Topic: Critical essay on article Sleep Deprivation Selectively Upregulates an Amygdala–Hypothalamic Circuit Involved in Food Reward
critical essay, cognitive scienceSystems/Circuits
Sleep Deprivation Selectively Upregulates an
Amygdala–Hypothalamic Circuit Involved in Food Reward
XJulia S. Rihm,1 XMareike M. Menz,1 Heidrun Schultz,2 Luca Bruder,3 Leonhard Schilbach,4 Sebastian M. Schmid,5
and Jan Peters3
Department for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany, 2
School of Psychology, University of
Birmingham, Birmingham B15 2TT, United Kingdom, 3
Department of Psychology, Biological Psychology, University of Cologne, Cologne 50969, Germany,
Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute for Psychiatry, Munich 80804, Germany, and 5
Internal Medicine I, Section of Endocrinology and Diabetes, University Hospital Schleswig-Holstein, Luebeck 23562, Germany
Sleep loss is associated with increased obesity risk, as demonstrated by correlations between sleep duration and change in body mass
index or bodyfat percentage. Whereas previous studies linkedthis weight gainto disturbed endocrine parameters after sleep deprivation
or restriction, neuroimaging studies revealed upregulated neural processing of food rewards after sleep loss in reward-processing areas
such as the anterior cingulate cortex, ventral striatum, and insula. To address this ongoing debate between hormonal versus hedonic
factors underlying sleep-loss-associated weight gain, we rigorously tested the association between sleep deprivation and food cue processing using high-resolution fMRI and assessment of hormones. After taking blood samples from 32 lean, healthy, human male participants, they underwent fMRI while performing a neuroeconomic, value-based decision-making task with snack food and trinket rewards
following a full night of habitual sleep and a night of sleep deprivation in a repeated-measures crossover design. We found that des-acyl
ghrelin concentrations were increased after sleep deprivation compared with habitual sleep. Despite similar hunger ratings duetofasting
in both conditions, participants were willing to spend more money on food items only after sleep deprivation. Furthermore, fMRI data
paralleled this behavioral finding, revealing a food-reward-specific upregulation of hypothalamic valuation signals and amygdala–
hypothalamic coupling after a single night of sleep deprivation. Behavioral and fMRI results were not significantly correlated with
changes in acyl, des-acyl, or total ghrelin concentrations. Our results suggest that increased food valuation after sleep loss might be due
to hedonic rather than hormonal mechanisms.
Key words: decision making; food intake; hypothalamus; obesity; reward; sleep deprivation
Numerous epidemiological studies suggest a link between reduced nocturnal sleep and increased risk for overweight and obesity (Patel and Hu, 2008). For example, short sleep duration
correlated positively with body mass index (BMI) (Shigeta et al.,
2001; Heslop et al., 2002; Cournot et al., 2004) and nocturnal
sleep duration correlated negatively with body fat percentage
(Rontoyanni et al., 2007). In addition to endocrine mechanisms,
Received Jan. 29, 2018; revised Sept. 18, 2018; accepted Oct. 15, 2018.
Author contributions: J.S.R., H.S., S.M.S., and J.P. edited the paper; J.P. wrote the first draft of the paper. J.S.R.,
L.S., and J.P. designed research; J.S.R. and M.M.M. performed research; H.S. and S.M.S. contributed unpublished
reagents/analytic tools; J.S.R., L.B., and J.P. analyzed data; J.S.R. and J.P. wrote the paper.
This work wassupported by the Deutsche Forschungsgemeinschaft (TR CRC 134, Project C05 toJ.P. and L.S.).We
thank Sophie Klusen for help with data acquisition.
The authors declare no competing financial interests.
J.S. Rihm’s present affiliation: Department of Psychology, Biological Psychology, University of Cologne, Cologne
Correspondence should be addressed to Julia Rihm at firstname.lastname@example.org.
