Learning and sleep-dependent consolidation of spatial and procedural memories are unaltered in young men under a fixed short sleep schedule
Jonathan Cedernaes
⇑, Filip Sand, Lisanne Liethof, Lauri Lampola, Sara Hassanzadeh, Emil K. Axelsson, Adine Yeganeh, Olof Ros, Jan-Erik Broman, Helgi B. Schiöth, Christian Benedict
⇑Department of Neuroscience, Uppsala University, Uppsala, Sweden
a r t i c l e i n f o
Article history:
Received 7 October 2015 Revised 11 March 2016 Accepted 16 March 2016 Available online 16 March 2016
Keywords:
Sleep-dependent memory consolidation Procedural memory
Spatial memory Sleep duration
a b s t r a c t
Objective: To investigate if a fixed short sleep schedule impairs one of the main functions of sleep, which is to consolidate newly learned memories.
Methods: Sixteen young men participated in two experimental conditions, each of which lasted for 3 con- secutive days and nights in our laboratory: a short sleep schedule (4.25-h sleep opportunity per night) versus a normal sleep schedule (8.5 h per night). In the evening after two experimental nights, partici- pants learned locations of 15 card pairs (spatial memory task) and a procedural finger tapping sequence task. Post-sleep retrieval of both memory tasks was tested the next morning.
Results: The short sleep schedule, compared with the normal sleep schedule, considerably altered sleep characteristics, e.g. the proportion of time in slow-wave sleep increased across the three experimental nights. In contrast, neither learning in the evening of day 2, nor subsequent overnight memory consoli- dation (i.e. concerning the change in memory performance between pre-sleep learning on day 2 and post- sleep retrieval on day 3) differed between the normal and short sleep schedule conditions.
Conclusions: Our findings suggest that learning in the evening and subsequent sleep-dependent consol- idation of procedural and spatial memories are unaltered in young men living under a fixed short sleep schedule. Future studies are warranted to validate our findings in other groups (e.g. adolescents and older subjects) and after more prolonged chronic sleep loss paradigms.
Ó 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction
Converging evidence from independent laboratories has demonstrated that sleep facilitates the transfer of newly acquired information from temporary to long-term memory storage sites in the human brain (Durrant, Cairney, McDermott, & Lewis, 2015; Maurer et al., 2015; Rasch, Büchel, Gais, & Born, 2007;
Rihm & Rasch, 2015; Walker, Brakefield, Hobson, & Stickgold, 2003). This concerns hippocampus-dependent (also called declara- tive) memories, including spatial (Moroni et al., 2014; Rasch et al., 2007; Talamini, Nieuwenhuis, Takashima, & Jensen, 2008) and semantic information (Lin & Yang, 2014; Ngo, Martinetz, Born, &
Mölle, 2013; Tamminen, Lambon Ralph, & Lewis, 2013). Sleep- dependent consolidation of declarative memories mainly takes place during slow-wave sleep (SWS) (Rasch & Born, 2013), a sleep stage that predominates during the first 2–3 h after sleep onset.
Another memory type that is strengthened during sleep is proce- dural memory, such as coordinated motor movements (e.g. playing a piano piece). The consolidation of procedural memories is believed to benefit mostly from rapid-eye movement (REM) sleep, which predominates during the second half of a typical nocturnal sleep period (Fischer, Hallschmid, Elsner, & Born, 2002; Karni, Tanne, Rubenstein, Askenasy, & Sagi, 1994; Mandai, Guerrien, Sockeel, Dujardin, & Leconte, 1989); however, there is also some evidence to the contrary (Rasch, Pommer, Diekelmann, & Born, 2009).
Given that an increasing number of adults on a daily basis in our 24/7-culture sleep less than 7 h per night (Ford, Cunningham, &
Croft, 2015), an obvious research question is: to what extent does the memory consolidation-enhancing effect of nocturnal sleep depend on its duration? At first glance, the answer seems to be a non-linear relationship. Previous studies have for instance demon- strated that information encoded during wakefulness is equally well consolidated after just a few hours of nocturnal sleep (i.e.
<5 h) as it is after an entire night of sleep (i.e.P7 h;Cedernaes, Rångtell et al., 2015; Tucker & Fishbein, 2009). However, a common
http://dx.doi.org/10.1016/j.nlm.2016.03.012
1074-7427/Ó 2016 The Authors. Published by Elsevier Inc.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
⇑ Corresponding authors at: Department of Neuroscience, Uppsala University, Husargatan 3, Box 593, 751 24 Uppsala, Sweden.
E-mail addresses: jonathan.cedernaes@neuro.uu.se (J. Cedernaes), christian.
benedict@neuro.uu.se(C. Benedict).
Contents lists available atScienceDirect
Neurobiology of Learning and Memory
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / y n l m e
experimental denominator of these studies is that their partici- pants were not sleep-deprived at the time of learning, i.e. prior to the sleep retention interval, but rather in the night(s) that were allowed to elapse after initial learning. While these experiments undoubtedly have advanced our understanding of how sleep dura- tion in the post-learning night affect memory consolidation, they have not conclusively addressed if this memory consolidation pro- cess is affected by preceding exposure to nightly recurring short sleep duration, as opposed to curtailed sleep duration only during the post-learning night(s).
