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Sleep is essential for regulating mood and affect, and it also consolidates emotional memories. The mechanisms underlying these effects may overlap. Here, we investigated whether the influence of sleep on affect may be moderated by emotional memory consolidation. Young adults viewed 45 negative and 45 neutral pictures before taking an afternoon nap measured with polysomnography. Following the nap period, participants viewed the same pictures intermixed with novel ones and indicated whether they remembered each picture. Affect was measured with the Positive and Negative Affect Schedule (PANAS) at baseline before the initial picture viewing task, immediately following the initial picture viewing task, and following the nap. The ratio of positive to negative affect declined over the task period and recovered over the nap period. When controlling for pre-nap affect, NREM sigma activity significantly predicted post-nap affect. Memory for negative pictures moderated this relationship such that a positive association between sigma activity and affect occurred when memory was low but not when memory was high. These results indicate that emotional memory consolidation influences the relationship between nap physiology and mood.
睡眠對於調節情緒和情緒至關重要，它也能鞏固情緒記憶。這些影響背後的機制可能會重疊。在這裡，我們研究了睡眠對情緒的影響是否可以透過情緒記憶鞏固來調節。年輕人在午睡前觀看了 45 張負面圖片和 45 張中性圖片，並透過多導睡眠圖測量。午睡後，參與者觀看了與新奇圖片混合在一起的相同圖片，並表明他們是否記得每張圖片。在初始圖片查看任務之前、初始圖片查看任務之後以及小睡之後，在基線處使用積極和消極情緒表（PANAS）測量情緒。正向情緒與負向情緒的比率在任務期間下降，並在午睡期間恢復。當控制小睡前的影響時，NREM 西格瑪活動顯著預測小睡後的影響。對負面圖片的記憶調節了這種關係，因此當記憶力較低時，西格瑪活動和情緒之間會出現正相關，但在記憶力較高時則不會。這些結果顯示情緒記憶鞏固影響午睡生理和情緒之間的關係。

Introduction 介紹

Sleep is important for multiple domains of emotional functioning. One such domain is the regulation of mood and affect. It is well known that sleep loss leads to impaired mood; both total sleep deprivation and sleep restriction have been linked to mood deficits (Pilcher and Huffcutt, 1996; Watling et al., 2017). Further, sleep disturbances and alterations are prevalent among those with mood disorders and may contribute to the development of these disorders (Meerlo et al., 2015; Murphy and Peterson, 2015). For example, major depressive disorder is marked by increased sleep latency and awakenings, decreased rapid eye movement (REM) sleep latency, increased REM density and REM sleep duration, and declines in slow wave sleep (SWS).
睡眠對於情緒功能的多個領域都很重要。其中一個領域是情緒和情緒的調節。眾所周知，睡眠不足會導致情緒受損。完全睡眠剝奪和睡眠限制都與情緒缺陷有關（Pilcher 和 Huffcutt，1996；Watling 等，2017）。此外，睡眠障礙和改變在情緒障礙患者中普遍存在，並可能導致這些疾病的發展（Meerlo 等人，2015 年；Murphy 和 Peterson，2015 年）。例如，重度憂鬱症的特徵是睡眠潛伏期和覺醒時間延長、快速動眼（REM）睡眠潛伏期縮短、快速動眼睡眠密度和快速動眼睡眠持續時間增加以及慢波睡眠（SWS）下降。

The mechanisms underlying the contribution of sleep to daily mood/affect in healthy individuals are not well understood, but REM sleep and SWS are both implicated by prior work. In healthy male adults who underwent two nights of normal sleep and two nights of sleep restriction, reduced REM sleep was associated with reduced functional connectivity between the medial prefrontal cortex and amygdala, which was in turn related to increased anxiety (Motomura et al., 2017). Collecting dream reports from healthy adults upon awakening from REM sleep indicated a decline in negative dream emotion throughout the night, which corresponded to an overnight reduction in negative mood (Cartwright et al., 1998). Slow wave sleep has also been linked to mood in healthy individuals. Compared to those undergoing restricted sleep, individuals undergoing forced awakenings had reduced SWS and positive mood, with the reduction in SWS mediating the reduction in positive mood (Finan et al., 2015). To our knowledge, no links between sleep spindles and mood have been reported in non-clinical samples. However, lower sleep spindle activity has been observed in individuals with anxiety and depression (Lopez et al., 2010; Wilhelm et al., 2017), suggesting that spindle activity may also be important for regulating mood.
睡眠對健康個體日常情緒/影響的影響機制尚不清楚，但 REM 睡眠和 SWS 都與先前的研究有關。在經歷了兩晚正常睡眠和兩晚睡眠限制的健康男性成年人中，快速動眼睡眠減少與內側前額葉皮質和杏仁核之間的功能連結減少有關，而這又與焦慮增加有關（Motomura et al., 2017） ）。從快速動眼睡眠中醒來後收集健康成年人的夢境報告表明，整個晚上的負面夢境情緒有所下降，這相當於夜間負面情緒的減少（Cartwright et al., 1998）。慢波睡眠也與健康人的情緒有關。與那些睡眠受限的人相比，經歷強迫覺醒的個體的 SWS 和積極情緒有所降低，而 SWS 的降低介導了積極情緒的降低（Finan 等人，2015）。據我們所知，在非臨床樣本中尚未報告睡眠紡錘波與情緒之間存在關聯。然而，在患有焦慮和憂鬱的個體中觀察到較低的睡眠紡錘體活動（Lopez et al., 2010; Wilhelm et al., 2017），這表明紡錘體活動對於調節情緒也可能很重要。

In addition to regulating mood, sleep consolidates emotional memories (Tempesta et al., 2018). Compared to those who remain awake, participants who sleep perform better at emotional memory tasks. For example, participants who slept for 3 h immediately after reading negative text performed better on a memory test 4 years later compared to participants who stayed awake immediately after reading the texts (Wagner et al., 2006). Moreover, the consolidation of emotional memory by sleep has been linked to mechanisms also implicated in the effects of sleep on mood. Negative memory consolidation has been associated with REM sleep (Wagner et al., 2001; Nishida et al., 2009; Cairney et al., 2014), slow wave sleep (Groch et al., 2011; Cairney et al., 2014; Payne et al., 2015; Alger et al., 2018), and spindle activity (Kaestner et al., 2013; Alger et al., 2018). The shared mechanisms by which sleep influences mood and emotional memory may result in interactions between these influences. For example, sleep-related emotional memory consolidation induces plasticity within the ventromedial prefrontal cortex, a key mood-regulatory center (Nieuwenhuis and Takashima, 2011). Such plasticity may contribute to or impact the effect of sleep on mood.
除了調節情緒外，睡眠還可以鞏固情緒記憶（Tempesta et al., 2018）。與那些保持清醒的人相比，睡眠的參與者在情緒記憶任務中表現得更好。例如，與讀完負面文字後立即保持清醒的參與者相比，在閱讀負面文字後立即睡 3 小時的參與者在 4 年後的記憶力測試中表現更好（Wagner 等，2006）。此外，睡眠對情緒記憶的鞏固也與睡眠對情緒的影響有關。負性記憶鞏固與快速動眼睡眠 (Wagner et al., 2001; Nishida et al., 2009; Cairney et al., 2014)、慢波睡眠 (Groch et al., 2011; Cairney et al., 2014; Payne 等人，2015；Alger 等人，2018）和紡錘體活動（Kaestner 等人，2013；Alger 等人，2018）。睡眠影響情緒和情緒記憶的共同機制可能會導致這些影響之間的相互作用。例如，與睡眠相關的情緒記憶鞏固會誘導腹內側前額葉皮質（關鍵的情緒調節中心）內的可塑性（Nieuwenhuis 和 Takashima，2011）。這種可塑性可能有助於或影響睡眠對情緒的影響。

Indeed, previous research suggests that emotional memory consolidation may influence the relationship between sleep and mood. We observed that when controlling for pre-sleep affect, percent of time spent in SWS during the night predicts morning affect (Jones et al., 2016). Specifically, more SWS was related to worse morning affect in young adults. This relationship was moderated by negative memory performance such that better post-sleep recognition for negative pictures was associated with a stronger negative relationship between SWS and morning affect.
事實上，先前的研究表明，情緒記憶鞏固可能會影響睡眠和情緒之間的關係。我們觀察到，在控制睡前影響時，夜間處於 SWS 的時間百分比可以預測早晨的影響（Jones 等，2016）。具體來說，更多的 SWS 與年輕人早晨情緒較差有關。這種關係受到負面記憶表現的調節，因此睡後對負面圖片的更好識別與 SWS 和早晨情緒之間更強的負面關係有關。

