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Olfactory connectivity mediates sleep-dependent food choices in humans

  1. Surabhi Bhutani
  2. James D Howard
  3. Rachel Reynolds
  4. Phyllis C Zee
  5. Jay Gottfried
  6. Thorsten Kahnt  Is a corresponding author
  1. Northwestern University, United States
  2. San Diego State University, United States
  3. University of Pennsylvania, United States
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Cite this article as: eLife 2019;8:e49053 doi: 10.7554/eLife.49053

Abstract

Sleep deprivation has marked effects on food intake, shifting food choices toward energy-dense options. Here we test the hypothesis that neural processing in central olfactory circuits, in tandem with the endocannabinoid system (ECS), plays a key role in mediating this relationship. We combined a partial sleep-deprivation protocol, pattern-based olfactory neuroimaging, and ad libitum food intake to test how central olfactory mechanisms alter food intake after sleep deprivation. We found that sleep restriction increased levels of the ECS compound 2-oleoylglycerol (2-OG), enhanced encoding of food odors in piriform cortex, and shifted food choices toward energy-dense food items. Importantly, the relationship between changes in 2-OG and food choices was formally mediated by odor-evoked connectivity between the piriform cortex and insula, a region involved in integrating feeding-related signals. These findings describe a potential neurobiological pathway by which state-dependent changes in the ECS may modulate chemosensory processing to regulate food choices.

https://doi.org/10.7554/eLife.49053.001

eLife digest

People who do not get enough sleep often start to favor sweet and fatty foods, which contributes to weight gain. While the exact mechanisms are still unknown, lack of sleep seems to change food preferences by influencing the levels of molecules that regulate food intake. In particular, it could have an effect on the endocannabinoid system, a complex network of molecules in the nervous system that controls biological processes such as appetite.

The sense of smell is also tightly linked to how and what organisms choose to eat. Recent experiments indicate that in rodents, endocannabinoids enhance food intake by influencing the activity of the brain areas that process odors. However, it is still unclear whether the brain regions that process odors play a similar role in humans.

To investigate, Bhutani et al. examined the impact of a four-hour night’s sleep on 25 healthy human volunteers. Blood analyses showed that after a short night, individuals had increased amounts of 2-oleoylglycerol, a molecule that is part of the endocannabinoid system. When sleep-deprived people were given the choice to eat whatever they wanted, those with greater levels of 2-oleoylglycerol preferred food higher in energy. Bhutani et al. also imaged the volunteers’ brains to examine whether these changes were connected to modifications in the way the brain processed smells. This revealed that, in people who did not sleep enough, an odor-processing region called the piriform cortex was encoding smells more strongly.

The piriform cortex is connected to another region, the insula, which integrates information about the state of the body to control food intake. Lack of sleep altered this connection, and this was associated with a preference for high-energy food. In addition, further analysis showed that changes in the amounts of 2-oleoylglycerol were linked to modifications in the connection between the two brain areas. Taken together, these results suggest that sleep deprivation influences the endocannabinoid system, which in turn alters the connection between piriform and insular cortex, leading to a shift toward foods which are high in calories.

In the United States alone, one in three people sleep less than six hours a night. Learning more about how sleep deprivation affects brain pathways and food choice may help scientists to develop new drugs or behavioral therapies for conditions like obesity.

https://doi.org/10.7554/eLife.49053.002

Introduction

Sleep deprivation profoundly impacts food choices. When individuals are sleep-deprived, their dietary behavior shifts toward increased consumption of foods high in sugar and fat, leading to weight gain (Markwald et al., 2013; Nedeltcheva et al., 2009). These effects on ingestive behavior are likely related to sleep-dependent changes in appetite-regulating compounds, including ghrelin (Rihm et al., 2019; Spiegel et al., 2004b), leptin (Spiegel et al., 2004a), and endocannabinoids (Hanlon et al., 2016). Indeed, the endocannabinoid system (ECS) exerts strong effects on food intake (Bellocchio et al., 2010; Di Marzo et al., 2001), and levels of the endocannabinoid 2-arachidonoylglycerol (2-AG) and its structural analog 2-oleoylglycerol (2-OG) are enhanced in sleep-deprived individuals (Hanlon et al., 2016). While previous studies have tested the effects of sleep deprivation on the human brain (Greer et al., 2013; Krause et al., 2017; Muto et al., 2016; Rihm et al., 2019), the neural pathways through which sleep-dependent alterations in the ECS influence food intake have not been investigated in humans.

One likely target for sleep-dependent neuromodulation of food intake is the olfactory system. Odors serve as powerful signals for the initiation and termination of feeding behavior (Saper et al., 2002; Shepherd, 2006), and animal studies have shown that olfactory processing is modulated in a state-dependent manner (Julliard et al., 2007; McIntyre et al., 2017; Murakami et al., 2005). In rodents (Aimé et al., 2007; Aimé et al., 2014) and humans (Hanci and Altun, 2016; Stafford and Welbeck, 2011), olfaction is altered by hunger and satiety, and satiety reduces neural activity in olfactory brain regions in parallel with a suppression of feeding behavior (Boesveldt, 2017; Gervais and Pager, 1979; O'Doherty et al., 2000; Prud'homme et al., 2009; Soria-Gómez et al., 2014). Moreover, recent work across different species suggests a link between the ECS, olfactory processing, and food intake, such that endocannabinoids may directly modulate neural activity in olfactory circuits (Breunig et al., 2010; Soria-Gómez et al., 2014). However, whether odor-evoked responses in the human olfactory system are similarly modulated by the ECS, and whether this accounts for the effects of sleep deprivation on food intake, is not known.

We hypothesized that sleep deprivation is associated with a cascade of metabolic and olfactory changes, ultimately steering food choices toward energy-dense options (Simon et al., 2015). We predicted that after a night of restricted sleep, relative levels of circulating ECS compounds will be increased (Hanlon et al., 2016), leading to changes in how olfactory brain regions in the medial temporal and basal frontal lobes respond to food odors (Soria-Gómez et al., 2014). We expected that such sleep-dependent changes in olfactory processing would manifest in odor-evoked activity patterns in piriform cortex (Howard and Gottfried, 2014; Howard et al., 2009), and that effects on food intake would involve interactions with areas downstream of piriform cortex, such as the insula. Olfactory, gustatory, homeostatic, and visceral signals are integrated in the insula (Craig, 2002; de Araujo et al., 2003; Johnson et al., 2000; Livneh et al., 2017; Small et al., 2008), optimally positioning this region to regulate ingestive behavior in a state-dependent manner (Dagher, 2012; de Araujo et al., 2006).

Results

Experimental design and sleep deprivation

To test these hypotheses, we utilized a within-subject crossover design with a partial sleep-deprivation protocol and pattern-based functional magnetic resonance imaging (fMRI) of food and non-food odors (Figure 1A). The experiment was designed to simultaneously measure the effects of sleep deprivation on ECS signaling, neural responses to food odors, and real-life food choices. After one week of sleep stabilization (7–9 hr sleep/night between 10:30 pm and 7:30 am), healthy-weight participants (N = 25, 10 male, age mean ± SEM: 26.6 ± 0.98 years) were randomly assigned to one night of deprived sleep (DS, 4 hr sleep between 1 am and 5 am) or non-deprived sleep (NDS, 8 hr sleep between 11 pm and 7 am). All subjects participated in both DS and NDS sessions, which were separated by 4 weeks to allow for sufficient recovery time and to control for potential effects of menstrual phase in female participants. Actigraphy-monitored sleep times did not differ between DS and NDS sessions during the 7-nights of sleep stabilization (NDS: 6.63 ± 0.18 hr, DS: 6.75 ± 0.19 hr; T22=−1.40, p=0.174). However, sleep times did differ during the night of sleep manipulation (NDS: 6.8 ± 0.12 hr, DS: 3.8 ± 0.18 hr; T24 = 14.70, p=1.68×10−13, Figure 1B, Figure 1—figure supplement 1, and Supplementary file 1), confirming that subjects complied with the sleep deprivation protocol.

Figure 1 with 4 supplements see all
Experimental design and behavioral effects of sleep deprivation.

(A) Study protocol for deprived sleep (DS) and non-deprived sleep (NDS) sessions with a 19 day washout period. Dinner was served at 6 pm, the fMRI session started at 7 pm, and the ad libitum buffet started after 8 pm. (B) Actigraphy data showed no differences in sleep duration during the sleep stabilization phase, but confirmed a significant difference between the two sleep conditions during the night of sleep manipulation (day 8). (C) Ratings of hunger on a visual analog scale showed significant effects of time, but no sleep-dependent effects (time-by-sleep ANOVA, main effect time, F5,120=70.86, p=3.63×10−34, main effect sleep, F1,24=0.06, p=0.81, interaction F5,120=0.24, p=0.95). (D) In the DS compared to the NDS session, participants reported lower sleep quality (DS 2.96 ± 0.19, NDS 3.96 ± 0.18, T22=−3.98, p=6.38×10−4), reduced alertness (DS 2.49 ± 0.14, NDS 4.09 ± 0.14, T22=−8.66, p=1.55×10−8), and felt less well-rested (DS 2.00 ± 0.14, NDS 4.17 ± 0.17, T22=−9.72, p=2.01×10−9). (E) Stanford Sleepiness Scale scores were higher in the DS session (DS 3.64 ± 0.24, NDS 2.08 ± 0.19, T24 = 6.96, p=3.40×10−7). (F) Energy-density (kcal/g) of food consumed after scanning at the ad libitum buffet, expressed as % change from NDS baseline. *p<0.05. Data are presented as mean ± SEM.

https://doi.org/10.7554/eLife.49053.003

The functional imaging sessions occurred in the evening after the night of sleep manipulation. In the 24 hr period leading up to these critical testing sessions, subjects received individually standardized isocaloric diets to ensure identical food intake in both sessions. Subjective states of sleep deprivation were assessed in the morning, and standardized sleepiness scores (Stanford sleepiness scale) were obtained upon arrival at the imaging center. Compared to the NDS session, participants in the DS session felt subjectively sleep-deprived, as indicated by reduced self-reported sleep quality, levels of alertness and well-restedness (Figure 1D), and increased sleepiness (Figure 1E). To account for potential effects of sleep deprivation-related stress and anxiety on food intake and associated brain responses (Maier et al., 2015), subjects completed the State Anxiety Inventory (SAI). We also measured serum cortisol levels as a physiological marker of stress. There were no significant changes in SAI scores (T24=−1.49, p=0.148) or cortisol (T24 = 0.70, p=0.584) between DS and NDS sessions (Figure 1—figure supplement 2), suggesting that stress and anxiety levels were not altered by sleep restriction.