Against this background, we sought to investigate whether learning and subsequent sleep-dependent memory consolidation are altered in young men following a three-day long fixed short vs. normal sleep schedule. To this aim, a procedural memory task and a spatial memory task were used. Both tasks have previously been shown to be reliable measures of sleep-dependent memory consolidation in humans (as shown by e.g.Rasch et al., 2007 and 2009).
Previous studies have demonstrated that nocturnal short sleep duration impairs a variety of cognitive functions the next day, such as general attention and working memory function (Fernandez- Mendoza et al., 2010; Lim & Dinges, 2010), encoding, retention and retrieval of hippocampus-dependent memories (Drummond et al., 2000; Harrison & Horne, 2000; Yoo, Hu, Gujar, Jolesz, &
Walker, 2007), and encoding of information that cannot be inte- grated with prior conceptual knowledge (Alberca-Reina, Cantero,
& Atienza, 2014). Thus, we hypothesized that living under a fixed short sleep schedule for two consecutive nights would impair par- ticipants’ ability to learn new information during evening hours (i.e. prior to the sleep retention interval during the third night) and attenuate their ability to consolidate these newly acquired memories during subsequent nocturnal sleep.
2. Methods 2.1. Participants
Sixteen normal-weight (BMI <25 kg/m2) men were included in the present study (mean age ± SD, 22.9 ± 2.7 years). Subjects were of general good health and free from psychiatric conditions and medications, as indicated by an anamnestic interview conducted by a physician (J.C.). One week prior to each experimental sleep schedule condition, subjects filled out a sleep diary. Average self- reported sleep duration (7–9 h per night) – calculated from partic- ipants’ sleep diaries – did not differ between the sleep schedule conditions (short vs. normal sleep schedule condition, p = 0.38, as determined by a paired t test). Within one week prior to the first experimental session, participants partook in an adaptation night that served to habituate them to our laboratory settings. The study was conducted in accordance with the Helsinki Declaration and was approved by the Regional Ethical Review Board in Uppsala (EPN 2014/242/1). Subjects received financial reimbursement for their participation.
2.2. Study design and procedure
According to a balanced crossover design, all subjects partici- pated in two experimental conditions, each of which lasted three consecutive days and nights in our sleep laboratories at Uppsala University (seeFig. 1for an experimental scheme, as well as for the order of experimental sessions and memory task versions across subjects). In one of the conditions (normal sleep schedule condition), subjects had an 8.5-h sleep opportunity between 2230 h (time when room ceiling lights were switched off) and 0700 h (time when room ceiling lights were switched on). In the
other condition (short sleep schedule condition), they were allowed to sleep 4.25 h each night, i.e. between 0245 h (low- intensity room lights were on between 2230 h and 0245 h in the short sleep schedule condition; at 0245 h all remaining room lights were switched off) and 0700 h (time when room ceiling lights were switched on). Before room lights were switched off prior to each experimental night, subjects were told that they could sleep until 0700 h the next morning; however, they were not free to do other activities once lights were switched off in the experimental room (e.g. reading). Experimental sessions were scheduled approxi- mately 5 weeks apart.
Sleep was recorded by use of Embla A10 recorders (Flaga hf, Reykjavik, Iceland). Seven channels were recorded (4 EEG, 2 EOG, and 1 submental EMG). EEG signals were derived from C3, C4, Fp1, Fp2 and referenced to the contralateral mastoid. Sleep was subsequently scored by an experienced scorer (J.E.B.) after high- pass (0.3 Hz) and low-pass (35 Hz) filtering with the Somnologica software (Version. 3.3.2) (Silber et al., 2007). Daytime naps were not allowed, and subjects were constantly monitored by the exper- imenters. During each experimental session, subjects were pro- vided with standardized meals (breakfast, lunch, dinner), but participants were only offered water to drink. When awake, sub- jects were engaged in sedentary activities such as reading books and magazines, or playing board games with experimenters, and they were allowed to watch movies and use electronic devices until 2000 h in the evening.
In the evening of the second day (i.e. after either two nights of full sleep or short sleep;2130 h), subjects learned a 2-D object location task and a procedural finger-tapping task (description can be found below). Performance on both memory tasks has pre- viously been shown to benefit from sleep (Rasch et al., 2007, 2009).
Note that upon awakening, cognitive performance is typically tran- siently impaired, in a state of grogginess known as sleep inertia (Tassi & Muzet, 2000). With this in mind, memory retrieval after the post-learning night was scheduled to occur about one hour after awakening to minimize possible confounding effects of sleep inertia (0810 h).
Self-reported sleepiness was measured by means of a 100-mm visual analogue scale (with 0 representing ‘‘not tired at all” and 100-mm representing ‘‘very tired”) at the following time points:
at1930 h on the second day, i.e. prior to the post-learning night and at0800 h on the third day, i.e. immediately before the post- sleep recall procedure.