The objective of the current study is to further investigate the influence of emotional memory consolidation on sleep-related change in affect. Here, we sought to determine the relationship between sleep physiology during a daytime nap and change in affect over the nap period. We further sought to determine whether negative memory performance would moderate any such relationship. Based on our previous findings, we hypothesized that SWS would be associated with worse affect upon wake and that higher negative memory performance would be associated with a stronger negative relationship between sleep and affect.
本研究的目的是進一步研究情緒記憶鞏固對睡眠相關情緒變化的影響。在這裡，我們試圖確定白天小睡期間的睡眠生理學與小睡期間情緒變化之間的關係。我們進一步試圖確定負記憶表現是否會緩和任何此類關係。根據我們先前的研究結果，我們假設 SWS 與起床後的不良情緒有關，而較高的負性記憶表現則與睡眠和情緒之間更強的負相關相關。

Materials and Methods 材料和方法

Participants 參加者

Data were collected from 50 young adults between 18 and 28 years of age (M = 20.94; SD = 2.29; 35 females). Participants had normal or corrected-to-normal vision and no history of neurological disease, sleep disorders, head injury, or use of medications known to affect sleep or cognitive function. Participants were instructed to refrain from alcohol, sleep at least 6 h the night before the experiment, wake up no later than 8:00 AM the morning of the experiment, and limit caffeine intake the day of the experiment. All participants were compensated with payment or course credit. Experimental procedures were approved by the University of Massachusetts, Amherst Institutional Review Board and written informed consent was obtained before the experiment.
數據收集自 50 名 18 至 28 歲的年輕人（M = 20.94；SD = 2.29；35 名女性）。參與者的視力正常或矯正至正常，且沒有神經系統疾病、睡眠障礙、頭部受傷或使用已知影響睡眠或認知功能的藥物的病史。請參與者避免飲酒，在實驗前一天晚上睡眠至少 6 小時，在實驗當天早上 8:00 之前起床，並限制實驗當天的咖啡因攝取量。所有參與者都獲得了付款或課程學分的補償。實驗程序得到了馬薩諸塞大學阿默斯特機構審查委員會的批准，並在實驗前獲得了書面知情同意書。

Seven participants were excluded from all analyses for sleeping less than 45 min (half of a typical sleep cycle), and 1 participant was excluded due to multiple awakenings due to construction noise near the sleep lab. Thus, analyses of affect are based on 42 participants. Due to data loss, sleep stage scoring was not possible for 13 participants. Thus, sleep stage analyses are based on 29 participants. Four additional participants were excluded from sigma and delta activity analyses due to poor recording quality at electrode site F3 and/or F4 (where these measures were calculated), leaving 25 participants for these analyses. Finally, 2 multivariate outliers were excluded from moderation analyses, leaving 23 participants (see Data Analysis for outlier detection). Demographic information and variables of interest showed similar characteristics across these four subsamples (see Supplementary Table 1).
7 名參與者因睡眠時間少於 45 分鐘（典型睡眠週期的一半）而被排除在所有分析之外，1 名參與者因睡眠實驗室附近的建築噪音導致多次醒來而被排除。因此，影響分析是基於 42 名參與者。由於數據遺失，無法對 13 名參與者進行睡眠階段評分。因此，睡眠階段分析是基於 29 名參與者。由於電極部位 F3 和/或 F4（計算這些測量值的地方）的記錄品質較差，另外 4 名參與者被排除在 Sigma 和 Delta 活動分析之外，只剩下 25 名參與者進行這些分析。最後，2 個多元離群值被排除在調節分析之外，留下 23 名參與者（有關離群值檢測，請參閱數據分析）。這四個子樣本的人口統計資訊和感興趣的變數顯示出相似的特徵（見補充表 1）。

Materials 材料

Stimuli were 90 emotionally negative and 90 emotionally neutral pictures. The majority of stimuli were obtained from the International Affective Picture System (IAPS; Lang et al., 2005). The rest were from an in-house set and were chosen to match the IAPS pictures in content and emotionality (Baran et al., 2012). Based on normative data and previous work in our lab (Jones et al., 2016), negative pictures were moderate to high in arousal, and neutral pictures were low in arousal.
刺激包括 90 張情緒消極的圖片和 90 張情緒中性的圖片。大多數刺激來自國際情緒圖片系統（IAPS；Lang 等，2005）。其餘的來自內部場景，並被選擇為在內容和情感上與 IAPS 圖片相匹配（Baran 等人，2012）。根據規範資料和我們實驗室先前的工作（Jones et al., 2016），負面圖片的喚醒程度為中等到高，而中性圖片的喚醒程度較低。

Procedure 程式

Participants arrived for the Encoding session between 12:30 and 1:00 PM. Following Encoding, an electrode cap was applied and a 2-h nap opportunity was given. Following the nap opportunity, the electrode cap was removed and participants completed the Recognition session. Affect was measured at three time points: immediately before Encoding (pre-Encoding), immediately after Encoding (post-Encoding), and immediately before Recognition (post-nap; Figure 1A). Approximately 30 min passed between waking and Recognition to allow for dissipation of sleep inertia.
參與者於中午 12:30 至 1:00 期間抵達參加程式設計會議。編碼後，戴上電極帽並給予 2 小時的午睡機會。午睡後，取下電極帽，參與者完成辨識課程。在三個時間點測量影響：編碼前（編碼前）、編碼後（編碼後）和識別前（午睡後；圖 1A）。醒來和識別之間大約相隔 30 分鐘，以消除睡眠慣性。

FIGURE 1 圖1

Figure 1. Experimental procedure and task. (A) Encoding took place in the early afternoon followed by a 2 h nap opportunity and then Recognition. Affect was measured prior to Encoding, just following Encoding, and then following the nap prior to Recognition. (B) During Encoding participants viewed 90 pictures (targets) and rated the valence and arousal of each on 9-point self-assessment manikin scales. During Recognition, participants viewed 180 pictures, a mixture of target and novel foil pictures, and rated each one on valence and arousal. Participants indicated whether or not they recognized the picture by responding yes/no.
圖 1. 實驗程序和任務。 (A) 編碼發生在下午早些時候，然後是 2 小時的午睡機會，然後是識別。在編碼之前、編碼之後、然後在識別之前的小睡之後測量情感。 (B) 在編碼過程中，參與者觀看了 90 張圖片（目標），並在 9 點自我評估人體模型量表上對每張圖片的效價和喚醒度進行評分。在辨識過程中，參與者觀看了 180 張圖片，其中包括目標圖片和新穎的陪襯圖片，並對每張圖片進行效價和喚醒度評分。參與者透過回答是/否來表示他們是否認識該圖片。

During Encoding, participants viewed 90 target stimuli (45 negative, 45 neutral) in random order (Figure 1B). Each picture appeared on the computer screen for 2 s, followed by a black screen for 6 s. After this black screen, participants were first prompted to rate the valence of the picture on a nine-item self-assessment manikin (SAM) valence scale (1 = negative, 5 = neutral, 9 = positive), and then prompted to rate its arousability on a nine-item SAM arousal scale (1 = no arousal, 9 = highly arousing; Bradley and Lang, 1994). Ratings were entered using numbers on a keyboard without any time limit. Following the rating scales, another black screen appeared for 10–14 s before the next picture. A long inter-stimulus interval was used in order to collect emotion physiology data (including skin conductance response), which are not presented here. Participants were not informed that their memory for the pictures would be tested later.
在編碼過程中，參與者以隨機順序查看 90 個目標刺激（45 個負面刺激，45 個中性刺激）（圖 1B）。每張圖片在電腦螢幕上出現2秒，然後黑屏6秒。在黑屏之後，首先提示參與者在九項自我評估人體模型(SAM) 效價量表上對圖片的效價進行評分（1 = 陰性，5 = 中性，9 = 陽性），然後提示其對其進行評分九題 SAM 喚醒量表的喚醒能力（1 = 無喚醒，9 = 高度喚醒；Bradley 與 Lang，1994）。使用鍵盤上的數字輸入評級，沒有任何時間限制。依照評分標準，在下一張圖片之前，另一個黑畫面出現 10-14 秒。使用較長的刺激間隔來收集情緒生理學數據（包括皮膚電導反應），此處未提供。參與者沒有被告知稍後將測試他們對圖片的記憶。

