Resting state EEG correlates of memory consolidation

YNLME 6377

No. of Pages 9, Model 5G

21 January 2016 Neurobiology of Learning and Memory xxx (2016) xxx–xxx 1

Contents lists available at ScienceDirect

Neurobiology of Learning and Memory journal homepage: www.elsevier.com/locate/ynlme 4 5 3

Resting state EEG correlates of memory consolidation

7

Kate Brokaw, Ward Tishler, Stephanie Manceor, Kelly Hamilton, Andrew Gaulden, Elaine Parr, Erin J. Wamsley ⇑

8

Furman University, Department of Psychology, United States

6

9 10 1 9 2 2 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

a r t i c l e

i n f o

Article history: Received 5 November 2015 Revised 11 January 2016 Accepted 16 January 2016 Available online xxxx Keywords: Memory consolidation Sleep Resting state Mindwandering Daydreaming EEG Slow oscillation Alpha Verbal memory

a b s t r a c t 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 identify the conditions under which non-sleep resting states benefit memory, we conducted an EEG (electroencephalographic) 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 distractor 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 associated with a particular EEG signature of increased slow oscillatory activity (<1 Hz), in concert with reduced alpha (8–12 Hz) activity. Mindwandering during the retention interval was also associated with improved 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 activity and characterized by decreased attention to the external environment. Slow oscillatory EEG rhythms are proposed to facilitate memory consolidation during sleep by promoting hippocampal–cortical communication. Our findings suggest that EEG slow oscillations could play a significant role in memory consolidation during other resting states as well. Ó 2016 Published by Elsevier Inc.

30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

48 49

1. Introduction

50

A growing literature confirms that memory is better retained when participants sleep after learning, as opposed to staying awake. It is widely proposed that this effect is due to an active process of memory consolidation during sleep (Diekelmann & Born, 2010; Stickgold, 2005). This hypothesis is supported by studies demonstrating that improved memory is associated with specific features of the sleep EEG linked to consolidation, including slow waves (Alger, Lau, & Fishbein, 2012; Diekelmann, Biggel, Rasch, & Born, 2012), slow oscillations (Huber, Ghilardi, Massimini, & Tononi, 2004; Marshall, Helgadóttir, Mölle, & Born, 2006), and sleep 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 not required to boost memory. Even a partial night of sleep or a short nap can facilitate memory, with effect sizes comparable to those following a full night (Mednick, Nakayama, & Stickgold, 2003; Plihal & Born, 1997; Tucker & Fishbein, 2009; Tucker et al.,

51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66

⇑ Corresponding author at: Furman University, Johns Hall 206K, United States. Fax: +1 864 294 2206. E-mail address: [email protected] (E.J. Wamsley).

2006). Furthermore, the duration of nap sleep is often unrelated to its memory effect, with even very short naps providing the same memory benefit as longer sleep periods (Payne et al., 2015; Tucker et al., 2006; Wamsley, Tucker, Payne, & Stickgold, 2010), although see (Alger et al., 2012; Mednick et al., 2003). Even a nap as short as 6 min has been reported to lead to a memory-enhancing effect (Lahl et al., 2008). What enables such short periods of sleep to enhance memory performance? One possibility is the presence of fast-acting offline consolidation mechanisms that do not require the completion of a full sleep cycle. Moreover, some propose that consolidation can occur during any state of sleep or alertness, when the encoding of new information is sufficiently reduced during the consolidation period (Mednick, Cai, Shuman, Anagnostaras, & Wixted, 2011). Might short periods of quiet wakefulness impact memory, even in the absence of sleep? Most studies investigating the effect of sleep on memory have done so in comparison to waking control conditions 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 leave the 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

http://dx.doi.org/10.1016/j.nlm.2016.01.008 1074-7427/Ó 2016 Published by Elsevier Inc.

Please cite this article in press as: Brokaw, K., et al. Resting state EEG correlates of memory consolidation. Neurobiology of Learning and Memory (2016), http://dx.doi.org/10.1016/j.nlm.2016.01.008

67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89

YNLME 6377

No. of Pages 9, Model 5G

21 January 2016 2

90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154

K. Brokaw et al. / Neurobiology of Learning and Memory xxx (2016) xxx–xxx

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 the absence of cognitive tasks, activities, and sensory stimulation – has not been sufficiently characterized. The notion that periods of unoccupied rest can retroactively facilitate memory actually dates back to the earliest days of experimental psychology, when Müller and Pilzecker first suggested that retroactive interference occurs even when the interpolated activity is highly dissimilar to the learned material (Müller & Pilzecker, 1900). But in more recent years, this question of whether a general reduction of mental effort during wakefulness (rest) facilitates consolidation has received little attention. Just in the last several years, emerging new evidence has begun to suggest that quiet wake does in 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). Several recent experiments report that a brief period of resting wake following 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 employed EEG-monitoring, it is uncertain whether participants might have obtained brief periods of sleep during the retention interval. Beyond this, we have little understanding of the mechanisms by which resting wakefulness might enhance memory, nor the conditions under which this benefit emerges. Neurophysiological correlates of memory changes across sleep have now been extensively documented (Clemens, Fabó, & Halász, 2005, 2006; Holz et al., 2012; Nishida & Walker, 2007; Schabus et al., 2004; van Dongen, Takashima, Barth, & Fernández, 2011), but corresponding studies of resting wakefulness are lacking. Quiet rest might facilitate memory via active consolidation mechanisms similar to those operating during sleep. Much of the neurophysiology purported to support consolidation during sleep is also present during resting wake. Like sleep, quiet rest is characterized by a dramatic reduction in sensory processing. Freed from the demands of stimulus processing, mental experience is focused inward, as participants engage in ‘‘mindwandering” – thinking about the past, imagining the future, and creating fictitious scenarios (Andrews-Hanna, 2011; Andrews-Hanna, Reidler, Huang, & Buckner, 2010; Antrobus, Singer, Goldstein, & Fortgang, 1970; Baird et al., 2012). Meanwhile, the ‘‘reactivation” of recent memory in the hippocampus and cortex that was first observed during slow wave 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 reactivation has not been directly observed in humans, the hippocampal ‘‘sharp-wave ripples” associated with reactivation are prevalent during quiet rest in humans (Axmacher, Elger, & Fell, 2008; Clemens et al., 2011). Consolidation-promoting neurochemical features of sleep are also partially replicated during rest, including decreased acetylcholine levels during quiet resting wakefulness (Marrosu et al., 1995). Finally, several EEG oscillations proposed to support consolidation during sleep also have analogs during quiet rest. Although the predominant frequencies are different, in comparison to more active states of wakefulness EEG slowing characterizes both sleep and eyes-closed quiet rest. In wakefulness, candidate oscillations that we hypothesized might relate to memory processing are the EEG alpha oscillation (8–12 Hz) and the slower theta (4–7 Hz) and slow/delta oscillations (0.5–2 Hz). Alpha is the primary EEG signature of eyes-closed waking rest that distinguishes this state from active wakefulness, and is one of the main EEG correlates of the fMRI-defined ‘‘default-mode” resting state network, which