2.3. 2-D object location task
This hippocampus-dependent computerized memory task has been utilized for instance to investigate the influence of odor- cued memory reactivation during sleep (Rasch et al., 2007). It con- sists of 15 colored card pairs (e.g. animals). Each of the 30 possible spatial locations is displayed on a computer screen as a gray square (each depicting the back side of each of the 30 cards); each square geometrically ordered in a checkerboard fashion (5 6 matrix).
In the present study, at learning (i.e. at2130 h in the evening of experimental day 2), one card of each pair was presented for 1 s.
Then, both cards were displayed for 3 s. Following an inter- stimulus interval of another 3 s, the next pair was presented in the same manner, until all 15 card pairs had been presented, after which the presentation was repeated; however the presentation order was different between the two presentations. After encoding, recall of card pair locations was tested using a cued-recall proce- dure; i.e. one card of each pair was presented, and the subject had to indicate the location of the second card with a computer mouse. After the subject had indicated the decided location of the second card, visual feedback was given by presenting the sec- ond card at the correct location for 2 s, independent of whether the
response was correct or not. After a card pair had been presented on screen, both cards were once more replaced by gray squares, so that guessing probability remained the same throughout each run. If participants correctly recalled 9 or more card pairs during the first learning trial, i.e. when all 15 card pair cues had been visu- ally presented, the computerized task was automatically stopped.
Otherwise, the cued-recall procedure was repeated until partici- pants passed this 60% criterion (i.e. trial 2, trial 3, trial 4, etc.).
The number of correctly recalled card pairs during the learning trial in which participants passed the 60% criterion was used as baseline memory performance. Note that the visual feedback pro- vided during the learning phase of this task facilitates re-encoding of card pair locations. This, together with the 60% criterion ensured that participants enter the post-learning retention period with a comparable encoding level and this paradigm has been utilized in e.g. (Rasch et al., 2007).
At retrieval testing the next morning (i.e. on day 3), the same cued-recall procedure was used as during the learning phase; how- ever, this time without visual feedback concerning the correctness of each successive response from the participants.
Two different versions of the task were set up, each with differ- ent pictures and different card pair locations on the 5 6 card matrix. The order of versions was balanced across subjects (see alsoFig. 1). Participants’ performance on the two versions of the spatial memory task did not differ during learning (mean ± SEM, version 1 vs. version 2: 10.7 ± 0.4 vs. 10.6 ± 0.4; t = 0.101, df = 15, p = 0.92).
2.4. Finger sequence-tapping task
In this task (also described inRasch et al., 2009), subjects were required to use the fingers of their non-dominant hand to repeat- edly tap a 5-element sequence (of digits 1–4) presented on a com- puter monitor (e.g. 4–1–3–2–4), as fast and accurately as possible on a computer keyboard. The training period before sleep consisted of twelve 30-s blocks. Between blocks, there was a break of 30 s.
The participants were instructed to press as quickly, but also as precisely, as possible. Retrieval after sleep consisted of three 30-s test blocks. Two digit sequences were used for the present study.
The order of sequences was balanced across subjects (see also Fig. 1). Participants’ performance on the two sequences did not dif- fer during learning (mean ± SEM, version 1 vs. version 2: 18.0 ± 1.5 vs. 16.8 ± 1.6; t = 1.52, df = 15, p = 0.15).
As has been previously described elsewhere (e.g.Herzog et al., 2012), the number of correctly tapped sequences (i.e. the entire sequence of digits displayed on the computer screen was tapped in the correct order) is typically used to calculate per-block mean values that represent memory consolidation and recall at learning and memory recall sessions, respectively. Mean values derived from the final 3 blocks during the learning period are then aver- aged to calculate the baseline memory performance. Mean values derived from the 3 blocks of the retrieval period (i.e. measured in the present study in the morning on day 3) are then averaged to calculate memory performance after the sleep retention interval.
However, it must be noted that averaging mean values derived Fig. 1. Experimental scheme. Upper panel: Sixteen male students learned a 2D object-location task (comprising 15 card pairs) and a motor memory finger tapping task in the evening (2130 h), after either two nights of recurrent short sleep duration or of full sleep, respectively. Retrieval of both memory tasks (0810 h) was tested in the morning after the third night of sleep, which as the previous two nights was also either curtailed (4.25-h sleep opportunity) or kept at full length (8.5 h). Bottom panel: Order of experimental sleep schedule conditions and order of versions of both memory tasks across participants. Abbreviations: Seq, sequence.
from 3 blocks (either in the evening or in the morning) may under- estimate a participant’s actual performance on this task. With this concern in mind, in the present study, in a secondary analysis the best per-block mean value of each participant during both learning and post-sleep retrieval was used to obtain an alternative measure of overnight memory consolidation.
