation patterns and n-back perform-
ance were very similar across scanners, so data were
pooled for group analyses. Both scanners used a T2*-
weighted, gradient-echo, echo planar imaging sequence
for the fMRI scans (TR = 3000 ms, TE = 38 ms, FOV = 25.6
× 25.6 cm, matrix = 64 × 64, 25 5-mm axial slices). A high-
resolution, T1-weighted, inversion-prepared MDEFT
whole brain scan [20,21] served as anatomical reference
for coregistration and overlay of functional data (TR =
16.5, TE = 8, inversion recovery time 550 ms, FOV = 25.6
× 19.2 × 19.2 cm, matrix = 256 × 128 × 128). Preprocess-
ing of each session included removal of imaging artifacts.
The initial 10 images were discarded to ensure T1 relaxa-
tion equilibrium. Spatial preprocessing using SPM5 [22]
included motion correction by realignment, normaliza-
tion to the MNI brain reference space [23], and spatial fil-
tering with an 8 mm Gaussian kernel. Signal intensity was
globally normalized for each functional image volume.
Analysis of functional data began with a voxel by voxel
assessment of contrast between the 2-back and the 0-back
tasks under the general linear model [24], regressing
against the n-back block time course with realignment
parameters used as covariates. A group composite T-score
map was then constructed for each sleep condition by ran-
dom effects analysis [25] and overlaid on the subjects'
average anatomical reference image.
The small sample did not afford enough statistical power
to make voxel-by-voxel comparisons across conditions.
Instead, we took a region of interest (ROI) approach,
using automated masks [26] to define two bilateral ROIs:
a task positive ROI comprised of the dorsolateral prefrontal
cortex and the inferior parietal lobes, and a task negative
ROI comprised of the medial frontal gyri and posterior
cingulate. Within each subject and condition, an activa-
tion score for the task positive ROI was calculated as the
mean positive T-score for voxels in the ROI, and a deacti-
vation score for the task negative ROI was calculated as the
mean negative T-score for voxels in that area. We then
applied a bootstrapping procedure for each ROI in each
condition by resampling the voxel T values 1000 times
(with replacement) to estimate the sampling distribution
of the ROI scores, thereby allowing us to examine the pos-
sibility that differences were due to sampling error.
Results
N-back performance was comparable across the two
experimental conditions (Figure 1). Consistent with prior
studies (e.g., [18,19]), in both conditions the 2-back task
resulted in relative activation in task positive regions and
deactivation in task negative regions. These are graphically
illustrated in Figure 2 and indexed in Tables 2 and 3 by
conventional label, Brodmann's area [27] and Talairach
coordinates to serve as a baseline reference. Though the
activation patterns were similar across conditions, the
intensity of such activation/deactivation differed across
Accuracy and reaction time were comparable across sleep conditions on the 0-back and 2-back tasks (p > .10)
Figure 1
Accuracy and reaction time were comparable across sleep conditions on the 0-back and 2-back tasks (p > .10).
SR = Sleep Restriction, HD = Healthy Sleep Duration.

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Composite activation/deactivation maps, showing contrast of 2-back task with 0-back task in each experimental sleep condition
Figure 2
Composite activation/deactivation maps, showing contrast of 2-back task with 0-back task in each experimen-
tal sleep condition. Warm colors (orange to yellow) reflect voxels that are more active during 2-back than the control task,
with a threshold of T > 3; Cool colors (blue) reflect relative deactivation during 2-back, T < -3. Slices shown are at -19, -15, -
11, -3, +9, +11, +13, +15, +23, +37, +43, and +45 mm.
Histograms reflecting results of the bootstrap resampling procedure across experimental sleep conditions
Figure 3
Histograms reflecting results of the bootstrap resampling procedure across experimental sleep conditions. The
panel on the left illustrates the greater activation in the task positive ROI during the Sleep Restriction (SR) condition than dur-
ing the Healthy Sleep Duration (HD) condition. The panel on the right illustrates the greater deactivation in the task negative
ROI during the SR condition than during the HD condition.