includes a number of memory-related brain regions including the hippocampus, parahippocamal cortex, and medial frontal cortex (Jann et al., 2009; Knyazev, Slobodskoj-Plusnin, Bocharov, & Pylkova, 2011). On the phenomenological level, alpha rhythms are associated with a decreased focus on external stimuli and increased attention to internal states, including memories of the past (Foulkes & Fleisher, 1975). Alpha has recently been studied as 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 quiet rest. In sleep, slow oscillations (1 Hz) and slow waves (up to 2 Hz) are thought to be major contributors to systems-level memory consolidation, synchronizing hippocampal sharp-wave ripples with cortical activity (Clemens et al., 2007, 2011; Mölle, Eschenko, Gais, Sara, & Born, 2009) and thus promoting hippocampal–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 of directed cognitive tasks (Alper et al., 2006; Demanuele, SonugaBarke, & James, 2010). Thus, a number of mechanisms proposed to account for the effects of sleep on memory are also present during quiet wake, which suggests the hypothesis that the memory benefits of rest and sleep could arise from overlapping active consolidation mechanisms. The aims of the current study were to (1) confirm that a period of EEG-verified quiet rest benefits memory, in the absence of any sleep, (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 15min interval with continuous EEG monitoring. We hypothesized that 15 min of quiet rest would lead to improved memory at a subsequent test, and expected to find that this effect was related to both EEG slowing and increased ‘‘mindwandering” (AndrewsHanna et al., 2010; Baird et al., 2012; Mason et al., 2007) during the rest period, both potential signatures of a sleep-like offline state conducive to memory consolidation.

155

2. Methods

192

2.1. Participants

193

29 college students (19 female) age 19–22 (M = 20 yrs (±0.8 SD)) were recruited by email, advertisement, or word-of-mouth, and paid $10/h for their participation. By self-report using a 3-day sleep log, participants stated that they slept an average of 7.4 h (±1.1 SD) per night on the 3 nights prior to the study.

194

Fig. 1. Experimental timeline. Participants learned a short story just prior to a 15 min retention interval during which they either rested quietly with eyes closed or completed a distractor task. A recall test was administered both immediately and following the retention interval.

Please cite this article in press as: Brokaw, K., et al. Resting state EEG correlates of memory consolidation. Neurobiology of Learning and Memory (2016), http://dx.doi.org/10.1016/j.nlm.2016.01.008

156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191

195 196 197 198

YNLME 6377

No. of Pages 9, Model 5G

21 January 2016 K. Brokaw et al. / Neurobiology of Learning and Memory xxx (2016) xxx–xxx 199

2.2. Procedure

200

248

The experimental timeline is illustrated in Fig. 1. Participants arrived at the laboratory, signed informed consent, and filled out initial paperwork including demographic surveys, the Epworth sleepiness scale (a measure of trait sleepiness (Johns, 1991)), and the Stanford Sleepiness Scale (a measure of state sleepiness (Hoddes, Zarcone, Smythe, Phillips, & Dement, 1973)). Participants were then prepared for EEG (electroencephalographic) recording by attaching electrodes to the scalp (C3, C4, O1, O2, F3, F4), with bilateral mastoid references, as well as chin leads for assessing muscle tone, and eye leads for recording EOG (electrooculography, right and left outer canthus). All participants completed 2 conditions in counterbalanced order: quiet rest and distractor task. There was a 10 min break in between conditions. In each condition, participants first listened to a short story, and were instructed to remember as much of the story as possible (details below). Immediately after listening to the story, participants were asked to freely recall as much of the story as possible. Participants then completed either the quiet rest or distractor task condition. In the quiet rest condition, participants sat in a comfortable chair with their eyes closed for 15 min. Participants were instructed to keep their eyes closed for the entire 15 min and to keep movement to a minimum. No instruction was given regarding what participants should be doing mentally during this time. In the distractor task condition, participants played the computer game ‘‘Snood” for 15 min (see below). EEG, EOG, and EMG were digitally recorded at 400 Hz during the 15 min interval. Immediately afterward, a delayed recall test was administered in which participants were instructed to again type as much of the story as they could remember. Two self-report measures assessed mental activity during the 15 min retention interval. Following each condition, participants completed a questionnaire on which they recorded any thoughts or imagery they could recall from the preceding interval, and rated the proportion of the 15 min interval that they had spent engaged in 14 predefined categories of mental activity, including ‘‘thinking about the past” (something else earlier today/yesterday to a week ago/past year or several years ago), ‘‘imagining the future” (later today/tomorrow to next week/next year or several years), ‘‘thinking about the short story”, ‘‘thinking about staying still”, ‘‘counting the time”, ‘‘mind was blank”, ‘‘meditating”, ‘‘sleeping”, ‘‘thinking about something else”, and ‘‘other”. Following the methods of Andrews-Hanna et al. (2010), participants recorded their responses by dividing a blank circle to reflect the proportional amount of time they had spent thinking about each topic. Following each condition, participants also completed a rehearsal questionnaire on which they used a 5-point scale to respond to the questions ‘‘how often did you think about the story?”, ‘‘how often did you imagine the story?” and ‘‘how often did you try to remember the story?” during the 15 min interval.

249

2.3. Materials

250

A short story recall task (adapted from the Wechsler Memory Scale (Wechsler, 1987)) was used to assess declarative memory (following Dewar et al., 2012). Participants listened to a digital recording of a short story, approximately 30 s long, and were then asked to freely recall as much of this story as they could, and as accurately as possible, by typing everything that they remembered into an electronic form. They were given as much time as needed to complete their responses. 15 min later, a delayed test was given in which they were again asked to freely recall as much of the story as 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

201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247

251 252 253 254 255 256 257 258 259 260 261

3

counterbalanced across subjects, and immediate recall performance 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 participants eliminate blocks of colors (‘‘snoods”) by creating groups of 3 or more of the same color. This visuospatial task was chosen in order to provide an engaging distractor activity that would have minimal overlap in specific content with the purely verbal short story learning task. Participants were instructed how to play the game by pointing and clicking the computer mouse to aim and release snoods. Once a game was lost or won, participants were instructed to click ‘‘restart” and continue playing until told to stop after 15 min had passed. Game difficulty level was set to ‘‘easy”.

