Fresh memories are strengthened while we sleep. However, we don’t remember every detail of our daily life experiences. Instead, it is essential that we retain information that promotes our survival, such as what we call "rewards" (including food, money or sex) and dangers that we should avoid.

Igloi et al. sought to find out how the human brain picks out important memories to be consolidated during sleep, while discarding irrelevant information. Healthy participants learned series of pictures associated with either high or low rewards. After learning, some of the participants had a nap, while others remained awake. Directly after this and three months later, all the participants returned for a memory test. Igloi et al. found that the highly rewarded pictures were better remembered at both time points (at the expense of lowly rewarded ones), but only for participants who had slept after learning.

Further analysis revealed that distinctive bursts of brain activity occurring during sleep, so-called “sleep spindles", favor the reorganization of memories stored in a region of the brain called the hippocampus, often considered to be the organ of memory.

These findings uncover how sleep enhances long-term memory selectivity thus demonstratethat sleep does not just passively increase the retention of all memories. In the future, this work may inspire educational strategies that combine the careful use of rewards followed by an overnight period of sleep.

It is well established that sleep contributes to memory consolidation processes (Diekelmann and Born, 2010; Maquet, 2001; Stickgold and Walker, 2013). In animals and humans memory traces are reinforced through neural reactivation and trimmed through synaptic downscaling. Critically, both neural replay and synaptic downscaling entail the tagging of neural elements coding for specific memories and coincide with the occurrence of specific oscillatory elements in sleep such as sleep spindles, and slow wave activity (Ringli and Huber, 2011; Tononi and Cirelli, 2003; Schwindel and Mcnaughton, 2011; Wilhelm et al., 2011; Káli and Dayan, 2004). For memory consolidation to be adaptive, information that is critical for survival, such as stimuli with an emotional or rewarding value, should be consolidated in priority during sleep (Perogamvros and Schwartz, 2012; Sterpenich et al., 2009). Consistent with this hypothesis, behavioral studies in humans suggest that reward may modulate sleep-related memory consolidation (Fischer and Born, 2009; Werchan and Gómez, 2013; Oudiette et al., 2013). At the neural level, animal studies provide evidence for spontaneous coordinated replay of neural activity in memory and reward brain structures during slow-wave sleep (Lansink et al., 2009; Pennartz et al., 2004), suggesting that the activation of reward circuits during sleep influences neural plasticity. Further, within the rat hippocampus, the firing patterns of CA1 and CA3 neurons encode the high or low reward outcome of items in an associative memory task (Mckenzie et al., 2014). In humans, at wake, the activation of the mesolimbic dopaminergic system enhances associative memory through interactions with the hippocampus (Shohamy and Wagner, 2008), as well as subjective confidence and feeling of goal attainment during successful retrieval (Shohamy and Wagner, 2008; Adcock et al., 2006; Wolosin et al., 2012). Yet, it is unclear whether the replay of rewarded memories during sleep primarily involves the transfer of hippocampal memories to cortical sites (Frankland and Bontempi, 2005; Gais et al., 2007; Takashima et al., 2009), or the strengthening of memory-reward associations, implicating hippocampal-striatal interactions (Lansink et al., 2009). Based on these findings and recent theoretical proposals suggesting that sleep may influence associative memory by facilitating the integration of multi-item sequences (Stickgold and Walker, 2013; Ellenbogen et al., 2007), a critical and unanswered question that we ask here is whether reward values linked to recent memory items control the remodeling of associative knowledge during sleep. Specifically, we test whether reward influences sleep-dependent memory consolidation by promoting associative processes in the hippocampus, ultimately prioritizing long-term retention and subjective confidence for highly (over lowly) rewarded stimuli.

We designed an associative memory task in which participants learned series of pictures yielding high or low reward outcomes. To assess the influence of sleep and reward, and their potential interaction, memory for these series was tested following a nap or a rest period, and then again 3 months later during the retest session (Figure 1A). The task was composed of eight series of six successive pictures each; four series were associated with high reward outcome (HR: dollar coin) and four with low reward outcome (LR: cent coin) all participants received the same total payment. The participants first saw the eight series once during the encoding session (Figure 1B and Figure 1—figure supplement 1A), followed by the 2-alternative forced choice learning session with feedback during which participants successively selected the next picture in the series among two presented pictures Figure 1—figure supplement 1B). Then, the participants either took a 90-min nap (Sleep group, n = 16) or spent an equivalent period quietly awake (Wake group; n = 15), both monitored by polysomnography EEG. After this delay and again three months later, the participants performed an associative memory task on pairs of pictures with different relational distances (direct, inference of order 1 and order 2). They were presented with one image and were then asked which one among two images was part of the same series as the first image (while the other image belonged to a different series) and gave confidence ratings for each answer (Figure 1C and Figure 1—figure supplement 1C). Functional MRI (fMRI) data were acquired during all experimental sessions and analyzed using SPM8 (see Materials and methods).

