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Behavioural Brain Research 180 (2007) 48–61

Research report

Dissociable learning-dependent changes in REM and non-REMsleep in declarative and procedural memory systems

Stuart M. Fogel a, Carlyle T. Smith b, Kimberly A. Cote a,∗a Brock University, St. Catharines, Ontario, Canadab Trent University, Peterborough, Ontario, Canada

Received 17 August 2006; received in revised form 14 February 2007; accepted 19 February 2007Available online 28 February 2007

bstract

Sleep spindles and rapid eye movements have been found to increase following an intense period of learning on a combination of proceduralemory tasks. It is not clear whether these changes are task specific, or the result of learning in general. The current study investigated changes

n spindles, rapid eye movements, K-complexes and EEG spectral power following learning in good sleepers randomly assigned to one of fourearning conditions: Pursuit Rotor (n = 9), Mirror Tracing (n = 9), Paired Associates (n = 9), and non-learning controls (n = 9). Following Pursuitotor learning, there was an increase in the duration of Stage 2 sleep, spindle density (number of spindles/min), average spindle duration, andn increase in low frequency sigma power (12–14 Hz) at occipital regions during SWS and at frontal regions during Stage 2 sleep in the secondalf of the night. These findings are consistent with previous findings that Pursuit Rotor learning is consolidated during Stage 2 sleep, and providedditional data to suggest that spindles across all non-REM stages may be a mechanism for brain plasticity. Following Paired Associates learning,

heta power increased significantly at central regions during REM sleep. This study provides the first evidence that REM sleep theta activity isnvolved in declarative memory consolidation. Together, these findings support the hypothesis that brain plasticity during sleep does not involveunitary process; that is, different types of learning have unique sleep-related memory consolidation mechanisms that act in dissociable brain

egions at different times throughout the night.2007 Elsevier B.V. All rights reserved.

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eywords: Sleep spindles; Stage 2 sleep; REM sleep; Learning; Memory

. Dissociable learning-dependent changes in REM andon-REM sleep in declarative and procedural memoryystems

There is now strong evidence in both humans and ani-als from behavioural, developmental, neural, and molecular

xperiments to support the hypothesis that sleep states playn important role in the consolidation of memory [For reviewee 41,52,66,75]. Rapid eye movement (REM) sleep has beendentified as being particularly important for memory consol-

dation in both animals [14,28,40,43,44,63,65], and humans42,66,70,74]. More recently, Stage 2 sleep has also beeneported to be important for memory consolidation of certain

∗ Corresponding author at: Brock University Sleep Research Laboratory,sychology Department, Brock University, St. Catharines, Ontario L2S 3A1,anada. Tel.: +1 905 688 5550x4806; fax: +1 905 688 6922.

E-mail address: kcote@brocku.ca (K.A. Cote).

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166-4328/$ – see front matter © 2007 Elsevier B.V. All rights reserved.oi:10.1016/j.bbr.2007.02.037

inds of tasks [21,24,49,60,69]. It has been suggested [66] that inumans, procedural tasks (usually implicit) require REM sleepor efficient memory consolidation. In particular, improvementn tasks such as the Wff’n Proof Task [68,72,64], the Tower ofanoi [12,70], and the Mirror Tracing Task [56,70] require REM

leep for normal improvement. One common characteristic ofhese memory tasks is that they involve learning of a complex orovel rule, or a procedure to improve task performance. Tradi-ionally, memory tasks have been categorized primarily based onhe pattern of impaired performance observed following brainamage in amnesic patients. Gabrielli [23] has categorized bothhe Mirror Tracing Task and the Pursuit Rotor as Sensorimotorasks, and tasks such as the Tower of Hanoi as a cognitive skillsask. Evidence for the distinction between these tasks comesrom learning deficits observed in both brain injured popula-

ions and in dementia observed in neurodegenerative disease. Inhe past, our group [66] has organized tasks according to the typef sleep deprivation which impairs performance, and accordingo the stage of sleep that is affected by learning. Tasks such as