262

2.4. Analysis

275

Free recall responses were scored by 2 raters blind to experimental condition. Correctly recalled elements were scored according to the methods described in the Wechsler Memory Scale Manual (Wechsler, 1987). In short, one point was awarded for each piece of correctly reported information, for a total of 25 points per story. All reports were scored by both raters, and the final score for each report was calculated as the average score of the two raters. Responses were also scored for the number of falsely recalled items, defined as story elements which were mentioned in the free recall response, but described incorrectly. For example, if a participant provided the protagonists’ first name (Anna), but incorrectly stated what the name was (Elsa, Annie, Astrid, etc.), this would be scored as a falsely recalled element. Inter-rater reliability for correct recall was r = .94, and for false recall was r = .66. For both measures, the primary dependent variable was change in performance across the 15-min memory retention interval. To assess fluctuations in the total amount of information provided, a total recall score was also calculated at both the immediate and delayed time points (correct recall + false recall). The amount of information contained in free recall responses was highly variable between immediate and delayed testing in some individuals, which might have been caused by lack of motivation to or fatigue with repeatedly supplying a lengthy written response. Thus, six performance outliers were thus excluded from further analysis due to scores on the performance change dependent measures lying >1.5 interquartile ranges above the 75th percentile or below the 25th percentile. The primary results of the study were robust to alternate methods of excluding outliers – Using the same method of outlier exclusion, but applied only to encoding performance on the Story Recall Task resulted in the exclusion of fewer outlying points (n = 2), but the same primary finding that memory performance is relatively preserved across a period of quiet wakefulness, in comparison to a significant decline across active wakefulness (see below). Five additional participants were excluded from all or some analyses because they failed to comply with instructions to keep their eyes closed during the quiet rest period (n = 1), had pervasive artifact in the EEG recording that prevented accurate spectral analysis (n = 1), or had corrupted or missing recall data (n = 3). Sleep stage was determined according the standard criteria established 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 other sources) was manually rejected via visual inspection. Spectral analysis via fast Fourier transform (Brain Products BrainVision Analyzer v2.0.2) was then applied to all artifact-free 4 s intervals (50% segment overlap, Hanning window) to assess mean power spectral density (lV2/Hz) in the frequency bands of interest, including alpha (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

276

Please cite this article in press as: Brokaw, K., et al. Resting state EEG correlates of memory consolidation. Neurobiology of Learning and Memory (2016), http://dx.doi.org/10.1016/j.nlm.2016.01.008

263 264 265 266 267 268 269 270 271 272 273 274

277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325

YNLME 6377

No. of Pages 9, Model 5G

21 January 2016 4

K. Brokaw et al. / Neurobiology of Learning and Memory xxx (2016) xxx–xxx

338

differences in EEG amplitude, relative power values were used (normalized such that total power across all frequency bands is rendered equivalent in all analyzed segments). Eye movements were automatically detected by marking peaks wherein amplitude of the right outer canthus recording exceeded an absolute threshold 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-state EEG was assessed using Pearson’s correlations. In cases where separate correlations were run for multiple electrodes, Type I error was controlled by using a Bonferroni-corrected significance threshold of a = .0083 (a = .05/6 electrodes).

339

3. Results

340

3.1. Effect of quiet rest on memory

341

349

As hypothesized, quiet rest led to improved memory for the story at 15 min, relative to the distractor task (change in correct recall: t18 = 2.60, p = 0.018; Fig. 2). In contrast, quiet rest did not significantly affect false recall scores (change in false recall: t18 = 1.82, p = 0.09; Fig. 2). At immediate recall, performance was equivalent between conditions for both correct (p > .3) and false (p > .8) recall. There was no effect of condition on the total amount of information reported (total recall score: p > .7). These performance data are reported in Table 1.

350

3.2. EEG correlates of memory retention

351

Memory retention during quiet rest was associated with an EEG signature of proportionally increased slow oscillation power (0.3–1 Hz), in concert with decreased power in the alpha frequency band (8–12 Hz). Slow oscillation power was strongly associated with improved recall following quiet rest (mean across all electrodes: r18 = 0.63, p = .005; Fig. 3 Left). The magnitude of this effect was strongest frontally, and survived Bonferroni correction for multiple comparisons at F3, F4 and C3 recording sites (Table 2; Fig. 3 Right; complete spectral analysis correlation results reported in Supplementary Table S1). As anticipated, compared with the distractor task, quiet rest was characterized by an increased proportion of EEG spectral power in the alpha frequency band (8–12 Hz; t20 = 6.68,

326 327 328 329 330 331 332 333 334 335 336 337

342 343 344 345 346 347 348

352 353 354 355 356 357 358 359 360 361 362 363

Fig. 2. Effect of condition on recall. Quiet rest significantly enhanced recall at 15 min. Error bars ± SEM.

Table 1 Story recall across quiet rest and distractor task conditions.

Correct recall False recall Total recall (Correct + False)

DRecall across quiet rest

DRecall across distractor

Mean

±SD

Mean

±SD

.61 .13 .56

1.30 .76 1.12

1.28 .73 .75

1.29 1.32 1.25

p

0.018 0.09 0.71

p-values derived from paired-samples t-tests.

p = 0.000002). Yet within quiet rest, the amount of alpha activity that participants expressed was negatively associated with correct recall (Table 2). Slow oscillation power during quiet rest was unrelated to memory following the distractor task (Table 2). In contrast, resting alpha marginally predicted memory both following the distractor condition and following quiet rest. There were no other significant associations between EEG power and correct recall across quiet wake. There were no significant associations between EEG measures and false recall. Supplemental analyses ruled out the possibility that residual eye movement artifact might have contributed to slow EEG frequencies during quiet rest. First, in contrast to the strong association with frontal EEG signals, story recall scores were not significantly correlated with slow oscillatory activity in the eye movement channels (p > .1). Second, the number of automatically detected eye movements during quiet rest (see Section 2) was correlated neither with story recall (p > .7), nor slow oscillation power in the EEG channels (p > .7).