Before fMRI scanning, subjects received a standardized isocaloric dinner based on individually estimated energy demands. Hunger ratings decreased immediately after dinner, and returned to pre-dinner levels 45 min after the meal (Figure 1C). Importantly, there were no sleep-dependent differences in hunger ratings made at any time point, indicating that measures of odor-evoked brain activity and food intake cannot be driven by differences in subjective levels of hunger. During fMRI scanning, subjects intermittently smelled a set of energy-dense food odors and non-food control odors (Supplementary file 2). There were no sleep-dependent differences in rated odor pleasantness and intensity, or respiratory behavior during fMRI scanning (Figure 1—figure supplement 3).

Sleep deprivation shifts food choices toward energy-dense options

Immediately after scanning, subjects were given ad libitum access to food items in a buffet-style setting to measure effects of sleep deprivation on food choices and intake. In the DS session, participants consumed food items with a significantly higher energy density (6.0 ± 2.43% change from NDS; T24 = 2.48, p=0.021, Figure 1F), with no significant difference in the overall calories consumed (18.6 ± 16.93% change from NDS; T24 = 1.10, p=0.282). Importantly, effects of sleep deprivation on dietary behavior persisted into the next day (after a night of unrestricted recovery sleep), with a higher percentage of calories consumed as fat (DS: 36.5 ± 1.28%, NDS: 30.6 ± 1.28%; T24 = 2.34, p=0.028), indicating that a single night of restricted sleep can have relatively long-lasting effects on food choices.

Sleep-dependent changes in the ECS correlate with energy-dense food choices

Based on previous reports that circulating levels of 2-AG and 2-OG are enhanced after sleep restriction (Hanlon et al., 2016), we next tested whether these ECS compounds were increased in the present study. Partially replicating the previous findings, circulating levels of 2-OG collected during fMRI scanning were increased in the DS relative to the NDS session (35.02 ± 17.82% change from NDS; T24 = 1.96, p=0.031, one-tailed, Figure 2A). However, although sleep-dependent changes in 2-AG and 2-OG were significantly correlated (r = 0.55, p=0.004), relative increases in 2-AG were not significant (5.29 ± 5.20% change from NDS; T24 = 1.02, p=0.16, one-tailed). We also observed no significant changes in other appetite-regulating hormones, including ghrelin, leptin, and insulin (Figure 1—figure supplement 2B). Interestingly, sleep-dependent increases in 2-OG correlated significantly with increases in the energy density of food consumed at the post-scanning buffet (robust regression, β = 0.47, p=0.027; permutation test, p=0.018; Figure 2B). Although correlative in nature, this finding suggests that the ECS may play a role in modifying dietary behavior after sleep deprivation.

Figure 2 with 1 supplement see all
Sleep-dependent changes in the ECS correlate with energy-dense food choices.

(A) Relative changes in 2-AG and 2-OG levels in the SD condition (% change from NDS baseline). Data are presented as mean ± SEM. (B) Percent changes in 2-OG in DS from NDS baseline correlate positively with % changes in energy density of food consumed at the ad libitum buffet in the DS condition relative to NDS baseline (robust regression, β = 0.47, p=0.027).

https://doi.org/10.7554/eLife.49053.030

Sleep deprivation enhances odor encoding in piriform cortex

Having established a link between sleep-dependent changes in the ECS and food choices, we next analyzed the fMRI data to examine whether this relationship was mediated by effects of sleep deprivation on central olfactory responses to odors. Based on previous rodent work showing that endocannabinoids affect feeding-related changes in olfactory processing (Soria-Gómez et al., 2014), we hypothesized that elevated levels of 2-OG would be accompanied by enhanced representations of odors in olfactory sensory cortices.

Both animal (Barnes et al., 2008; Illig and Haberly, 2003; Stettler and Axel, 2009) and human studies (Howard et al., 2009; Zelano et al., 2011) have shown that odors are encoded in piriform cortex by sparsely distributed patterns of ensemble activity, with no apparent topographical organization. Such distributed representations cannot be detected by univariate fMRI analyses, in which activity is typically averaged across voxels, thus blurring information contained within fine-grained patterns of activity. To examine such distributed responses to food odors (Figure 3A), here we used a searchlight-based multi-voxel pattern analysis (MVPA), which enables unbiased whole-brain decoding based on activity patterns (Haynes et al., 2007; Kahnt et al., 2011; Kriegeskorte et al., 2006). Specifically, we used a support vector machine (SVM) classifier to decode information about food vs. non-food odors from patterns of odor-evoked fMRI activity (Figure 3B). Across sleep sessions, we found significant decoding of odor information in the piriform cortex (x = 16, y=−2, z=−14, T24 = 4.20, PFWE-SVC = 0.012, Figure 3—figure supplement 1) and insula (left x=−32, y=−4, z = 16, T24 = 4.75, PFWE-SVC = 0.021; right x = 44, y = 8, z = 10, T24 = 4.84, PFWE-SVC = 0.017). Importantly, comparing odor encoding between DS and NDS sessions, we found significantly higher decoding accuracy in the piriform cortex in the DS session (x = 20, y = 8, z=−12, T24 = 5.91, PFWE-SVC = 0.001; Figure 3C), suggesting that sleep deprivation enhances encoding of odor information in olfactory brain areas. In contrast, a univariate analysis of odor-evoked fMRI responses in piriform cortex showed no sleep-dependent effects or interactions (Figure 4).

Figure 3 with 2 supplements see all
Sleep deprivation enhances encoding of odor information in piriform cortex.

(A) Sweet and savory food odors and non-food control odors presented during fMRI. (B) Schematic of the searchlight decoding analysis used to reveal information about food vs. non-food odors in the DS compared to the NDS session. (C) Decoding accuracy for food vs. non-food odors in the piriform cortex (black circle) was significantly higher in the DS compared to the NDS session (T24 = 5.91, PFWE-SVC = 0.001). This result did not change when including covariates for head motion (translation and rotation) into the group-level model (x = 20 y = 8 z=−12, T23 = 6.12, PFWE-SVC = 0.0001). In addition, results were still significant when including covariates for odor pleasantness in the first- (x = 20, y = 8, z=−10, T24 = 3.12, PFWE-SVC = 0.045) and group-level models (x = 20, y = 8, z=−12, T23 = 6.34, PFWE-SVC = 0.0001). Finally, controlling for respiratory response functions (Birn et al., 2008) did not change the result (x = 20, y = 8, z=−12, T24 = 4.83, PFWE-SVC = 0.001). Whole-brain map can be viewed at neurovault.org/images/132917/.

https://doi.org/10.7554/eLife.49053.036
Sleep deprivation does not enhance univariate fMRI responses to odors.

(A) Significant univariate odor-evoked fMRI responses (food and non-food odors > clean air) in piriform cortex, averaged across both sleep sessions (right, x = 22, y = −4, z = −18, T24 = 12.53, PFWE = 4.42×10−7; left, x = −24, y = 4, z = −18, T24 = 9.06, PFWE = 2.83×10−4). Whole-brain map can be viewed at neurovault.org/images/132916/ (B) Parameter estimates in piriform cortex show no differences between food and non-food odors, and no sleep-dependent effects (two-way ANOVA, main effect of sleep, F1,24=0.003, p=0.954; main effect of odor, F2,48=112.88, p=7.12×10−19; sleep-by-odor interaction, F2,482=0.29, p=0.748). Data are represented as mean ± SEM.

https://doi.org/10.7554/eLife.49053.039

Enhanced encoding of odor information after sleep deprivation could be the mediating neural factor behind the observed relationship between sleep-dependent changes in the ECS and food choices. However, decoding accuracy in piriform cortex was not correlated with either sleep-dependent changes in 2-OG (r=−0.24, p=0.248) or energy-dense food choices (r=−0.05, p=0.80), suggesting that the relationship between the ECS and food intake was not directly mediated by changes in odor encoding. In the next step, we therefore considered the possibility that changes in the propagation of olfactory signals from piriform cortex to downstream regions may account for the effects of sleep deprivation on food choices.

Piriform-insula connectivity mediates the link between ECS and food intake

One downstream region particularly relevant for integrating chemosensory, interoceptive, and homeostatic signals to guide food intake is the insula (Dagher, 2012; de Araujo et al., 2006; Livneh et al., 2017). To test whether sleep deprivation altered the functional connectivity between piriform and insular cortex, we utilized a psychophysiological interaction (PPI) model, with piriform cortex as the seed region and odor presentation (food and non-food odors > clean air) as the psychological variable. We found that sleep-dependent changes (DS >NDS) in piriform connectivity (odorized >clean air) with the right insula correlated with changes in the energy density of food consumed immediately after the fMRI session (x = 40, y = 6, z = 0, T23 = 6.04, PFWE-SVC = 0.005; Figure 5A), such that reduced odor-evoked connectivity was associated with enhanced intake of energy-dense food (Figure 5B). A similar effect in the left insula was present but did not survive correction for multiple comparisons (x=−44, y = 4, z = 0, T23 = 4.20, PFWE-SVC = 0.17). In addition, while not part of our main set of hypotheses, we found a positive correlation between sleep-dependent changes in energy-dense food choices and changes in odor-evoked connectivity between the piriform cortex and the right anterior hippocampus (x = 28, y=−10, z=−18, T23 = 5.36, Puncorr = 1.0×10−5). In contrast, sleep-dependent changes in piriform connectivity for food vs. non-food odors did not show a significant relationship with changes in food choices. These results suggest that the connectivity between olfactory signals in the piriform cortex and downstream areas may play a role in linking sleep-dependent changes in olfactory processing to changes in food choices.

Piriform-insula connectivity mediates the effects of 2-OG on sleep-dependent food choices.

