The Role Of Sleep And Wakefulness in The Recognition Of Emotional Pictures Part 1

Summary

Sleep has a beneficial effect on memory consolidation. However, its role in emotional memory is currently debated. Here, we investigate the role of sleep and a similar period of wakefulness on the recognition of emotional pictures and subjective emotional reactivity. Forty participants without any significant physical, neurological, or psychological condition were randomly assigned to the Sleep First Group or Wake First Group. The two groups underwent the encoding phase of an emotional images task with negative and neutral pictures at 09:00 hours (Wake First Group) or 21:00 hours (Sleep First Group). Then participants performed an immediate recognition test (T1), and two delayed tests 12 hr (T2) and 24 hr (T3) later. 

The relationship between sleep and memory has attracted much attention. Sleep is an important part of memory. Sleep not only helps people improve memory but also helps to consolidate and strengthen memory, thereby improving language, mathematics, and intellectual ability.

While the body rests, sleep also helps people consolidate and strengthen memories. During sleep, the brain replays the experiences and information we experience during the day, such as learned knowledge, language memory, emotional experience, and so on. This process is called "memory recall".

During deep sleep, the brain transfers memory to the long-term memory area, where the memory is stored for a long time. Therefore, sleep can help us consolidate the knowledge and experience we have learned, and improve language, mathematics, and intellectual abilities. In addition, good sleep can also help us remember new information better, especially when studying and researching, getting enough sleep will become an important condition for us.

When we are sleep deprived, memory will be affected, and while we may retain some information, our performance will be weaker over long periods for storage and consolidation. Therefore, maintaining good sleep habits will be of great help to improve our memory.

Finally, a reminder that maintaining good sleep habits does not mean sleeping for long periods or sleeping more times during the day. The correct sleep habits should be to ensure adequate sleep, to ensure the full use of deep sleep within the specified time, and to ensure the normal performance of physical health and memory. It can be seen that we need to improve our memory. Cistanche can significantly improve memory, because Cistanche can also regulate the balance of neurotransmitters, such as increasing the level of acetylcholine and growth factors. These substances are very important for memory and learning. In addition, meat can also improve blood flow and promote oxygen delivery, which can ensure that the brain receives sufficient nutrition and energy, thereby improving the vitality and endurance of the brain.

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Perceived arousal and valence levels were collected for each picture. Sleep parameters were recorded at participants' homes with a portable device. No differences were observed at T1, whereas at T2 the Sleep First Group showed a higher memory performance than the Wake First Group. At T3, performance decreased in the Sleep First Group (who spent the previous 12 hours awake), but not in the Wake First Group (who slept in the last 12 hr). Overall, negative images were remembered better than neutral ones. 

We also observed a positive association between memory performance for negative items at the immediate test and the percentage of rapid eye movement sleep the night before the encoding. Our data confirm that negative information is remembered better over time than neutral information and that sleep benefits the retention of declarative knowledge. However, rest seems not to preferentially improve emotional memory, although it may affect the encoding of negative news.

KEYWORDS

Arousal, emotional memory, recognition test, rapid eye movement sleep, retention period, valence.

1 | INTRODUCTION

Since the first mention of a “sleep effect”, which refers to the ability of sleep to enhance the recall of declarative memories (Jenkins & Dallenbach, 1924), a consistent pool of sleep research has been focused on its role in memory consolidation, leading to the development of several theoretical models (Born & Wilhelm, 2012; Genzel, Kroes, Dresler, & Battaglia, 2014; Tononi & Cirelli, 2006). 

For example, according to the Synaptic Downscaling Hypothesis (Tononi & Cirelli, 2006), slow-wave sleep promotes the reduction of synaptic strength to optimize memory retention and facilitate successive learning. On the other hand, the Active System Consolidation Hypothesis (Born & Wilhelm, 2012) posits that information encoded during wakefulness undergoes a process of reactivation and reorganization during sleep, mainly involving hippocampal and neocortical networks.

However, for a long time, sleep and memory have been investigated without taking into account the possible contribution of emotional experience. Nevertheless, it has been demonstrated that emotions and emotion-derived states (e.g. mood) can have a large impact on most brain and body functions, including memory formation and retention (Coughlin Della Selva, 2006; LaBar & Cabeza, 2006; McGaugh, 2004). 

More recently, given the consistent findings linking mood disorders with altered sleep patterns, researchers have begun to explore the relationship between sleep, emotional memory, and affective regulation (for a comprehensive review of these topics, see Walker, 2009). Some evidence suggests that sleep preferentially enhances memory for emotional rather than neutral stimuli (Hu, Stylos-Allan, & Walker, 2006; Wagner, Gais, & Born, 2001). 