Copyright © 2019 the authors 0270-6474/19/390888-12$15.00/0
Epidemiological studies suggest an association between overweight and reduced nocturnal sleep, but the relative contributions of
hedonic and hormonal factors to overeating after sleep loss are a matter of ongoing controversy. Here, we tested the association
between sleep deprivation and food cue processing in a repeated-measures crossover design using fMRI. We found that willingness to pay increased for food items only after sleep deprivation. fMRI data paralleled this behavioral finding, revealing a foodreward-specific upregulation of hypothalamic valuation signals and amygdala-hypothalamic coupling after a single night of sleep
deprivation. However, there was no evidence for hormonal modulations of behavioral or fMRI findings. Our results suggest that
increased food valuation after sleep loss is due to hedonic rather than hormonal mechanisms.
888 • The Journal of Neuroscience, January 30, 2019 • 39(5):888 – 899
decision-related mechanisms likely contribute to the regulation
of food intake (D’Agostino and Small, 2012; Rangel, 2013) and
both may consequently affect overeating following sleep loss
(Chaput and St-Onge, 2014; Cedernaes et al., 2015).
The disbalance that sleep loss exerts on homeostasis can be
manifested in altered levels of hormones involved in hunger and
satiety. Two such candidate hormones are the orexigenic hormone ghrelin and the anorexic hormone leptin. Receptors for
ghrelin (Howard et al., 1996) and leptin (Schwartz et al., 1996)
are expressed in the hypothalamus, which is involved in the regulation of hunger (Anand and Brobeck, 1951) and circadian
rhythm (Economo, 1930). For brain activation in response to
food stimuli, ghrelin has been found to act as modulator in
reward-processing areas (Malik et al., 2008; Kroemer et al., 2013;
Goldstone et al., 2014). When also taking sleep loss into account,
previous studies found elevated ghrelin concentrations, whereas
leptin levels were decreased after sleep restriction (Spiegel et al.,
2004; Taheri et al., 2004; Schmid et al., 2008; Morselli et al., 2010).
For example, compared witha7h sleep period, total ghrelin
levels and subjective hunger were significantly increased after
restricting sleep to 4.5 h and even more after a night of total sleep
deprivation (Schmid et al., 2008). However, studies focusing on
hormonal changes as a mechanistic account of increased food
intake after sleep loss often use controlled, artificial laboratory
environments with little resemblance to realistic life situations
(Spiegel et al., 2004; Schmid et al., 2008). Thus, endocrine modulation has recently been questioned as a major contributor to
changes in food choice and the consideration of increased hedonic values of food after sleep deprivation was suggested as a
possible explanation for overeating after sleep loss (Chaput and
St-Onge, 2014). Therefore, control for both endocrine and hedonic aspects seems crucial in this context. At the same time, highly
controlled experimental setups might not yield ecologically valid
fMRI studies revealed increased neural responses to food images in regions involved in reward and motivation following total
or partial sleep deprivation, such as the anterior cingulate cortex,
amygdala, insula, orbitofrontal cortex, nucleus accumbens, and
putamen (Benedict et al., 2012; St-Onge et al., 2012; Greer et al.,
2013). However, these studies did not examine more general
changes in reward processing, for example, by comparing food
with nonfood control rewards or subjective reward valuation via
parametric contrasts. Finally, effects on reward processing might
in part be driven by sleep-deprivation-induced hormonal
changes (Spiegel et al., 2004; Chaput and St-Onge, 2014). However, previous imaging studies lacked control for neuroendocrine factors.
Here, we specifically address these issues by combining approaches from decision neuroscience and endocrinology. We recorded fMRI during a neuroeconomic decision-making task
(Becker et al., 1964; Chib et al., 2009) assessing subjective values
of food and nonfood stimuli in a counterbalanced repeatedmeasures design after habitual sleep and a single night of total
sleep deprivation. This enabled us to investigate food valuation
and sleep deprivation on an intraindividual level, controlling for
unspecific behavioral and fMRI effects by comparing food and
nonfood rewards in each condition. Additionally, circulating
hormones were assessed via blood samples obtained before fMRI
scanning. We hypothesized that a full night of sleep deprivation
selectively increases subjective valuation of food versus nonfood
rewards, paralleled by selective increases in BOLD signals in
reward- and homeostasis-related brain structures. Furthermore,
we predicted elevated ghrelin concentrations after sleep deprivation versus habitual sleep as well as a modulation of behavior and
brain activity in response to food stimuli by changes in circulating
Materials and Methods
Thirty-two healthy, lean, male participants (mean SD age: 26.13
3.80 years, range: 19 –33 years; BMI: 23.32 1.44 kg/m2
, range: 20.52–
) participated in two fMRI sessions in counterbalanced order
following a single night of sleep deprivation or habitual sleep. No blood
samples could be collected from two participants and the plasma sample
of one participant was not frozen immediately after centrifuging in one
session, so endocrine data are only reported for 29 participants. All participants were right-handed nonsmokers who had normal or correctedto-normal vision and no history of neurological or psychiatric disorders.