(A) Sleep-dependent changes in odor-evoked connectivity (odor >clean air) between piriform cortex and insula negatively correlated with the energy density of foods consumed at the post-fMRI buffet (T23 = 6.04, PFWE-SVC = 0.005). This result did not change when including covariates for head motion (translation and rotation) into the group-level model (x = 40, y = 6, z = 0, T21 = 5.42, PFWE-SVC = 0.021). In addition, controlling for respiratory response functions (Birn et al., 2008) did not change the result (x = 40, y = 6, z = 0, T23 = 5.59, PFWE-SVC = 0.011). Whole-brain map can be viewed at neurovault.org/images/132919/ (B and C) For illustrative purposes, scatter plots depict association between sleep-dependent changes in piriform-insula connectivity and (B) energy density of food intake (r=−0.78, p=3.4×10−6) and (C) 2-OG (r=−0.58, p=0.002). (D) Mediation analysis. Sleep-dependent changes in 2-OG separately correlated with choices of energy-dense foods (c = 0.46, p=0.019) and piriform-insular connectivity (a=−0.58, p=0.002), and piriform-insula connectivity correlated with food intake (b = 0.785, p=3.39×10−6). The association between 2-OG and food choices was no longer significant when the indirect effect of piriform-insula connectivity on food choice was included in the regression model (c’=0.01, p=0.956), which itself remained significant when controlling for 2-OG (b’=−0.78, p=0.0001).

https://doi.org/10.7554/eLife.49053.041

Given that energy-dense food choices were associated with changes in 2-OG levels, it is possible that the observed connectivity effects were also related to the ECS. Indeed, we found that piriform-insula connectivity was significantly correlated with sleep-dependent changes in 2-OG levels (r=−0.58, p=0.002; Figure 5C), raising the possibility that the association between sleep-dependent changes in ECS and food choices is mediated by piriform-insula connectivity. To directly test this hypothesis, we employed a formal mediation analysis (Baron and Kenny, 1986), including a direct path from sleep-dependent changes in 2-OG (x) to changes in the energy density of the food consumed after fMRI (y), and an indirect path with changes in piriform-insula connectivity as mediator (m, Figure 5D). Importantly, the direct path between 2-OG levels and food intake was fully explained by the indirect path through piriform-insula connectivity, establishing a significant mediation effect (Sobel test, z = 2.97, p=0.003). This suggests that sleep deprivation affects the ECS, which then modulates the connectivity between piriform and insular cortex, and in turn shifts food choices toward energy-dense options.

Discussion

Clinical and epidemiological studies have linked reduced sleep to elevated food intake and weight gain (Kant and Graubard, 2014; Markwald et al., 2013; Patel and Hu, 2008). This relationship has been confirmed in studies using experimentally induced sleep deprivation, demonstrating that sleep restriction increases the desire for foods high in sugar and fat content (Cain et al., 2015; Greer et al., 2013; Hogenkamp et al., 2013; Simon et al., 2015), and leads to excessive consumption of such food options (Brondel et al., 2010; Nedeltcheva et al., 2009). Several different factors have been proposed to account for this relationship (Patel and Hu, 2008), including changes in hunger induced by appetite-regulating hormones such as ghrelin and leptin (Spiegel et al., 2004a; Spiegel et al., 2004b), and the ECS (Hanlon et al., 2016). In addition, previous imaging studies have reported sleep-dependent activity changes in response to food cues (Benedict et al., 2012; Greer et al., 2013; Rihm et al., 2019; St-Onge et al., 2014), but whether and how these neural changes are related to actual food choices and the ECS has remained unclear.

In the current study, we tested the hypothesis that central olfactory mechanisms, in conjunction with the ECS, play a role in mediating the effects of sleep deprivation on dietary choices. This hypothesis was based on previous findings that experimentally induced sleep deprivation elevates relative levels of ECS compounds (Hanlon et al., 2016), and animal work suggesting that ECS activity drives changes in food intake through modulatory effects on olfactory circuits (Breunig et al., 2010; Soria-Gómez et al., 2014; Wang et al., 2012). In line with this idea, we found that sleep deprivation increased consumption of energy-dense foods, proportional to relative increases in 2-OG, and that it enhanced pattern-based encoding of odor information in the piriform cortex. Finally, we found that the effects of the ECS on food intake were mediated by changes in connectivity between the piriform cortex and the insula.

Previous animal studies have established an important role for the ECS in regulating feeding behavior (Bellocchio et al., 2010; Di Marzo et al., 2001; Rodríguez de Fonseca et al., 2001). More recently, it has been shown that levels of endocannabinoids in humans are elevated after sleep restriction (Hanlon et al., 2016), suggesting that this may drive altered dietary choices. However, unlike previous studies (Hanlon et al., 2016), we did not find a significant increase in 2-AG that paralleled increases in 2-OG. This may be due to the shorter duration of sleep restriction used in our study, but may also indicate different roles of the two compounds. Whereas 2-AG stimulates food intake and lipogenesis by activating CB1 receptors (DiPatrizio and Simansky, 2008; Osei-Hyiaman et al., 2005), the exact role of 2-OG and its relation to 2-AG is not fully understood (Murataeva et al., 2016). Our study shows that sleep-dependent increases in 2-OG are associated with changes in the consumption of energy-dense foods, providing novel evidence for a link between sleep, ECS, and dietary behavior.

We found that sleep restriction induced qualitative changes in food intake, biasing choices toward energy-dense options, without altering total calorie intake. Although some studies have shown increases in calorie intake with sleep deprivation (Al Khatib et al., 2017; Broussard et al., 2016; Markwald et al., 2013; Patel and Hu, 2008), our findings are in line with several other studies (Cain et al., 2015; Nedeltcheva et al., 2009; Simon et al., 2015) and suggest that sleep deprivation induces nuanced changes in food-based decision making, rather than simply increasing hunger or the motivation to eat.

Our results further elaborate on the effects of sleep deprivation on the human brain, suggesting that neural processing of odors is enhanced in primary olfactory brain areas after sleep restriction. Although decoding accuracy can be influenced by factors other than the strength of neural encoding, such as reduced variability (noise), our results indicate that encoding of food vs. non-food odors was more robust in the piriform cortex in a sleep-deprived state. In theory, such enhanced encoding of olfactory information could facilitate odor-evoked approach and consummatory responses (Aimé et al., 2007; Soria-Gómez et al., 2014). However, we did not observe a direct correlation between encoding in piriform cortex and food intake, and changes in odor information were not directly related to changes in circulating levels of 2-OG. It is possible that nonlinear effects or interactions among different ECS compounds (Ho et al., 2008; Murataeva et al., 2016) may have obscured a direct linear relationship between 2-OG and encoding in piriform cortex. In any case, our findings indicate that sleep-dependent increases in odor information may not directly mediate the relationship between the ECS and food intake. Instead, they suggest that interactions between olfactory cortex and downstream areas may translate altered chemosensory encoding into changes in food intake.

We found that changes in piriform-insula connectivity were correlated with the effects of sleep deprivation on food choices, suggesting a relationship between sleep-dependent food intake and neural processing in extended olfactory pathways. A formal mediation analysis showed that relative increases in 2-OG were related to reductions in odor-evoked piriform-insula connectivity, which in turn was correlated with increased choices of energy-dense food options. Although these findings need to be confirmed in future studies, they suggest a broader role for neural processing of chemosensory signals along piriform-insula pathways in the regulation of food intake. There are several possible ways by which reduced piriform-insula connectivity could promote choices of energy-dense foods in the presence of enhanced odor information in piriform cortex. Previous studies show that sensory and visceral-homeostatic signals are integrated in the insula, and that this integration is critical for guiding food intake (de Araujo et al., 2003; Livneh et al., 2017; Small, 2012). Reduced piriform-insula connectivity could reflect a diminished integration of chemosensory and homeostatic-visceral information, and a failure to adequately integrate elevated olfactory signals with homeostatic information may drive excess intake of energy-dense food. Alternatively, reduced piriform-insula connectivity could indicate an aberrant assignment of value to energy-dense foods (Balleine and Dickinson, 2000; Gottfried et al., 2003). Finally, to the degree that sleep deprivation decreases activity in other cortical areas (Greer et al., 2013; Muto et al., 2016), it is possible that reduced piriform-insula connectivity is related to diminished top-down control over elevated olfactory signals in piriform cortex, which may promote impulsive behavior in response to energy-dense food (Cedernaes et al., 2014; Krause et al., 2017).

In the current study, we compared brain responses to food odors with high palatability and non-food items with low palatability. As expected, the food odors were rated as higher in pleasantness than non-food odors. In principle, it is therefore possible that our observed effects for food vs. non-food odor encoding in the brain were fully explained by this corresponding pleasantness difference. However, our results remained significant when including pleasantness as a covariate in the statistical models, indicating that in this case pleasantness does not account for our results.

Taken together, our findings show that sleep-dependent changes in food choices are associated with changes in an olfactory pathway that is related to the ECS. This pathway is likely not restricted to sleep-dependent changes in food intake but may also account for dietary decisions more generally. In this regard, our current findings may help to guide the identification of novel targets for treatments of obesity.

Materials and methods

Subjects

We consented and screened 41 healthy, right handed, non-smoking, and non-obese men and women between the ages of 18–40 year and body mass index (BMI) between 18.5 and 24.9 kg/m2, with no history of neurological disorders. We included individuals with a self-reported habitual sleep duration of 7–9 hr, and regular sleep time between 9 pm and midnight. The 7–9 hr habitual sleep inclusion criteria was based on guidelines set by National Sleep Foundation for healthy adults (Hirshkowitz et al., 2015). This ensured that all participants had sleep duration and sleep timing that are within the normal range for this age group. A regular sleep time between 9 pm and midnight ensured circadian rhythms were aligned across all participants (Burgess and Eastman, 2004), minimizing between-subject variance. Additional exclusion criteria were daytime nap, variable sleep habits, regular night work, travel across time zone during the study, use of medications affecting sleep, and caffeine intake of >300 mg/day. We also administered the Center for Epidemiologic Studies Depression Scale (cut off 16) (Radloff, 1977), and the Pittsburgh Sleeping Quality Index (cut off 5) (Buysse et al., 1989). Only individuals with scores below the cut off were included in the study. Only non-pregnant women were included.