364

3.3. Mental activity correlates of memory retention

383

Improved memory was associated with decreased attention to the external environment. First, during quiet rest, participants spent much less time thinking about what they were currently doing, as compared to during the distractor task (t21 = 6.02, p = 0.000006; Fig. 4). Instead, they spent more time thinking about the past and imagining the future. This included increased time during 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, which occurred more commonly than during the distractor task (t21 = 2.4, p = 0.02). Second, within the distractor task condition, participants showed superior memory retention when spending less time thinking about the Snood game itself (r20 = 0.48, p = 0.03), and more time thinking about other things, including the past week (r20 = .56, p = 0.01), the rest of the day (r20 = 0.46, p = 0.04), tomorrow (r20 = 0.51, p = 0.02), and meditating (r20 = 0.51, p = 0.02). There was no correlation between mental activity during quiet rest and memory performance.

384

3.4. Rehearsal effects

404

Condition did not significantly affect the extent to which participants reported effortfully trying to remember the story (t21 = 1.80, p = 0.09) or imagine the story (t21 = 1.50, p = 0.15). Participants did report ‘‘thinking” about the story significantly more in the quiet rest condition than during the distractor task (t21 = 3.46, p = 0.002). However, thinking about the story was not correlated with the improved recall seen during quiet rest (p > .3), nor did any other rehearsal questionniare responses correlate with memory change during either quiet rest or the distractor task.

405

Please cite this article in press as: Brokaw, K., et al. Resting state EEG correlates of memory consolidation. Neurobiology of Learning and Memory (2016), http://dx.doi.org/10.1016/j.nlm.2016.01.008

365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382

385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403

406 407 408 409 410 411 412 413

YNLME 6377

No. of Pages 9, Model 5G

21 January 2016 5

K. Brokaw et al. / Neurobiology of Learning and Memory xxx (2016) xxx–xxx

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 recall score across the quiet resting period. Right: This association was strongest at frontal electrodes.

Table 2 EEG correlates of change in story recall across the 15 min retention interval. Memory following quiet rest r

Memory following distractor p

r

p

Quiet rest EEG Slow oscillation lV2/Hz (0.3–1 Hz) C3 .51 C4 .69 O1 .57 O2 .52 F3 .61 F4 .65

.03* .002** .01* .03* .008** .004**

.12 .20 .25 .28 .15 .08

.62 .41 .30 .25 .54 .74

Alpha lV2/Hz (8–12 Hz) C3 C4 O1 O2 F3 F4

.03* .02* .09 .11 .009* .02*

.42 .41 .45 .45 .51 .31

.08 .09 .05 .05 .03* .20

Distractor task EEG Slow oscillation lV2/Hz (0.3–1 Hz) C3 .22 C4 .15 O1 .27 O2 .33 F3 .18 F4 .09

.37 .57 .28 .19 .48 .71

.04 .12 .39 .19 .18 .07

.86 .64 .10 .44 .46 .77

Alpha lV2/Hz (8–12 Hz) C3 C4 O1 O2 F3 F4

.78 .54 .45 .78 .36 .41

.01 .01 .06 .16 .04 .10

.96 .96 .86 .69 .82 .53

.51 .54 .41 .39 .60 .53

.07 .16 .19 .07 .23 .21

Pearson’s correlations testing the association between EEG during the retention interval and change in story recall following the quiet rest and distractor task conditions. * p < .05. ** Survives Bonferroni correction for multiple comparisons at a = .0083.

414

3.5. Effects of sleep and sleepiness

415

During quiet rest, 5 participants fell asleep for an average of 2.0 min (±2.6 SD). However, falling asleep did not improve recall scores. In fact, those who fell asleep for this brief time actually performed numerically worse at delayed recall (change in correct recall = 1.2 ± 0.6 SD) than those who did not fall asleep (change in correct recall = 0.39 ± 1.4 SD, p > .2). Additionally, when participants who slept were excluded from analysis, the memory benefit of quiet rest was still apparent (t12 = 2.5, p = .03). Sleep also had no

416 417 418 419 420 421 422

effect on change in false recall scores following quiet rest (p > .4). Neither slow oscillation power nor alpha power showed a detectable difference between those who fell asleep during the retention interval and those who did not (slow oscillation: p = .4; alpha: p = .7). Self-reported sleepiness was equivalent between conditions at baseline. However, participants became significantly sleepier following quiet rest compared to following the distractor task (t21 = 2.25, p = 0.04). The sleepiness induced by quiet rest did not correlate with memory change across rest (p > .7).

Please cite this article in press as: Brokaw, K., et al. Resting state EEG correlates of memory consolidation. Neurobiology of Learning and Memory (2016), http://dx.doi.org/10.1016/j.nlm.2016.01.008

423 424 425 426 427 428 429 430 431 432

YNLME 6377

No. of Pages 9, Model 5G

21 January 2016 6

K. Brokaw et al. / Neurobiology of Learning and Memory xxx (2016) xxx–xxx

Fig. 4. Mental activity during the retention interval. Proportion of the 15 min retention interval that participants spent engaged in various categories of mental activity, by self-report.

433

3.6. Order effects

434

447

Order of experimental condition affected memory, but this was unrelated to the memory benefit of quiet rest. Order of condition significantly affected immediate (F1,18 = 13.9, p = 0.002) and delayed recall (F1,18 = 16.9, p = 0.001) within the quiet rest condition, such that participants remembered the story better when quiet rest followed the distractor task condition, as opposed to when quiet rest preceded the distractor task condition. This could represent a facilitation effect in which the distractor task benefits subsequent new encoding. But importantly, when we controlled for this order effect by including counterbalancing order as a factor in the ANOVA model, we found that order did not impact the effect of rest on memory (p > 0.4), and that the main effect of quiet rest vs. distractor task was still statistically significant (F1,18 = 6.43, p = 0.02). There was no such order effect for false recall scores.

448

4. Discussion

449

Here, we confirm that a short period of eyes-closed rest facilitates declarative memory, and we identify two novel predictors of this phenomenon that elucidate the neurophysiological and phenomenological conditions under which the effect occurs. On the neurophysiological level, an increase in slow oscillatory EEG rhythms and decrease in alpha rhythms predicted improved mem-