On average, they were good sleepers during the last 4 weeks as assed with
the Pittsburgh Sleep Quality Index (Buysse et al., 1989) (PSQI Score:
4.43 0.36). The BMI exclusion criterion was a BMI of 25 kg/m2 or
higher. However, if men interested in participating in the study failed this
BMI criterion during screening but had at the same time 20% body fat,
we included them in our study. This was the case for two participants
(BMI: 25.66 and 25.23 kg/m2
; percentage body fat: 15.3% and 18.4%,
respectively). Participants were not on a special diet and did not have any
food allergies. All experimental procedures were approved by the local
ethics committee (Hamburg Board of Physicians) and all experimental
appointments took place at the Institute for Systems Neuroscience at the
University Hospital Hamburg-Eppendorf in Hamburg, Germany.
Participants visited the institute for three appointments, further described below: one screening and two counterbalanced experimental
fMRI sessions with either a habitual sleep or a sleep deprivation condition separated by 1 week. The evening before the fMRI scan, they received
a standardized dinner at the institute, after which they went home to
sleep as usual (habitual sleep session) or stayed at the institute to spend
the whole night awake under constant supervision (sleep deprivation
Pre-experimental screening session. After recruitment by online advertisements and a short phone interview, participants were invited to the
institute 1–5 d before the first experimental session for a preexperimental screening. During this appointment, participants read the
study description carefully and had the opportunity to ask questions
concerning the procedure before giving written informed consent. We
measured height, weight, body fat percentage, and the familiarity of the
stimuli used in the fMRI experiment. We determined body fat percentage
via the bioelectric impedance method with a BF306 device (OMRON).
Furthermore, a physician conducted a short medical examination to
ensure fMRI compatibility. At the end of the screening, we did not reveal
the order of the two experimental sessions. Additionally, to prevent
participants from sleeping during the afternoon before the sleep deprivation session when habitual sleep was the first condition, we instructed them that all combinations of the two conditions could be
possible, including two habitual sleep or two sleep deprivation sessions. Therefore, participants did not know if they were to stay awake
or could go home to sleep until they came in for the evening appointment. After the completion of all three sessions, we gave full disclosure of the experimental procedure.
Experimental sessions. Both experimental sessions started at 8:00 P.M.
with groups of two or three participants undergoing the same experimental condition. Upon arrival, they were told the experimental condition of the night and received a standardized dinner with 741 kcal per
serving (pasta with veal strips in cream and mushroom sauce: 582 kcal
total, per 100 g serving: 142 kcal, fat: 6.5 g, carbohydrate: 13.2 g, protein
7.7 g; apple: 68 kcal total, per 100 g serving: 52 kcal, fat: 0.2 g, carbohydrate: 14.0 g, protein: 0.3 g; strawberry yogurt: 91 kcal total, per 100 g
serving: 91 kcal, fat: 3.0 g, carbohydrate: 13.0 g, protein: 2.9 g). Importantly, participants fasted overnight in both conditions because we instructed them to refrain from food and caloric beverages until the
appointment in the morning.
Rihm et al. • Sleep Deprivation and Food Reward J. Neurosci., January 30, 2019 • 39(5):888 – 899 • 889
In the habitual sleep condition, participants wore an Actiwatch 2
(Philips Respironics) to track their sleep and wake times until the next
morning (average sleep duration: 6.73 0.93 h). They were instructed to
sleep as they normally do during a typical work week, went home to
spend the night as usual, and were invited again for the fMRI session the
next morning between 7:30 A.M. and 9:30 A.M. In the sleep deprivation
condition, participants stayed at the institute under constant supervision
and spent the whole night awake. During this time, they played card
games, parlor games, games on game consoles, watched television and
movies, and took walks at the university area.