Of the 41 individuals screened, 29 proceeded with the experimental procedures. Of those, three participants dropped out of the study due to discomfort inside the scanner, and one was excluded from the analysis because of a large number of missed responses. This resulted in a final sample of N = 25 participants (10 male), whose data are reported here. The main outcome measures did not differ between male and female participants (energy density: T23 = 0.618, p=0.542; 2-OG: T23=−0.826, p=0.416; piriform encoding: T23 = 0.095, p=0.924) and did not correlate with body weight (energy density: β = 0.019, p=0.865; 2-OG: β=−1.334, p=0.103; piriform encoding: β = 0.024, p=0.611). All experimental procedures of this study (STU00203395) were approved by the Institutional Review Board of Northwestern University.

Experimental procedures overview

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In a randomized within-subject crossover protocol, all subjects participated in two sleep sessions that included one night of deprived sleep (DS; 4 hr in bed 1 am – 5 am) and one night of non-deprived sleep (NDS; 8 hr in bed 11 pm – 7 am) at home. There was a washout period of 19 days between the last day of the first session (7 days of sleep stabilization phase, 1 day sleep manipulation, 1 day fMRI session) and the first day of second session (Figure 1A). This ensured that the two fMRI days were separated by 28 days for all participants, such that female participants were tested in the same phase of the menstrual cycle. In addition, we recorded self-reported menstrual cycle phase and compared sleep-dependent changes in our primary outcome measures between female participants in the follicular and luteal phase. No significant differences were found between the two menstrual phases (energy-dense food intake: T10=−0.76, p=0.465; 2-OG: T10=−1.41, p=0.188; piriform encoding: T10=−0.05, p=0.960). During the week preceding each session, participants were instructed to maintain a standardized sleep schedule of 7–9 hr sleep (between 10:30 pm and 7:30 am) in order to align the phase of the circadian rhythm across participants. Compliance with the sleep schedule during the sleep stabilization and sleep manipulation phase was monitored using wrist-worn actigraphy and a self-reported sleep diary. Subjects also rated their subjective sleep quality, alertness, and restfulness every morning using an online questionnaire. For the ratings, subjects used a 5-point scale, where one indicated ‘poor’ or ‘least’ and five indicated ‘excellent’ or ‘highest’. No naps were allowed during both sleep stabilization and sleep manipulation periods. Participants were also instructed to abstain from alcohol, caffeine, and drugs, including all recreational drugs, to avoid interference with sleep and hormone levels. To ensure that participants limited their caffeine intake to <300 mg/day on sleep stabilization days, they were instructed to consume not more than one small caffeinated drink per day. Foods and drinks high in caffeine (e.g., coffee, chocolate, soda, most tea, including ice tea, traditional black tea, and green tea) were listed in the instruction sheet provided to participants. Subjects were verbally reminded of the caffeine restriction at every study visit. In addition, subjects were instructed to not consume any caffeinated drinks on the scanning day.

On the evening following the night of the sleep manipulation, fMRI scanning was performed after subjects consumed a standardized isocaloric dinner (subjects received exactly the same meal in both sessions). We collected imaging data and blood samples after dinner in the evening following the night of sleep manipulation because previous studies found that experimentally induced sleep deprivation affects ECS compounds and the desire for and consumption of energy-dense food most prominently in this time window (Hanlon et al., 2016; Nedeltcheva et al., 2009). During fMRI scanning, participants were presented with food odors, non-food odors, and clean air, and rated odor pleasantness and intensity. Before the fMRI scan in both sessions, participants also rated their subjective sleepiness using the Stanford Sleepiness Scale. To equate food intake leading up to the two fMRI sessions, isocaloric meals were provided for the 24 hr preceding both sessions.

Sleep protocol and monitoring

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Our study used an in-home setting to render the sleep-deprivation protocol as ecologically valid as possible without the additional distractions and stressors of being in an unfamiliar hospital laboratory environment. However, because in-home settings come with potential limitations related to non-compliance, we took several measures to reinforce and monitor compliance with the sleep stabilization and sleep manipulation schedule. For both sleep conditions, participants were instructed to strictly follow the sleep protocol (DS: sleep from 1:00 am to 5:00 am; NDS: Sleep between 11:00 pm and 7:00 am). To encourage compliance with the instructions, research staff discussed strategies to stay awake, such as watching TV, standing up, etc. Sleep and wake-up times were monitored for 8 days (7 days of stabilization, 1 day of manipulation) using a wrist-worn 3-axis accelerometers (ActiGraph GT9X Link, ActiGraph, LLC, Pensacola, FL) (Ancoli-Israel et al., 2003; Marino et al., 2013). Due to a technical failure, actigraphy data from two participants collected during the sleep stabilization phase of the NDS session was lost (data from both critical nights of sleep manipulation were not affected). Data from one additional participant recorded during one day of the sleep stabilization phase was also lost. Actigraphy data were classified as sleep or awake using Cole-Kripke algorithm, as implemented in the Actilife software. Total sleep time (TST), time in bed (TIB), sleep efficiency (SE), and wake after sleep onset (WASO) were also calculated using the same algorithm. TIB began at sleep onset and ended at awakening, and TST was defined as time sleeping within TIB. WASO was defined as the wake time within TIB, and SE was computed as the ratio between TST and TIB.

In addition to wearing the actigraphy device, subjects also logged their bed time, sleep duration, and sleep quality immediately after scheduled sleep hours in an online sleep diary. Information entered in the sleep diary was time stamped, and study personnel cross-referenced these time stamps and entries with the actigraphy data. Participants were also instructed to avoid daytime naps, and to not consume alcoholic or caffeinated drinks. Participants who failed to follow the sleep protocol were excluded from the study.

Controlled food intake

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During the 24 hr period before each fMRI session, participants were provided with an isocaloric diet. All meals were planned and packaged by a registered dietician at the Clinical Research Unit (CRU) at Northwestern Memorial Hospital, and were based on individually estimated energy requirements according to height, weight, age, and sex. Estimated calorie requirements ranged from 1400 to 2600 kcal/day. Meals were composed of 55–60% carbohydrate, 15–20% protein, and 25–30% fat. On the evening preceding the sleep manipulation, participants arrived at the laboratory and consumed a dinner at 6:00 pm. They were also provided with a take-out breakfast and lunch to be consumed on the following day at 8:00 am and 12:00 pm, respectively. Participants were instructed to not consume any additional foods or drinks, other than water, during the 24 hr period preceding the fMRI session. Breakfast consisted of ~30% of total caloric needs, while the lunch and dinner each consisted of ~35% of the estimated calorie requirement. The standardized pre-scan dinner consisted of an entrée (e.g., hamburger, veggie burger, grilled chicken with dinner roll, ham/turkey sandwich, etc.), a fruit/snack (e.g., apple, banana, granola bar, etc.), and a small non-alcoholic and non-caffeinated drink. The total calorie content of this dinner ranged from 490 to 910 kcal (~35% of estimated calorie requirements) and was composed of 55–60% carbohydrate, 15–20% protein, and 25–30% fat. All participants consumed the entire meal. For each participant, the same meals were provided in both fMRI sessions. All subjects reported that they did not consume any other foods or caffeinated and caloric drinks.

Collection of blood samples and assay

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Upon arrival at the imaging center, an MRI safe catheter was placed in participant’s left arm and samples were collected through antecubital venipuncture. Blood plasma samples were collected at baseline (before dinner) and at four time points following the dinner. Blood serum samples for ECS analysis were only collected at 7:30 pm while subjects were inside the MRI scanner, 90 min after the initiation of dinner (in between the 2nd and 3rd fMRI run). Prior to drawing each sample, 1.5–2 mL of blood were drawn off to remove potentially diluted blood from the dead space of the catheter. Samples were put on ice, centrifuged, aspirated, divided into aliquots, and stored at −80°C until assay.

Serum levels of 2-arachidonoylglycerol (2-AG) and 2-oleoylglycerol (2-OG) were extracted using the Bond Elut C18 solid-phase extraction columns (1 ml; Varian Inc, Lake Forest, CA). Serum samples were processed and the two compounds were quantified using chemical ionization liquid chromatography/mass spectrometry (LC-ESI-MS; Agilent LC-MSD 1100 series, Ramsey, MN), as previously described (Patel et al., 2005).

We used enzyme-linked immunosorbent assay (ELISA) kits for total plasma ghrelin (human ghrelin, Millipore), plasma leptin (human leptin, Millipore), and cobas e411 analyzer (Roche) for plasma insulin, and serum cortisol.

Assessment of hunger

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On the day of fMRI scanning, subjects completed both paper and computerized versions of visual analogue scales to measure motivation to eat at baseline (before dinner) and 30 min, 60 min, 90 min, and 120 min after dinner initiation and after the ad libitum buffet (see below). At each timepoint, sensation of hunger, fullness, satisfaction, and prospective food consumption was assessed with following questions: (1) How hungry do you feel? (2) How full do you feel? (3) How satisfied do you feel? (4) How much do you think you can eat? Ratings were made on a 10 cm visual analogue scale with text at each end indicating the most positive and most negative rating.

Ad libitum buffet and next day food intake

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After completion of the fMRI scan, participants were presented with excessive portion sizes of energy-dense sweet (cinnamon roll, donut holes, chocolate chip cookies, mini muffins) and savory food items (hash browns, garlic bread, pizza bites, potato chips) in an all-you-can-eat buffet-style setting. In both sessions participants were instructed to wait in a separate room for 30 min before filling out another questionnaire. This waiting room contained the buffet of food options, and participants were told that they could consume the food freely while they waited, if they wanted. Food items were weighed before and after to determine the amount of food consumed. Total calorie and energy density (kcal/g) of consumed food was calculated from the product nutrition labels. Participants were presented with identical buffet options in both testing sessions.

To track food intake on the day following the experiment (after a night of unrestricted sleep), participants were asked to record their food intake for 24 hr following the fMRI scan using a food diary. To estimate total calories and fat consumed, nutritional information for each consumed food item was obtained from the United States Department of Agriculture (USDA) Food Composition Databases (ndb.nal.usda.gov/ndb/). Percent of calories consumed as fat was calculated by multiplying total grams of consumed fat by nine kcal/g and dividing this number by the total number of calories consumed.