During Recognition, participants were shown 180 pictures: the same 90 targets seen during Encoding intermixed with 90 novel pictures (foils; 45 neutral and 45 negative). The stimulus presentation procedure was identical to Encoding with the following exceptions: (1) following valence and arousal ratings, participants were prompted to indicate whether they had seen each picture before by pressing “y” for yes and “n” for no, and (2) a 1-s inter-stimulus interval was used during the second half of the session in order to prevent fatigue.
在辨識過程中，參與者看到了 180 張圖片：編碼過程中看到的 90 個目標與 90 張新穎的圖片混合在一起（箔片；45 張中性圖片和 45 張負面圖片）。刺激呈現程序與編碼相同，但有以下例外：（1）在效價和喚醒評級之後，系統提示參與者通過按“y”表示是和“n”表示否來表明他們之前是否看過每張圖片，並且（ 2) 在訓練的後半段使用 1 秒的刺激間隔，以防止疲勞。

Polysomnography 多導睡眠圖

Polysomnography (PSG) was recorded in the sleep laboratory using the Comet Plus PSG system (Grass Technologies) combined with a 32-electrode cap (EasyCap GmbH, Germany) that included two electrooculography (EOG; right and left ocular canthi), two chin electromyography (EMG), and 27 electroencephalography (EEG) leads (Fz, F3, F4, F7, F8, FCz, FC1, FC2, FC5, FC6, Cz, C3, C4, CP1, CP2, CP5, CP6, Pz, P3, P4, P7, P8, POz, O1, O2, M1, M2). PSG data were collected at a sampling rate of 200 Hz with a bandpass of 0.1–100 Hz. EOG and EEG channels were referenced to Cz during recording and re-referenced to the contralateral mastoid for scoring. Recordings were obtained and scored according to the specifications provided by the American Academy of Sleep Medicine (Iber et al., 2007).
在睡眠實驗室中使用Comet Plus PSG 系統（Grass Technologies）結合32 電極帽（EasyCap GmbH，德國）記錄多導睡眠圖(PSG)，其中包括兩次眼電圖（EOG；左右眼角）、兩次下巴肌電圖(EMG) 和27 個腦電圖(EEG) 導極（Fz、F3、F4、F7、F8、FCz、FC1、FC2、FC5、FC6、Cz、C3、C4、CP1、CP2、CP5、 CP6、Pz、P3、 P4、P7、P8、POz、O1、O2、M1、M2）。 PSG 資料以 200 Hz 的取樣率、0.1–100 Hz 的帶通進行收集。 EOG 和 EEG 通道在記錄期間參考 Cz，並重新參考對側乳突進行評分。根據美國睡眠醫學會提供的規範取得記錄並評分（Iber 等，2007）。

Data Analysis 數據分析

Participants’ individual valence ratings of the pictures were used to categorize stimuli for analyses (as in St. Jacques et al., 2009). Due to individual differences in emotional response, individualized categorization may provide the most accurate measures. Targets were categorized based on ratings during the Encoding session, and foils were categorized based on ratings during the Recognition session. Negative and neutral pictures were defined as those rated 1–3 and 4–6 on valence, respectively. Hence, the analyzed picture sets were unique for each participant. On average, 36.92 ± 8.55 target pictures were rated as negative (valence: M = 1.83, SD = 0.53; arousal: M = 5.89, SD = 1.92) and 43.05 ± 11.60 target pictures were rated as neutral (valence: M = 5.02, SD = 0.13; arousal: M = 2.13, SD = 1.36). Hit rate, defined as the percentage of target pictures correctly identified as previously seen, was chosen as the memory measure based on our previous findings (Baran et al., 2012; Jones et al., 2016).
參與者對圖片的個人化合價評分用於對刺激進行分類以進行分析（如 St. Jacques 等人，2009 年）。由於情緒反應的個體差異，個體化分類可能提供最準確的衡量標準。目標根據編碼會話期間的評分進行分類，箔片根據識別會話期間的評分進行分類。負面和中性圖片分別定義為價數 1-3 和 4-6 的圖片。因此，分析的圖片集對於每位參與者來說都是獨一無二的。平均而言，36.92±8.55張目標圖片被評為負面（效價：M = 1.83，SD = 0.53；喚醒：M = 5.89，SD = 1.92），43.05±11.60張目標圖片被評為中性（效價：M = 5.02， SD = 0.13；喚醒：M = 2.13，SD = 1.36）。根據我們先前的研究結果，命中率（定義為正確識別出先前看到的目標圖片的百分比）被選為記憶測量（Baran 等人，2012 年；Jones 等人，2016 年）。

Affect was measured using the Positive and Negative Affect Schedule (PANAS; Watson et al., 1988). The PANAS consists of 10 positive and 10 negative attributes that participants rate on a scale from 1 to 5 according to their current feelings, resulting in a possible score of 10–50 for each valence. Higher scores indicate higher affect. Affect Ratio was calculated as an adjusted ratio of positive to negative affect for each of the three time points. Because a simple affect ratio (positive affect divided by negative affect) would range from 1 to 5 when positive affect is equal to or higher than negative, but would only range from 0.20 to 0.98 when negative affect is higher than positive, we calculated an adjusted ratio to keep negative and positive affect on the same scale. The adjusted ratio was calculated by dividing the higher valence score by the lower valence score, multiplying by -1 if negative affect was higher, and then subtracting 1 from positive values and adding 1 to negative values. This transformation resulted in a linearly spaced composite measure with a possible range of -4 to 4. Thus, positive scores indicate that positive affect was higher than negative affect, and negative scores indicate that negative affect was higher than positive affect.
使用正面和負面影響表（PANAS；Watson 等，1988）來測量影響。 PANAS 由 10 個正面屬性和 10 個負面屬性組成，參與者根據自己目前的感受以 1 到 5 的等級進行評分，每個價的可能得分為 10-50。分數越高表示影響力越高。影響比計算為三個時間點中每個時間點的正面與負面影響的調整比率。因為當正面情緒等於或高於負面情緒時，簡單的情緒比率（正面情緒除以負面情緒）的範圍為1 到5，但當負面情緒高於正面情緒時，其範圍僅為0.20 到0.98，因此我們計算了調整後的比率以保持負面和正面影響在同一範圍內。調整後的比率的計算方法是，將較高效價分數除以較低效價分數，如果負面影響較高，則乘以-1，然後從正值中減去1，並在負值中加1 。此轉換導致線性間隔的複合測量，可能的範圍為 -4 到 4。

EEG amplitude density was measured in the delta (0.5–4 Hz) and sigma (12–16 Hz) bands over frontal scalp regions (F3, F4) by extracting the amplitude envelope of bandpass-filtered EEG, summing it within identified sleep stages, and normalizing by time. The use of the Hilbert-transformation-derived amplitude envelope to quantify signal dynamics is a common method in engineering that has been previously applied to EEG analysis in multiple contexts, including sleep (Clochon et al., 1996; Freeman, 2004; Díaz et al., 2018). We opt to use this approach over the potentially more familiar short-time Fourier transform or wavelet decomposition methods of signal dynamics quantification because it is more computationally efficient, and because recent evidence suggests that Hilbert-transformation-derived envelopes may more accurately capture arrhythmic elements of the EEG (Díaz et al., 2018).
透過擷取帶通濾波腦電圖的振幅包絡，在辨識的睡眠階段內將其求和，在額頭皮區域（F3、F4）的delta（0.5-4 Hz）和sigma（12-16 Hz）頻帶中測量EEG 振幅密度，並依時間標準化。使用希爾伯特變換導出的幅度包絡來量化訊號動態是工程中的常見方法，該方法先前已應用於多種情況下的腦電圖分析，包括睡眠（Clochon 等人，1996 年； Freeman，2004 年；Díaz 等人） .，2018）。我們選擇使用這種方法，而不是可能更熟悉的訊號動態量化的短時傅立葉變換或小波分解方法，因為它的計算效率更高，並且因為最近的證據表明希爾伯特變換導出的包絡可以更準確地捕捉訊號動態量化的心律不整元素。