Each experimental session in the morning started with hunger and
appetite ratings on a 7-point Likert scale, followed by Becker-deGrootMarschak (BDM) pre-scan bidding (see “BDM auction task” section),
blood drawing immediately before scanning, the BDM choice phase in
the scanner, and the BDM auction after scanning. We monitored participants in the scanner by online eye tracking to ensure wakefulness during
the fMRI task.
BDM auction task
Participants performed a BDM (Becker et al., 1964) auction to assess
their willingness to pay (WTP, i.e., subjective value) for a range of snack
foods (food reward) and trinkets (nonfood reward) (Plassmann et al.,
2007; Chib et al., 2009). In this task, participants had the opportunity to
win a trinket and a snack item (factor reward category with levels “food”
and “nonfood”). The task consisted of three phases, reported below in
more detail: (1) a prescan free bidding phase to obtain subjective value
estimates for all items, (2) a decision phase in the scanner, and (3) a
postscan auction phase. The procedure for the BDM auction closely
followed previous studies (Plassmann et al., 2007; Chib et al., 2009). In
particular, care was taken to ensure that all participants understood the
auction procedure and that the best strategy was to bid exactly the maximum amount that they were willing to pay for an item in the prescan
phase. Also, participants were instructed that, following scanning on
each day, one trial per item category (food and nonfood) from the combined set of trials from the bidding and decision phase would be selected
and played out. All snacks and trinkets were available and arranged in the
testing room such that all choices involved the prospect of obtaining the
Prescan bidding phase. In the bidding phase (Fig. 1a), participants
received 3 € to spend on snacks and 3 € to spend on trinkets. They saw all
food and nonfood images and indicated their WTP for each item on a
scale from 0 € to 3 € in steps of 0.25 €. They were instructed to bid the
maximal amount that they were willing to spend on the item and that
they could use the full range of the 3 € for each item because only one item
per category was drawn in the auction at the end. After bidding on all
items, the median of all food and nonfood items bid values was calculated
separately for each participant and session and used as reference price in
the respective fMRI decision phase (Chib et al., 2009). We informed
participants about the reference price and carefully ensured that they
correctly understood it by checking repeatedly if they remembered and
could reproduce the correct reference price. Additionally, the reference
price was displayed on the task screen while positioning the participants
in the scanner as well as during the first minutes during positioning.
Before the fMRI task started, we briefly instructed the participants again
over an intercom with the choice task and the reference price.
fMRI decision phase. During the choice phase in the fMRI scanner,
participants underwent a task in which they made repeated choices between buying or rejecting an item for the median price over all items (Fig.
1b). Participants saw all snack and trinket images again and had to indicate if they would buy or reject them for the reference price, which was
the median bid over all food and nonfood items and was calculated for
each participant and session. In a typical trial, participants saw a green
dot for 0.5 s, followed by the food or nonfood item for 6 s. Subsequently,
a red cross (reject) and a green check mark (accept) appeared randomly
left and right of the image for 2 s, indicating the decision-making phase.
Participants made choices via a MRI-compatible button box. The intertrial interval was marked by a red dot with a randomized presentation
time between 2 and 6 s sampled from a uniform distribution.
Postscan auction. In the auction after scanning, one trial per category
was randomly drawn from all trials of the bidding and choice phases. For
trials of the bidding phase, the participants’ auction bid was the bidding
value, whereas for trials of the choice phase, the participants’ auction bid
was the reference price in case the participants accepted the item or 0 € in
case they rejected it. The participants’ bid competed against a randomly
generated price by the computer between 0 € and 3 € in 0.25 € steps. If the
participants’ price was higher than or equal to that of the computer, then
they purchased the snack for the lower price and additionally received the
difference amount to 3 €. If the computer-generated price was higher
than the participants’ price, then the item could not be bought but participants received the full 3 €. After the auction, participants stayed another 30 min at the institute and could not eat anything except the snack
if they had purchased one.