2.5. Statistical analysis
Data are presented as means ± SEMs. Repeated measures ANOVA utilizing a 2⁄ 2 design was chosen as the main statistical test, including within-subject factors ‘sleep schedule’ (reflecting the short and normal sleep schedule conditions) and ‘time of recall’
or ‘night’ (reflecting the different time points of measurement for the memory tasks and sleep architecture, respectively). The Green- house–Geisser method was used to correct for sphericity devia- tions. ANOVA tests were followed by post hoc comparisons with the paired Student’s t-test. Overall, a two-sided P < 0.05 was con- sidered significant, unless otherwise specified. SPSS version 21.0 (SPSS Inc, Chicago, IL) was used for statistical analysis.
Four of 96 sleep polysomnographic recordings (one in the nor- mal sleep schedule condition during the second night, one in the normal sleep schedule condition during the third night, two in the short sleep schedule condition during the third night) could not be entered into the repeated measures ANOVA analysis because of technical failure during data collection. Importantly, participants’ sleepiness ratings measured in the morning after these nights were within the 95%-confidence interval of the respective condition. Thus, sleep data from 16 subjects were used for pairwise comparisons concerning the first night, sleep data from 15 subjects concerning the second night, and sleep data from 13 concerning the third night. For all other dependent variables, there were no missing data.
3. Results 3.1. Sleep
A detailed summary of repeated measures ANOVA main and interaction effects of within-subject factors sleep schedule and nights on sleep parameters is shown in Table 1. Results derived
from post hoc pairwise t-test comparisons and corresponding descriptive statistics can be found inTable 2. Briefly, living under a fixed short sleep schedule reduced the time in sleep stages 1, 2, and REM sleep in each of the three nights, while time in SWS was only reduced in the first night compared to the normal sleep schedule condition (Table 2). Moreover, compared to the normal sleep schedule condition, in their short sleep schedule condition participants’ were less awake after sleep onset in all experimental nights. Finally, there was a shift from time in sleep stage 2 toward time in SWS in the short sleep schedule condition, also reflected by an inverse correlation between minutes in sleep stage 2 and SWS in each of the experimental nights (first night: r = 0.75, p = 0.001; second night: r = 0.58, p = 0.022; third night):
r = 0.54, p = 0.044; as derived from two-tailed Pearson’s correlation).
3.2. Declarative memory performance
Participants’ performance on the 2-D object-location memory task is summarized inTable 3. In the evening prior to the third night (i.e. the post-learning night), the number of trials to pass the 60% criterion did not differ between the sleep schedule condi- tions (Table 3). A repeated measures ANOVA utilizing within- subject factors sleep schedule and time of recall (i.e. before vs. after sleep) did not reveal a main effect of sleep schedule (F(1, 15)
= 0.03; p = 0.87); however, a main effect of time of recall was found in that participants recalled less card pair locations in the morning after sleep than they did in the evening before (10.6 ± 0.3 vs.
9.3 ± 0.6; F(1, 15) = 5.1, p = 0.04, Table 3). No interaction was observed between within-subject factors sleep schedule and time of recall (F(1, 15) = 0.004; p = 0.95), indicating that the overnight decrease in correctly recalled card pairs was similar for both sleep schedules. Accordingly, post hoc t-test comparisons yielded no sig- nificant differences in overnight memory consolidation between the sleep schedule conditions (seeTable 3).
Given that the time in SWS and S2 has been proposed to play a major role for the consolidation of hippocampus-dependent mem- ory (Diekelmann, Biggel, Rasch, & Born, 2012; Ruch et al., 2012), participants’ data from both sleep schedule conditions were pooled to examine if time in SWS and S2 (in min) correlated with the over- night change in recall performance on the declarative memory Table 1
Repeated measures ANOVA main and interaction effects of within-subject factors sleep schedule and nights on sleep parameters.