Odor selection and delivery

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During initial screening, participants rated the pleasantness of six energy-dense food odors in randomized order (pot roast, potato chips, garlic bread, cinnamon roll, caramel, and gingerbread, provided by International Flavors and Fragrances [New York City, NY] and Kerry [Tralee, Ireland]). Ratings were made on visual analog scales using a scroll wheel and mouse button press. Anchors were ‘most-liked sensation imaginable’ (10) and ‘most disliked sensation available’ (−10). Based on these ratings, two savory and two sweet odors were chosen for each participant such that they were matched in pleasantness, and these odors were used for the remainder of the experiment. After odor selection, participant also rated the pleasantness, intensity (anchors ‘strongest sensation imaginable’ and ‘weakest sensation imaginable’), edibility (anchors ‘certainly edible’ and ‘certainly inedible’), and quality (anchors ‘clearly savory’ and ‘clearly sweet’) of the four selected food odors and two non-food control odors (fir needle and celery seed). Odor ratings collected during screening are summarized in Supplementary file 2. Most importantly, edibility ratings for food and non-food odors collected during the screening session differed significantly between food and non-food odors (T24 = 12.69, p=3.87×10−12).

For all odor ratings and the experimental task, odors were delivered directly to subjects’ nose using a custom-built MR-compatible olfactometer (Howard and Kahnt, 2017; Howard and Kahnt, 2018; Suarez et al., 2019), capable of redirecting medical grade air with precise timing at a constant flow rate of 3.2 L/min through the headspace of amber bottles containing liquid solutions of the odors. The olfactometer is equipped with two independent mass flow controllers (Alicat, Tucson, AZ), allowing for dilution of odorants with odorless air. At all times throughout the experiment, a constant stream of odorless air is delivered to participants’ noses, and odorized air is mixed into this airstream at specific time points, without changing in the overall flow rate. Thus, odor presentation does not involve a change in somatosensory stimulation induced by the airstream.

Olfactory fMRI task

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On the evening following the sleep manipulation, all participants arrived at the scanning center after a 6 hr fast and consumed dinner at 6:00 pm before entering the scanner at 6:45 pm. Immediately before the fMRI scan, participants repeated rated the pleasantness, edibility, and quality of the four selected food odors and the two non-food control odors. Each scanning session consisted of four fMRI runs, and each run consisted of 63 pseudo-randomized trials of olfactory stimulation. On each trial, after a 2 s countdown, the white crosshair in the center of the screen turned blue, cuing participants to sniff the odor for 2.5 s. The sniff cue was followed by a rating scale (pleasantness or intensity, counterbalanced) for 5 s and a 1–8 s inter-trial interval. Each run consisted of nine presentations of the two sweet food odors, the two savory food odors, the two non-food control odors, and clean air (totaling 63 trials/run).

fMRI data acquisition

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Functional MRI data were acquired on a Siemens 3T PRISMA system equipped with a 64-channel head-neck coil. In each scanning run, 382 Echo-Planar Imaging (EPI) volumes were acquired with a parallel imaging sequence with the following parameters: repetition time, 2 s; echo time, 22 ms; matrix size, 104 × 96; field-of-view, 208 × 192 mm (resulting in an in-plane resolution of 2 × 2 mm2); flip angle, 90o; multi-band acceleration factor, 2; slice thickness, 2 mm; 58 slices; no gap; acquisition angle, ~30o rostral to intercommissural line to minimize susceptibility artifacts in piriform cortex (Deichmann et al., 2003; Weiskopf et al., 2006). Figure 3—figure supplement 2 shows a normalized group average EPI. A high-resolution (1 mm isotropic) T1-weighted structural scan was also acquired at the beginning of the fMRI session. To support the co-registration of functional and structural images, we also collected 10 whole-brain EPI volumes using the same parameters as the functional EPIs, except for 96 slices and a repetition time of 3.22 s.

Respiratory data acquisition and analysis

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Respiration, as an indirect measure of nasal sniffing, was measured using a MR-compatible breathing belt (BIOPAC Systems Inc, Goleta, CA) affixed around the participant’s torso, and recorded at 1 kHz using PowerLab equipment (ADInstruments, Dunedin, New Zealand). Respiratory traces for each fMRI run were temporally smoothed using a moving window of 500 ms, high-pass filtered (cutoff, 50 s) to remove slow-frequency drifts, normalized by subtracting the mean and dividing by the standard deviation across the run trace, and down-sampled to 0.5 Hz for use as nuisance regressors in fMRI data analyses (see below).

For quantification of respiratory peak amplitude and respiratory latency, trial-specific respiratory traces were baseline corrected by subtracting the mean signal across the 0.5 s window preceding sniff cue onset, and then normalized by dividing by the maximum respiratory amplitude of all trials in the run. Respiratory traces were sorted by sleep session and odor, and averaged across trials. Respiratory amplitude was then calculated as the max signal within 5 s of sniff cue onset, and respiratory latency was calculated as the time from sniff cue onset to max amplitude.

fMRI pre-processing

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Pre-processing of fMRI data was performed using SPM12 (http://www.fil.ion.ucl.ac.uk/spm/software/spm12/). For each participant, we aligned all functional volumes for both sleep sessions to the first acquired functional volume to correct for head motion. We then realigned and averaged the ten whole-brain EPI volumes, and co-registered the mean whole-brain EPI to the anatomical T1 image. The mean functional volume was then co-registered to the mean whole-brain EPI, and this transformation was applied to all functional volumes. Spatial normalization was performed by normalizing the T1 anatomical images to the MNI (Montreal Neurological Institute) space using the six tissue probability map provided by SPM12. For multivariate analysis, the resulting deformation fields were applied to searchlight-based maps of decoding accuracy (see below). The normalized decoding accuracy maps were spatially smoothed with a 6 × 6 × 6 mm full-width half-maximum (FWHM) Gaussian kernel before group-level statistical testing. For functional connectivity and univariate analyses (see below), the motion-corrected and co-registered functional images were normalized to MNI space using the previously estimated deformation fields and spatially smoothed with a 6 × 6 × 6 mm FWHM Gaussian kernel.

To quantify and compare head motion between sessions we computed the average (across scans) of the absolute volume-by-volume displacements for each of the six realignment parameters for each session. We then computed composite scores for translation and rotation parameters and compared them between sessions. There were no sleep-dependent differences in these composite head motion parameters (translation, T24 = 0.25, p=0.803; rotation, T24=−0.14, p=0.891).

Multivoxel pattern analysis

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We implemented a searchlight-based multi-voxel pattern analysis (MVPA) (Howard and Kahnt, 2018; Kahnt et al., 2011) to decode information about food vs. non-food odors. We first estimated general linear models (GLM) for each subject, separately for each session, using the non-normalized and un-smoothed functional images. The GLM included three regressors of interest specifying onset times for the following conditions: 1) food odors, 2) non-food odors, 3) clean air. We also included the following nuisance regressors: the smoothed and normalized respiratory trace, down-sampled to scanner temporal resolution (0.5 Hz); the six realignment parameters (three translations, three rotations), calculated for each volume during motion correction; the derivate, square, and the square of the derivative of each realignment regressor; the absolute signal difference between even and odd slices, and the variance across slices in each functional volume (to account for fMRI signal fluctuation caused by within-volume head motion); additional regressors as needed to model out individual volumes in which particularly strong head motion occurred (absolute difference between odd and even slices >5 SD or slice variance >4 SD). The parameter estimates from the first two regressors of this GLM reflect the voxel-wise response amplitudes for food and non-food odors, separately for each run and sleep session.

Next, we used these voxel-wise parameter estimates in a searchlight-based, leave-one-run-out cross-validated decoding approach. We decoded food vs. non-food odors from patterns of odor-evoked activity, separately for each of the two sleep sessions. We used The Decoding Toolbox (TDT) to implement the searchlight (Hebart et al., 2014) and LIBSVM (Chang and Lin, 2011) for the linear support vector machine (SVM) classifier. To test for brain regions that encoded food vs. non-food odors, at each searchlight (sphere with 8 mm radius), we trained a SVM to discriminate between activity patterns evoked by food vs. non-food odors in three of the four runs per session (DS or NDS), and tested it on activity patterns evoked by food vs. non-food from the fourth ‘left out’ run of the same session. The procedure was repeated four times leaving a different run out, and decoding accuracies were averaged and mapped to the center voxel of the searchlight. This procedure was repeated for every voxel within a 10% gray-matter mask (based on SPMs tissue probability map that was inverse-normalized into the individual native space, as described in Howard and Kahnt, 2018). The resulting accuracy maps for food vs. non-food odors for DS and NDS sessions were subtracted (DS >NDS), normalized, and smoothed (6 mm FWHM). We tested for significant differences between DS and NDS sessions at the group level using voxel-wise one-sample t-tests. Statistical thresholds were set to p<0.05, family-wise error (FWE) small-volume corrected for multiple comparisons at the voxel-level in a functional mask of piriform cortex that was obtained from a one-sample t-test of decoding accuracy for food vs. non-food odors, averaged across sleep sessions (p<0.001, see Figure 3—figure supplement 1).

Functional connectivity analysis

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We used the generalized psycho-physiological interaction (PPI) model (McLaren et al., 2012) to test for regions in which sleep-dependent changes in functional connectivity with the piriform cortex correlated with sleep-dependent changes in food intake. The seed region in the piriform cortex was defined from significant voxels (p<0.001) in the contrast DS >NDS for decoding food vs. non-food odors. We first specified session-wise (DS or NDS) PPI models at the single-subject level using normalized and smoothed functional images. Odor presentation (odor vs. clean air) was used as ‘psychological variable’ and mean piriform cortex activity as ‘physiological variable’. The PPI models also included the same nuisance regressors as described above for the GLM for the MVPA analysis. Estimated connectivity parameters for odor vs. no-odor were contrasted between DS and NDS sessions, and entered into a group-level model with changes in energy-dense food intake as regressor of interest. We tested for regions in which sleep-dependent changes in odor-evoked functional connectivity (odor >clean air) correlated significantly with sleep-dependent changes in food intake. Statistical thresholds were set to p<0.05, FWE small-volume corrected for multiple comparisons at the voxel-level in an anatomical mask of insula cortex obtained using the Automated Anatomical Labeling (AAL) atlas.