Visual stimuli consisted of 48 different snack food and 48 different trinket images. The presented snacks were familiar snack foods available in
Germany [mean SD familiarity: 3.88 0.66, scale from 1 (not familiar) to 5 (highly familiar)] such as chocolate bars and chips as compiled
from an internet search and a previous study (Gluth et al., 2015). Trinkets
were familiar [mean SD familiarity: 3.64 0.75, scale from 1 (not
familiar) to 5 (highly familiar)] everyday items such as office, drugstore,
or university merchandise items compiled from an internet search and
inspired by the trinket items used in Chib et al. (2009).
All images were resized to 400 pixels in the largest dimension, superimposed on a gray background image, and presented with Presentation
software version 18 (Neurobehavioral Systems; RRID:SCR_002521). For
each participant, half of the 48 stimuli from each category were randomly
chosen and presented on the first scanning day, the other half on the
second scanning day. Because we did not take into account caloric density of the snack food items for randomization, we conducted a post hoc
analysis to confirm that the randomized stimuli sets of the habitual sleep
and sleep deprivation sessions were matched according to caloric density
by analyzing relatively high and relatively low caloric density between
sessions. For this purpose, we median split the snack food stimulus set
based on caloric content per 100 g (kcal/100 g) into relatively high and
relatively low caloric items. A repeated-measures ANOVA with the factors sleep state (habitual sleep vs sleep deprivation) and caloric density
(relatively higher vs lower caloric density) revealed no main effect of sleep
state (F(1,31) 0.24, p 0.63) and no interaction between the two factors
(F(1,31) 0.08, p 0.78). However, there was a significant difference
between the caloric content of the relatively high and low caloric density
items (F(1,31) 75337.05, p 0.001; mean kcal/100 g for low caloric
density: habitual sleep: 404.72 1.66, sleep deprivation: 406.39 1.67;
for high caloric density: habitual sleep: 546.79 1.31, sleep deprivation:
Images from both categories were mixed and randomly presented in
two runs per session. In the scanner, images were projected on a wall and
participants saw them via a mirror mounted on the head coil.
Blood sampling and analyses
We took blood plasma and serum samples in both sessions to determine
circulating levels of ghrelin, leptin, insulin, cortisol, and glucose. CollecFigure 1. Outline of the BDM task structure of the prescan bidding phase (a) and the fMRI
decision phase (b).
890 • J. Neurosci., January 30, 2019 • 39(5):888 – 899 Rihm et al. • Sleep Deprivation and Food Reward
tion of blood samples took place immediately before fMRI scanning to
measure endocrine concentrations during the fMRI BDM choice task.
Plasma blood samples of 8.5 ml were collected in BD P800 tubes (BD
Biosciences) containing K2EDTA anticoagulant for ghrelin concentration determination. They were immediately preprocessed by centrifuging at 4°C for 10 min at 1200 g, pipetting off the supernatant, and
stored at 80°C.
Serum blood samples of 7.5 ml were collected in Serum Gel Monovettes (Sarstedt) to determine leptin, cortisol, and insulin concentrations. The blood soaked for 45 min in the gel solution before the samples
were centrifuged at room temperature for 10 min at 2000 g. The
supernatant was pipetted off and stored at 80°C.
Two additional serum samples of 2.7 ml each for glucose determination were collected in Sarstedt S-Monovettes with Fluorid EDTA and also
soaked for 45 min in gel solution before centrifuging at room temperature for 10 min at 2000 g. The supernatant was pipetted off and stored
All serum blood samples were analyzed by the LADR laboratory in
Geesthacht, Germany. Leptin was analyzed with a sandwich enzymelinked immunosorbent assay (ELISA) from DRG, cortisol and insulin
with an electro-chemiluminescence immunoassay (ECLIA) method
from Roche, and glucose with a photometric AU 5800 from Beckman
Total and acyl ghrelin concentrations were analyzed at the Metabolic Core Unit, CBBM, in Luebeck, Germany with a radioimmune
fMRI data acquisition
fMRI data were obtained on a Siemens Magnetom Trio 3 T whole-body
scanner using a 32-channel head coil. Functional images were collected
using single-shot echoplanar imaging with parallel imaging (GRAPPA,
in-plane acceleration factor 2) (Griswold et al., 2002) and simultaneous
multi-slice acquisitions (“multiband,” slice acceleration factor 2) (Feinberg et al., 2010; Moeller et al., 2010; Xu et al., 2013) as described previously (Setsompop et al., 2012) (TR 2260 ms, TE 30 ms, number of
slices 60, flip angle 80°, voxel size 1.5 * 1.5 * 1.5 mm). The
corresponding image reconstruction algorithm was provided by the University of Minnesota Center for Magnetic Resonance Research.