In particular, negatively valenced information seems to be more resistant to forgetting, as it is often better remembered than positive or neutral material after a period of sleep deprivation (Tempesta et al., 2016; Walker, 2009). A discrete amount of research has been focused on the association between emotional memory and rapid eye movement sleep (REM). The focus on REM is justified by its unique physiology, and the evidence linking selective REM deprivation with an impaired encoding of emotional stimuli and altered emotional reactivity (Goldstein & Walker, 2014; Walker & van Der Helm, 2009). 

REM sleep is characterized by the activation of those cerebral regions implied in fear learning and broader forms of emotional processing during wakefulness, such as the amygdala, the anterior cingulate cortex, the entorhinal cortex, the ventromedial prefrontal cortex, and the hippocampus (Tempesta, Socci, De Gennaro, & Ferrara, 2018). Its unique neurochemical environment is characterized by a reduced concentration of monoamines (e.g. adrenaline and noradrenaline) and by an increased concentration of acetylcholine. Although both neuromodulators are implicated in synaptic plasticity and memory consolidation, the absence of monoamines during REM sleep is thought to allow the reactivation of emotional aspects of memories without enacting their associated physiological activation (Walker & van Der Helm, 2009). 

At the same time, cholinergic transmission is thought to play a critical role in high-level cognitive activities such as learning and memory processes (Diekelmann & Born, 2010; Teber et al., 2004). Furthermore, the electroencephalogram (EEG) during REM sleep is dominated by the theta band, which is thought to be related to the consolidation of information acquired during the day (Hutchison & Rathore, 2015). Considering these peculiar characteristics, a well-known theoretical model introduced by Walker & van der Helm (2009) proposes a possible role of REM sleep in supporting emotional regulation processes by strengthening salient memory representations while gradually reducing their affective tone over time.

Nonetheless, these hypothesized functions remain highly debated among scientists, who seem unable to find a clear agreement on the possible mechanisms linking sleep with memory consolidation and emotional processing (Lipinska, Stuart, Thomas, Baldwin, & Bolinger, 2019). Among the many questions that remain unanswered, it is still not clear how much of the “sleep effect” could be explained by the reduction of interference during sleep (i.e. the continuous flow of new sensory and cognitive information that is constantly processed by the brain during wakefulness; Jenkins & Dallenbach, 1924) or could be attributed to sleep-exclusive active mechanisms supporting memory consolidation (Benson & Feinberg, 1977). 

Moreover, the role of REM sleep in emotional regulation and emotional memory consolidation has been confirmed in some studies (Hu et al., 2006; Van Der Helm et al., 2011; Wagner et al., 2001), but not in others (Baran, Pace-Schott, Ericson, & Spencer, 2012; Cellini, Torre, Stegagno, & Sarlo, 2016; Groch, Wilhelm, Diekelmann, & Born, 2013). However, some of these results may be partially biased by the methodological differences across studies: for example, selective sleep deprivation and split-night paradigms often produce incompatible results although being widely adopted to investigate the separate contributions of REM sleep and non-REM sleep on memory consolidation (Wagner et al., 2001; Wagner, Fischer, & Born, 2002). 

Also, although deprivation studies offered a significant contribution to the effect of sleep on emotional processing (Tempesta et al., 2016), their observations are limited to the consequences of sleep loss (whether partial or total) but do not allow to assess the direct contribution of specific sleep parameters on memory processes. Other researchers have obtained significant results through the use of nap paradigms (Cellini et al., 2016; Nishida & Walker, 2007), although this type of experimental design does not allow for examining the effect of whole nights of sleep on emotional memory consolidation and emotional reactivity. 

Moreover, while the effects of sleep on emotional memory consolidation have already been studied using a 1-week longitudinal design, showing a negative association between sleep efficiency and negative picture discrimination (Cellini, Mercurio, & Sarlo, 2019), the monitoring of multiple consecutive nights with polysomnography (PSG)-like accuracy has not yet been achieved for this aim. 

Lastly, although sleep has been extensively studied in memory consolidation, far less interest has been directed to its effect on emotional information encoding, except for deprivation studies (Kaida, Niki, & Born, 2015).

Starting from this theoretical background, we conducted a study to assess the separate roles of sleep and wakefulness on emotional memory consolidation during 24 hr. 

Specifically, we used an emotional image recognition task and a portable sleep tracker to (i) investigate the effect of nocturnal sleep compared with an equal period of daytime wakefulness on subjective emotional reactivity and memory performance; (ii) determine whether a positive effect of sleep on memory consolidation remains stable after a subsequent period of wakefulness; and (iii) explore the effect of nighttime sleep on the encoding of emotional information.