Univariate fMRI analysis

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We conducted a univariate analysis using the traditional GLM approach on normalized and spatially smoothed functional images. The session-wise GLM included three regressors of interest specifying onsets for the following conditions: 1) food odors, 2) non-food odors, 3) clean air. The GLM included the same nuisance regressors as the GLM described in the MVPA section. To test for odor-evoked fMRI activity in the piriform cortex, contrast images for food and non-food odors > clean air trials were created at the single-subject level and averaged across sessions. Group-level analyses were carried out using voxel-wise one-sample t-tests thresholded at p<0.05, FWE whole-brain corrected. To test for sleep-dependent differences in odor-evoked activity in the piriform cortex, we extracted parameter estimates for the three odor conditions per sleep session from a piriform cortex region of interest (defined using the odor >clean air contrast, at p<0.001, see Figure 4). We computed a two-way ANOVA on the parameter estimates to test for sleep-dependent main effects and interactions.

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Decision letter

  1. Timothy Verstynen
    Reviewing Editor; Carnegie Mellon University, United States
  2. Christian Büchel
    Senior Editor; University Medical Center Hamburg-Eppendorf, Germany
  3. Timothy Verstynen
    Reviewer; Carnegie Mellon University, United States
  4. Noam Sobel
    Reviewer; Weizmann Institute of Science, Israel

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Olfactory contributions to sleep-dependent food intake in humans" for consideration by eLife. Your article has been reviewed by three peer reviewers, including, including X as the Reviewing Editor and Reviewer #1 as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Christian Büchel as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Noam Sobel (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

This study examines how a manipulation of sleep duration impacts both food choice and circulating endocannabinoid system (ECS) compounds, as well as how this association is mediated by altered connectivity of neural olfactory regions. Consistent with previous work they found that sleep restriction increases circulating levels of an ECS compound (2-OG) and increases food intake (measured as energy density in food selected from a buffet). Using an MVPA approach, the authors identified regions that were selective in their representations of food vs. non-food smells, with the right piriform cortex exhibiting a group difference in representational distances of food vs. non-food odors. Using this as a seed region, the authors found that individual differences in odor vs. clean air task connectivity between the piriform cortex and insula mediated the individual differences observed in ECS compounds and food choice.

All three reviewers felt that this is potentially a very important study that addresses a very important question, with immediate interest for both scientists and the general public alike. Moreover, it is clearly the product of "high level" rigorous science. More specifically, the authors tackle their question from several directions at once. This is on one side a clear advantage, yet on the other, when you do a lot of different work, there are lots of potential complications that need to be addressed before the manuscript can be accepted.

Essential revisions:

1) Imaging analysis.

Reviewer 1 pointed out that the critical finding that sets this work apart from prior studies is the observation that task-related piriform-insula connectivity differences statistically mediate a relationship between 2-OG and food choice. Yet a lot relies on the veracity of the neuroimaging analysis. Both estimates of task effects (e.g., GLM) and connectivity measures in the BOLD response are highly sensitive to motion and physiological artifacts that can sometimes lead to spurious observations. This brings up several concerns.

First, reviewer 1 is concerned that the food > non-food difference in piriform representations may be due to differences in head motion across groups. It seems possible that sleep manipulations can lead to differences in head motion in the scanner, that in turn would alter the reliability of the representational distance estimates that the SVN decoder picks up on (leading to a potential spurious group difference). The authors should both report whether there were differences in framewise displacement measures across groups and include a displacement measure as a nuisance variable in the estimation of the group differences.

Second, reviewer 1 pointed out that while there is no statistical difference in sniff amplitude (Figure 1—figure supplement 3E), there does appear to be a mean difference such that the sleep restricted group is taking lower amplitude sniffs. Even if this isn't significant at the behavioral level, it might lead to differences in the expression of respiratory artifacts in the BOLD response. In addition, there may be differences in respiration variability, both during the odor presentation and in-between, which is also known to contribute to artifacts in the BOLD signal (see Birn et al., (2008). Therefore, the authors should include models of respiration artifacts, particularly in the connectivity estimates, in order to rule out possible spurious associations due to group differences in respiration. This concern was echoed by reviewer 3, who pointed out that, they were not convinced that there were not, for instance, motoric differences in the sniff that contribute to the effects. Although Figure 1—figure supplement 3E and F show a non-significant differences, when one cuts out C and D and overlay them up against the light, it sure appears that the odors for NDS are equal to the clean air in DS. In other words, it is not clear where the amplitude and latency measures come from, but they don't seem to capture the differences you can see if you overlap the curves in C and D. One other concern in this regard is that if the piriform-insula connectivity measure is for odors (collapsed) v. clean air, then this sniff response comparison should be for odors (collapsed) v. clean air. (One note on food odors v. non-food odors: it's confusing to me that celery seed is considered a non-food odor as this is a not-uncommon ingredient in food dishes.)

Finally, all three reviewers raised concerns about the use of different contrasts used in the analyses. Reviewer 1 points out that the search for group differences in representational distances in the piriform uses the food > non-food contrast, but then the connectivity results between piriform and insula are using the odor > clean air contrast. It is likely that this is because the food > non-food contrast did not produce a significant result. This should still be reported and clarified in the text.

2) Pleasantness confounds.

Both reviewers 1 and 2 had concerns regarding the role of subjective pleasantness preferences had on the task. Reviewer 2 pointed out that a major analysis in this manuscript is the searchlight-based multi-voxel pattern analysis contrasting the response to "food" and "non-food" odors. As clearly indicated in Figure 1—figure supplement 3, "food" odors were much (as in p = 2.5x10-6) more pleasant than "non-food" odors. Thus, why is this to be considered a contrast of "food vs. non-food" and not a contrast of "pleasant vs. less-pleasant". The impact of sleep deprivation on hedonics is also interesting, but not the aim of this manuscript. This potential confound is so blatant, that the reader must be missing something. Thus, what is missing? Why is the sharp difference in pleasantness between the edible and non-edible odorants not a concern? The authors can do one of two things: Either better explain how this relates directly to food vs. non-food odors, or run analyses to address this. For example, either regress out pleasantness differences, or select subsets of data devoid of this potential confound.

Related to this, reviewer 2 pointed out that the selection of non-food odorants was slightly odd. After all, celery seed is not that far from a food. Why didn't the authors just use perfume? It would have addressed their pleasantness difference, and it's clearly not edible. Odorant selection, however, is behind us. Thus, what the authors should at least add is the actual edibility ratings of the odorants. This should be provided in a supplementary table with the associated statistics of the differences in edibility.

3) Validity of inferences.

Reviewers 2 and 3 had concerns about the certainty of the conclusions being made. Reviewer 2 pointed out that the authors conclude that "sleep deprivation induces changes in food intake through the modulation of an olfactory pathway that is related to the endocannabinoid system". This very strong statement implies causation, that it is not 100% certain the authors have in hand. There is a relationship, but the authors to make such a strong causal claim, wouldn't they need to somehow find a way to independently manipulate the olfactory pathway, and show that it is indeed responsible for the effect? Would sleep deprivation fail to impact eating behaviour of individuals with anosmia? This is a major concern, but it is of course trivial to address: The authors should either slightly tone down the claims on causation (the manuscript is strong enough as it is) or make a better substantiated claim on causation.

Reviewer 3 pointed out that the conclusions of the paper emphasize a finding that olfaction contributes to sleep-dependent food intake in humans (see Title). This is an overstatement of the results. First, unlike other papers, the present study did not find a difference in food intake (caloric intake) based on sleep deprivation. The authors did find a difference in food preference (food decisions). Second, the measure of olfaction in the result is not olfaction per se but rather piriform-insula connectivity when sleep deprived v. not sleep deprived. This should be made more clear and up front throughout the manuscript.

4) 2-OG vs. food choice association.

All three reviewers had a concern about the association between 2-OG and food choice. An inspection of Figure 2B suggests that the association between ECS system and food choice may be driven by 5 participants. Reviewers 2 and 3 pointed out that if these outliers are removed, the association appears to become negative. The authors seem to be aware of this since they used a robust regression analysis; however, robust regression approaches simply account for differences in variance of each observation in the overall estimate. Reviewer 1 recommend a non-parametric statistic, such as a bootstrap or permutation test. (Note: This is listed as a Major concern because this is one of the critical observations for the authors' primary conclusions).

5) Compliance.

Reviewer 3 raised concerns about subject compliance. There is a lot of trust in participants. Trust that they did not consume additional food and drinks, trust they did not take caffeine, and trust in both the participant and actigraph that the sleep restriction instructions were complied with. Given the importance of these, it is surprising that participants were not kept in-lab for the sleep deprivation manipulation. The authors should address this.

6) Sniffing vs. inhalation.

Reviewer 2 raised a concern about the nature of the inhalation measure identified as sniffing. This reviewer points out that authors don't directly measure sniffing; they measure respiration with respiratory belts. However, the path from thoracic and abdominal movements to airflow patterns in the nose is complex, and more critically, it is variable. In fact, even in direction; an expanding abdomen can reflect inhalation and it can reflect exhalation. Moreover, belts don't discriminate between nasal and oral inhalation. What if a participant didn't like a particular odorant, and shifted to oral inhalation every time it was presented? How would the authors know this?

Moreover, as the authors know, sniffing is odorant dependent. Thus, the pleasant food odors may have been greeted with slightly more vigorous sniffs, yet the less pleasant non-food odors with slightly less vigorous sniffs. If this difference was very small, it would not be picked up by the belts. Such a small difference, however, may also be very consistent, and thus meaningful. All this is critical, because the authors may attribute an activation pattern to a difference in odor, where in fact it may reflect a difference in sniffing. Alas, these are indeed often very difficult to untangle, but this still needs to be addressed. This is especially true in light of the simplicity of precisely measuring sniffing at the nose. If the olfactometer uses a mask, then a simple pressure tube off the mask accurately converts sniffing. If no mask is used, then a nasal cannula provides an even better measure of sniffing. As sniffing is very easy to measure, it is not clear why the authors did not do so.

As to the current manuscript, the authors should use more careful terminology. In Figure 1—figure supplement 3, the authors should refer to respiratory amplitude, not sniff amplitude. Reviewer 2 points out that this is a problem with the entire field, but should be reasonably addressed here. In future, reviewer 2 suggests the authors precisely measure sniffing in their studies. It's cheap, easy, and informative.

https://doi.org/10.7554/eLife.49053.048

Author response

Summary:

This study examines how a manipulation of sleep duration impacts both food choice and circulating endocannabinoid system (ECS) compounds, as well as how this association is mediated by altered connectivity of neural olfactory regions. […] More specifically, the authors tackle their question from several directions at once. This is on one side a clear advantage, yet on the other, when you do a lot of "things", there are lots of potential complications that need to be addressed before the manuscript can be accepted.

