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Neurobiology of Learning and Memory xxx (2016) xxx–xxx
YNLME 6377 No. of Pages 9, Model 5G
21 January 2016
Contents lists available at ScienceDirect
Neurobiology of Learning and Memory
journal homepage: www.elsevier .com/ locate/ynlme
Resting state EEG correlates of memory consolidation
http://dx.doi.org/10.1016/j.nlm.2016.01.0081074-7427/� 2016 Published by Elsevier Inc.
⇑ Corresponding author at: Furman University, Johns Hall 206K, United States.Fax: +1 864 294 2206.
E-mail address: [email protected] (E.J. Wamsley).
Please cite this article in press as: Brokaw, K., et al. Resting state EEG correlates of memory consolidation. Neurobiology of Learning and Memoryhttp://dx.doi.org/10.1016/j.nlm.2016.01.008
Kate Brokaw, Ward Tishler, Stephanie Manceor, Kelly Hamilton, Andrew Gaulden, Elaine Parr,Erin J. Wamsley ⇑Furman University, Department of Psychology, United States
a r t i c l e i n f o a b s t r a c t
303132333435363738394041424344
Article history:Received 5 November 2015Revised 11 January 2016Accepted 16 January 2016Available online xxxx
Keywords:Memory consolidationSleepResting stateMindwanderingDaydreamingEEGSlow oscillationAlphaVerbal memory
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Numerous studies demonstrate that post-training sleep benefits human memory. At the same time,emerging data suggest that other resting states may similarly facilitate consolidation. In order to identifythe conditions under which non-sleep resting states benefit memory, we conducted an EEG (electroen-cephalographic) study of verbal memory retention across 15 min of eyes-closed rest. Participants(n = 26) listened to a short story and then either rested with their eyes closed, or else completed a dis-tractor task for 15 min. A delayed recall test was administered immediately following the rest period.We found, first, that quiet rest enhanced memory for the short story. Improved memory was associatedwith a particular EEG signature of increased slow oscillatory activity (<1 Hz), in concert with reducedalpha (8–12 Hz) activity. Mindwandering during the retention interval was also associated withimproved memory. These observations suggest that a short period of quiet rest can facilitate memory,and that this may occur via an active process of consolidation supported by slow oscillatory EEG activityand characterized by decreased attention to the external environment. Slow oscillatory EEG rhythms areproposed to facilitate memory consolidation during sleep by promoting hippocampal–cortical communi-cation. Our findings suggest that EEG slow oscillations could play a significant role in memory consolida-tion during other resting states as well.
� 2016 Published by Elsevier Inc.
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1. Introduction
A growing literature confirms that memory is better retainedwhen participants sleep after learning, as opposed to stayingawake. It is widely proposed that this effect is due to an active pro-cess of memory consolidation during sleep (Diekelmann & Born,2010; Stickgold, 2005). This hypothesis is supported by studiesdemonstrating that improved memory is associated with specificfeatures of the sleep EEG linked to consolidation, including slowwaves (Alger, Lau, & Fishbein, 2012; Diekelmann, Biggel, Rasch, &Born, 2012), slow oscillations (Huber, Ghilardi, Massimini, &Tononi, 2004; Marshall, Helgadóttir, Mölle, & Born, 2006), andsleep spindles (Cox, Hofman, & Talamini, 2012; Mednick et al.,2013; Schabus et al., 2004).
Yet it is increasingly clear that a full night of sleep is notrequired to boost memory. Even a partial night of sleep or a shortnap can facilitate memory, with effect sizes comparable to thosefollowing a full night (Mednick, Nakayama, & Stickgold, 2003;Plihal & Born, 1997; Tucker & Fishbein, 2009; Tucker et al.,
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2006). Furthermore, the duration of nap sleep is often unrelatedto its memory effect, with even very short naps providing the samememory benefit as longer sleep periods (Payne et al., 2015; Tuckeret al., 2006; Wamsley, Tucker, Payne, & Stickgold, 2010), althoughsee (Alger et al., 2012; Mednick et al., 2003). Even a nap as short as6 min has been reported to lead to a memory-enhancing effect(Lahl et al., 2008). What enables such short periods of sleep toenhance memory performance? One possibility is the presence offast-acting offline consolidation mechanisms that do not requirethe completion of a full sleep cycle. Moreover, some propose thatconsolidation can occur during any state of sleep or alertness,when the encoding of new information is sufficiently reduced dur-ing the consolidation period (Mednick, Cai, Shuman, Anagnostaras,& Wixted, 2011).