Sleep parameter Within-subject factors Interaction
Sleep schedule (S) Nights (N) S⁄ N
F(df1, df2) P F(df1, df2) P F(df1, df2) P
In min
TST F(1, 12) = 3495 0.000 F(1.6, 19.5) = 0.8 0.44 F(1.9, 23.3) = 4.4 0.03
SOL F(1, 12) = 16.4 0.002 F(1.6, 19.5) = 0.8 0.44 F(1.9, 23.3) = 4.4 0.03
SWS-L F(1, 12) = 0.7 0.43 F(2, 24) = 2.1 0.14 F(1.8, 21.4) = 0.7 0.49
REM-L F(1, 12) = 0.2 0.71 F(1.9, 22.4) = 0.9 0.40 F(1.8, 21.4) = 2.2 0.14
%TST
WASO F(1, 12) = 6.2 0.03 F(1.9, 22.9) = 2.0 0.17 F(1.6, 19.6) = 1.7 0.21
S1 F(1, 12) = 5.5 0.04 F(1.1, 13.2) = 4.3 0.054 F(1.6, 18.6) = 1.4 0.27
S2 F(1, 12) = 26.5 0.000 F(1.9, 22.7) = 7.5 0.004 F(1.9, 22.2) = 6.0 0.01
SWS F(1, 12) = 97.1 0.000 F(1.8, 22) = 5.2 0.02 F(1.9, 22.3) = 5.3 0.01
REM F(1, 12) = 1.4 0.26 F(1.6, 18.9) = 3.8 0.051 F(1.5, 17.6) = 0.2 0.73
In min
WASO F(1, 12) = 10.9 0.006 F(1.7, 20.9) = 2.0 0.17 F(1.6, 19.1) = 1.8 0.19
S1 F(1, 12) = 24.3 0.000 F(1.2, 13.8) = 4.6 0.047 F(1.4, 16.9) = 3.1 0.09
S2 F(1, 12) = 169.3 0.000 F(2, 23.4) = 4.6 0.02 F(2, 23.9) = 1.7 0.28
SWS F(1, 12) = 4.7 0.050 F(2, 23.4) = 4.3 0.03 F(2, 23.6) = 4.8 0.02
REM F(1, 12) = 108.6 0.000 F(1.5, 18.2) = 5.5 0.02 F(1.7, 20.8) = 1.0 0.37
Abbreviations: REM, rapid eye movement sleep; REM-L, rapid eye movement sleep onset latency; SWS, slow-wave sleep; SWS-L, slow-wave sleep onset latency; SOL, sleep onset latency; S1, sleep stage 1, S2, sleep stage 2; WASO, wake after sleep onset. The Greenhouse–Geisser method was used to correct for sphericity deviations. Significant p- values (P < 0.05) are indicated by italic bold font.
task. However, no significant association was found (SWS: r = 0.03, p = 0.88; S2: r = 0.23, p = 0.41; as derived from two-tailed Pearson’s correlation).
3.3. Procedural memory performance
Subjects’ scores on the finger tapping memory task are shown in Fig. 2and Table 3. Pre-sleep finger sequence tapping perfor- mance was not different between the sleep schedule conditions (Table 3). A repeated measures ANOVA utilizing within-subject factors sleep schedule and time of recall did not yield a significant main effect of sleep schedule (F(1, 15) = 0.04, p = 0.85); however, a main effect of time of recall was observed. Compared to the perfor- mance in the evening, participants correctly recalled more sequences after sleep (17.4 ± 1.5 vs. 20.9 ± 1.7, F(1, 15) = 48.9, p < 0.001). No interaction between sleep schedule⁄time of recall was observed (F(1, 15) = 0.85; p = 0.37), indicating an equal over- night increase of correctly recalled sequences for both sleep sched- ules. Finally, the number of correctly tapped finger sequences after
sleep divided by the number of correctly tapped finger sequences before sleep did not differ between the sleep schedule conditions (seeTable 3andFig. 2B).
Similar findings were obtained when utilizing the best per- block mean value of each participant during learning and morn- ing retrieval (F(1, 15) = 0.07, p = 0.80 for the main effect of sleep schedule; F(1, 15) = 15.6, p = 0.001 for the main effect of time of recall; and (F(1, 15) = 0.03; p = 0.87 for the interaction between sleep schedule and time of recall; post hoc comparisons see Table 3).
Previous studies have shown that time in both REM sleep and sleep stage 2 is correlated with the consolidation of procedural memories (e.g. Fischer et al., 2002; Walker, Brakefield, Morgan, Hobson, & Stickgold, 2002). Thus, we pooled data from both sleep schedule conditions to investigate if time in these sleep stages was associated with the overnight change in recall performance on the finger tapping sequence memory task. No association was found (REM sleep: r = 0.27, p = 0.16; S2: r = 0.16, p = 0.39; as derived from two-tailed Pearson’s correlation).
Table 2
Sleep characteristics across the three nights in the normal and short sleep schedule conditions.