2 | MATERIALS AND METHODS

2.1 | Participants

Forty participants between the ages of 18 and 34 years (17 M, 23 F; mean age ± standard deviation = 23.575 ± 3.161), and without any major physical, neurological, or psychological condition took part in this study. Participants were randomly assigned to a Sleep First Group (SF; 8 M, 12 F) or a Wake First Group (WF; 9 M, 11 F), which provided for two different experimental conditions. All participants provided informed consent. The study protocol was approved by the local Ethics Committee.

2.2 | Stimuli and task

Neutral and emotional images (N = 180) were selected from the EmoMadrid database (Carretié, Tapia, Lopez-Martín, & Albert, 2019), already validated for affective research, and further tested in a pilot sample of students from Padua University to confirm its validity in an Italian sample (N = 20; pilot data will not be presented in this manuscript). Each image in the database comes with a series of parameters, including Valence and Arousal values expressed on a 5-point Likert scale ranging from
2 (very negative/very calm) to +2 (very positive/ very arousing). 

The reason EmoMadrid has been chosen over the more popular International Affective Picture System (IAPS; Lang, Bradley, & Cuthbert, 2008) is that although the latter has been widely used for affective research, it presents some major issues regarding the obsolescence of some pictures and their cultural-geographical background, which is mostly representative of USA (Carretié et al., 2019; Henrich, Heine, & Norenzayan, 2010).

The experimental task included an encoding phase and three memory tests (T1, T2, T3), all of which were built employing the PsychoPy software (Peirce et al., 2019). The Encoding set included 90 images (45 negatives + 45 neutral). During the encoding, each image was displayed on the screen for 2 seconds, after which participants were asked to report their perceived ratings of Valence and Arousal.

Memory tests included 60 images each, 30 of which (15 negatives + 15 neutral) had already been shown during the Encoding phase, while the other 30 (15 negatives + 15 neutral) were new. All test sets were built to balance thematic content (every set included the same number of images depicting animals, people, landscapes and objects) and Arousal/Valence levels (Negative Valence Mean ± Standard Deviation =
1.38 ± 0.08; Negative Arousal Mean ± Standard Deviation = 1.13 ± 0.04; Neutral Valence Mean ± Standard Deviation = 0.16 ± 0.06; Neutral Arousal Mean ± Standard Deviation =
0.03 ± 0.10). Every image appeared on the screen for 2 seconds, after which participants were asked to respond whether they had already seen it during the encoding or not, and to report their perceived ratings of valence and arousal. 

Memory performance was assessed through the d-prime index. Specifically, we computed the Hit Rate (HR; the proportion of old pictures correctly identified as “already seen”) and False Alarm Rate (FAR; the proportion of new pictures mistakenly identified as “already seen”). From these two indices, according to Signal Detection Theory (Macmillan & Creelman, 2004), we calculated the discrimination index d-prime as the difference between HR and FAR z-scores, using the formula d-prime = zHR
zFAR. For the d-prime, HR, and FAR, we also computed the change in memory performance between T2 and T1 as T2score/T1score*100 and the change in memory performance between T3 and T2 as T3score/ T2score*100.

2.3 | Sleep monitoring device

Sleep nights were monitored using the Dreem Headband (DH; Dreem SAS, Paris), a wearable device that has been validated as a portable alternative to PSG (Arnal et al., 2020). The headband comes with various sensors including five dry electrodes (O1, O2, FpZ, F7, F8) yielding seven bipolar EEG derivations (FpZ–O1, FpZ–O2, FpZ–F7, F8–F7, F7–01, F8–O2, FpZ–F8), a 3D accelerometer to measure the respiration rate and keep track of movements and positions, and a pulse oximeter to measure heart rate. The DH can collect and store physiological measures in real-time during the night; raw recorded data are available from a dedicated cloud service. The DH uses a validated automatic sleep scoring to provide classical sleep metrics (e.g. sleep duration, time spent in different sleep stages).

2.4 | Procedure

The whole procedure lasted 3 days for each participant (Figure 1), with a different distribution of tasks for the SF and WF. Each participant was given detailed oral and written instructions on how to record sleep nights and complete experimental tasks.