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conditions. Relative to HD, in the SR condition subjects
showed greater activation in the task positive ROI and
more deactivation in the task negative ROI (Figure 3). The
estimated sampling distributions did not overlap across
conditions, suggesting that the mean differences were not
due to sampling error.
Discussion
As the first published imaging study of the functional neu-
ral consequences of sleep restriction in a pediatric sample,
this study provides important evidence of feasibility and
preliminary findings in this understudied population. The
adolescents in this study overall appeared resilient to
chronic sleep restriction in their n-back performance, but
their neural activation differed across conditions. Adoles-
cents showed greater relative activation of the task positive
attention network and deactivation in the task negative
network after chronic sleep restriction than when well-
rested. A compensatory cerebral response appeared to be
elicited during sleep restriction [11] that may have helped
focus attention and suppressed extraneous mental proc-
esses [5], resulting in relatively preserved performance.
This is consistent with prior studies of adults, in which
sleep-deprived subjects who maintain their performance
on tasks that require concentrated effort show particularly
heightened activation in the task-positive attention net-
work, particularly low activation in the task-negative
attention network, or both [6-12].
Future research will be needed to determine whether or
how this compensatory response breaks down, as adoles-
cents who have experienced chronic sleep restriction ulti-
mately show reduced attention in applied settings [3].
Based upon findings from prior adult research, one might
expect a correspondence between task failure and attenu-
ation or loss of activation in the task-positive network, rel-
ative activation within the task-negative network, or both
[6,7,12-16]. Such an effect may not be unique to the sleep
deprived state, as studies of subjects who have not been
intentionally deprived of sleep have shown that task-neg-
Table 2: Brain regions activated under the n-back task for the sleep restriction (SR) and healthy duration (HD) conditions
SR HD
Brain regionBrodmann areas Centroid locationVolume [cc] Centroid location Volume [cc]
Precentral gyrus
Precuneus
9
7, 19
L, -33, 10, 30
R, 21, -64, 45
1.4
3.0L, -27, -77, 35
L, -15, -68, 50
R, 18, -62, 48
1.8
0.6
0.7
Superior parietal lobule
Middle frontal gyrus
Dorsolateral prefrontal gyrus
Insula
7
6
9
13
L, -23, -60, 445.5
L, -24, 0, 53
L, -32, 31, 34
L, -41, 18, 3
R, 31, 25, -1
L, -41, -49, 37
B, 1, 20, 39
2.1
2.8
3.8
0.8
3.8
0.6
Inferior parietal lobule
Cingulate gyrus
40
32
L = left hemisphere, R = right hemisphere, B = bilateral. Clusters satisfy a significance threshold of p < 0.005 (uncorrected) and a volume threshold
of 0.5 cc. Centroid locations are in Talairach coordinates.
Table 3: Brain regions deactivated under the n-back task for the sleep restriction (SR) and healthy duration (HD) conditions
SR HD
Brain regions Brodmann areas Centroid locationVolume [cc]Centroid locationVolume [cc]
Precentral gyrus
Postcentral gyrus
Inferior parietal lobule
Inferior frontal gyrus
Lingual gyrus, vermis
6
3
40
46
17
R, 51, -2, 210.9
L, -33, -31, 591.0
R, 47, -59, 36
R. 49, 35, 8
B, 1, -74, -8
1.0
1.7
16.7R, 24, -65, -17
R, 23, -87, 2
0.5
1.5
Cingulate gyrus
Anterior cingulated gyrus, medial frontal gyrus
Posterior cingulate gyrus
Parahippocampal gyrus, fusiform gyrus
Superior temporal gyrus
Middle temporal gyrus
31
32, 10
23, 29, 30
35, 36, 37
22
21
R, 10, -34, 35
L, -13, 55, 12
0.6
1.6 B, 4, 47, 8
B, -6, -56, 6
R, 38, -25, -10
L, -47, -18, 1
L, -51, -11, -16
9.8
30.7
12.2
7.0
0.7
L = left hemisphere, R = right hemisphere, B = bilateral. Clusters satisfy a significance threshold of p < 0.005 (uncorrected) and a volume threshold
of 0.5 cc. Centroid locations are in Talairach coordinates.