Statistical analysis for behavioral data
Paired-samples t tests with a significance threshold of p 0.05, twotailed, were computed using MATLAB R2016b (RRID:SCR_001622) and
repeated-measures ANOVAs were computed using JASP 0.8.1.2 (RRID:
Hunger ratings. Subjective hunger ratings before the BDM task were
compared between sessions with a paired-samples t test.
Bids and reaction times. We assessed differences between bids and reaction times from the BDM prescan bidding phase using 2 2 repeatedmeasures ANOVAs with the factors sleep state (habitual sleep, sleep
deprivation) and reward category (food, nonfood).
As suggested by one reviewer, we conducted a post hoc analysis to
examine the impact of caloric density (kcal per 100 g) of the snack food
items on WTP for these items. For this purpose, we median split food
datasets of each participant and each session into relatively high and
relatively low caloric items and computed a repeated-measures ANOVA
with the factors sleep state (habitual sleep, sleep deprivation) and caloric
density (relatively low caloric density, relatively high caloric density).
As another post hoc analysis requested by one reviewer, we additionally
correlated the change in subjective value of the items reflected by the
change in the difference between bids for food and bids for nonfood
items between the two states with the change between the two states for
food and nonfood items in all behavioral (change in hunger, change in
reaction times, and change in probability to buy items for the reference
price) and neural parameters (change in values for parametric WTP
modulation in the and right hypothalamus, change in values related to
image onsets in the amygdala, and change in values for PPI image
onset-related activity for the connectivity between amygdala and hypothalamus) as exploratory analysis. Due to multiple correlations, we applied a Bonferroni correction for multiple comparisons (p 0.05/7).
Percentage irrational choices. Irrational choices are choices during the
choice phase that are not predicted based on the subjective values from
the bidding phase. Irrational choices were thus defined as rejections
when the reference price was equal to or smaller than the subjective value
assessed during bidding or as acceptance when the reference price was
higher than the subjective value assessed during bidding. The number of
irrational choices was divided by the total number of decision trials and
multiplied by 100.
Probability to buy. The probability to buy an item [p(buy)] for the
individual median bid during the scanning choice phase was calculated
by dividing the number of accepted by the total number of items. Differences between sleep states and reward categories were compared with a
Hierarchical Bayesian drift diffusion modeling (DDM). To further supplement analyses of bidding behavior, individual participant’s choice
and reaction time data were fit with a DDM (Ratcliff and McKoon, 2008).
During the scanning choice phase, participants made repeated choices
between the presented reward (trinket or snack) and the prescanning
median bid from the BDM auction. The DDM is a frequently used model
describing two-alternative forced-choice tasks and has been shown to be
valid in the context of value-based choices (Milosavljevic et al., 2010).
The choice process is modeled as a noisy evidence accumulation process
over time between two boundaries. As soon as one of the boundaries is
crossed, the associated response is executed. This evidence accumulation
is described by several underlying parameters, which in turn give rise to
the reaction time distributions for correct and incorrect choices (Ratcliff
and McKoon, 2008). The drift rate v reflects the rate of evidence accumulation over time; the boundary separation a reflects the amount of
evidence required to execute a choice; the nondecision time parameter t
captures the time needed to perceive the stimulus and execute a motor
response; and z corresponds to the starting point of the evidence accumulation process between the two boundaries and was fixed at the midpoint between the two boundaries in the model. In addition, intertrial
variability in nondecision time (St) was included in the model because
setting it to zero might be problematic for model estimation (Voss et al.,
2015). Correct choices were defined as choices consistent with the median bid from the prescan bidding phase; that is, buying the item if the bid
was greater than or equal to the median bid and rejection if the bid was
less than median bid, whereas incorrect choices were defined as choices
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