Might short periods of quiet wakefulness impact memory, evenin the absence of sleep? Most studies investigating the effect ofsleep on memory have done so in comparison to waking controlconditions in which participants watch videos (Lau, Tucker, &Fishbein, 2010; Tucker et al., 2006), listen to music (Elizabeth &McDevitt, 2014; Mednick, Makovski, Cai, & Jiang, 2009), or leavethe laboratory to go about their daily activities (Ellenbogen,Hulbert, Stickgold, Dinges, & Thompson-Schill, 2006; Payne et al.,2012). These studies have clearly established that sleep benefits
(2016),
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Fig. 1. Experimental timeline. Participants learned a short story just prior to a15 min retention interval during which they either rested quietly with eyes closedor completed a distractor task. A recall test was administered both immediately andfollowing the retention interval.
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memory relative to an equivalent duration of active wakefulness,during which participants encode new sensory information. In
contrast, the effect of quiet resting wake on memory – in theabsence of cognitive tasks, activities, and sensory stimulation –has not been sufficiently characterized.
The notion that periods of unoccupied rest can retroactivelyfacilitate memory actually dates back to the earliest days of exper-imental psychology, when Müller and Pilzecker first suggested thatretroactive interference occurs even when the interpolated activityis highly dissimilar to the learned material (Müller & Pilzecker,1900). But in more recent years, this question of whether a generalreduction of mental effort during wakefulness (rest) facilitates con-solidation has received little attention. Just in the last several years,emerging new evidence has begun to suggest that quiet wake doesin fact facilitate memory, at least under some conditions (Craig,Dewar, Della Sala, & Wolbers, 2015; Dewar, Alber, Butler, Cowan,& Della Sala, 2012; Dewar, Alber, Cowan, & Della Sala, 2014). Sev-eral recent experiments report that a brief period of resting wakefollowing learning can improve later memory in both elderly(Dewar et al., 2012, 2014) and young participants (Craig et al.,2015; Mercer, 2015). But because these studies have not employedEEG-monitoring, it is uncertain whether participants might haveobtained brief periods of sleep during the retention interval.Beyond this, we have little understanding of the mechanisms bywhich resting wakefulness might enhance memory, nor the condi-tions under which this benefit emerges. Neurophysiological corre-lates of memory changes across sleep have now been extensivelydocumented (Clemens, Fabó, & Halász, 2005, 2006; Holz et al.,2012; Nishida & Walker, 2007; Schabus et al., 2004; van Dongen,Takashima, Barth, & Fernández, 2011), but corresponding studiesof resting wakefulness are lacking.
Quiet rest might facilitate memory via active consolidationmechanisms similar to those operating during sleep. Much of theneurophysiology purported to support consolidation during sleepis also present during resting wake. Like sleep, quiet rest is charac-terized by a dramatic reduction in sensory processing. Freed fromthe demands of stimulus processing, mental experience is focusedinward, as participants engage in ‘‘mindwandering” – thinkingabout the past, imagining the future, and creating fictitious scenar-ios (Andrews-Hanna, 2011; Andrews-Hanna, Reidler, Huang, &Buckner, 2010; Antrobus, Singer, Goldstein, & Fortgang, 1970;Baird et al., 2012). Meanwhile, the ‘‘reactivation” of recent memoryin the hippocampus and cortex that was first observed during slowwave sleep is also expressed during resting wake in rodents (Carr,Jadhav, & Frank, 2011; Davidson, Kloosterman, & Wilson, 2009;Foster & Wilson, 2006; Gupta, van der Meer, Touretzky, & Redish,2010; Karlsson & Frank, 2009). Although this form of memory reac-tivation has not been directly observed in humans, the hippocam-pal ‘‘sharp-wave ripples” associated with reactivation areprevalent during quiet rest in humans (Axmacher, Elger, & Fell,2008; Clemens et al., 2011). Consolidation-promoting neurochem-ical features of sleep are also partially replicated during rest,including decreased acetylcholine levels during quiet restingwakefulness (Marrosu et al., 1995).