On the first day, all participants were asked to complete a series of online questionnaires to obtain basic demographics (age, gender, occupation), check for the presence of anxiety or depressive symptoms with the Hospital Anxiety and Depression Scale (HADS; Zigmond & Snaith, 1983), investigate global sleep quality through the Pittsburgh Sleep Quality Index (PSQI; Buysse, Reynolds III, Monk, Berman, & Kupfer, 1989), and circadian preferences through the ultrashort version of the Munich ChronoType Questionnaire (MCTQ; Ghotbi et al., 2020) and the Morningness–Eveningness Questionnaire reduced version (MEQ-r; Natale, Esposito, Martoni, & Fabbri, 2006), and to record the first night of sleep.

Beginning from the second day, instructions changed depending on the experimental condition: for the WF, initial learning (Encoding) took place at 09:00 ± 1:00 hours, followed by an immediate memory test (T1), while the second test (T2) had to be completed at 21:00 ± 1:00 hours. 

After that, participants had to record the second night of sleep and complete the last memory test (T3) at 09:00 ± 1:00 hours on the following day. Instructions for the SF were similar, except for the fact that experimental tasks were shifted by a 12-hour interval so that initial learning would take place just before going to sleep. Thus, the Encoding phase and T1 had to be completed at 21:00 ± 1:00 hours on the second day, while the remaining tests (T2 and T3) were scheduled, respectively, for 09:00 ± 1:00 hours and 21:00 ± 1:00 hours on the following day, after the second night of sleep was recorded. To prevent any order effects, all tests were counterbalanced between participants for a total of six possible combinations. 

Moreover, the presentation order of pictures was randomized for each task. Before each experimental session, participants had to complete a short series of questionnaires that changed depending on the time of the day. The morning questionnaires included a PSQI (Buysse et al., 1989) to test sleep quality related to the previous night, a Samn–Perelli Scale (Samn & Perelli, 1982) and a Stanford Sleepiness Scale (Hoddes, Zarcone, Smythe, Phillips, & Dement, 1973) to, respectively, investigate vigilance and tiredness levels, whereas the evening questionnaires included only a Samn–Perelli and a Stanford Sleepiness Scale.

2.5 | Statistical analysis

Demographics (age, gender), and psychological and sleep parameters of the two groups were compared using independent t-tests and χ2 - tests. For each comparison, we reported Cohen's d as a measure of effect size.

Changes in memory performances across testing sessions have been analyzed using the following plan. First, for each variable of interest, we conducted an omnibus mixed-ANOVA with testing Sessions (T1, T2, T3) and type of image (Negative, Neutral) as within-subjects factors, and Group (SF, WF) as between-subject factor.

Then, to test the first experimental hypothesis (i.e. lower forgetting after a night of sleep), we conducted a mixed-ANOVA using changes scores from T1 to T2 as dependent variables, with Type of image (Negative, Neutral) as within-subjects factor and Group (SF, WF) as between-subjects factor. To test our second experimental hypothesis (i.e. the memory performance after a night of sleep remains stable after subsequent wake), we conducted another mixed ANOVA using changes scores from T2 to T3 as dependent variables, Type of image (Negative, Neutral) as within-subjects factor and Group (SF, WF) as between-subjects factor.

To assess the changes in emotional reactivity (Arousal and Valence), we conducted two separate omnibus mixed-ANOVA with testing Sessions (Encoding, T1, T2, T3) and Type of image (Negative, Neutral) as within-subjects factors, and Group (SF, WF) as between-subjects factor. For all the ANOVAs, we reported η2 p as a measure of effect size for the main factors and interactions, and we reported both uncorrected and Holm test post hoc analysis, using Cohen's d as a measure of effect size for post hoc comparisons.

The relationship between sleep parameters and performance as well as Arousal and Valence has been explored separately for the two groups using Pearson's correlations. For WF, we explored the relationship between sleep parameters recorded the night before encoding and the variables of interest, such as d-prime, valence, and arousal ratings at T1. For the SF, we explored the relationship between sleep parameters recorded the night after encoding and the change in performance from T1 to T2. A value of p < 0.05 was used as the significant level.

Besides null hypothesis significance testing, we also employed Bayesian statistics to estimate the probability of the alternative hypothesis being true given the data. Specifically, we reported the Bayes Factor (BF10), with values larger than three indicating moderate evidence for the alternative hypothesis (H1), and BF10 values lower than 0.3 moderately supporting the null hypothesis (H0; Jarosz & Wiley, 2014). For the ANOVAs, BF10 was computed using the across-matched models approach. All the analyses were conducted using JASP version 0.16.2 (JASP Team, 2022). The study was not preregistered.

FIGURE 2 (a) Valence and (b) arousal ratings as a function of the type of stimuli (negative and neutral pictures) at the encoding (Enc) and the three testing sessions (T1, T2, T3). Error bars represent the standard error of the mean.

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