EEG data were first re-referenced offline to the averaged mastoid recording, then filtered separately into delta activity using a Butterworth infinite-impulse response filter (order = 2) that did not remove mean recording bias, and sigma activity using a forward impulse response filter (order = 164) that did remove mean recording bias. Regions of continuous filtered EEG exceeding frequency-band specific thresholds (delta: ±250 μV, sigma: ±75 μV) within a moving 500 ms window were marked as artifact. Delta and sigma amplitude envelopes were then calculated for each electrode as the magnitude (absolute value) of the analytic signal (z) of the filtered EEG, where the analytic signal is the sum of the filtered EEG and its discrete Hilbert transformation multiplied by the imaginary unit: z(EEG) = EEG + i ^∗ Hilbert(EEG). Amplitude envelopes were then averaged across electrodes. Samples not previously marked as artifact at either electrode were then summed across stage 2 non-rapid eye movement (NREM2) sleep and SWS epochs, and divided by the combined number of artifact-free seconds spent in NREM2 sleep and SWS. Less than 0.04 and 0.02% of samples were marked as artifact for any participant for delta and sigma, respectively. EEG analyses were conducted in MATLAB using a combination of EEGLAB (Delorme and Makeig, 2004), ERPLAB (Lopez-Calderon and Luck, 2014), and custom in-house functions (available upon request).
EEG 數據首先離線重新參考平均乳突記錄，然後使用巴特沃斯無限脈衝響應濾波器（階數= 2）單獨過濾為delta 活動，該濾波器不會消除平均記錄偏差，並使用前向脈衝響應濾波器器過濾sigma 活動（階數 = 164）確實消除了平均記錄偏差。在移動 500 ms 視窗內超過頻帶特定閾值（δ：±250 μV，sigma：±75 μV）的連續濾波 EEG 區域被標記為偽影。然後計算每個電極的 Delta 和 sigma 幅度包絡，作為濾波 EEG 的分析訊號 (z) 的振幅（絕對值），其中分析訊號是濾波 EEG 及其離散希爾伯特變換乘以虛數的總和。單位：z(腦電圖) = 腦電圖+ ^∗ 希爾伯特(腦電圖)。然後對電極上的振幅包絡進行平均。然後，將先前在任一電極上未標記為偽影的樣本在第2 階段非快速動眼(NREM2) 睡眠和SWS 時期進行求和，並除以NREM2 睡眠和SWS 中花費的無偽影秒數的總和。對於任何參與者來說，對於 delta 和 sigma，分別有不到 0.04% 和 0.02% 的樣本被標記為偽影。 EEG 分析是在 MATLAB 中結合使用 EEGLAB（Delorme 和 Makeig，2004 年）、ERPLAB（Lopez-Calderon 和 Luck，2014 年）和自訂內部函數（可根據要求提供）進行的。

Within-subject comparisons of means were conducted using repeated-measures analyses of variance (ANOVAs), and post hoc pairwise comparisons were made using Student’s paired-sample t-tests. Pearson’s r was used to assess bivariate linear relationships. Hierarchical multiple linear regression was used to conduct moderation analyses. Independent variables were mean-centered before being entered into regression models. Significant interactions were decomposed according to the guidelines of Aiken and West (1991), with fitted regression lines plotted at high (+1 SD) and low (-1 SD) levels of the moderating variable using estimates obtained from the final model. Simple slopes testing was conducted to assess relationships at high and low levels of the moderator. Multivariate outliers were detected and removed based on a studentized residual greater than 2.5 (1 data point removed) or a Cook’s Distance greater than 3 SD from the mean Cook’s Distance (1 data point removed). Significance levels were set to p < 0.05. A “marginal” effect was defined as having a p-value ≥0.05 and <0.075. Statistical analyses were conducted in SPSS, and interaction plots were created using open-source tools¹.
使用重複測量變異數分析 (ANOVA) 進行受試者內平均值比較，並使用學生配對樣本 t 檢定進行事後配對比較。 Pearson’s r 用於評估二元線性關係。使用分層多元線性迴歸進行調節分析。自變數在進入迴歸模型之前以平均值為中心。根據 Aiken 和 West (1991) 的指導方針分解顯著的交互作用，並使用從最終模型獲得的估計值在調節變量的高 (+1 SD) 和低 (-1 SD) 水平上繪製擬合回歸線。進行簡單的斜率測試來評估調節器高水平和低水平的關係。根據學生化殘差大於 2.5（刪除 1 個資料點）或庫克距離與平均庫克距離大於 3 SD（刪除 1 個資料點）來偵測和刪除多變量異常值。顯著水準設定為 p < 0.05。 「邊際」效應定義為 p 值≥0.05 且<0.075。在 SPSS 中進行統計分析，並使用開源工具 ¹ 建立交互圖。

Results 結果

Change in Affect Over the Encoding and Nap Periods
編碼和午睡期間的影響變化

Mean positive affect and negative affect scores measured at the three time points are reported in Table 1. A repeated-measures ANOVA with Time (pre-Encoding, post-Encoding, post-nap) as the within-subjects factor was conducted on Affect Ratio. There was a main effect of Time [F_(1.7,70.3) = 21.676, p < 0.001, Huynh-Feldt correction]. Follow-up paired-sample t-tests indicated that affect decreased over the encoding task period (t = 7.258, p < 0.001) and increased/recovered between the post-Encoding and post-nap time points (t = -5.670, p < 0.001; Figure 2).
表 1 報告了在三個時間點測量的平均正向情緒和負向情緒得分。時間有主效果 [F _(1.7,70.3) = 21.676，p < 0.001，Huynh-Feldt 校正]。後續配對樣本 t 檢定表明，影響在編碼任務期間下降（t = 7.258，p < 0.001），而在編碼後和午睡後時間點之間增加/恢復（t = -5.670，p < 0.001；圖2) 。

TABLE 1 表格1

Table 1. Affect [mean (SE)].
表 1. 影響 [平均值 (SE)]。

FIGURE 2 圖2

Figure 2. Mean Affect Ratio (adjusted ratio of positive to negative affect) at the pre-Encoding, post-Encoding, and post-nap time-points. Error bars represent standard errors of means.
圖 2. 編碼前、編碼後和午睡後時間點的平均情緒比（調整後的正向與負向情緒的比率）。誤差線代表平均值的標準誤差。

We next analyzed positive and negative affect separately. For positive affect, there was a main effect of Time [F_(1.8,74.7) = 28.719, p < 0.001, Huynh-Feldt correction]. Follow-up comparisons indicated that positive affect decreased over the encoding task period (t = 8.717, p < 0.001) and increased/recovered over the nap period (t = -4.333, p < 0.001). For negative affect, there was also a main effect of Time [F(2,82) = 5.659, p = 0.005], with follow-up comparisons indicating that negative affect did not significantly change over the encoding task period (t = -0.382, p = 0.704) but did decrease over the nap period (t = 3.812, p < 0.001). Given that both positive and negative affect change over the nap period, we use Affect Ratio for subsequent analyses in order to capture overall affect while limiting the number of comparisons/tests.
接下來我們分別分析正面和負面影響。對於正面影響，時間具有主效果 [F _(1.8,74.7) = 28.719，p < 0.001，Huynh-Feldt 校正]。後續比較表明，積極情緒在編碼任務期間下降（t = 8.717，p < 0.001），在午睡期間增加/恢復（t = -4.333，p < 0.001）。對於負面影響，時間也有主要影響[F(2,82) = 5.659，p = 0.005]，後續比較顯示負面影響在編碼任務期間沒有顯著變化（t = -0.382 ，p = 0.704），但在午睡期間確實有下降（t = 3.812，p < 0.001）。鑑於午睡期間正面和負面影響都會發生變化，我們使用影響比進行後續分析，以便在限制比較/測試數量的同時捕捉整體影響。

Relationships Between Nap Physiology and Affect
午睡生理與情緒之間的關係

Average nap parameters are reported in Table 2. Of the 29 participants for whom sleep stage scoring was possible, 26 obtained SWS, and 16 obtained REM sleep. Nineteen participants were in NREM2 when they woke from the nap, 6 were in SWS, and 4 were in REM sleep.
表 2 報告了平均小睡參數。 19 名參與者從午睡中醒來時處於 NREM2 狀態，6 名參與者處於 SWS 狀態，4 名參與者處於 REM 睡眠狀態。