Finally, several EEG oscillations proposed to support consolida-tion during sleep also have analogs during quiet rest. Although thepredominant frequencies are different, in comparison to moreactive states of wakefulness EEG slowing characterizes both sleepand eyes-closed quiet rest. In wakefulness, candidate oscillationsthat we hypothesized might relate to memory processing are theEEG alpha oscillation (8–12 Hz) and the slower theta (4–7 Hz)and slow/delta oscillations (0.5–2 Hz). Alpha is the primary EEGsignature of eyes-closed waking rest that distinguishes this statefrom active wakefulness, and is one of the main EEG correlates ofthe fMRI-defined ‘‘default-mode” resting state network, which
Please cite this article in press as: Brokaw, K., et al. Resting state EEG correlathttp://dx.doi.org/10.1016/j.nlm.2016.01.008
includes a number of memory-related brain regions including thehippocampus, parahippocamal cortex, and medial frontal cortex(Jann et al., 2009; Knyazev, Slobodskoj-Plusnin, Bocharov, &Pylkova, 2011). On the phenomenological level, alpha rhythmsare associated with a decreased focus on external stimuli andincreased attention to internal states, including memories of thepast (Foulkes & Fleisher, 1975). Alpha has recently been studiedas a mediator of effective memory encoding and retrieval(Klimesch, 1997; Klimesch, Schimke, & Schwaiger, 1994; Vogt,Klimesch, & Doppelmayr, 1998; Williams, Ramaswamy, & Oulhaj,2006). But slower EEG frequencies are also present during quietrest. In sleep, slow oscillations (�1 Hz) and slow waves (up to2 Hz) are thought to be major contributors to systems-level mem-ory consolidation, synchronizing hippocampal sharp-wave rippleswith cortical activity (Clemens et al., 2007, 2011; Mölle,Eschenko, Gais, Sara, & Born, 2009) and thus promoting hippocam-pal–cortical communication and synaptic plasticity (Rosanova &Ulrich, 2005). �1 Hz rhythms are present during quiet rest as well,and these may be relatively attenuated during the execution ofdirected cognitive tasks (Alper et al., 2006; Demanuele, Sonuga-Barke, & James, 2010). Thus, a number of mechanisms proposedto account for the effects of sleep on memory are also present dur-ing quiet wake, which suggests the hypothesis that the memorybenefits of rest and sleep could arise from overlapping active con-solidation mechanisms.
The aims of the current study were to (1) confirm that a periodof EEG-verified quiet rest benefits memory, in the absence of anysleep, (2) isolate EEG correlates of this memory effect, and (3)describe the mental activity associated with this memory effect.We examined memory retention for a short story across a 15-min interval with continuous EEG monitoring. We hypothesizedthat 15 min of quiet rest would lead to improved memory at a sub-sequent test, and expected to find that this effect was related toboth EEG slowing and increased ‘‘mindwandering” (Andrews-Hanna et al., 2010; Baird et al., 2012; Mason et al., 2007) duringthe rest period, both potential signatures of a sleep-like offlinestate conducive to memory consolidation.
2. Methods
2.1. Participants
29 college students (19 female) age 19–22 (M = 20 yrs (±0.8 SD))were recruited by email, advertisement, or word-of-mouth, andpaid $10/h for their participation. By self-report using a 3-day sleeplog, participants stated that they slept an average of 7.4 h (±1.1 SD)per night on the 3 nights prior to the study.
es of memory consolidation. Neurobiology of Learning and Memory (2016),
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2.2. Procedure
The experimental timeline is illustrated in Fig. 1. Participantsarrived at the laboratory, signed informed consent, and filled outinitial paperwork including demographic surveys, the Epworthsleepiness scale (a measure of trait sleepiness (Johns, 1991)), andthe Stanford Sleepiness Scale (a measure of state sleepiness(Hoddes, Zarcone, Smythe, Phillips, & Dement, 1973)). Participantswere then prepared for EEG (electroencephalographic) recordingby attaching electrodes to the scalp (C3, C4, O1, O2, F3, F4), withbilateral mastoid references, as well as chin leads for assessingmuscle tone, and eye leads for recording EOG (electrooculography,right and left outer canthus).
All participants completed 2 conditions in counterbalancedorder: quiet rest and distractor task. There was a 10 min break inbetween conditions. In each condition, participants first listenedto a short story, and were instructed to remember as much ofthe story as possible (details below). Immediately after listeningto the story, participants were asked to freely recall as much ofthe story as possible. Participants then completed either the quietrest or distractor task condition. In the quiet rest condition, partic-ipants sat in a comfortable chair with their eyes closed for 15 min.Participants were instructed to keep their eyes closed for the entire15 min and to keep movement to a minimum. No instruction wasgiven regarding what participants should be doing mentally duringthis time. In the distractor task condition, participants played thecomputer game ‘‘Snood” for 15 min (see below). EEG, EOG, andEMG were digitally recorded at 400 Hz during the 15 min interval.Immediately afterward, a delayed recall test was administered inwhich participants were instructed to again type as much of thestory as they could remember.