435 436 437 438 439 440 441 442 443 444 445 446

450 451 452 453 454

ory following rest. On the phenomenological level, improved memory was associated with decreased attention to the external environment and an increase in ‘‘mindwandering”, as participants turned to thinking of the past and imagining the future. In several respects, the resting state in the present study resembles sleep. Taken together, our observations suggest that eyes-closed rest could facilitate memory consolidation via mechanisms similar to those that operate during sleep. Although 1 Hz rhythms are not a visually prominent feature of the resting EEG, these oscillations are present during rest, and may be relatively attenuated during the execution of directed cognitive tasks (Alper et al., 2006; Demanuele et al., 2010). During wakefulness, EEG oscillations in the slow and delta bands have been associated with the activation of memory-related structures including the parahippocampal gyrus (Chen, Feng, Zhao, Yin, & Wang, 2008) and medial prefrontal cortex (Alper et al., 2006). Importantly, these oscillations are also among the neurophysiological signatures most strongly associated with memory consolidation during sleep. Hippocampal sharp-wave ripple bursts, during which memory ‘‘reactivation” is seen in rodents, are temporally synchronized with the up-state of the sleep slow oscillation, which is thought to group functionally relevant faster brain rhythms. As such, slow oscillations are indirectly linked to the reactivation and 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 oscillatory EEG activity during waking rest is at least superficially similar to that seen during sleep, one possibility is that there is similar hippocampal–cortical communication occurring during this time. It is unknown whether this frequency of EEG oscillation during wakefulness is generated by the same mechanisms as the sleep EEG slow oscillation, but recent work demonstrates that slow membrane potential oscillations are indeed present during quiet rest (Crochet & Petersen, 2006; Poulet & Petersen, 2008). Attenuated alpha was also associated with enhanced memory across quiet rest. The fact that reduced resting alpha was associated with memory in both experimental conditions suggests that the alpha correlation may arise from a trait-like association between alpha generation and memory, rather than a specific effect of the EEG state immediately following encoding. Of note, the disappearance of alpha from eyes-closed resting EEG is a primary indicator of entry into Stage 1 sleep (Iber et al., 2007). But in this case, there was no association between decreased resting alpha and visually-identified sleep onset, nor were there any other indications that sleep might account for the observed memory effects. Although several participants did fall asleep for a brief time, this was unrelated to memory performance. We cannot rule out periods of spatially localized sleep (Hung et al., 2013; Vyazovskiy et al., 2011) or very short ‘‘microsleeps” below the threshold of visual detection as contributors to the memory effect and the EEG findings. Future studies using high-density EEG might seek to test the hypothesis that experience-dependent local sleep (Hung et al., 2013; Vyazovskiy et al., 2011) accounts for the effect of quiet rest on memory. During rest, participants’ thoughts differed substantially from those during active wake. While resting, participants reported thinking about the past and/or the future and most noticeably not of what they were doing presently. This is different from the distractor task condition, during which participants were highly focused on their current stimulus environment. However, there were substantial individual differences in the extent to which participants were focused on the distractor task during the retention interval. Memory retention was superior in the distractor task condition when participants reported more mindwandering and less task-related thought, echoing the mental activity profile of quiet rest. In both cases, improved memory followed a period of time

Please cite this article in press as: Brokaw, K., et al. Resting state EEG correlates of memory consolidation. Neurobiology of Learning and Memory (2016), http://dx.doi.org/10.1016/j.nlm.2016.01.008

455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520

YNLME 6377

No. of Pages 9, Model 5G

21 January 2016 K. Brokaw et al. / Neurobiology of Learning and Memory xxx (2016) xxx–xxx 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586

during which participants attended more to internal, taskunrelated thoughts, and less to the ongoing task. This observation is consistent with a hypothesized role for mindwandering in memory consolidation, but alternatively, it could be that increased mindwandering during the distractor task signifies decreased attention to external stimuli, rather than itself enhancing the consolidation process in any way. Because this non-verbal distractor task had no content overlap with the short story, stimulusspecific retroactive interference induced by the distractor is an unlikely explanation for the results. In contrast, interference caused by general mental effort devoted to stimulus processing is a potential explanation for the findings. Our data are broadly consistent with theoretical accounts of memory consolidation which propose that any encoding activity during wakefulness interferes with the initiation of offline consolidation processes (Dewar, Cowan, & Della Sala, 2007; Mednick et al., 2011; Wixted, 2004). Notably however, quiet rest was not associated with a lack of mental activity in the present study. Participants experienced rich and varied mental activity during the rest period, which differed from the distractor task primarily in that this mentation was inwardly focused, rather than directed toward the current stimulus environment. Thus, our observations are consistent with the notion that stimulus-oriented mental effort interferes with consolidation, whereas inwardly-focused mental activity does not. In fact, the present observations suggest that mindwandering may serve as an indicator that the brain has entered an offline state conducive to consolidation. During rest, amongst the thought and imagery reported were thoughts specifically about the experimental learning task. However, no participants reported effortfully rehearsing the story, and thinking about the learning task was unrelated to memory performance. Thus, in line with the observations of one prior report (Dewar et al., 2014), we conclude that active rehearsal of the learned material is an unlikely explanation for the memory outcome. Numerous questions remain unanswered. First, the current study does not establish the duration of this memory effect (although prior research suggests a long-lasting impact (Dewar et al., 2012)). Second, rest may be beneficial for some forms of memory and not others. The small body of literature to date has focused on learning tasks which are presumably hippocampusdependent (Craig et al., 2015; Dewar et al., 2012, 2014; Mercer, 2015). Future research should extend this work to tasks in the motor, procedural, and perceptual domains, which rely on distinct brain systems but also show a benefit of post-training sleep. Third, numerous forms of offline memory processing have been proposed to exist – insight formation (Wagner, Gais, Haider, Verleger, & Born, 2004), memory integration (Tamminen, Payne, Stickgold, Wamsley, & Gaskell, 2010) and the extraction of ‘‘gist” (Payne et 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 same active consolidation processes that are proposed to account for sleep’s effect on memory. Although prior research supports our hypothesis of a causal role for slow oscillations in resting state memory consolidation, our current data cannot directly support causation. And although we argue that the mechanisms accounting for the benefit of rest could overlap with those operating during sleep, future research will be required to determine the extent to which rest and sleep benefit memory via distinct vs. overlapping mechanisms. In summary, these data demonstrate that under the right conditions, non-sleep resting states can facilitate declarative memory. Prior research has demonstrated that during human rest, patterns of brain activity associated with recent learning are re-expressed, and that this ‘‘reactivation” of learning-related activity predicts

7

subsequent memory (Deuker et al., 2013; Tambini & Davachi, 2013; Tambini, Ketz, & Davachi, 2010). This mechanism may explain the behavioral memory effect demonstrated here. Our current data complement these observations by elucidating the global brain state under which this ‘‘reactivation” of memory during human rest may occur. It appears critical that a particular state is entered characterized on the neural level by increased slow oscillatory and decreased alpha activity, and on the phenomenological level by an increase in task-unrelated mental processing (e.g. ‘‘mindwandering”). Together, these features of rest may indicate that the brain has entered a state of reduced encoding-related activity optimal for offline memory reactivation and consolidation.