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First, we assessed the effect of reward on learning performance (hit rate) during the learning phase using an ANOVA with Reward (high, low) and learning Block (1,2,3, see Materials and methods) as within-subject factors and Group (Sleep, Wake) as between-subject factor. Performance was superior for high reward (HR) than for low reward (LR) picture pairs (F(1,28) = 35.86, p<0.001), with a significant learning effect over Blocks (F(2,56) = 20.68, p<0.001), and a Reward x Block interaction effect (F(2,56) = 4.72, p=0.012). Importantly, there was no Group difference (F(1,28) = 2.59, p>0.05) and participants of the two groups reached similar levels of performance for HR and LR trials at the third block of the learning (post-hoc test p>0.05; Figure 2—figure supplement 1A). For all three learning blocks, performance was above chance level (one-way T-tests comparing performance to 50%, all p<0.05). Reward-related performance improvement during the learning phase (HR vs. LR) was paralleled by an increase in midbrain activity, in a region compatible with the ventral tegmental area (VTA) [z-score = 3.71 (-3x, -13y, -20z), p<0.05, small-volume corrected (SVC) for familywise error (Bunzeck and Düzel, 2006); see Materials and methods] (Figure 2—figure supplement 1B and Supplementary file 2).

Next, we tested whether the consolidation of rewarded associative memory was selectively enhanced by post-encoding sleep. After a nap (Sleep group) or rest (Wake group), participants were tested on direct pairs of pictures in the series, and on non-consecutive pairs of pictures, that is inference of order 1 and 2 measuring associative memory, namely the strength of the integration of non-consecutive images in a sequence (Ellenbogen et al., 2007) (Figure 1C and Figure 1—figure supplement 1C). Both groups performed above chance level for HR and LR trials and direct, inference 1 and inference 2 trials (one-way T-tests p<0.05). Participants performed better for HR than LR trials (F(1,84) = 15.88, p<0.001), and for close relational distance trials (F(2,84) = 4.16, p=0.018). Further, the Sleep group performed better than the Wake group (F(1,84) = 4.52, p=0.036; Figure 2A). Notably, within HR trials, the sleep Group performed better than the wake group (F(1,84) = 5.07, p=0.027), which was not the case for LR trials (F(1,84) = 1.52, p=0.22). However the Group x Reward interaction was not significant (F(1,84) = 0.23, p=0.63). In the Sleep group, the number of slow spindles (11–13 Hz) correlated with memory improvement from learning to test specifically for HR (R = 0.69, p<0.001) and not for LR trials (R = -0.27, p>0.05; Figure 2B). The correlation for HR trials was significantly higher than that for LR trials (Fisher’s z-score = 2.15, two-tailed p=0.01) (Lee and Preacher, 2013). Additionally, the correlation effect was not linked to sleep duration as there was no correlation between performance improvement in the HR trials and sleep duration, while the correlation with performance improvement in HR persisted when considering slow spindles density (number of slow spindles per second of sleep; R = 0.586, p=0.028).

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To investigate fMRI responses from the Sleep and Wake groups during the test session, we used a general linear model distinguishing successful trials according to Reward (HR, LR), Relational Distance (direct, inference 1, inference 2) and Confidence (‘certain’ vs lower confidence judgments, i.e. ‘not sure’, ‘guess’, ‘by elimination’). A direct comparison between the Sleep and Wake groups for HR versus LR (independently of Relational Distance) revealed increased activity in the right hippocampus [z-score = 4.04 (30x, -10y, -23z), p<0.05 SVC; Figure 2C)]. Within this interaction, post-hoc analysis of the extracted beta values showed that both for Sleep and Wake groups HR condition was different from LR condition (both p=0.02). Because the integration of distant associative memories possibly relies on sleep spindles (Tamminen et al., 2010), we tested for the brain correlates of this functional relationship. To test specifically for the integration of associative memories (and not the consolidation of previously seen associations, as in direct trials), we considered associations between non-consecutive images with distinct relational distances by comparing inference 2 to inference 1 trials (inference 2 > inference 1). We found that the number of slow spindles in the Sleep group positively correlated with activation in the right hippocampus for HR distant associations [HR inference 2 versus HR inference 1; z-score = 3.75 (27x, -16y, -11z), p<0.05 SVC; Figure 2D], and not for LR associations.