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he Mirror Tracing Task, and the Tower of Hanoi have distinctharacteristics from tasks such as the Pursuit Rotor in that theyeem to require the use of new cognitive strategies before theyan be solved; these have been referred to as “cognitive procedu-al” [66,65]. Recently, researchers have described a relationshipetween Stage 2 sleep and procedural tasks including the Pur-uit Rotor which have been described as simple procedural tasks21]. These tasks have very simple cognitive attributes, primar-ly involve implicit motor skills learning, and do not appear toequire the acquisition of any new cognitive strategy for taskmprovement [67]. Tasks such as the ball-and-cup task [47], theimple tracing task [2], the Pursuit Rotor [69], and the finger-apping task [80] have been reported to be dependent on Stage 2leep for maximum learning efficiency. More recently, we havedentified initial skill level to be an important determinant ofhe nature of the changes observed in sleep after learning [54].n low-skill individuals, learning-dependent changes in REMleep are observed, whereas in high-skill individuals, learning-ependent changes in Stage 2 sleep are observed. Thus, it seemshat when learning novel tasks which are more difficult for thendividual to acquire, the task appears to be REM sleep depen-ent, whereas when only the refinement of existing well-learnedkills are improved upon, the task appears to be Stage 2 sleepependent. Declarative memory (usually explicit) has, to thisoint, been less clearly related to any stage of sleep [3,66],lthough recent work suggests that non-rapid eye movementNREM) sleep state characteristics may be correlated with thecquisition of declarative material [26].

Fogel and Smith [21] found that motor skills learningncreased the duration of Stage 2 sleep and sleep spindle den-ity (spindles/minute), and that the increase in the number ofleep spindles was correlated with task performance improve-ent. A number of questions remained unanswered, including

he topographic distribution of the phenomena, possible qual-tative differences in the spindles compared to baseline andon-learning control values, and whether the increases in spin-les were confined solely to Stage 2 sleep as opposed to slowave sleep (SWS), i.e., Non-REM sleep phenomena in general.iapas and Wilson [61] found that sleep spindles during SWSre temporally correlated with hippocampal ripples. Both sleeppindles and hippocampal ripples have been hypothesized toe mechanisms for memory consolidation, which suggests thatleep spindles during SWS (or NREM sleep in general) may benvolved in the interplay between hippocampal and neocorticaltructures and may be involved in the consolidation of newlyormed memory traces during sleep. A recent series of stud-es [9,10] have demonstrated that the number of automaticallyetected sleep spindles in left fronto-central regions are corre-ated with free recall of verbal material, while sleep spindles inentro-parietal regions are correlated with visual spatial mem-ry performance. The present study was designed to investigatehanges in Stage 2, SWS (Stages 3 and 4), and REM sleep afterew learning. Three different types of memory tasks (Pursuit

otor, Mirror Tracing, and Paired Associates) were chosen to

nvestigate the changes to sleep states.Based on the results of Fogel and Smith [21], it was hypoth-

sized that following learning on the Pursuit Rotor, an increase

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n Research 180 (2007) 48–61 49

n spindles and the duration of Stage 2 sleep would be observedver baseline levels. No change in the number of rapid eye move-ents (REMs) or the duration of REM sleep was expected afterursuit Rotor learning, since this type of learning requires theefinement of motor skills without the acquisition of new cog-itive rules or strategies, and memory performance appears toe uniquely sensitive to Stage 2 sleep interruption [69], andecause there was no change in REM sleep following Pursuitotor learning in a previous study [21]. It has been found that

igma power does not change in response to declarative learningespite changes in sleep spindle activity [26]. Brain activity inhe sigma range includes both sigma which is associated withleep spindles, and sigma that does not originate from sleeppindle activity. If non-spindle sigma is independent of spin-le activity, then only robust changes in sleep spindles woulde expected to affect sigma power over and above non-spindleigma. A recent study [21] has shown that robust changes in sleeppindles occur following Pursuit Rotor learning, which could bearge enough to affect sigma power during Stage 2 sleep. Thus, itas expected that sigma power during Stage 2, and SWS would