TABLE 2 表2

Table 2. Nap characteristics [mean (SE)].
表 2. 午睡特徵 [平均值 (SE)]。

To investigate whether specific sleep stages were associated with the recovery in affect, correlation analyses were conducted. Neither percent time spent in SWS (r = -0.187, p = 0.331) nor REM sleep (r = -0.222, p = 0.247) was significantly related to post-nap affect. However, percent time spent in NREM2 sleep was positively related to post-nap affect (r = 0.459, p = 0.012; Figure 3A). Since sleep measures may be related to trait characteristics of affect, we next controlled for pre-nap (post-Encoding) affect to identify relationships with change in affect over the nap period. When controlling for post-Encoding affect, the relationship with percent time in NREM2 sleep remained marginally significant (partial r = 0.344, p = 0.073), suggesting that NREM2 sleep during the nap may be associated with improvement in affect. Neither SWS nor REM sleep was significantly related to post-nap affect when controlling for post-Encoding affect (p’s > 0.16). Furthermore, neither total sleep time nor sleep efficiency was significantly related to post-nap affect (p’s > 0.52). Post-nap affect also did not significantly vary according to the sleep stage from which participants awoke (p’s > 0.43), suggesting that individual differences in sleep inertia was not a factor in these results.
為了調查特定的睡眠階段是否與情緒恢復相關，進行了相關分析。 SWS 時間百分比（r = -0.187，p = 0.331）和 REM 睡眠時間百分比（r = -0.222，p = 0.247）皆與午睡後情緒有顯著相關。然而，NREM2 睡眠時間百分比與午睡後情緒呈正相關（r = 0.459，p = 0.012；圖 3A）。由於睡眠測量可能與情感的特徵特徵相關，因此我們接下來控制午睡前（編碼後）情感，以確定與午睡期間情感變化的關係。當控制編碼後情緒時，與 NREM2 睡眠百分比時間的關係仍然略有顯著（部分 r = 0.344，p = 0.073），這表明小睡期間的 NREM2 睡眠可能與情緒的改善相關。當控制編碼後影響時，SWS 與 REM 睡眠與午睡後影響皆不顯著相關（p > 0.16）。此外，總睡眠時間和睡眠效率與午睡後情緒皆不顯著相關（p > 0.52）。根據參與者醒來的睡眠階段，午睡後的影響也沒有顯著變化（p>0.43），這表明睡眠慣性的個體差異並不是這些結果的一個因素。

FIGURE 3 圖3

Figure 3. Relationships between sleep and affect. (A) Relationship between the percent of time spent in NREM2 sleep and the Affect Ratio (adjusted ratio of positive to negative affect) after the nap. (B) Partial correlation between sigma density and post-nap Affect Ratio when controlling for post-Encoding Affect Ratio. Residuals were obtained by regressing sigma density and post-nap Affect Ratio against the post-Encoding Affect Ratio. Sigma density values are reported in arbitrary amplitude envelope units summed per second. These values can be converted to mean amplitude envelope units (comparable to microvolts) by dividing by the sampling rate (200 Hz).
圖 3. 睡眠和情緒之間的關係。 (A) 午睡後 NREM2 睡眠時間百分比與影響比（調整後的正向情緒與負向情緒的比率）之間的關係。 (B) 當控制編碼後影響比時，西格瑪密度和午睡後影響比之間的部分相關。透過將西格瑪密度和午睡後影響比與編碼後影響比進行迴歸來獲得殘差。西格瑪密度值以每秒求和的任意振幅包絡單位來報告。這些值可以透過除以取樣率 (200 Hz) 轉換為平均幅度包絡單位（相當於微伏特）。

Given the relationship with NREM2 sleep, we investigated whether sigma activity (a hallmark of NREM2 sleep) was associated with improvement in affect. Controlling for post-Encoding affect, greater NREM sigma density was associated with higher post-nap affect (partial r = 0.439, p = 0.036; Figure 3B). To determine whether this relationship was specific to the sigma band, we also calculated delta density during NREM sleep. There was no significant relationship with delta activity during NREM sleep (partial r = -0.175, p = 0.414).
鑑於與 NREM2 睡眠的關係，我們研究了 sigma 活動（NREM2 睡眠的標誌）是否與情緒改善有關。控制編碼後影響，較大的 NREM 西格瑪密度與較高的午睡後影響相關（部分 r = 0.439，p = 0.036；圖 3B）。為了確定這種關係是否特定於西格瑪帶，我們也計算了 NREM 睡眠期間的 delta 密度。 NREM 睡眠期間與 delta 活動沒有顯著關係（部分 r = -0.175，p = 0.414）。

Moderation by Memory 透過記憶進行調節

We next asked whether emotional memory consolidation influenced the relationship between sleep physiology and affect. Memory performance and relationships between memory and affect are reported in Supplementary Tables 1 and 2, respectively. A moderation analysis was conducted using hierarchical linear regression with post-Encoding affect entered in level 1 as the control variable, NREM sigma density and memory performance (hit rate for negative pictures) entered in level 2 as the predictor variable and moderator variable, respectively, and the NREM sigma density X memory performance interaction term entered in level 3. Sigma density (β = 0.259, p = 0.045) and memory performance (β = -0.289, p = 0.028) each predicted change in affect. Adding the interaction term significantly increased model fit, indicating that memory moderated the relationship between sigma density and improvement in affect (Table 3). Specifically, simple slopes testing indicated there was a positive relationship between NREM sigma density and change in affect at low (-1 SD) memory levels (β = 0.592, p = 0.003) but not high (+1 SD) memory levels (β = -0.090, p = 0.631; Figure 4). The moderation effect remained when we included false alarm rate as an additional control variable in the level 1 model (R² change = 0.071, p = 0.024), suggesting that the effect is not driven by response bias. Additionally, the moderation was not significant using hit rate of neutral pictures (R² change = 0.009, p = 0.445), indicating it is specific to negative memory.
接下來我們詢問情緒記憶鞏固是否會影響睡眠生理與情緒之間的關係。補充表 1 和補充表 2 分別報告了記憶表現以及記憶與情緒之間的關係。使用分層線性回歸進行調節分析，其中在第 1 級中輸入編碼後影響作為控制變量，在第 2 級中輸入 NREM 西格瑪密度和記憶表現（負面圖片的命中率）分別作為預測變量和調節變量， NREM 西格瑪密度X 記憶表現交互項在等級3 中輸入。增加交互項顯著提高了模型適配度，顯示記憶調節了西格瑪密度和情緒改善之間的關係（表 3）。具體來說，簡單的斜率測試表明，NREM 西格瑪密度與低(-1 SD) 記憶水平(β = 0.592，p = 0.003) 的影響變化之間存在正相關關係，但在高(+1 SD) 記憶水平(β = -0.090，p = 0.631；圖 4)。當我們將誤報率作為 1 級模型中的附加控制變數時，調節效應仍然存在（R ² 變化 = 0.071，p = 0.024），這表明該效應不是由反應偏差驅動的。此外，使用中性圖片的命中率進行的調節並不顯著（R ² 變化 = 0.009，p = 0.445），表明它特定於負面記憶。

TABLE 3 表3

Table 3. Multiple regression analysis.
表 3. 多元迴歸分析。

FIGURE 4 圖4

Figure 4. Interaction between NREM sigma density and negative memory in predicting the post-nap Affect Ratio (adjusted ratio of positive to negative affect), while controlling for the post-Encoding Affect Ratio. Neg HR, negative hit rate.
圖 4. NREM 西格瑪密度和負性記憶之間的交互作用在預測午睡後影響比（調整後的正面與負面影響的比率）時，同時控制編碼後影響比。負HR，負命中率。

Discussion 討論

Here we show that percent time spent in NREM2 sleep as well as NREM sigma density during an afternoon nap predict recovery of affect over the nap period following a decline related to viewing negative pictures. The relationship between sigma density and affect was moderated by memory performance for negative pictures. Specifically, this relationship was present for those with low memory performance but not for those with high memory performance. Further, this effect was specific to negative memory, as memory performance for neutral pictures did not moderate the relationship between sigma density and affect. These results may suggest that processing of emotional memories during sleep contributes to the impact of sleep on subsequent affect.
在這裡，我們表明，在 NREM2 睡眠中花費的時間百分比以及下午小睡期間的 NREM 西格瑪密度可以預測在與觀看負面圖片相關的下降之後的小睡期間情緒的恢復。西格瑪密度和情緒之間的關係受到負面圖片的記憶表現的調節。具體來說，這種關係對於記憶體效能低的人來說存在，但對於記憶體效能高的人則不存在。此外，這種效應是負面記憶所特有的，因為中性圖片的記憶表現並不能調節西格瑪密度和情緒之間的關係。這些結果可能表明，睡眠期間情緒記憶的處理有助於睡眠對後續情緒的影響。