587

Acknowledgments

599

We thank Yvette Graveline for technical support, and Matthew Tucker for comments on the draft manuscript. This research was supported by intramural funding through the Furman Advantage Research Fellowship Program.

600

Appendix A. Supplementary material

604

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.nlm.2016.01.008.

605

References

607

Alger, S. E., Lau, H., & Fishbein, W. (2012). Slow wave sleep during a daytime nap is necessary for protection from subsequent interference and long-term retention. Neurobiology of Learning and Memory. http://dx.doi.org/10.1016/j. nlm.2012.06.003. Alper, K. R., John, E. R., Brodie, J., Günther, W., Daruwala, R., & Prichep, L. S. (2006). Correlation of PET and qEEG in normal subjects. Psychiatry Research: Neuroimaging, 146(3), 271–282. http://dx.doi.org/10.1016/j. pscychresns.2005.06.008. Andrews-Hanna, J. R. (2011). The brain’s default network and its adaptive role in internal mentation. The Neuroscientist. http://dx.doi.org/10.1177/ 1073858411403316. Andrews-Hanna, J. R., Reidler, J. S., Huang, C., & Buckner, R. L. (2010). Evidence for the default network’s role in spontaneous cognition. Journal of Neurophysiology, 104(1), 322–335. http://dx.doi.org/10.1152/jn.00830.2009. Antrobus, J. S., Singer, J. L., Goldstein, S., & Fortgang, M. (1970). Mindwandering and cognitive structure. Transactions of the New York Academy of Sciences, 32(2 Series II), 242–252. Axmacher, N., Elger, C. E., & Fell, J. (2008). Ripples in the medial temporal lobe are relevant for human memory consolidation. Brain: A Journal of Neurology, 131(Pt 7), 1806–1817. http://dx.doi.org/10.1093/brain/awn103. Baird, B., Smallwood, J., Mrazek, M. D., Kam, J. W. Y., Franklin, M. S., & Schooler, J. W. (2012). Inspired by distraction: mind wandering facilitates creative incubation. Psychological Science, 23(10), 1117–1122. http://dx.doi.org/10.1177/ 0956797612446024. Carr, M. F., Jadhav, S. P., & Frank, L. M. (2011). Hippocampal replay in the awake state: A potential substrate for memory consolidation and retrieval. Nature Neuroscience, 14(2), 147–153. http://dx.doi.org/10.1038/nn.2732. Chen, A. C. N., Feng, W., Zhao, H., Yin, Y., & Wang, P. (2008). EEG default mode network in the human brain: Spectral regional field powers. NeuroImage, 41(2), 561–574. http://dx.doi.org/10.1016/j.neuroimage.2007.12.064. Clemens, Z., Fabó, D., & Halász, P. (2005). Overnight verbal memory retention correlates with the number of sleep spindles. Neuroscience, 132(2), 529–535. http://dx.doi.org/10.1016/j.neuroscience.2005.01.011. Clemens, Z., Fabó, D., & Halász, P. (2006). Twenty-four hours retention of visuospatial memory correlates with the number of parietal sleep spindles. Neuroscience Letters, 403(1–2), 52–56. http://dx.doi.org/10.1016/j. neulet.2006.04.035. Clemens, Z., Mölle, M., Eross, L., Barsi, P., Halász, P., & Born, J. (2007). Temporal coupling of parahippocampal ripples, sleep spindles and slow oscillations in humans. Brain: A Journal of Neurology, 130(Pt 11), 2868–2878. http://dx.doi.org/ 10.1093/brain/awm146. Clemens, Z., Mölle, M., Eross, L., Jakus, R., Rásonyi, G., Halász, P., & Born, J. (2011). Fine-tuned coupling between human parahippocampal ripples and sleep spindles. The European Journal of Neuroscience, 33(3), 511–520. http://dx.doi. org/10.1111/j.1460-9568.2010.07505.x. Cox, R., Hofman, W. F., & Talamini, L. M. (2012). Involvement of spindles in memory consolidation is slow wave sleep-specific. Learning & Memory (Cold Spring Harbor, N.Y.), 19(7), 264–267. http://dx.doi.org/10.1101/lm.026252.112. Craig, M., Dewar, M., Della Sala, S., & Wolbers, T. (2015). Rest boosts the long-term retention of spatial associative and temporal order information. Hippocampus. http://dx.doi.org/10.1002/hipo.22424.

608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658

Please cite this article in press as: Brokaw, K., et al. Resting state EEG correlates of memory consolidation. Neurobiology of Learning and Memory (2016), http://dx.doi.org/10.1016/j.nlm.2016.01.008