We thank the reviewers for their positive and thoughtful comments, and the reviewing editor for preparing the summary and the consolidated comments. Below we outline how we have addressed the essential revisions in the revised manuscript.

Essential revisions:

1) Imaging analysis.

Reviewer 1 pointed out that the critical finding that sets this work apart from prior studies is the observation that task-related piriform-insula connectivity differences statistically mediate a relationship between 2-OG and food choice. Yet a lot relies on the veracity of the neuroimaging analysis. Both estimates of task effects (e.g., GLM) and connectivity measures in the BOLD response are highly sensitive to motion and physiological artifacts that can sometimes lead to spurious observations. This brings up several concerns.

First, reviewer 1 is concerned that the food > non-food difference in piriform representations may be due to differences in head motion across groups. It seems possible that sleep manipulations can lead to differences in head motion in the scanner, that in turn would alter the reliability of the representational distance estimates that the SVN decoder picks up on (leading to a potential spurious group difference). The authors should both report whether there were differences in framewise displacement measures across groups and include a displacement measure as a nuisance variable in the estimation of the group differences.

We thank the reviewers for these suggestions. We were also particularly concerned about potential differences in head motion between the two sleep conditions. We therefore included 24 (instead of the standard 6) motion regressors along with nuisance regressors that capture within-volume motion (absolute difference between odd and even slices, variance across slices) into all GLMs.

We also directly compared head motion between the two sessions (i.e., NDS and DS). Specifically, we computed the average (across scans) of the absolute volume-by-volume displacements for each of the 6 realignment parameters for each session. We found no significant sleep-dependent differences in any of these parameters (x, P=0.476; y, P=0.962; z, P=0.922; pitch, P=0.879; roll, P=0.9182; yaw, P=0.894). In addition, we computed composite scores for translation and rotation parameters, which showed no sleep-dependent differences (translation, T24=0.25, P=0.803; rotation, T24=−0.14, P=0.891).

Finally, as suggested, we added these two composite scores as covariates in the second level models. Adding these covariates did not change the results for the decoding (x=20 y=8 z=−12, T23=6.12, PFWE-SVC=0.0001) or the connectivity analysis (right insula, x=40, y=6, z=0, T21=5.42, PFWE-SVC=0.021).

We have added the results of these control analyses to the manuscript.

Materials and methods section:

“To quantify and compare head motion between sessions we computed the average (across scans) of the absolute volume-by-volume displacements for each of the 6 realignment parameters for each session. We then computed composite scores for translation and rotation parameters and compared them between sessions. There were no sleep-dependent differences in these composite head motion parameters (translation, T24=0.25, P=0.803; rotation, T24=−0.14, P=0.891).”

Legend Figure 3:

“This result did not change when including covariates for head motion (translation and rotation) into the group-level model (x=20 y=8 z=−12, T23=6.12, PFWE-SVC=0.0001).”

Legend Figure 5:

“This result did not change when including covariates for head motion (translation and rotation) into the group-level model (x=40, y=6, z=0, T21=5.42, PFWE-SVC=0.021).”

Second, reviewer 1 pointed out that while there is no statistical difference in sniff amplitude (Figure 1—figure supplement 3E), there does appear to be a mean difference such that the sleep restricted group is taking lower amplitude sniffs. […] One other concern in this regard is that if the piriform-insula connectivity measure is for odors (collapsed) v. clean air, then this sniff response comparison should be for odors (collapsed) v. clean air. (One note on food odors v. non-food odors: it's confusing to me that celery seed is considered a non-food odor as this is a not-uncommon ingredient in food dishes.)

The reviewers point out a number of important issues. Similar to head motion, we controlled for potential respiratory confounds by including respiratory traces as covariates in all GLMs used for the decoding and connectivity analyses. We also tested whether the respiratory measures differed between sessions, the results of which are reported in the legend accompanying Figure 1—figure supplement 3.

In addition to these controls, we have computed new GLMs which include respiratory nuisance regressors that model the “respiration response function” as defined in Birn et al., (2008). Controlling for breathing using these more advanced nuisance regressors did not change the results of the decoding (x=20, y=8, z=−12, T24=4.83, PFWE-SVC=0.001) or connectivity analyses (x=40, y=6, z=0, T23=5.59, PFWE-SVC=0.011). Taken together, these new control analyses suggest that our findings are unlikely to be driven by respiratory artifacts.

Regarding sleep-dependent differences in odor-evoked respiratory amplitude and latency, we do not fully understand the reviewer’s concern when they write “it is not clear where the amplitude and latency measures come from but they don't seem to capture the differences you can see if you overlap the curves in C and D”. We fully agree that the peak of the respiratory trace for food in NDS (light pink trace in Figure 1—figure supplement 3C) appears to roughly line up with the peak of the respiratory trace for clean air in DS (dark gray trace in Figure 1—figure supplement 3D). We would like to point out that these respiratory traces (averaged across trials and subjects) are presented in panels C and D only for visualization, and the across-subjects mean values of respiratory peak amplitude and latency directly corresponding to these traces are plotted in panels E and F of the same figure using bar plots with error bars. We suspect that the confusion may have originated from the fact that the respiratory traces themselves in Figure 1—figure supplement 3C and D did not contain error bars, which are necessary to interpret the mean differences. We now include error bars on these traces to better illustrate that differences between respiratory responses to different odors are within what can be expected by chance.

Finally, as requested, we also computed statistical tests for comparing odors (collapsed) vs. clean air trials which show that there are no sleep-dependent effects in respiratory peak amplitude or latency (sleep-by-odor ANOVA on amplitude; main effect sleep F1,24=1.45, P=0.240; main effect odor F1,24=22.41, P<0.0001; sleep-by-odor interaction, F1,24=1.24, P=0.277; sleep-by-odor ANOVA on latency; main effect sleep F1,24=0.14, P=0.715; main effect odor F1,24=3.50, P=0.074; sleep-by-odor interaction, F1,24=0.53, P=0.474).

We have added the results of these additional analyses to the manuscript.

Legend Figure 3:

“Finally, controlling for respiratory response functions (Birn et al., 2008) did not change the result (x=20, y=8, z=−12, T24=4.83, PFWE-SVC=0.001).”

Legend Figure 5:

“In addition, controlling for respiratory response functions (Birn et al., 2008) did not change the result (x=40, y=6, z=0, T23=5.59, PFWE-SVC=0.011).”

Legend Figure 1—figure supplement 3:

“There were also sleep-depended effects on amplitude and latency when comparing odor (collapsed across food and non-food odors) vs. clean air trials (sleep-by-odor ANOVA on amplitude; main effect sleep F1,24=1.45, P=0.240; main effect odor F1,24=22.41, P<0.0001; sleep-by-odor interaction, F1,24=1.24, P=0.277; sleep-by-odor ANOVA on latency; main effect sleep F1,24=0.14, P=0.715; main effect odor F1,24=3.50, P=0.074; sleep-by-odor interaction, F1,24=0.53, P=0. 474).”

We address the question about the choice of celery seed as a non-food odor below.

Finally, all three reviewers raised concerns about the use of different contrasts used in the analyses. Reviewer 1 points out that the search for group differences in representational distances in the piriform uses the food > non-food contrast, but then the connectivity results between piriform and insula are using the odor > clean air contrast. It is likely that this is because the food > non-food contrast did not produce a significant result. This should still be reported and clarified in the text.

We used the food vs. non-food contrast in our decoding analysis to test for differences in encoding of odor information. For the connectivity analysis, we tested odor-evoked connectivity, thus contrasting connectivity during odor vs. clean air trials. Reviewers are correct that the contrast between food and non-food odor for sleep-dependent changes in connectivity did not reveal significant effects. We now report this in the manuscript.

Results section:

“In contrast, sleep-dependent changes in piriform connectivity for food vs. non-food odors did not show a significant relationship with changes in food choices.”

2) Pleasantness confounds.

Both reviewers 1 and 2 had concerns regarding the role of subjective pleasantness preferences had on the task. Reviewer 2 pointed out that a major analysis in this manuscript is the searchlight-based multi-voxel pattern analysis contrasting the response to "food" and "non-food" odors. As clearly indicated in Figure 1—figure supplement 3, "food" odors were much (as in p = 2.5x10-6) more pleasant than "non-food" odors. Thus, why is this to be considered a contrast of "food vs. non-food" and not a contrast of "pleasant vs. less-pleasant". The impact of sleep deprivation on hedonics is also interesting, but not the aim of this manuscript. This potential confound is so blatant, that the reader must be missing something. Thus, what is missing? Why is the sharp difference in pleasantness between the edible and non-edible odorants not a concern? The authors can do one of two things: Either better explain how this relates directly to food vs. non-food odors, or run analyses to address this. For example, either regress out pleasantness differences, or select subsets of data devoid of this potential confound.

The objective of this experiment was to study sleep-dependent brain responses to food odors corresponding to highly palatable energy-dense food items (caramel, cinnamon bun, potato chips, pot roast, garlic bread, etc.), relative to odors of objects (in principle edible) with low palatability and low energy density (celery seed and fir needle). We expected that higher palatability would be reflected in higher pleasantness ratings, and thus view the pleasantness difference as validation of our experimental design with respect to stimulus selection.

However, we understand the reviewers’ concerns and conducted additional analyses to test whether our effects are merely driven by pleasantness. In these analyses, we controlled for pleasantness in the single-subject GLMs used to estimate activity patterns evoked by food and non-food odors by adding the ratings as a nuisance regressor. Interestingly, despite the fact that food and non-food odors differed in pleasantness, we still found a significant (but much weaker) effect in our piriform ROI, such that odor encoding was enhanced in the sleep-deprived session (x=20, y=8, z=−10, T24=3.12, PFWE-SVC=0.045).

In addition, it is worth emphasizing that pleasantness ratings for food and non-food odors did not differ between the two sleep sessions, and that controlling for individual differences in the pleasantness of food vs. non-food odors in the group-level analysis did not change our decoding results (x=20, y=8, z=−12, T23=6.34, PFWE-SVC=0.0001).