Two self-report measures assessed mental activity during the15 min retention interval. Following each condition, participantscompleted a questionnaire on which they recorded any thoughtsor imagery they could recall from the preceding interval, and ratedthe proportion of the 15 min interval that they had spent engagedin 14 predefined categories of mental activity, including ‘‘thinkingabout the past” (something else earlier today/yesterday to a weekago/past year or several years ago), ‘‘imagining the future” (latertoday/tomorrow to next week/next year or several years), ‘‘think-ing about the short story”, ‘‘thinking about staying still”, ‘‘countingthe time”, ‘‘mind was blank”, ‘‘meditating”, ‘‘sleeping”, ‘‘thinkingabout something else”, and ‘‘other”. Following the methods ofAndrews-Hanna et al. (2010), participants recorded their responsesby dividing a blank circle to reflect the proportional amount oftime they had spent thinking about each topic. Following each con-dition, participants also completed a rehearsal questionnaire onwhich they used a 5-point scale to respond to the questions‘‘how often did you think about the story?”, ‘‘how often did youimagine the story?” and ‘‘how often did you try to remember thestory?” during the 15 min interval.
2.3. Materials
A short story recall task (adapted from the Wechsler MemoryScale (Wechsler, 1987)) was used to assess declarative memory(following Dewar et al., 2012). Participants listened to a digitalrecording of a short story, approximately 30 s long, and were thenasked to freely recall as much of this story as they could, and asaccurately as possible, by typing everything that they rememberedinto an electronic form. They were given as much time as neededto complete their responses. 15 min later, a delayed test was givenin which they were again asked to freely recall as much of the storyas they could. There were two equivalent versions of the story used(version A and version B), one for each experimental condition.Assignment of story version to experimental condition was
Please cite this article in press as: Brokaw, K., et al. Resting state EEG correlathttp://dx.doi.org/10.1016/j.nlm.2016.01.008
counterbalanced across subjects, and immediate recall perfor-mance was equivalent between versions (p > .4).
The computer game Snood was used as a distractor task (http://snoodworld.com). Snood is a simple puzzle game in which the par-ticipants eliminate blocks of colors (‘‘snoods”) by creating groupsof 3 or more of the same color. This visuospatial task was chosenin order to provide an engaging distractor activity that would haveminimal overlap in specific content with the purely verbal shortstory learning task. Participants were instructed how to play thegame by pointing and clicking the computer mouse to aim andrelease snoods. Once a game was lost or won, participants wereinstructed to click ‘‘restart” and continue playing until told to stopafter 15 min had passed. Game difficulty level was set to ‘‘easy”.
2.4. Analysis
Free recall responses were scored by 2 raters blind to experi-mental condition. Correctly recalled elements were scored accord-ing to the methods described in the Wechsler Memory ScaleManual (Wechsler, 1987). In short, one point was awarded for eachpiece of correctly reported information, for a total of 25 points perstory. All reports were scored by both raters, and the final score foreach report was calculated as the average score of the two raters.Responses were also scored for the number of falsely recalleditems, defined as story elements which were mentioned in the freerecall response, but described incorrectly. For example, if a partic-ipant provided the protagonists’ first name (Anna), but incorrectlystated what the name was (Elsa, Annie, Astrid, etc.), this would bescored as a falsely recalled element. Inter-rater reliability for cor-rect recall was r = .94, and for false recall was r = .66. For both mea-sures, the primary dependent variable was change in performanceacross the 15-min memory retention interval. To assess fluctua-tions in the total amount of information provided, a total recallscore was also calculated at both the immediate and delayed timepoints (correct recall + false recall).
The amount of information contained in free recall responseswas highly variable between immediate and delayed testing insome individuals, which might have been caused by lack of moti-vation to or fatigue with repeatedly supplying a lengthy writtenresponse. Thus, six performance outliers were thus excluded fromfurther analysis due to scores on the performance change depen-dent measures lying >1.5 interquartile ranges above the 75th per-centile or below the 25th percentile. The primary results of thestudy were robust to alternate methods of excluding outliers –Using the same method of outlier exclusion, but applied only toencoding performance on the Story Recall Task resulted in theexclusion of fewer outlying points (n = 2), but the same primaryfinding that memory performance is relatively preserved across aperiod of quiet wakefulness, in comparison to a significant declineacross active wakefulness (see below).
Five additional participants were excluded from all or someanalyses because they failed to comply with instructions to keeptheir eyes closed during the quiet rest period (n = 1), had pervasiveartifact in the EEG recording that prevented accurate spectral anal-ysis (n = 1), or had corrupted or missing recall data (n = 3).