ollow the same pattern as visually detected sleep spindles, thats, increase only following Pursuit Rotor learning. Investigat-ng the topographic distribution of sigma power may providedditional information regarding the underlying brain structuresnvolved in the generation of spindle activity, and allow theifferentiation between frontal slow activity and posterior fastctivity. If large enough increases in sleep spindles are observed,hen presumably sigma power should also increase, since sleeppindles oscillate in the 12–16 Hz range. Learning-dependenthanges in Stage 2 sleep would be expected to be largest in theecond half of the night [80]. We also investigated the K-complexs a potential marker of memory consolidation during sleep sincet is another phasic event that is characteristic of non-REM sleep.t has been demonstrated using animal models that the combinedffect of sleep spindles and slow oscillations during NREM sleepeads to the production of K-complexes [73]. However, there isebate whether the K-complex generator is independent of thepindle generators [13] and there is little behavioural evidence touggest that the sleep spindle and the K-complex serve functionselated to synaptic plasticity. We therefore hypothesized that theumber of K-complexes would not increase following new learn-ng, and that spindle-dependent memory consolidation would beissociable from K-complex activity. Next, it was hypothesizedhat following learning on the Mirror Tracing Task there woulde an increase in number and density of rapid eye movementsuring REM sleep. No change was expected in sleep spindles ortage 2 sleep as a result of Mirror Trace learning since this taskequires the acquisition of new cognitive rules or strategies tomprove performance and since this type of memory has beenound to be uniquely sensitive to REM sleep deprivation [2,68].inally, declarative learning has sometimes been associated withWS [25,56,57]. However, during wakefulness, the formationf declarative memory has also been related to theta activity,

hich is thought to play a role in hippocampal communicationith other structures [34], as well as in the induction of long-

erm potentiation (LTP) [37]. Hippocampal theta predominatesuring REM sleep [7]; thus, REM sleep may be an ideal time for

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eclarative memory consolidation to occur. It was hypothesizedhat there would be an increase in theta power during REM sleepollowing Paired Associates learning.

To investigate the questions above, an experimental designas implemented in which good sleepers were randomly

ssigned to one of four learning conditions: (1) Pursuit RotorPR); (2) Mirror Tracing (MT); (3) Paired Associates (PA); and4) non-learning controls (C). Using this approach, the learning-ependent changes in sleep macrostructure (sleep stages) andhe temporal and spatial changes to sleep microstructure (phasicnd tonic EEG activity) could be characterized. These learning-ependent changes to sleep were expected to implicate particulartages of sleep, and phasic activity such as sleep spindles,-complexes, and rapid eye movements as either markers of

ncreased brain plasticity or mechanisms for memory consoli-ation during sleep. Furthermore, the topographic distributionf learning-dependent changes in the sigma and theta bands wasnvestigated to determine whether the consolidation of differ-nt types of learning during sleep might be localized to specificrain regions.

. Method

.1. Participants

An initial telephone interview was used to exclude participants for left-andedness, atypical sleep patterns (sleep time outside the approximate hours of1:00 PM to 7:00 AM), shift work, head injury, cigarette smoking, and chronicain. In addition, participants were excluded for activities that involved theevelopment of simple motor skills (for example; dance lessons or other sportsctivities), and complex motor skills (for example; piano lessons, video games)nd strategy games such as chess. If participants were engaged in this typef activity more than once per week for a period of several hours, they werexcluded from participating in the experiment. If they engaged in this type ofctivity less than once per week for a period of several hours, they were asked toestrict themselves from this activity for the duration of the study. It was expectedhat all participants would be engaged in regular amounts of declarative learningince they were recruited from a university population. However, participantsere screened for above normal levels of declarative learning. If participantsere preparing for exams or writing papers in the week immediately prior tor during overnight testing and recording sessions, they were not scheduled toarticipate at that time.