Based on our previous study of overnight sleep (Jones et al., 2016), we hypothesized that higher percent time in SWS would predict less improvement in affect over the nap. Instead, we did not find any significant relationship between SWS during a nap and affect. However, we did see that percent time in NREM2 sleep, as well as sigma activity [which is most prevalent in NREM2 sleep (De Gennaro and Ferrara, 2003)], were positively related to change in affect over the nap. Since percent time in NREM2 sleep and SWS are often inversely related, these findings may simply reflect this inverse relationship. Alternatively, there may be separate mechanisms acting on affect during NREM2 sleep and SWS, with a longer sleep period needed to uncover the relationship with SWS.
根據我們先前對夜間睡眠的研究（Jones 等人，2016 年），我們假設 SWS 時間百分比越高，則預示小睡後的情緒改善幅度較小。相反，我們沒有發現小睡期間的 SWS 與情緒之間有任何顯著關係。然而，我們確實發現 NREM2 睡眠的百分比時間以及 sigma 活動（在 NREM2 睡眠中最普遍（De Gennaro 和 Ferrara，2003））與小睡期間的情緒變化呈正相關。由於 NREM2 睡眠時間百分比與 SWS 通常呈負相關，因此這些發現可能只是反映了這種負相關關係。或者，NREM2 睡眠和 SWS 期間可能有不同的機製作用於影響，需要更長的睡眠時間才能揭示與 SWS 的關係。

Sigma activity predominately reflects sleep spindles, though it should be kept in mind that they are not necessarily the same. While previous studies have more often linked REM sleep (Cartwright et al., 1998; Palagini et al., 2013; Motomura et al., 2017) and SWS/slow wave activity (Landsness et al., 2011; Cheng et al., 2015; Finan et al., 2015) to mood, some recent studies have implicated sleep spindles in relation to mood. Reduced spindle activity has been seen in children and adolescents with social anxiety, with greater fast spindle activity (13–16 Hz) related to less severe symptoms (Wilhelm et al., 2017). A reduction in spindle activity was also observed in children and adolescents with or at risk for depression (Lopez et al., 2010). In adults, reduced spindles have been reported in depressed individuals compared to controls (de Maertelaer et al., 1987), though there have also been reports of no difference between groups (Ferrarelli et al., 2007) or increased spindle activity in depressed individuals (Plante et al., 2013). Sleep spindles reflect synchronization between cortical and subcortical structures and promote synaptic plasticity, which may be integral to regulating structural and functional connectivity and effective communication among brain regions regulating mood and affect (Meerlo et al., 2015; Ulrich, 2016). Thus, sigma activity during sleep may generally benefit and restore mood. Lower trait-level sigma activity may predispose individuals to anxiety and mood disorders due to a reduction in the capacity of sleep to restore mood. Future research could investigate whether experimentally manipulating spindles could influence mood regulation.
西格瑪活動主要反映睡眠紡錘波，但應記住它們不一定相同。雖然先前的研究通常將快速動眼睡眠（Cartwright et al., 1998; Palagini et al., 2013; Motomura et al., 2017）與SWS/慢波活動連結起來（Landsness et al., 2011; Cheng et al., 2015；Finan 等人，2015）與情緒有關，最近的一些研究顯示睡眠紡錘波與情緒有關。在患有社交焦慮的兒童和青少年中，紡錘體活動減少，快速紡錘體活動（13-16 Hz）越大，症狀越輕（Wilhelm et al., 2017）。在患有憂鬱症或有憂鬱風險的兒童和青少年中也觀察到紡錘體活動減少（Lopez 等，2010）。在成年人中，據報道，與對照組相比，憂鬱症患者的紡錘體減少（de Maertelaer 等人，1987 年），儘管也有報導各組之間沒有差異（Ferrarelli 等人，2007 年）或憂鬱症患者的紡錘體活動增加（普蘭特等人，2013）。睡眠紡錘波反映了皮質和皮質下結構之間的同步性，並促進突觸可塑性，這可能是調節結構和功能連接以及調節情緒和情緒的大腦區域之間有效溝通的組成部分（Meerlo et al. , 2015; Ulrich, 2016）。因此，睡眠期間的西格瑪活動通常可能有益於恢復情緒。由於睡眠恢復情緒的能力降低，較低的特質水準西格瑪活動可能使個體容易焦慮和情緒障礙。未來的研究可能會調查實驗性操縱紡錘體是否會影響情緒調節。

In the current study negative (but not neutral) memory performance influenced the relationship between sigma activity and affect. Sigma density predicted improvement in affect only when memory performance was low and not when memory performance was high. These results are in some ways consistent with our prior findings. We previously observed a negative relationship between the percent time spent in SWS overnight and next morning affect (ratio of positive to negative affect; Jones et al., 2016). However, negative memory performance moderated this relationship. There was a significant negative relationship between SWS and affect only when negative memory was high, and not when it was low. Thus, in both the previous and current study, high negative memory was associated with worse affect than low negative memory. Since high memory performance suggests strong consolidation during sleep, these results may suggest that negative memory consolidation hinders the extent to which sleep benefits and restores mood.
在目前的研究中，負面（但不是中性）記憶表現影響了西格瑪活動和情緒之間的關係。西格瑪密度僅在記憶表現較低時預測情緒改善，在記憶表現較高時則不預測。這些結果在某些方面與我們先前的發現是一致的。我們先前觀察到過夜 SWS 花費的時間百分比與第二天早上的情緒之間存在負相關關係（正面與負面情緒的比率；Jones 等人，2016）。然而，負記憶表現緩和了這種關係。只有當負面記憶較高時，SWS 和情感之間才存在顯著的負相關關係，而當負面記憶較低時，則不存在顯著的負相關關係。因此，在先前和目前的研究中，高負性記憶比低負性記憶與更糟糕的情緒有關。由於高記憶表現顯示睡眠期間有強烈的鞏固作用，這些結果可能表明負面的記憶鞏固會阻礙睡眠有益和恢復情緒的程度。

Together, these current and past findings may suggest a multi-step process during sleep with regard to affect: sigma activity, most prevalent during NREM2 sleep, may benefit affect, but if subsequent mechanisms (particularly during SWS) lead to strong consolidation of negative memory (and thus high memory performance), affect is diminished. Thus, more sigma activity predicts better affect when negative memory is low, and more SWS predicts worse affect when negative memory is high. Additionally, we previously observed that high (but not low) positive memory performance was associated with a significant positive relationship between percent time spent in overnight SWS and morning affect in older adults (Jones et al., 2016). Thus, while consolidation of negative memories may adversely affect sleep-related restoration of mood, consolidation of positive memories may have the opposite effect. Since emotional memory consolidation involves mood-regulating circuitry, such as the ventromedial prefrontal cortex (Nieuwenhuis and Takashima, 2011), it may lead to functional changes within this circuitry that impact mood. More research manipulating memory valence and consolidation mechanisms such as slow wave activity is needed to investigate these possibilities.
總之，這些當前和過去的發現可能表明睡眠期間關於影響的多步驟過程：西格瑪活動（在NREM2 睡眠期間最普遍）可能有益於影響，但如果後續機制（特別是在SWS 期間）導致負面記憶的強烈鞏固（從而提高記憶表現），影響力減弱。因此，當負面記憶較低時，較多的 sigma 活動預測較好的影響，而當負面記憶較高時，較多的 SWS 預測較差的影響。此外，我們先前觀察到，老年人的高（但不是低）正向記憶表現與夜間 SWS 花費的時間百分比和早晨情緒之間存在顯著的正相關關係（Jones 等，2016）。因此，雖然負面記憶的鞏固可能會對與睡眠相關的情緒恢復產生不利影響，但正向記憶的鞏固可能會產生相反的效果。由於情緒記憶鞏固涉及情緒調節迴路，例如腹內側前額葉皮質（Nieuwenhuis 和 Takashima，2011），因此可能會導致此迴路內的功能發生變化，從而影響情緒。需要更多的研究來操縱記憶效價和鞏固機制（例如慢波活動）來調查這些可能性。