Sleep stage was determined according the standard criteriaestablished by the American Academy of Sleep Medicine (Iber,Ancoli-Israel, Chesson, & Quan, 2007). Prior to analysis, EEG artifact(due to movement, muscle activity, eye movement, and othersources) was manually rejected via visual inspection. Spectral anal-ysis via fast Fourier transform (Brain Products BrainVision Analyzerv2.0.2) was then applied to all artifact-free 4 s intervals (50% seg-ment overlap, Hanning window) to assess mean power spectraldensity (lV2/Hz) in the frequency bands of interest, includingalpha (8–12 Hz), beta (13–25 Hz), theta (4–7 Hz), delta (1–4 Hz),and slow oscillation (0.3–1 Hz). To compensate for individual
es of memory consolidation. Neurobiology of Learning and Memory (2016),
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Table 1Story recall across quiet rest and distractor task conditions.
DRecall acrossquiet rest
DRecall acrossdistractor
p
Mean ±SD Mean ±SD
Correct recall �.61 1.30 �1.28 1.29 0.018False recall .13 .76 .73 1.32 0.09Total recall (Correct + False) �.56 1.12 �.75 1.25 0.71
p-values derived from paired-samples t-tests.
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differences in EEG amplitude, relative power values were used(normalized such that total power across all frequency bands isrendered equivalent in all analyzed segments). Eye movementswere automatically detected by marking peaks wherein amplitudeof the right outer canthus recording exceeded an absolute thresh-old of 30 lV.
Effect of experimental condition (quiet rest vs. distractor task)on memory retention was assessed using paired-samples t-tests,and the association between memory retention and resting-stateEEG was assessed using Pearson’s correlations. In cases where sep-arate correlations were run for multiple electrodes, Type I errorwas controlled by using a Bonferroni-corrected significance thresh-old of a = .0083 (a = .05/6 electrodes).
3. Results
3.1. Effect of quiet rest on memory
As hypothesized, quiet rest led to improved memory for thestory at 15 min, relative to the distractor task (change in correctrecall: t18 = 2.60, p = 0.018; Fig. 2). In contrast, quiet rest did notsignificantly affect false recall scores (change in false recall:t18 = 1.82, p = 0.09; Fig. 2). At immediate recall, performance wasequivalent between conditions for both correct (p > .3) and false(p > .8) recall. There was no effect of condition on the total amountof information reported (total recall score: p > .7). These perfor-mance data are reported in Table 1.
3.2. EEG correlates of memory retention
Memory retention during quiet rest was associated with an EEGsignature of proportionally increased slow oscillation power(0.3–1 Hz), in concert with decreased power in the alpha frequencyband (8–12 Hz). Slow oscillation power was strongly associatedwith improved recall following quiet rest (mean across allelectrodes: r18 = 0.63, p = .005; Fig. 3 Left). The magnitude of thiseffect was strongest frontally, and survived Bonferroni correctionfor multiple comparisons at F3, F4 and C3 recording sites (Table 2;Fig. 3 Right; complete spectral analysis correlation results reportedin Supplementary Table S1).
As anticipated, compared with the distractor task, quiet restwas characterized by an increased proportion of EEG spectralpower in the alpha frequency band (8–12 Hz; t20 = 6.68,
Fig. 2. Effect of condition on recall. Quiet rest significantly enhanced recall at15 min. Error bars ± SEM.
Please cite this article in press as: Brokaw, K., et al. Resting state EEG correlathttp://dx.doi.org/10.1016/j.nlm.2016.01.008
p = 0.000002). Yet within quiet rest, the amount of alpha activitythat participants expressed was negatively associated with correctrecall (Table 2).
Slow oscillation power during quiet rest was unrelated to mem-ory following the distractor task (Table 2). In contrast, resting alphamarginally predicted memory both following the distractor condi-tion and following quiet rest. There were no other significant asso-ciations between EEG power and correct recall across quiet wake.There were no significant associations between EEG measuresand false recall.
Supplemental analyses ruled out the possibility that residualeye movement artifact might have contributed to slow EEG fre-quencies during quiet rest. First, in contrast to the strong associa-tion with frontal EEG signals, story recall scores were notsignificantly correlated with slow oscillatory activity in the eyemovement channels (p > .1). Second, the number of automaticallydetected eye movements during quiet rest (see Section 2) was cor-related neither with story recall (p > .7), nor slow oscillation powerin the EEG channels (p > .7).
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3.3. Mental activity correlates of memory retention
Improved memory was associated with decreased attention tothe external environment. First, during quiet rest, participantsspent much less time thinking about what they were currentlydoing, as compared to during the distractor task (t21 = 6.02,p = 0.000006; Fig. 4). Instead, they spent more time thinking aboutthe past and imagining the future. This included increased timeduring quiet rest thinking about the past week (t21 = 2.87,p = 0.009), the past years (t21 = 2.05, p = .05), the rest of that day(t21 = 2.04, p = 0.05), as well as the future year (t21 = 2.6, p = 0.02).10 participants also reported meditating during quiet rest, whichoccurred more commonly than during the distractor task(t21 = 2.4, p = 0.02).