First night Second night Third night
NSS SSS t (15) P NSS SSS t (14) P NSS SSS t (12) P
In min
TST 477 ± 8 238 ± 3 28.3 0.000 475 ± 6 244 ± 2 35.7 0.000 481 ± 6 247 ± 1 39.8 0.000
SOL 33 ± 8 17 ± 3 1.9 0.08 35 ± 6 11 ± 2 3.7 0.002 29 ± 6 8 ± 1 3.7 0.003
SWS-L 13 ± 2 13 ± 1 0.03 0.98 14 ± 1 17 ± 2 1.6 0.14 12 ± 1 13 ± 1 0.8 0.47
REM-L 101 ± 9 88 ± 10 1.2 0.24 91 ± 8 92 ± 14 0.05 0.96 81 ± 9 84 ± 9 0.20 0.85
%TST
WASO 7 ± 2 5 ± 1 1.2 0.25 8 ± 2 3.4 ± 0.7 2.6 0.02 6 ± 1 3 ± 0.5 2.3 0.04
S1 1 ± 0.3 1 ± 0.2 0.6 0.58 3 ± 2 1.7 ± 0.4 2.6 0.02 2 ± 0.3 1 ± 0.2 1.4 0.20
S2 45 ± 2 38 ± 3 4.2 0.001 45 ± 2 37.6 ± 2.6 2.1 0.057 43 ± 2 25 ± 2 7.7 0.000
SWS 24 ± 2 38 ± 2 10.4 0.000 23 ± 2 37.8 ± 3.1 5.4 0.000 25 ± 2 48 ± 2 10.3 0.000
REM 22 ± 2 18 ± 2 2.7 0.017 21 ± 2 19.5 ± 2.4 0.7 0.52 25 ± 1 24 ± 2 0.8 0.46
In min
WASO 33 ± 10 11 ± 2 2.3 0.035 39 ± 10 8.2 ± 1.7 3.3 0.006 27 ± 5 7 ± 4 3.7 0.003
S1 7 ± 1 3 ± 1 2.9 0.012 13 ± 3 4.1 ± 1.0 4.5 0.000 7 ± 1 3 ± 1 3.7 0.003
S2 216 ± 12 90 ± 6 14.2 0.000 214 ± 12 91.2 ± 5.8 9.5 0.000 206 ± 12 61 ± 6 12.8 0.000
SWS 115 ± 8 91 ± 6 5.1 0.000 108 ± 8 92.8 ± 8.0 1.9 0.084 118 ± 10 118 ± 6 0.04 0.968
REM 107 ± 7 43 ± 4 11.6 0.000 101 ± 9 47.4 ± 5.7 5.5 0.000 122 ± 6 58 ± 6 8.5 0.000
Data are shown as mean ± SEM, with significant p-values (P < 0.05) indicated by italic bold font. Percent values refer to total sleep time (TST). Sleep onset latency was referenced to lights off at 2230 h and 0245 h, respectively. Onset latencies of SWS and REM (SWS-L and REM-L, respectively) were referenced to sleep onset. The right-handed columns for each night display p-values derived from pairwise t-test comparisons. Abbreviations: SSS, short sleep schedule (sleep opportunity between 0245 and 0700 h);
NSS, normal sleep schedule (sleep opportunity between 2230 and 0700 h); REM, rapid eye movement; SWS, slow-wave sleep; S1, sleep stage 1, S2, sleep stage 2; WASO, wake after sleep onset.
Table 3
Participants’ performance on the procedural and spatial memory task before and after the post-learning night in the normal and short sleep schedule conditions.
Memory task NSS SSS t (15) p
2-D object location task
No. of trials needed to pass the 60% criterion 2.4 ± 0.4 2.6 ± 0.4 0.11 0.91
No. of correctly recalled card pairs during the trial where participants passed the 60% criterion (A) 10.7 ± 0.3 10.6 ± 0.4 0.14 0.89
No. of correctly recalled card pairs in the morning after sleep (B) 9.4 ± 0.8 9.3 ± 0.7 0.54 0.60
Overnight change (B/A⁄ 100) 87.8 ± 7.2 88.2 ± 6.7 0.04 0.97
Finger tapping sequence task
No. of correctly recalled sequences averaged across the last 3 learning blocks (C) 17.8 ± 1.7 17.0 ± 1.4 0.94 0.36 No. of correctly recalled sequences during participants’ best learning block (D) 20.7 ± 1.6 20.8 ± 1.7 0.16 0.88 No. of correctly recalled sequences averaged across the 3 retrieval blocks in the morning after sleep (E) 20.7 ± 2.2 21.1 ± 1.5 0.28 0.78 No. of correctly recalled sequences during participants’ best retrieval block in the morning after sleep (F) 23.1 ± 2.3 23.5 ± 1.6 0.24 0.81
Overnight change for the 3 retrieval blocks (E/C⁄ 100) 108.8 ± 7.7 128.3 ± 7.1 1.80 0.09
Overnight change for participants’ best retrieval block (F/D⁄ 100) 109.5 ± 6.4 114.5 ± 4.6 0.61 0.55
Data are shown as mean ± SEM. Note that repeated measures ANOVA did not reveal main effects of the within-subject factor sleep schedule on participants’ memory performance (all P > 0.05, see also results). Moreover, no interaction effects between within-subject factors sleep schedule and time of recall (i.e. scheduled before and after the post-learning night) on participants’ memory performance were found (all P > 0.05, see also results). Thus, p-values displayed in the right-handed column, derived from pairwise t-test comparisons, must be interpreted with caution. Abbreviations: SSS, short sleep schedule (sleep opportunity between 0245 h and 0700 h); NSS, normal sleep schedule (sleep opportunity between 2230 h and 0700 h).
3.4. Subjective sleepiness
Repeated measures ANOVA revealed a significant interaction effect of within-subject factors sleep schedule and time (i.e. before learning in the evening on day 2 vs. before recall in the morning on day 3) on sleepiness (F(1) = 5.78, p = 0.03). At both time points, par- ticipants felt sleepier in their short sleep schedule condition than they did in their normal sleep schedule condition (short vs. normal sleep schedule, day 2: 47 ± 4 vs. 35 ± 4 mm, t = 2.47, df = 15, 1- tailed p = 0.013; day 3: 56 ± 3 vs. 31 ± 4 mm, t = 6.79, df = 15, 1- tailed p < 0.001).