588 589 590 591 592 593 594 595 596 597 598

601 602 603

606

YNLME 6377

No. of Pages 9, Model 5G

21 January 2016 8 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744

K. Brokaw et al. / Neurobiology of Learning and Memory xxx (2016) xxx–xxx

Crochet, S., & Petersen, C. C. H. (2006). Correlating whisker behavior with membrane potential in barrel cortex of awake mice. Nature Neuroscience, 9(5), 608–610. http://dx.doi.org/10.1038/nn1690. Davidson, T. J., Kloosterman, F., & Wilson, M. A. (2009). Hippocampal replay of extended experience. Neuron, 63(4), 497–507. http://dx.doi.org/10.1016/j. neuron.2009.07.027. Demanuele, C., Sonuga-Barke, E. J. S., & James, C. J. (2010). Slow neuronal oscillations in the resting brain vs task execution: A BSS investigation of EEG recordings. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1638–1641), doi: http://dx.doi.org/10. 1109/IEMBS.2010.5626645. Deuker, L., Olligs, J., Fell, J., Kranz, T. A., Mormann, F., Montag, C., ... Axmacher, N. (2013). Memory consolidation by replay of stimulus-specific neural activity. The Journal of Neuroscience, 33(49), 19373–19383. http://dx.doi.org/10.1523/ JNEUROSCI.0414-13.2013. Dewar, M., Alber, J., Butler, C., Cowan, N., & Della Sala, S. (2012). Brief wakeful resting boosts new memories over the long term. Psychological Science. http:// dx.doi.org/10.1177/0956797612441220. Dewar, M., Alber, J., Cowan, N., & Della Sala, S. (2014). Boosting long-term memory via wakeful rest: Intentional rehearsal is not necessary, consolidation is sufficient. PLoS ONE, 9(10), e109542. http://dx.doi.org/10.1371/journal. pone.0109542. Dewar, M., Cowan, N., & Della Sala, S. (2007). Forgetting due to retroactive interference: A fusion of early insights into everyday forgetting and recent research on anterograde amnesia. Cortex; A Journal Devoted to the Study of the Nervous System and Behavior, 43(5), 616. Diekelmann, S., Biggel, S., Rasch, B., & Born, J. (2012). Offline consolidation of memory varies with time in slow wave sleep and can be accelerated by cuing memory reactivations. Neurobiology of Learning and Memory, 98(2), 103–111. http://dx.doi.org/10.1016/j.nlm.2012.07.002. Diekelmann, S., & Born, J. (2010). The memory function of sleep. Nature Reviews. Neuroscience, 11(2), 114–126. http://dx.doi.org/10.1038/nrn2762. Elizabeth, A., & McDevitt, K. M. R. (2014). The benefit of offline sleep and wake for novel object recognition. Experimental Brain Research. http://dx.doi.org/10.1007/ s00221-014-3830-3. Ellenbogen, J. M., Hulbert, J. C., Stickgold, R., Dinges, D. F., & Thompson-Schill, S. L. (2006). Interfering with theories of sleep and memory: Sleep, declarative memory, and associative interference. Current Biology, 16(13), 1290–1294. Foster, D. J., & Wilson, M. A. (2006). Reverse replay of behavioural sequences in hippocampal place cells during the awake state. Nature, 440(7084), 680–683. http://dx.doi.org/10.1038/nature04587. Foulkes, D., & Fleisher, S. (1975). Mental activity in relaxed wakefulness. Journal of Abnormal Psychology, 84(1), 66–75. http://dx.doi.org/10.1037/h0076164. Gupta, A. S., van der Meer, M. A. A., Touretzky, D. S., & Redish, A. D. (2010). Hippocampal replay is not a simple function of experience. Neuron, 65(5), 695–705. http://dx.doi.org/10.1016/j.neuron.2010.01.034. Hoddes, E., Zarcone, V., Smythe, H., Phillips, R., & Dement, W. C. (1973). Quantification of sleepiness: A new approach. Psychophysiology, 10(4), 431–436. Holz, J., Piosczyk, H., Feige, B., Spiegelhalder, K., Baglioni, C., Riemann, D., & Nissen, C. (2012). EEG sigma and slow-wave activity during NREM sleep correlate with overnight declarative and procedural memory consolidation. Journal of Sleep Research. http://dx.doi.org/10.1111/j.1365-2869.2012.01017.x. Huber, R., Ghilardi, M. F., Massimini, M., & Tononi, G. (2004). Local sleep and learning. Nature, 430(6995), 78–81. http://dx.doi.org/10.1038/nature02663. Hung, C.-S., Sarasso, S., Ferrarelli, F., Riedner, B., Ghilardi, M. F., Cirelli, C., & Tononi, G. (2013). Local experience-dependent changes in the wake EEG after prolonged wakefulness. Sleep. http://dx.doi.org/10.5665/sleep.2302. Iber, C., Ancoli-Israel, S., Chesson, A., & Quan, S. (2007). The AASM manual for the scoring of sleep and associated events: Rules, terminology, and technical specifications. Westchester, IL: American Academy of Sleep Medicine. Jann, K., Dierks, T., Boesch, C., Kottlow, M., Strik, W., & Koenig, T. (2009). BOLD correlates of EEG alpha phase-locking and the fMRI default mode network. NeuroImage, 45(3), 903–916. Johns, M. W. (1991). A new method for measuring daytime sleepiness: The Epworth sleepiness scale. Sleep, 14(6), 540–545. Karlsson, M. P., & Frank, L. M. (2009). Awake replay of remote experiences in the hippocampus. Nature Neuroscience, 12(7), 913–918. http://dx.doi.org/10.1038/ nn.2344. Klimesch, W. (1997). EEG-alpha rhythms and memory processes. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology, 26(1–3), 319–340. Klimesch, W., Schimke, H., & Schwaiger, J. (1994). Episodic and semantic memory: An analysis in the EEG theta and alpha band. Electroencephalography and Clinical Neurophysiology, 91(6), 428–441. Knyazev, G. G., Slobodskoj-Plusnin, J. Y., Bocharov, A. V., & Pylkova, L. V. (2011). The default mode network and EEG alpha oscillations: An independent component analysis. Brain Research, 1402, 67–79. http://dx.doi.org/10.1016/j. brainres.2011.05.052. Lau, H., Tucker, M. A., & Fishbein, W. (2010). Daytime napping: Effects on human direct associative and relational memory. Neurobiology of Learning and Memory, 93(4), 554–560. http://dx.doi.org/10.1016/j.nlm.2010.02.003. Marrosu, F., Portas, C., Mascia, M. S., Casu, M. A., Fà, M., Giagheddu, M., ... Gessa, G. L. (1995). Microdialysis measurement of cortical and hippocampal acetylcholine release during sleep-wake cycle in freely moving cats. Brain Research, 671(2), 329–332.