Second, within the distractor task condition, participantsshowed superior memory retention when spending less time think-ing about the Snood game itself (r20 = �0.48, p = 0.03), and moretime thinking about other things, including the past week(r20 = .56, p = 0.01), the rest of the day (r20 = 0.46, p = 0.04), tomor-row (r20 = 0.51, p = 0.02), and meditating (r20 = 0.51, p = 0.02).There was no correlation between mental activity during quiet restand memory performance.
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3.4. Rehearsal effects
Condition did not significantly affect the extent to which partic-ipants reported effortfully trying to remember the story (t21 = 1.80,p = 0.09) or imagine the story (t21 = 1.50, p = 0.15). Participants didreport ‘‘thinking” about the story significantly more in the quietrest condition than during the distractor task (t21 = 3.46,p = 0.002). However, thinking about the story was not correlatedwith the improved recall seen during quiet rest (p > .3), nor didany other rehearsal questionniare responses correlate with mem-ory change during either quiet rest or the distractor task.
es of memory consolidation. Neurobiology of Learning and Memory (2016),
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Fig. 3. Slow oscillation power predicts memory benefit of quiet rest. Left: The amount of slow oscillatory EEG power (0.3–1 Hz) was correlated strongly with change in recallscore across the quiet resting period. Right: This association was strongest at frontal electrodes.
Table 2EEG correlates of change in story recall across the 15 min retention interval.
Memory following quiet rest Memory following distractor
r p r p
Quiet rest EEGSlow oscillation lV2/Hz (0.3–1 Hz)C3 .51 .03* .12 .62C4 .69 .002** .20 .41O1 .57 .01* .25 .30O2 .52 .03* .28 .25F3 .61 .008** .15 .54F4 .65 .004** .08 .74
Alpha lV2/Hz (8–12 Hz)C3 �.51 .03* �.42 .08C4 �.54 .02* �.41 .09O1 �.41 .09 �.45 .05O2 �.39 .11 �.45 .05F3 �.60 .009* �.51 .03*
F4 �.53 .02* �.31 .20
Distractor task EEGSlow oscillation lV2/Hz (0.3–1 Hz)C3 �.22 .37 .04 .86C4 .15 .57 �.12 .64O1 �.27 .28 �.39 .10O2 .33 .19 .19 .44F3 .18 .48 .18 .46F4 .09 .71 �.07 .77
Alpha lV2/Hz (8–12 Hz)C3 .07 .78 .01 .96C4 �.16 .54 .01 .96O1 .19 .45 �.06 .86O2 �.07 .78 �.16 .69F3 �.23 .36 �.04 .82F4 �.21 .41 .10 .53
Pearson’s correlations testing the association between EEG during the retention interval and change in story recall following the quietrest and distractor task conditions.
* p < .05.** Survives Bonferroni correction for multiple comparisons at a = .0083.
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3.5. Effects of sleep and sleepiness
During quiet rest, 5 participants fell asleep for an average of2.0 min (±2.6 SD). However, falling asleep did not improve recallscores. In fact, those who fell asleep for this brief time actually per-formed numerically worse at delayed recall (change in correctrecall = �1.2 ± 0.6 SD) than those who did not fall asleep (changein correct recall = �0.39 ± 1.4 SD, p > .2). Additionally, when partic-ipants who slept were excluded from analysis, the memory benefitof quiet rest was still apparent (t12 = 2.5, p = .03). Sleep also had no
Please cite this article in press as: Brokaw, K., et al. Resting state EEG correlathttp://dx.doi.org/10.1016/j.nlm.2016.01.008
effect on change in false recall scores following quiet rest (p > .4).Neither slow oscillation power nor alpha power showed a detect-able difference between those who fell asleep during the retentioninterval and those who did not (slow oscillation: p = .4; alpha:p = .7).
Self-reported sleepiness was equivalent between conditions atbaseline. However, participants became significantly sleepier fol-lowing quiet rest compared to following the distractor task(t21 = 2.25, p = 0.04). The sleepiness induced by quiet rest did notcorrelate with memory change across rest (p > .7).
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Fig. 4. Mental activity during the retention interval. Proportion of the 15 minretention interval that participants spent engaged in various categories of mentalactivity, by self-report.