We assessed long-term memory in a retest session identical to the test session, three months later. Both Sleep and Wake groups performed above chance level for HR and LR series (one-way T-tests, all p<0.05; Figure 3A). While we found no main effect of Group, Reward, or Relational Distance (ANOVA all p>0.05), we found a Group x Reward interaction (F(1,66) = 4.21, p=0.044). Only the Sleep group remembered HR compared to LR series better (T(32) = 2.91, p=0.006), while no such difference was found in the Wake group (T(38) = -0.25, p=0.80). In fMRI, we tested for Group differences between HR and LR trials according to Relational Distance. We observed a selective increase of left hippocampus activity for HR versus LR during inference 2 trials in the Sleep group compared to the Wake group [z-score = 3.78 (-36x, -28y, -8z), p<0.05 SVC; Figure 3B], we observed no Group difference for direct and inference 1 trials. Post-hoc analyses of the hippocampal beta values showed that the interaction was due to an increased hippocampal response to HR vs. LR trials in the Sleep group only (p=0.03). Further, using psychophysiological interaction (PPI), we asked whether sleep had some long-term effects on the functional coupling between the hippocampus and other brain regions during the processing of HR (vs. LR) trials (see Materials and methods). Both the caudate nucleus [z-score = 3.41 (15x, 20y, 7z); p<0.05 SVC] and the medial prefrontal cortex [z-score = 2.99 (15x, 59y, -5z); p<0.05 SVC] showed such pattern of increased functional connectivity in the Sleep (compared to the Wake) group (Figure 3C). Because striatal activation may be relevant for the reprocessing of rewarded associations during sleep (Lansink et al., 2009), we tested whether the strength of the caudate-hippocampal functional coupling correlated with the number of slow sleep spindles during the nap in the Sleep group, and found that this correlation was significant (R = 0.591, p=0.028, Figure 3D). Note that no such correlation was observed for the connectivity between the hippocampus and medial prefrontal cortex (mPFC).

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Beyond performance effects, it has been shown that remembering emotional events elicits a stronger feeling of recollection (Sharot et al., 2004), yet higher confidence seems adaptive only if associated with accurate memory recall (Schultz, 2006). We therefore analyzed ‘certain’ confidence responses. To correct for potential performance biases, we considered the percentage of ‘certain’ responses for hits only. For the test session, we found a main effect of Group (F(1,78) = 4.90, p=0.029; more ‘certain’ responses for the Sleep group), of Reward (F(1,78) = 59.06,p<0.001; higher confidence for HR (Figure 4A). Further, we found a Group effect (Sleep>Wake) within HR hits (F(1,78) = 6.63, p=0.01), and not for LR hits (F(1,78) = 0.73, p=0.39). Importantly, the Group effect was not due to an unspecific influence of sleep, as confidence judgments for incorrect responses did not differ between the groups, neither for HR trials (U = 72.50, p=0.35) nor for LR trials (U = 84, p=0.51). Note that we performed U-Tests because of small sample size and non-normal distribution of incorrect responses. For the retest session, we found a main effect of Reward (higher confidence for HR trials; F(1,66) = 18.92, p<0.001) and a post-hoc difference between HR and LR confidence within the Sleep group only (p=0.003 for the Sleep group and p=0.38 for Wake group) (Figure 4B). At retest, there were very few certain responses. To obtain correctly sized samples to analyze confidence judgments at retest, we grouped ‘certain’, ‘by elimination’ and ‘not sure’ answers together and contrasted them to ‘guess’ answers. In line with our main imaging results for inference 2 trials (Figure 3B), this analysis on confidence yielded higher activation in the parahippocampus [z-score = 3.49 (27x, -25y, 26z) p<0.05 SVC] and in the putamen [z-score = 3.46 (-18x, -1y, 7z), p<0.05 SVC] for Sleep > Wake selectively for HR inference 2 when comparing some confidence to guess (Figure 4C).

Figure 4

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Here we show that sleep favors the selectivity of memory consolidation and confidence for these memories by promoting the integration and long-term retention of the most important (i.e. here highly rewarding) memories.