We now include these results into the manuscript and mention the potential pleasantness confound in the Discussion section.

Legend Figure 3:

“In addition, results were still significant when including covariates for odor pleasantness in the first- (x=20, y=8, z=−10, T24=3.12, PFWE-SVC=0.045) and group-level models (x=20, y=8, z=−12, T23=6.34, PFWE-SVC=0.0001).”

Discussion section:

“In the current study, we compared brain responses to food odors with high palatability and non-food items with low palatability. As expected, the food odors were rated as higher in pleasantness than non-food odors. In principle, it is therefore possible that our observed effects for food vs. non-food odor encoding in the brain were fully explained by this corresponding pleasantness difference. However, our results remained significant when including pleasantness as a covariate in the statistical models, indicating that in this case pleasantness does not account for our results.”

Related to this, reviewer 2 pointed out that the selection of non-food odorants was slightly odd. After all, celery seed is not that far from a food. Why didn't the authors just use perfume? It would have addressed their pleasantness difference, and it's clearly not edible. Odorant selection, however, is behind us. Thus, what the authors should at least add is the actual edibility ratings of the odorants. This should be provided in a supplementary table with the associated statistics of the differences in edibility.

Edibility ratings did indeed differ significantly between food and non-food odors (T24=12.69, P=3.87x10-12). We have now included a new supplementary table (Supplementary file 2) summarizing the odor ratings (collected during the screening session) for all odors used in the study.

Materials and methods section:

“Odor ratings collected during screening are summarized in Supplementary file 2. Most importantly, edibility ratings for food and non-food odors collected during the screening session differed significantly between food and non-food odors (T24=12.69, P=3.87x10-12).”

3) Validity of inferences.

Reviewers 2 and 3 had concerns about the certainty of the conclusions being made. Reviewer 2 pointed out that the authors conclude that "sleep deprivation induces changes in food intake through the modulation of an olfactory pathway that is related to the endocannabinoid system". This very strong statement implies causation, that it is not 100% certain the authors have in hand. There is a relationship, but the authors to make such a strong causal claim, wouldn't they need to somehow find a way to independently manipulate the olfactory pathway, and show that it is indeed responsible for the effect? Would sleep deprivation fail to impact eating behaviour of individuals with anosmia? This is a major concern, but it is of course trivial to address: The authors should either slightly tone down the claims on causation (the manuscript is strong enough as it is) or make a better substantiated claim on causation.

We have toned down our conclusions regarding causation throughout the manuscript. In particular, we have changed the sentence referenced above to:

Discussion section:

“Taken together, our findings show that sleep-dependent changes in food choices are associated with changes in an olfactory pathway that is related to the ECS.”

Reviewer 3 pointed out that the conclusions of the paper emphasize a finding that olfaction contributes to sleep-dependent food intake in humans (see Title). This is an overstatement of the results. First, unlike other papers, the present study did not find a difference in food intake (caloric intake) based on sleep deprivation. The authors did find a difference in food preference (food decisions). Second, the measure of olfaction in the result is not olfaction per se but rather piriform-insula connectivity when sleep deprived v. not sleep deprived. This should be made more clear and up front throughout the manuscript.

The reviewer is correct that the relationship between energy-dense food choices and total calorie intake is not evident in our study. We further agree that energy-dense food choice was correlated with connectivity of the olfactory cortex, not olfaction per se.

We have changed wording throughout the manuscript and also changed the Title to:

“Olfactory connectivity mediates sleep-dependent food choices in humans”

4) 2-OG vs. food choice association.

All three reviewers had a concern about the association between 2-OG and food choice. An inspection of Figure 2B suggests that the association between ECS system and food choice may be driven by 5 participants. Reviewers 2 and 3 pointed out that if these outliers are removed, the association appears to become negative. The authors seem to be aware of this since they used a robust regression analysis; however, robust regression approaches simply account for differences in variance of each observation in the overall estimate. Reviewer 1 recommend a non-parametric statistic, such as a bootstrap or permutation test. (Note: This is listed as a Major concern because this is one of the critical observations for the authors' primary conclusions).

Following this suggestion, we computed a permutation test to verify the result from the robust regression analysis. This permutation test (100,000 permutations) confirmed that the correlation between 2-OG and food choice is statistically significant (p=0.018).

We have added this result to the manuscript.

Results section:

“Interestingly, sleep-dependent increases in 2-OG correlated significantly with increases in the energy density of food consumed at the post-scanning buffet (robust regression, β=0.47, P=0.027; permutation test, P=0.018; Figure 2B).”

5) Compliance.

Reviewer 3 raised concerns about subject compliance. There is a lot of trust in participants. Trust that they did not consume additional food and drinks, trust they did not take caffeine, and trust in both the participant and actigraph that the sleep restriction instructions were complied with. Given the importance of these, it is surprising that participants were not kept in-lab for the sleep deprivation manipulation. The authors should address this.

We agree that the issue of compliance is an inherent concern in sleep and dietary manipulation studies conducted in the field. We opted for an in-home sleep manipulation in order to render the sleep deprivation as ecologically valid as possible without the typical distractors and stressors of being in an unfamiliar hospital laboratory environment. However, we took several measures to monitor compliance and to control for potential confounds. Most importantly, we used wrist actigraphy to monitor subjects’ sleep-wake schedule, as reported in Figure 1—figure supplement 1. We also collected a time stamped self-reported sleep diary and ratings of sleep quality during both sessions (Figure 1D and E) to verify that our sleep protocol was effective.

Importantly, non-compliance with the sleep instructions would have presumably increased between subject variability, thereby decreasing the likelihood of observing significant effects. Thus, assuming that some subjects may have “cheated”, the resulting observations are a conservative estimate of the true effect of sleep deprivation on the brain activity and behavior reported here.

With regards to food intake, two of the four meals provided to the participants prior to scanning were consumed at the laboratory and imaging center, respectively. We also provided subjects with repeated instructions to not consume any additional foods or snacks in the 24 hours preceding the scanning session. Regarding the remaining two take-out meals, subjects were required to report whether they consumed all of the provided food, and they were also asked to report any additional foods or drinks consumed.

We now discuss these issues in the manuscript.

Materials and methods section:

“Our study used an in-home setting to render the sleep-deprivation protocol as ecologically valid as possible without the additional distractions and stressors of being in an unfamiliar hospital laboratory environment. However, because in-home settings come with potential limitations related to non-compliance, we took several measures to reinforce and monitor compliance with the sleep stabilization and sleep manipulation schedule.”

6) Sniffing vs. inhalation.

Reviewer 2 raised a concern about the nature of the inhalation measure identified as sniffing. […] In future, reviewer 2 suggests the authors precisely measure sniffing in their studies. It's cheap, easy, and informative.

We appreciate the methodological advice from reviewer 2. We now refer to “respiration” not “sniff” in Figure 1—figure supplement 3 and throughout the manuscript. We also now clearly state in the methods that respiratory effort bands only provide an indirect measure of sniffing.

Materials and methods section:

“Respiration, as an indirect measure of nasal sniffing, was measured using a MR-compatible breathing belt (BIOPAC Systems Inc, Goleta, CA) affixed around the participant’s torso, and recorded at 1 kHz using PowerLab equipment (ADInstruments, Dunedin, New Zealand).”

https://doi.org/10.7554/eLife.49053.049

Article and author information

Author details

  1. Surabhi Bhutani

    1. Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, United States
    2. School of Exercise and Nutritional Sciences, College of Health and Human Services, San Diego State University, San Diego, United States
    Contribution
    Conceptualization, Formal analysis, Investigation, Writing—original draft
    Competing interests
    No competing interests declared
  2. James D Howard

    Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, United States
    Contribution
    Formal analysis, Investigation, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9309-3773
  3. Rachel Reynolds

    Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, United States
    Contribution
    Investigation, Writing—review and editing
    Competing interests
    No competing interests declared
  4. Phyllis C Zee

    Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, United States
    Contribution
    Conceptualization, Writing—review and editing
    Competing interests
    No competing interests declared
  5. Jay Gottfried

    1. Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, United States
    2. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    3. Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, United States
    Contribution
    Conceptualization, Writing—review and editing
    Competing interests
    No competing interests declared
  6. Thorsten Kahnt

    1. Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, United States
    2. Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, United States
    3. Department of Psychology, Weinberg College of Arts and Sciences, Northwestern University, Evanston, United States
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Writing—original draft
    For correspondence
    thorsten.kahnt@northwestern.edu
    Competing interests
    Reviewing editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3575-2670

Funding

National Institute of Diabetes and Digestive and Kidney Diseases (R21 DK118503)

  • Thorsten Kahnt

National Institute on Deafness and Other Communication Disorders (R01 DC015426)

  • Thorsten Kahnt

National Center for Advancing Translational Sciences (UL1 TR001422)

  • Thorsten Kahnt

National Heart, Lung, and Blood Institute (T32 HL007909)

  • Surabhi Bhutani

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors thank Dr. CJ Hillard and team for MS analysis of serum samples, International Flavor and Fragrances (A Dumer and RS. Santos) and Kerry (JL Buckley) for providing food odorants. This work was supported by the Comprehensive Metabolic Core at Northwestern University, the National Center for Advancing Translational Sciences, Grant number UL1 TR001422 (to TK), the National Blood Lung and Heart Institute grant T32 HL007909 (to SB), the National Institute of Diabetes and Digestive and Kidney Diseases grant R21 DK118503 (to TK), and the National Institute on Deafness and Other Communication Disorders grant R01 DC015426 (to TK).

Ethics

Human subjects: Informed consent was obtained from all subjects and all experimental procedures of this project (STU00203395) were approved by the Institutional Review Board of Northwestern University.

Senior Editor

  1. Christian Büchel, University Medical Center Hamburg-Eppendorf, Germany

Reviewing Editor

  1. Timothy Verstynen, Carnegie Mellon University, United States

Reviewers

  1. Timothy Verstynen, Carnegie Mellon University, United States
  2. Noam Sobel, Weizmann Institute of Science, Israel

Publication history

  1. Received: June 5, 2019
  2. Accepted: September 3, 2019
  3. Version of Record published: October 8, 2019 (version 1)

Copyright

© 2019, Bhutani et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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