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3.6. Order effects
Order of experimental condition affected memory, but this wasunrelated to the memory benefit of quiet rest. Order of conditionsignificantly affected immediate (F1,18 = 13.9, p = 0.002) anddelayed recall (F1,18 = 16.9, p = 0.001) within the quiet rest condi-tion, such that participants remembered the story better whenquiet rest followed the distractor task condition, as opposed towhen quiet rest preceded the distractor task condition. This couldrepresent a facilitation effect in which the distractor task benefitssubsequent new encoding. But importantly, when we controlledfor this order effect by including counterbalancing order as a factorin the ANOVA model, we found that order did not impact the effectof rest on memory (p > 0.4), and that the main effect of quiet restvs. distractor task was still statistically significant (F1,18 = 6.43,p = 0.02). There was no such order effect for false recall scores.
4. Discussion
Here, we confirm that a short period of eyes-closed rest facili-tates declarative memory, and we identify two novel predictorsof this phenomenon that elucidate the neurophysiological andphenomenological conditions under which the effect occurs. Onthe neurophysiological level, an increase in slow oscillatory EEGrhythms and decrease in alpha rhythms predicted improved mem-
Please cite this article in press as: Brokaw, K., et al. Resting state EEG correlathttp://dx.doi.org/10.1016/j.nlm.2016.01.008
ory following rest. On the phenomenological level, improved mem-ory was associated with decreased attention to the externalenvironment and an increase in ‘‘mindwandering”, as participantsturned to thinking of the past and imagining the future. In severalrespects, the resting state in the present study resembles sleep.Taken together, our observations suggest that eyes-closed restcould facilitate memory consolidation via mechanisms similar tothose that operate during sleep.
Although �1 Hz rhythms are not a visually prominent feature ofthe resting EEG, these oscillations are present during rest, and maybe relatively attenuated during the execution of directed cognitivetasks (Alper et al., 2006; Demanuele et al., 2010). During wakeful-ness, EEG oscillations in the slow and delta bands have been asso-ciated with the activation of memory-related structures includingthe parahippocampal gyrus (Chen, Feng, Zhao, Yin, & Wang,2008) and medial prefrontal cortex (Alper et al., 2006). Impor-tantly, these oscillations are also among the neurophysiologicalsignatures most strongly associated with memory consolidationduring sleep. Hippocampal sharp-wave ripple bursts, during whichmemory ‘‘reactivation” is seen in rodents, are temporally synchro-nized with the up-state of the sleep slow oscillation, which isthought to group functionally relevant faster brain rhythms. Assuch, slow oscillations are indirectly linked to the reactivationand consolidation of memory in hippocampal–cortical circuits(Carr et al., 2011; Davidson et al., 2009; Foster & Wilson, 2006;Gupta et al., 2010; Karlsson & Frank, 2009). Because slow oscilla-tory EEG activity during waking rest is at least superficially similarto that seen during sleep, one possibility is that there is similar hip-pocampal–cortical communication occurring during this time. It isunknown whether this frequency of EEG oscillation during wake-fulness is generated by the same mechanisms as the sleep EEGslow oscillation, but recent work demonstrates that slow mem-brane potential oscillations are indeed present during quiet rest(Crochet & Petersen, 2006; Poulet & Petersen, 2008).
Attenuated alpha was also associated with enhanced memoryacross quiet rest. The fact that reduced resting alpha was associ-ated with memory in both experimental conditions suggests thatthe alpha correlation may arise from a trait-like associationbetween alpha generation and memory, rather than a specificeffect of the EEG state immediately following encoding. Of note,the disappearance of alpha from eyes-closed resting EEG is a pri-mary indicator of entry into Stage 1 sleep (Iber et al., 2007). Butin this case, there was no association between decreased restingalpha and visually-identified sleep onset, nor were there any otherindications that sleep might account for the observed memoryeffects. Although several participants did fall asleep for a brief time,this was unrelated to memory performance. We cannot rule outperiods of spatially localized sleep (Hung et al., 2013; Vyazovskiyet al., 2011) or very short ‘‘microsleeps” below the threshold ofvisual detection as contributors to the memory effect and theEEG findings. Future studies using high-density EEG might seekto test the hypothesis that experience-dependent local sleep(Hung et al., 2013; Vyazovskiy et al., 2011) accounts for the effectof quiet rest on memory.