This study provides evidence that emotional memory consolidation may impact the influence of sleep on mood. However, limitations of this research should be considered. First, these findings are associative in nature, and further research is needed to establish a causal relationship and determine the underlying mechanisms. Furthermore, though post-sleep memory performance is expected to reflect sleep-dependent memory consolidation to some extent, future studies should use over-sleep change in memory performance, as this change may be a more accurate representation of sleep-dependent consolidation. Finally, although fairly standard in the sleep and memory field, the sample sizes used in our sleep analyses (n = 23–29) are still relatively low, particularly for moderation analyses, and thus future studies with larger sample sizes are warranted.
這項研究提供了證據表明情緒記憶鞏固可能會影響睡眠對情緒的影響。然而，應該考慮這項研究的局限性。首先，這些發現本質上是相關的，需要進一步的研究來建立因果關係並確定潛在的機制。此外，儘管睡眠後記憶表現預計在一定程度上反映了睡眠依賴性記憶鞏固，但未來的研究應該使用記憶表現的睡眠過度變化，因為這種變化可能更準確地代表睡眠依賴性鞏固。最後，儘管在睡眠和記憶領域相當標準，但我們的睡眠分析（n = 23-29）中使用的樣本量仍然相對較低，特別是對於調節分析而言，因此未來有必要進行更大樣本量的研究。

Data Availability 數據可用性

The raw data supporting the conclusions of this manuscript will be made available to a qualified researcher upon reasonable request.
支持本手稿結論的原始數據將根據合理要求提供給合格的研究人員。

Author Contributions 作者貢獻

BJ designed the experiments and collected the data. BJ and AF analyzed the data and wrote the manuscript. RS supervised the entire project.
BJ 設計了實驗並收集了數據。 BJ 和 AF 分析了數據並撰寫了手稿。 RS 監督了整個專案。

Funding 資金

This work was supported by NIH R01 AG040133.
這項工作得到了 NIH R01 AG040133 的支持。

Conflict of Interest Statement
利益衝突聲明

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
作者聲明，該研究是在不存在任何可能被視為潛在利益衝突的商業或財務關係的情況下進行的。

Acknowledgments 致謝

We thank Kristin Jones for assistance with data entry and preliminary analysis.
我們感謝克里斯汀瓊斯在數據輸入和初步分析方面提供的幫助。

Supplementary Material 補充資料

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2019.00500/full#supplementary-material
本文的補充資料可在網路上找到：https://www.frontiersin.org/articles/10.3389/fpsyg.2019.00500/full#supplementary-material

Footnotes 註腳

1. ^www.jeremydawson.co.uk/slopes.htm

References 參考

Aiken, L. S., and West, S. G. (1991). Multiple Regression: Testing and Interpreting Interactions. Newbury Park, CA: Sage.
艾肯，L.S. 和韋斯特，S.G. (1991)。多元迴歸：測驗和解釋交互作用。加州紐伯里公園：Sage。

Google Scholar  谷歌學術

Alger, S. E., Kensinger, E. A., and Payne, J. D. (2018). Preferential consolidation of emotionally salient information during a nap is preserved in middle age. Neurobiol. Aging 68, 34–47. doi: 10.1016/j.neurobiolaging.2018.03.030
Alger, S.E.、Kensinger, E.A. 與 Payne, J.D. (2018)。在中年時期，小睡期間優先鞏固情感上的重要訊息會被保留。神經生物學。年齡 68 歲、34-47 歲。 doi：10.1016/j.neurobiolaging.2018.03.030

PubMed Abstract | CrossRef Full Text | Google Scholar
考研摘要|全文交叉引用 |谷歌學術

Baran, B., Pace-Schott, E. F., Ericson, C., and Spencer, R. M. C. (2012). Processing of emotional reactivity and emotional memory over sleep. J. Neurosci. 32, 1035–1042. doi: 10.1523/JNEUROSCI.2532-11.2012
Baran, B.、Pace-Schott, E. F.、Ericson, C. 與 Spencer, R. M. C. (2012)。睡眠期間情緒反應和情緒記憶的處理。 J.神經科學。 32、1035–1042。 doi：10.1523/JNEUROSCI.2532-11.2012

CrossRef Full Text | Google Scholar
全文交叉引用 |谷歌學術

Bradley, M. M., and Lang, P. J. (1994). Measuring emotion: the Self-Assessment Manikin and the semantic differential. J. Behav. Ther. Exp. Psychiatry 25, 49–59. doi: 10.1016/0005-7916(94)90063-9
Bradley, M. M. 與 Lang, P. J. (1994)。測量情緒：自我評估模型和語義差異。 J.行為。瑟爾。過期。精神病學 25, 49–59。編號：10.1016/0005-7916(94)90063-9

CrossRef Full Text | Google Scholar
全文交叉引用 |谷歌學術

Cairney, S. A., Durrant, S. J., Power, R., and Lewis, P. A. (2014). Complementary roles of slow-wave sleep and rapid eye movement sleep in emotional memory consolidation. Cereb. Cortex doi: 10.1093/cercor/bht349
Cairney, S.A.、Durrant, S.J.、Power, R. 與 Lewis, P.A. (2014)。慢波睡眠和快速動眼睡眠在情緒記憶鞏固中的互補作用。塞雷布。皮質 doi：10.1093/cercor/bht349

PubMed Abstract | CrossRef Full Text | Google Scholar
考研摘要|全文交叉引用 |谷歌學術

Cartwright, R., Luten, A., Young, M., Mercer, P., and Bears, M. (1998). Role of REM sleep and dream affect in overnight mood regulation: a study of normal volunteers. Psychiatry Res. 81, 1–8. doi: 10.1016/S0165-1781(98)00089-4
Cartwright, R.、Luten, A.、Young, M.、Mercer, P. 與 Bears, M. (1998)。快速動眼睡眠和夢境在夜間情緒調節中的作用：對正常志願者的研究。精神病學研究中心。 81、1-8。編號：10.1016/S0165-1781(98)00089-4

PubMed Abstract | CrossRef Full Text | Google Scholar
考研摘要|全文交叉引用 |谷歌學術

Cheng, P., Goldschmied, J., Casement, M., Kim, H. S., Hoffmann, R., Armitage, R., et al. (2015). Reduction in delta activity predicted improved negative affect in Major Depressive Disorder. Psychiatry Res. 228, 715–718. doi: 10.1016/j.psychres.2015.05.037
Cheng, P.、Goldschmied, J.、Casement, M.、Kim, H.S.、Hoffmann, R.、Armitage, R. 等。 （2015）。三角洲活動的減少預示著重度憂鬱症的負面影響有所改善。精神病學研究中心。 228、715–718。 doi：10.1016/j.psychres.2015.05.037

PubMed Abstract | CrossRef Full Text | Google Scholar
考研摘要|全文交叉引用 |谷歌學術

Clochon, P., Fontbonne, J.-M., Lebrun, N., and Etévenon, P. (1996). A new method for quantifying EEG event-related desynchronization: amplitude envelope analysis. Electroencephal. Clin. Neurophysiol. 98, 126–129. doi: 10.1016/0013-4694(95)00192-1
Clochon, P.、Fontbonne, J.-M.、Lebrun, N. 與 Etévenon, P. (1996)。量化腦電圖事件相關去同步的新方法：振幅包絡分析。腦電圖。臨床。神經生理學。 98、126–129。號碼：10.1016/0013-4694(95)00192-1

PubMed Abstract | CrossRef Full Text | Google Scholar
考研摘要|全文交叉引用 |谷歌學術

De Gennaro, L., and Ferrara, M. (2003). Sleep spindles: an overview. Sleep Med. Rev. 7, 423–440. doi: 10.1053/smrv.2002.0252
De Gennaro, L. 與 Ferrara, M. (2003)。睡眠紡錘波：概述。睡眠醫學。啟示錄 7，423–440。 DOI：10.1053/smrv.2002.0252

CrossRef Full Text | Google Scholar
全文交叉引用 |谷歌學術

de Maertelaer, V., Hoffman, G., Lemaire, M., and Mendlewicz, J. (1987). Sleep spindle activity changes in patients with affective disorders. Sleep 10, 443–451. doi: 10.1093/sleep/10.5.443
de Maertelaer, V.、Hoffman, G.、Lemaire, M. 與 Mendlewicz, J. (1987)。情感障礙患者睡眠紡錘體活動的變化。睡眠 10，443–451。 doi：10.1093/睡眠/10.5.443

CrossRef Full Text | Google Scholar

Delorme, A., and Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21. doi: 10.1016/j.jneumeth.2003.10.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Díaz, J., Bassi, A., Coolen, A., Vivaldi, E. A., and Letelier, J.-C. (2018). Envelope analysis links oscillatory and arrhythmic EEG activities to two types of neuronal synchronization. Neuroimage 172, 575–585. doi: 10.1016/j.neuroimage.2018.01.063