First, our results show that slow sleep spindles were critically involved in the consolidation of series with high reward outcome (Figure 2B), consistent with Schmidt et al. (Schmidt et al., 2006) who reported that memory improvement for word-pairs over a nap implicated slow spindles. Sleep spindles may induce long-term synaptic plastic changes (Marshall et al., 2006), thus consolidating newly learned information into a more stable form of long-term memory. Slow spindles (11–13 Hz) are dominant over the prefrontal lobe (Saletin and Walker, 2012) and are related to a cross-linking of transferred information within prefrontal circuitry (Clemens et al., 2007). Moreover, activity in dopaminergic reward regions such as the VTA is increased during slow spindles in humans (Schabus et al., 2007). Consistent with these observations, we found that slow spindles after learning were related to the functional interplay between the hippocampus and the caudate nucleus during the long-term recall of high rewarded series. From these connectivity findings, we conclude that slow spindles may primarily favor the long-term consolidation of rewarded stimuli across striatal and hippocampal networks. We may speculate that enhanced hippocampal recruitment for HR trials at retest could possibly foster recall processes subserved by the mPFC, a brain region reportedly involved in the retrieval of episodic memories (Düzel et al., 1999).

Second, our results confirm that sleep does not only strengthen memory for recently (and possibly more strongly) encoded items (Diekelmann and Born, 2010) but also boosts the integration of associative memories, via a hippocampus-dependent mechanism (Werchan and Gómez, 2013; Ellenbogen et al., 2007; Lau et al., 2010) (Figure 2D). Importantly, there was no difference between reaction times for inference trials and direct trials (Table 1), supporting the idea that the consolidation of associative memory involved the integration of discrete events (i.e., generalization of memories) rather than sequential inferential reasoning (Shohamy and Wagner, 2008). This mechanism could promote the conversion of implicit forms of memory into more explicit and conscious memories (Wilhelm et al., 2013), and also facilitate the access to remote associations (Zeithamova et al., 2012).

Third, we show that sleep after learning reinforces the subjective feeling of recollection for correct recall (Figure 4A), and at long-term retest selectively enhances performance for rewarded information (Sharot et al., 2004) (Figure 3A), although this interaction effect was not present at short-term. These effects were not present for missed trials and were thus not due to a general increase in confidence judgments after sleep. Moreover, we report that medium to high confidence is characterized by parahippocampal and striatal activation at retest. Both these structures have previously been linked to confidence measures (Daniel and Pollmann, 2012; Eichenbaum et al., 2007), with parahippocampal activation involved in remembering contextual information about a memory, which contributes to increased confidence about the information being recalled (Eichenbaum et al., 2007), while striatum (in particular putamen) activation has been shown to reflect an internal error signal about probable outcome in the absence of feedback (or prediction error of confidence measures) (Daniel and Pollmann, 2012).

At a more conceptual level, rewards may act as a ‘relevance tag’ that would prioritize the neural reprocessing of associative memories during sleep via a 2-step process: (i) during encoding, potential rewards activate dopaminergic midbrain regions, which would seal a ‘relevance tag’ to recently learned and rewarded information; (ii) during sleep (in particular sleep oscillations), the activation of a striatal-hippocampal network would favor the reprocessing of recent memories with a high relevance (Perogamvros and Schwartz, 2012).

Overall, by determining the fate of motivationally-relevant memory traces, post-learning sleep, even in the form of a nap, sharpens the skyline of our memories.

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Author details

1. Kinga Igloi

   1. Department of Neuroscience, Faculty of Medicine, University of Geneva, Geneva, Switzerland
   2. Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
   3. Geneva Neuroscience Center, University of Geneva, Geneva, Switzerland

   Contribution

   KI, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   kinga.igloi@unige.ch

   Competing interests

   The authors declare that no competing interests exists.

   "This ORCID iD identifies the author of this article:" 0000-0002-3428-8474

2. Giulia Gaggioni

   Department of Neuroscience, Faculty of Medicine, University of Geneva, Geneva, Switzerland

   Contribution

   GG, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exists.

3. Virginie Sterpenich

   1. Department of Neuroscience, Faculty of Medicine, University of Geneva, Geneva, Switzerland
   2. Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
   3. Geneva Neuroscience Center, University of Geneva, Geneva, Switzerland

   Contribution

   VS, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exists.

4. Sophie Schwartz

   1. Department of Neuroscience, Faculty of Medicine, University of Geneva, Geneva, Switzerland
   2. Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
   3. Geneva Neuroscience Center, University of Geneva, Geneva, Switzerland

   Contribution

   SS, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exists.

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