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ative regions are relatively more active when adults subjec-
tively report daydreaming [28] or objectively display
momentary lapses in attention [29].
This study also illustrates the utility of a protocol that
could advance research into the neuroscience of pediatric
attention and its disorders. The most common approach
to understanding neurological aspects of attention in chil-
dren has been to compare those with known deficits to
healthy controls, which carries potential confounds that
can be difficult to disentangle. Our within-subjects exper-
imental method could provide a less confounded tool for
understanding attention network functioning under
healthy and sub-optimal conditions.
Finally, present findings demonstrate that it is feasible to
examine functional activation patterns after chronic sleep
restriction. To our knowledge, such research has not been
previously published, despite the ubiquity of chronic
sleep restriction among adolescents and its frequent
occurrence in adults [30]. Present results are consistent
with the hypothesis that the cumulative effects of chronic
sleep restriction are similar to those seen after acute sleep
deprivation, but more research is needed to verify and
elaborate upon our findings. Of note, it is not known how
changes in functional activation unfold over the course of
chronic sleep restriction; recent quantitative EEG results
suggest region-specific, nonlinear changes in neural activ-
ity over the course of a week of restricted sleep in healthy
adults [31].
Limitations
Findings from this study are considered preliminary
because of several limitations, including the reliance on a
single working memory task, the use of multiple scanners,
and a fixed sleep protocol across all subjects. More impor-
tantly, the small sample precluded voxel-wise compari-
sons across conditions and diminished statistical power to
detect effects on n-back accuracy or reaction time. Never-
theless, the within-subjects experimental design produced
robust ROI-based findings and reduced possible con-
founds. If replicated, these findings have the potential to
guide targeted palliative treatments for adolescents with
refractory sleep disorders or sleep problems that are sec-
ondary to medical illness. Our findings also set the stage
for future research investigating questions of reversible
versus irreversible effects of chronic sleep restriction dur-
ing brain development.
Conclusion
Adolescents in our sample displayed a cerebral response
to chronic sleep restriction that appeared similar to that
described in acutely sleep-deprived adults. Although
present findings have limitations, they provide important
evidence of feasibility and guidance for novel lines of
research into the functional neurological effects of chronic
sleep restriction, the effects of pediatric sleep restriction,
and the neuroscience of attention in children.
Abbreviations
FOV: Field of View; fMRI: functional Magnetic Resonance
Imaging; HD: Healthy Duration (condition requiring 10
hours in bed Monday-Friday nights); ISI: Interstimulus
Interval; SR: Sleep Restriction (condition limiting time in
bed to 6.5 hours Monday-Friday nights); SPM: Statistical
Parametric Mapping software; TE: Echo Time; TR: Repeti-
tion Time; ROI: Region of Interest; MDEFT: Modified
Driven Equilibrium Fourier Transform; MNI: Montreal
Neurological Institute; SPM5: Statistical Parametric Map-
ping software.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
DWB oversaw the project, directed the experimental sleep
manipulation, monitored subject adherence to the sleep
manipulation, conducted the behavioral performance
analyses, and drafted the manuscript. MWD and SKH
oversaw the collection of anatomical and functional
imaging data. MWD also conducted primary imaging
analyses, drafted portions of the manuscript, and had con-
ceptual input in the research design and interpretation of
findings. SJT and KAM assisted in data collection and con-
tributed to manuscript revisions. SKH oversaw the imag-
ing analyses, had conceptual input in the research design,
and contributed to manuscript revisions. All authors read
and approved the final manuscript.
Acknowledgements
This research was conducted with funding from the Cincinnati Children's
Division of Behavioral Medicine and Clinical Psychology, and National Insti-
tutes of Health grants K23HL075369 and M01RR08084; neither organiza-
tion altered the design of the study, conduct of analyses, or interpretation
of results.
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