PubMed Abstract | CrossRef Full Text | Google Scholar

Ferrarelli, F., Huber, R., Peterson, M. J., Massimini, M., Murphy, M., Riedner, B. A., et al. (2007). Reduced sleep spindle activity in schizophrenia patients. Am. J. Psychiatry 164, 483–492. doi: 10.1176/ajp.2007.164.3.483

PubMed Abstract | CrossRef Full Text | Google Scholar

Finan, P. H., Quartana, P. J., and Smith, M. T. (2015). The effects of sleep continuity disruption on positive mood and sleep architecture in healthy adults. Sleep 38, 1735–1742. doi: 10.5665/sleep.5154

PubMed Abstract | CrossRef Full Text | Google Scholar

Freeman, W. J. (2004). Origin, structure, and role of background EEG activity. Part 1. Analytic amplitude. Clin. Neurophysiol. 115, 2077–2088. doi: 10.1016/j.clinph.2004.02.029

PubMed Abstract | CrossRef Full Text | Google Scholar

Groch, S., Wilhelm, I., Diekelmann, S., Sayk, F., Gais, S., and Born, J. (2011). Contribution of norepinephrine to emotional memory consolidation during sleep. Psychoneuroendocrinology 36, 1342–1350. doi: 10.1016/j.psyneuen.2011.03.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Iber, C., Ancoli-Israel, S., Chesson, A. L. Jr., and Quan, S. F. (2007). The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, 1st Edn. Westchester, IL: American Academy of Sleep Medicine.

Google Scholar

Jones, B. J., Schultz, K. S., Adams, S., Baran, B., and Spencer, R. M. C. (2016). Emotional bias of sleep-dependent processing shifts from negative to positive with aging. Neurobiol. Aging 45, 178–189. doi: 10.1016/j.neurobiolaging.2016.05.019

PubMed Abstract | CrossRef Full Text | Google Scholar

Kaestner, E. J., Wixted, J. T., and Mednick, S. C. (2013). Pharmacologically increasing sleep spindles enhances recognition for negative and high-arousal memories. J. Cogn. Neurosci. 25, 1597–1610. doi: 10.1162/jocn_a_00433

PubMed Abstract | CrossRef Full Text | Google Scholar

Landsness, E. C., Goldstein, M. R., Peterson, M. J., Tononi, G., and Benca, R. M. (2011). Antidepressant effects of selective slow wave sleep deprivation in major depression: a high-density EEG investigation. J. Psychiatr. Res. 45, 1019–1026. doi: 10.1016/j.jpsychires.2011.02.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Lang, P. J., Bradley, M. M., and Cuthbert, B. N. (2005). International Affective Picture System (IAPS): Affective Ratings of Pictures and Instruction Manual. Tech. Rep. A-6. University of Florida, Gainesville, FL.

Google Scholar

Lopez, J., Hoffmann, R., and Armitage, R. (2010). Reduced sleep spindle activity in early-onset and elevated risk for depression Running head: sleep Spindles and Adolescent MDD. J. Am. Acad. Child Adolesc. Psychiatry 49, 934–943. doi: 10.1016/j.jaac.2010.05.014

PubMed Abstract | CrossRef Full Text | Google Scholar

Lopez-Calderon, J., and Luck, S. J. (2014). ERPLAB: an open-source toolbox for the analysis of event-related potentials. Front. Hum. Neurosci. 8:213. doi: 10.3389/fnhum.2014.00213

PubMed Abstract | CrossRef Full Text | Google Scholar

Meerlo, P., Havekes, R., and Steiger, A. (2015). “Chronically restricted or disrupted sleep as a causal factor in the development of depression,” in Sleep, Neuronal Plasticity and Brain Function Current Topics in Behavioral Neurosciences, eds P. Meerlo, R. M. Benca, and T. Abel (Berlin: Springer), 459–481. doi: 10.1007/7854_2015_367

PubMed Abstract | CrossRef Full Text | Google Scholar

Motomura, Y., Katsunuma, R., Yoshimura, M., and Mishima, K. (2017). Two days’ sleep debt causes mood decline during resting state via diminished amygdala-prefrontal connectivity. Sleep 40:zsx133. doi: 10.1093/sleep/zsx133

PubMed Abstract | CrossRef Full Text | Google Scholar

Murphy, M., and Peterson, M. J. (2015). Sleep disturbances in depression. Sleep Med. Clin. 10, 17–23. doi: 10.1016/j.jsmc.2014.11.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Nieuwenhuis, I. L., and Takashima, A. (2011). The role of the ventromedial prefrontal cortex in memory consolidation. Behav. Brain Res. 218, 325–334. doi: 10.1016/j.bbr.2010.12.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Nishida, M., Pearsall, J., Buckner, R. L., and Walker, M. P. (2009). REM sleep, prefrontal theta, and the consolidation of human emotional memory. Cereb. Cortex 19, 1158–1166. doi: 10.1093/cercor/bhn155

PubMed Abstract | CrossRef Full Text | Google Scholar

Palagini, L., Baglioni, C., Ciapparelli, A., Gemignani, A., and Riemann, D. (2013). REM sleep dysregulation in depression: state of the art. Sleep Med. Rev. 17, 377–390. doi: 10.1016/j.smrv.2012.11.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Payne, J. D., Kensinger, E. A., Wamsley, E. J., Spreng, R. N., Alger, S. E., Gibler, K., et al. (2015). Napping and the selective consolidation of negative aspects of scenes. Emotion 15, 176–186. doi: 10.1037/a0038683

PubMed Abstract | CrossRef Full Text | Google Scholar

Pilcher, J. J., and Huffcutt, A. I. (1996). Effects of sleep deprivation on performance: a meta-analysis. Sleep 19, 318–326. doi: 10.1093/sleep/19.4.318

CrossRef Full Text | Google Scholar

Plante, D. T., Goldstein, M. R., Landsness, E. C., Peterson, M. J., Riedner, B. A., Ferrarelli, F., et al. (2013). Topographic and sex-related differences in sleep spindles in major depressive disorder: a high-density EEG investigation. J. Affect Disord. 146, 120–125. doi: 10.1016/j.jad.2012.06.016

PubMed Abstract | CrossRef Full Text | Google Scholar

St. Jacques, P. L., Dolcos, F., and Cabeza, R. (2009). Effects of aging on functional connectivity of the amygdala for subsequent memory of negative pictures: a network analysis of functional magnetic resonance imaging data. Psychol. Sci. 20, 74–84. doi: 10.1111/j.1467-9280.2008.02258.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Tempesta, D., Socci, V., De Gennaro, L., and Ferrara, M. (2018). Sleep and emotional processing. Sleep Med. Rev. 40, 183–195. doi: 10.1016/j.smrv.2017.12.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Ulrich, D. (2016). Sleep spindles as facilitators of memory formation and learning. Neural Plast. 2016:1796715. doi: 10.1155/2016/1796715

PubMed Abstract | CrossRef Full Text | Google Scholar

Wagner, U., Gais, S., and Born, J. (2001). Emotional memory formation is enhanced across sleep intervals with high amounts of rapid eye movement sleep. Learn. Mem. 8, 112–119. doi: 10.1101/lm.36801

PubMed Abstract | CrossRef Full Text | Google Scholar

Wagner, U., Hallschmid, M., Rasch, B., and Born, J. (2006). Brief sleep after learning keeps emotional memories alive for years. Biol. Psychiatry 60, 788–790. doi: 10.1016/j.biopsych.2006.03.061

PubMed Abstract | CrossRef Full Text | Google Scholar

Watling, J., Pawlik, B., Scott, K., Booth, S., and Short, M. A. (2017). Sleep loss and affective functioning: more than just mood. Behav. Sleep Med. 15, 394–409. doi: 10.1080/15402002.2016.1141770

PubMed Abstract | CrossRef Full Text | Google Scholar

Watson, D., Clark, L. A., and Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: the PANAS scales. J. Pers. Soc. Psychol. 54, 1063–1070. doi: 10.1037/0022-3514.54.6.1063

CrossRef Full Text | Google Scholar

Wilhelm, I., Groch, S., Preiss, A., Walitza, S., and Huber, R. (2017). Widespread reduction in sleep spindle activity in socially anxious children and adolescents. J. Psychiatr. Res. 88, 47–55. doi: 10.1016/j.jpsychires.2016.12.018

PubMed Abstract | CrossRef Full Text | Google Scholar