Pearson correlation analyses (pooling data from both sleep schedule conditions) did not reveal any association between self- reported sleepiness and recall performances for either memory task, neither in the evening prior to the post-learning night nor in the morning after sleep (pP 0.42 for all correlations).
4. Discussion
Today, many adults habitually sleep less than the recommended 7 h per night, e.g.70 million in the U.S. alone (Ford et al., 2015).
With these alarming figures in mind, the present study involving 16 men sought to investigate whether restricted sleep for three nights would influence one of the main functions of sleep, which is to consolidate newly learned memories (Diekelmann & Born, 2010; Rasch & Born, 2013). As learning has been shown to benefit from greater sleep duration (Fernandez-Mendoza et al., 2010; Lim
& Dinges, 2010), we also tested whether living under a fixed short sleep schedule would impair men’s learning ability. Our main find- ing is that both learning and sleep-dependent consolidation of both spatial and procedural memories are unaltered in young men under a fixed short sleep schedule. Thus, our study does not provide com- pelling evidence that at least intermittent periods with short sleep schedules exert negative effects on either learning or subsequent sleep-dependent memory consolidation in healthy young men.
Sleep restriction has been shown to impair learning the next day (Alberca-Reina et al., 2014; Drummond et al., 2000;
Fernandez-Mendoza et al., 2010; Harrison & Horne, 2000; Lim &
Dinges, 2010; Yoo et al., 2007). Thus, our finding that living under a fixed short sleep schedule did not impair participants’ learning performance – e.g. as indicated by the trials to reach the 60% crite- rion during the spatial memory task – may seem surprising at first
glance. However, it must be borne in mind that detrimental effects of insufficient sleep duration on learning may depend on various factors, e.g. the time of day during which learning is tested and the type of memory that is examined. Moreover, since we included only young male students in the present study, it cannot be ruled out that recurrent short sleep duration may impair learning in other age groups, e.g. elders and adolescents, although some stud- ies suggest that adolescents are not either susceptible to living on a short sleep schedule with regards to their declarative memory per- formance (Biggs et al., 2010). Finally, as we tested learning on experimental day 2 (i.e. after either 2 nights of short sleep or nor- mal sleep) but not on experimental day 1, it could be speculated that the human brain may gradually develop coping strategies that may at least partially under certain conditions compensate for learning deficits during periods of shortened sleep.
Another main finding of our study is that the overnight change in spatial memory did not differ between the short and normal sleep schedule conditions. The consolidation of hippocampus- dependent memories has been shown to be ‘dose’-dependently associated with time spent in SWS (Diekelmann et al., 2012). Given that the time in SWS during the post-learning night did not differ between the sleep schedule conditions may therefore offer an explanation as to why sleep-dependent consolidation of spatial memory remained unaffected by the short sleep schedule. How- ever, this explanation must be considered with caution since no correlation between the time in SWS and overnight consolidation of card pair location was found in our study. Importantly, recent studies have found that the hippocampus-dependent memory consolidation-enhancing effect of nocturnal sleep is primarily dri- ven by early SWS, which is not affected by short sleep duration (Diekelmann et al., 2012). This may also explain as to why we did no find a correlation between the total time in SWS and over- night change in spatial memory performance.
We also tested the effects of a short sleep schedule on sleep- dependent memory consolidation of a finger tapping sequence.
Studies have shown that the consolidation of such procedural memories benefits mostly from time in REM sleep (Fischer et al., 2002; Karni et al., 1994; Mandai et al., 1989). With this in mind, it could be hypothesized that short sleep hallmarked by reduced time spent in REM sleep would result in an impaired consolidation of the finger tapping sequence. However, no difference was found in the performance on this task between the experimental sleep schedule conditions. With this in mind, the question is: how does short sleep hallmarked by reduced time in REM sleep still facilitate the consolidation of procedural memories? One hypothesis could be that the mere occurrence of REM sleep during nocturnal sleep, even if just for a short duration, may already represent a powerful trigger of neural processes involved in the stabilization of – at least newly acquired – procedural memories. This is also supported by evidence from pharmacological studies that have utilized antide- pressant medication. While REM sleep was greatly reduced – but not completely suppressed – by these medications in the tested subjects, overnight consolidation of procedural memories was not found to be negatively affected (Rasch et al., 2009). Another explanation could be that non-REM sleep that is largely preserved during short sleep conditions may have facilitated the consolida- tion of the newly acquired finger taping sequence. For instance, one recent study has demonstrated that auditory reactivation dur- ing early SWS-rich sleep improves overnight performance on a fin- ger tapping task (Schönauer Geisler, & Gais, 2014).