During rest, participants’ thoughts differed substantially fromthose during active wake. While resting, participants reportedthinking about the past and/or the future and most noticeablynot of what they were doing presently. This is different from thedistractor task condition, during which participants were highlyfocused on their current stimulus environment. However, therewere substantial individual differences in the extent to which par-ticipants were focused on the distractor task during the retentioninterval. Memory retention was superior in the distractor task con-dition when participants reported more mindwandering and lesstask-related thought, echoing the mental activity profile of quietrest. In both cases, improved memory followed a period of time
es of memory consolidation. Neurobiology of Learning and Memory (2016),
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during which participants attended more to internal, task-unrelated thoughts, and less to the ongoing task. This observationis consistent with a hypothesized role for mindwandering in mem-ory consolidation, but alternatively, it could be that increasedmindwandering during the distractor task signifies decreasedattention to external stimuli, rather than itself enhancing the con-solidation process in any way. Because this non-verbal distractortask had no content overlap with the short story, stimulus-specific retroactive interference induced by the distractor is anunlikely explanation for the results.
In contrast, interference caused by general mental effortdevoted to stimulus processing is a potential explanation for thefindings. Our data are broadly consistent with theoretical accountsof memory consolidation which propose that any encoding activityduring wakefulness interferes with the initiation of offline consol-idation processes (Dewar, Cowan, & Della Sala, 2007; Mednicket al., 2011; Wixted, 2004). Notably however, quiet rest was notassociated with a lack of mental activity in the present study. Par-ticipants experienced rich and varied mental activity during therest period, which differed from the distractor task primarily inthat this mentation was inwardly focused, rather than directedtoward the current stimulus environment. Thus, our observationsare consistent with the notion that stimulus-oriented mental effortinterferes with consolidation, whereas inwardly-focused mentalactivity does not. In fact, the present observations suggest thatmindwandering may serve as an indicator that the brain hasentered an offline state conducive to consolidation.
During rest, amongst the thought and imagery reported werethoughts specifically about the experimental learning task. How-ever, no participants reported effortfully rehearsing the story,and thinking about the learning task was unrelated to memoryperformance. Thus, in line with the observations of one prior report(Dewar et al., 2014), we conclude that active rehearsal of thelearned material is an unlikely explanation for the memoryoutcome.
Numerous questions remain unanswered. First, the currentstudy does not establish the duration of this memory effect(although prior research suggests a long-lasting impact (Dewaret al., 2012)). Second, rest may be beneficial for some forms ofmemory and not others. The small body of literature to date hasfocused on learning tasks which are presumably hippocampus-dependent (Craig et al., 2015; Dewar et al., 2012, 2014; Mercer,2015). Future research should extend this work to tasks in themotor, procedural, and perceptual domains, which rely on distinctbrain systems but also show a benefit of post-training sleep. Third,numerous forms of offline memory processing have been proposedto exist – insight formation (Wagner, Gais, Haider, Verleger, &Born, 2004), memory integration (Tamminen, Payne, Stickgold,Wamsley, & Gaskell, 2010) and the extraction of ‘‘gist” (Payneet al., 2009), to name only a few (Robert Stickgold & Walker,2013). Waking rest may affect memory in some of these ways,but not others. Finally, the effect of rest may not be due to the sameactive consolidation processes that are proposed to account forsleep’s effect on memory. Although prior research supports ourhypothesis of a causal role for slow oscillations in resting statememory consolidation, our current data cannot directly supportcausation. And although we argue that the mechanisms accountingfor the benefit of rest could overlap with those operating duringsleep, future research will be required to determine the extent towhich rest and sleep benefit memory via distinct vs. overlappingmechanisms.
In summary, these data demonstrate that under the right condi-tions, non-sleep resting states can facilitate declarative memory.Prior research has demonstrated that during human rest, patternsof brain activity associated with recent learning are re-expressed,and that this ‘‘reactivation” of learning-related activity predicts
Please cite this article in press as: Brokaw, K., et al. Resting state EEG correlathttp://dx.doi.org/10.1016/j.nlm.2016.01.008
subsequent memory (Deuker et al., 2013; Tambini & Davachi,2013; Tambini, Ketz, & Davachi, 2010). This mechanism mayexplain the behavioral memory effect demonstrated here. Our cur-rent data complement these observations by elucidating the globalbrain state under which this ‘‘reactivation” of memory duringhuman rest may occur. It appears critical that a particular state isentered characterized on the neural level by increased slow oscil-latory and decreased alpha activity, and on the phenomenologicallevel by an increase in task-unrelated mental processing (e.g.‘‘mindwandering”). Together, these features of rest may indicatethat the brain has entered a state of reduced encoding-relatedactivity optimal for offline memory reactivation and consolidation.
Acknowledgments
We thank Yvette Graveline for technical support, and MatthewTucker for comments on the draft manuscript. This research wassupported by intramural funding through the Furman AdvantageResearch Fellowship Program.
Appendix A. Supplementary material
Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.nlm.2016.01.008.
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