Marshall, L., Helgadóttir, H., Mölle, M., & Born, J. (2006). Boosting slow oscillations during sleep potentiates memory. Nature, 444(7119), 610–613. http://dx.doi. org/10.1038/nature05278. Mason, M. F., Norton, M. I., Van Horn, J. D., Wegner, D. M., Grafton, S. T., & Macrae, C. N. (2007). Wandering minds: The default network and stimulus-independent thought. Science, 315(5810), 393–395. http://dx.doi.org/ 10.1126/science.1131295. Mednick, S. C., Cai, D. J., Shuman, T., Anagnostaras, S., & Wixted, J. T. (2011). An opportunistic theory of cellular and systems consolidation. Trends in Neurosciences. http://dx.doi.org/10.1016/j.tins.2011.06.003. Mednick, S. C., Makovski, T., Cai, D. J., & Jiang, Y. V. (2009). Sleep and rest facilitate implicit memory in a visual search task. Vision Research, 49(21), 2557–2565. http://dx.doi.org/10.1016/j.visres.2009.04.011. Mednick, S. C., McDevitt, E. A., Walsh, J. K., Wamsley, E., Paulus, M., Kanady, J. C., & Drummond, S. P. A. (2013). The critical role of sleep spindles in hippocampaldependent memory: A pharmacology study. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 33(10), 4494–4504. http://dx.doi. org/10.1523/JNEUROSCI.3127-12.2013. Mednick, S. C., Nakayama, K., & Stickgold, R. (2003). Sleep-dependent learning: A nap is as good as a night. Nature Neuroscience, 6(7), 697–698. http://dx.doi.org/ 10.1038/nn1078. Mercer, T. (2015). Wakeful rest alleviates interference-based forgetting. Memory, 23 (2), 127–137. http://dx.doi.org/10.1080/09658211.2013.872279. Mölle, M., Eschenko, O., Gais, S., Sara, S. J., & Born, J. (2009). The influence of learning on sleep slow oscillations and associated spindles and ripples in humans and rats. European Journal of Neuroscience, 29(5), 1071–1081. Müller, G., & Pilzecker, A. (1900). Experimentelle Beiträge zur Lehre vom Gedächtniss. Zeitschrift Für Psychologie, Ergänzungsband (1), 1–300. Nishida, M., & Walker, M. P. (2007). Daytime naps, motor memory consolidation and regionally specific sleep spindles. PLoS ONE, 2(4), e341. Payne, J. D., Kensinger, E. A., Wamsley, E. J., Spreng, R. N., Alger, S. E., Gibler, K., ... Stickgold, R. (2015). Napping and the selective consolidation of negative aspects of scenes. Emotion (Washington, D.C.), 15(2), 176–186. http://dx.doi.org/ 10.1037/a0038683. Payne, J. D., Schacter, D. L., Propper, R. E., Huang, L.-W., Wamsley, E. J., Tucker, M. A., ... Stickgold, R. (2009). The role of sleep in false memory formation. Neurobiology of Learning and Memory, 92(3), 327–334. http://dx.doi.org/10.1016/j. nlm.2009.03.007. Payne, J. D., Tucker, M. A., Ellenbogen, J. M., Wamsley, E. J., Walker, M. P., Schacter, D. L., & Stickgold, R. (2012). Memory for semantically related and unrelated declarative information: The benefit of sleep, the cost of wake. PLoS ONE, 7(3), e33079. http://dx.doi.org/10.1371/journal.pone.0033079. Plihal, W., & Born, J. (1997). Effects of early and late nocturnal sleep on declarative and procedural memory. Journal of Cognitive Neuroscience, 9(4), 534–547. Poulet, J. F. A., & Petersen, C. C. H. (2008). Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice. Nature, 454(7206), 881–885. http://dx.doi.org/10.1038/nature07150. Rosanova, M., & Ulrich, D. (2005). Pattern-specific associative long-term potentiation induced by a sleep spindle-related spike train. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 25(41), 9398–9405. http://dx.doi.org/10.1523/JNEUROSCI.2149-05.2005. Schabus, M., Gruber, G., Parapatics, S., Sauter, C., Klosch, G., Anderer, P., ... Zeitlhofer, J. (2004). Sleep spindles and their significance for declarative memory consolidation. Sleep, 27(8), 1479–1485. Stickgold, R. (2005). Sleep-dependent memory consolidation. Nature London, 7063, 1272. Stickgold, R., & Walker, M. P. (2013). Sleep-dependent memory triage: Evolving generalization through selective processing. Nature Neuroscience, 16(2), 139–145. http://dx.doi.org/10.1038/nn.3303. Tambini, A., & Davachi, L. (2013). Persistence of hippocampal multivoxel patterns into postencoding rest is related to memory. Proceedings of the National Academy of Sciences, 110(48), 19591–19596. http://dx.doi.org/10.1073/ pnas.1308499110. Tambini, A., Ketz, N., & Davachi, L. (2010). Enhanced brain correlations during rest are related to memory for recent experiences. Neuron, 65(2), 280–290. http:// dx.doi.org/10.1016/j.neuron.2010.01.001. Tamminen, J., Payne, J. D., Stickgold, R., Wamsley, E. J., & Gaskell, M. G. (2010). Sleep spindle activity is associated with the integration of new memories and existing knowledge. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 30(43), 14356–14360. http://dx.doi.org/10.1523/ JNEUROSCI.3028-10.2010. Tucker, M. A., & Fishbein, W. (2009). The impact of sleep duration and subject intelligence on declarative and motor memory performance: How much is enough? Journal of Sleep Research, 18(3), 304–312. http://dx.doi.org/10.1111/ j.1365-2869.2009.00740.x. Tucker, M. A., Hirota, Y., Wamsley, E. J., Lau, H., Chaklader, A., & Fishbein, W. (2006). A daytime nap containing solely non-REM sleep enhances declarative but not procedural memory. Neurobiology of Learning and Memory, 86(2), 241–247. van Dongen, E. V., Takashima, A., Barth, M., & Fernández, G. (2011). Functional connectivity during light sleep is correlated with memory performance for facelocation associations. NeuroImage, 57(1), 262–270. http://dx.doi.org/10.1016/j. neuroimage.2011.04.019. Vogt, F., Klimesch, W., & Doppelmayr, M. (1998). High-frequency components in the alpha band and memory performance. Journal of Clinical Neurophysiology: Official Publication of the American Electroencephalographic Society, 15(2), 167–172.

Please cite this article in press as: Brokaw, K., et al. Resting state EEG correlates of memory consolidation. Neurobiology of Learning and Memory (2016), http://dx.doi.org/10.1016/j.nlm.2016.01.008

745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830

YNLME 6377

No. of Pages 9, Model 5G

21 January 2016 K. Brokaw et al. / Neurobiology of Learning and Memory xxx (2016) xxx–xxx 831 832 833 834 835 836 837 838

Vyazovskiy, V. V., Olcese, U., Hanlon, E. C., Nir, Y., Cirelli, C., & Tononi, G. (2011). Local sleep in awake rats. Nature, 472(7344), 443–447. http://dx.doi.org/ 10.1038/nature10009. Wagner, U., Gais, S., Haider, H., Verleger, R., & Born, J. (2004). Sleep inspires insight. Nature, 427(6972), 352–355. http://dx.doi.org/10.1038/nature02223. Wamsley, E. J., Tucker, M. A., Payne, J. D., & Stickgold, R. (2010). A brief nap is beneficial for human route-learning: The role of navigation experience and EEG spectral power. Learning & Memory, 17(7), 332.

9

Wechsler, D. (1987). WMS-R: Wechsler memory scale-revised: Manual. Harcourt Brace Jovanovich. Williams, J., Ramaswamy, D., & Oulhaj, A. (2006). 10 Hz flicker improves recognition memory in older people. BMC Neuroscience, 7, 21. http://dx.doi.org/10.1186/ 1471-2202-7-21. Wixted, J. T. (2004). The psychology and neuroscience of forgetting. Annual Review of Psychology, 55(1), 235–269. http://dx.doi.org/10.1146/annurev. psych.55.090902.141555.

839 840 841 842 843 844 845 846 847

Please cite this article in press as: Brokaw, K., et al. Resting state EEG correlates of memory consolidation. Neurobiology of Learning and Memory (2016), http://dx.doi.org/10.1016/j.nlm.2016.01.008