5. Limitations
Several limitations apply to our study. It must first be recog- nized that successive nights of curtailed or misaligned sleep in a Fig. 2. Number of correctly tapped finger sequences before and after the sleep
retention interval. Data are presented as mean ± SEM. Note that repeated measures ANOVA did not reveal a main effect of the within subject factor sleep schedule on participants’ ability to correctly tap the target finger sequence (all P > 0.05, see also the Results section). Moreover, no interaction effect between within-subject factors sleep schedule and time of recall (i.e. scheduled before and after the post-learning night) for participants’ memory performance was observed (P > 0.05, further described in Results).
cumulative fashion can impair other cognitive domains than those tested in the present study, such as reaction time and working memory (Cohen et al., 2010; Van Dongen, Maislin, Mullington, &
Dinges, 2003). The findings from these studies could also suggest that even longer periods of recurrent sleep loss than what we stud- ied may also have a negative impact on e.g. memory consolidation.
Our study design enabled us to investigate how living under a fixed short sleep schedule influences participants’ ability to learn new information and subsequently consolidate it during subse- quent nocturnal sleep. However, in this context it must be borne in mind that our study was not designed to examine if an increased sleep pressure as a result of a fixed short sleep schedule would alter memory consolidation during recovery sleep.
Another limitation is that our study was relatively small, and only involved young healthy men. Thus extrapolation of our find- ings to the general population and to other groups (e.g. elders, females, and adolescents) would necessitate validation in separate experiments.
Studies have shown that sleep disturbances that do not neces- sarily lead to restricted bedtime (e.g. sleep-disordered breathing) impair oversleep memory consolidation (Varga et al., 2014). Thus, it is possible that recurrent changes to sleep quality rather than sleep duration may adversely impact sleep-dependent memory consolidation in humans.
Features of the chosen memory tasks may have hampered our ability to capture how living on a fixed short sleep schedule affects participants’ learning performance. For instance, during encoding of the spatial memory task, visual feedback was given for the cor- rectness of each chosen card pair location. Thus, it remains difficult to draw firm conclusions about how living under a short sleep schedule may impact learning without visual feedback.
Finally, it must be borne in mind that following the post- learning night, participants’ ability to recall card pair locations was tested under resting conditions. It has recently been shown that following a single night of short sleep, men were unable to recall as many card pair locations after a half-hour long psycholog- ical stress protocol as they had done before (Cedernaes Rångtell et al., 2015). Thus, studies are needed to investigate how the com- bination of living under a fixed sleep schedule (e.g. due to occupa- tional duties) and acute psychological stress affects post-sleep recall of memories.
6. Conclusions and perspectives
Our results suggest that sleep-dependent memory consolida- tion does not rely on a consistent sleep schedule of at least 7–8 h of sleep per night in subjects who habitually obtain at least 7 h of sleep per night. However, the majority of epidemiological and experimental studies support the notion that sleeping less than 7 h per night is associated with general poor health, both in the short (Cedernaes, Osler et al., 2015; Cedernaes, Schiöth, &
Benedict, 2015; Christoffersson et al., 2014) and long term (Cappuccio, D’Elia, Strazzullo, & Miller, 2010; Shan et al., 2015).
Importantly, we and others have also demonstrated that short to long-term sleep loss or sleep problems may harm neurons in the brain and increase the risk of neurodegenerative disease (Benedict et al., 2014; Benedict et al., 2015; Cedernaes, Lampola et al., 2016; Cedernaes, Osorio et al., 2016; Ooms et al., 2014;
Sprecher et al., 2015). Furthermore, studies have shown that sleep in the night after learning has a beneficial effect on memory that can still be observed several years later (Wagner, Hallschmid, Rasch, & Born, 2006). Altogether, this implies that our study find- ings should not be generalized to the influence of sleep duration on other biological functions, which in the long run also may impact on other cognitive domains that also play a role for how
the normal physiology of sleep is able to help the encoding, consol- idation and retrieval of memories.
Funding
Work from the authors’ laboratory is supported by AFA Försäkring (CB), Erik, Karin and Gösta Selander’s Foundation (JC), Fredrik och Ingrid Thuring’s Foundation (JC), the Lars Hierta Memorial Foundation (JC), Novo Nordisk Foundation (CB), the Tore Nilson Foundation (JC), the Swedish Brain Foundation (JC, CB), the Swedish Society for Medical Research (JC), the Swedish Society of Medicine (JC), the Swedish Research Council (CB, JC, HBS) and the Åke Wiberg Foundation (JC).
Duality of interest
The authors are unaware of any affiliation, funding, or financial holdings that might be perceived as affecting the objectivity of this manuscript. The authors declare that there is no duality of interest associated with this manuscript.
Contribution statement
JC and CB designed the study; JC and CB wrote the protocol; JC, FS, LaL, LiL, EKA, SH, AY, OR and JEB collected the data; JC, LaL, LiL and CB conducted the analyses. All authors interpreted the data;
and all authors contributed to writing. All authors have approved the final manuscript.
Conflict of Interest
The authors have nothing to disclose and no conflicts of interest to report.
Disclosure statement
The authors are unaware of any affiliation, funding, or financial holdings that might be perceived as affecting the objectivity of this article.
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