Seventeen participants were excluded based on the telephone interview cri-eria, and three dropped out after the first screening night. Two participantsere excluded following the clinical screening night due to the appearance oferiodic limb movements associated with regular arousals throughout the night.ne participant was excluded from all analyses because of poor sleep qualityue to alpha intrusion on the EEG throughout both baseline and test nights, andepeated awakenings throughout the night. This data was replaced by pilot datahat was complete with the exception that data was missing on the re-test for theursuit Rotor Task due to a computer malfunction, and because the participantlected not to have IQ measured. The final sample size included 36 participants6 males), aged 18–26 (M = 20.28, S.D. = 5.26) who spent three consecutiveights in the Sleep Research Laboratory at Brock University. While there weregreater proportion of females than males overall, the number of males and

emales were evenly distributed so that each group included 1–2 males.

.2. Screening questionnaires and sleep log

A sleep-wake questionnaire was used to screen candidates for sleep quality,ntake of illicit drugs, alcohol, chronic pain, shift work, family sleep history,nd health. All participants were medication-free. In addition, the Horne andstberg [29] Circadian Rhythm Questionnaire was used to screen participants

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n Research 180 (2007) 48–61

or extreme morningness or eveningness; and the Fatigue Questionnaire [84]as used to screen candidates for excessive daytime fatigue. If participants met

he inclusion criteria, they were given a sleep and activity diary that loggednformation on sleep and wake times, caffeine and alcohol consumption, typend duration of physical exercise, studying, and other leisure activities. Thectivities measured with this instrument were reported daily for 10 days untilhe completion of the last overnight spent in the sleep laboratory.

.3. Learning tasks

.3.1. Mirror Tracing TaskThe Mirror Tracing Task was adapted from Plihal and Born [56]. The task

nvolved tracing around 14 figures with a pen as quickly and accurately asossible (including two star-shaped practice figures, six human-like figures withharp corners, six human-like figures with curved corners). Two concentric linesmm apart outlined the figures. The goal of the task was to trace around thegure, keeping between the lines without touching them. The participant musto this by watching their hand in a mirror. A shade blocked the participant fromirectly tracking their hand movements. The dependent measure for this taskas the number of times the participant touched the outline of the figure. The

otal trial duration was not measured, thus analysis of a speed-accuracy trade offas not possible. However, the primary goal of measuring performance was to

ecord an index of the efficacy of the experimental manipulation. Participantsere instructed to trace around the figure as quickly as possible, without making

dditional errors due to the speed at which they completed the task. Both speednd accuracy increase with practice on the Mirror Tracing Task [33], while botheasures provide valuable information about the nature of improvement on the

ask, they would also be expected to be highly correlated with one another, thusnly one index of performance was used here.

.3.2. Pursuit Rotor TaskThe Pursuit Rotor Task required the participant to follow a rotating target in a

quare track using a (LogitechTM) hand-held computer cordless optical mouse. Aomputerized version of the Pursuit Rotor Task was used as its reliability with theursuit Rotor apparatus had been established [20]. Participants completed fortyets of 30-s trials (for 15 rotations per trial, or 600 rotations in total) with 60-s restntervals between sets of trials to minimize fatigue. The target revolved aroundhe track at 30 revolutions per minute. Time on target was counted when contactas made between the rotating target and the crosshairs of the computer mouse.he number of occurrences off-target could not be measured due to limitations of

he rotor software program. A tone sounded (440 Hz, 60 dB) when the crosshairsere on target which served as positive feedback for task performance. The

rack, target and crosshairs were displayed on a computer screen. The targetas a 1 cm diameter red circle which revolved around a 20 cm diameter square

rack.

.3.3. Paired-Associates TaskA modified version of the Paired-Associates Task [26] was used in this

xperiment, such that words pairs were presented on the screen individually aspposed to in a block of several pairs displayed on the screen at once. Wordsere selected for high concreteness, low emotionality, and word length (3–11

etters). Words that fit these criteria were randomly selected from a revisedersion of the General Service List [4] which contains 2284 words commonlysed in the English language. Words were randomly paired, and subsequentlycreened to ensure that semantically related words were not paired together. Par-icipants learned 168 word pairs presented in semantically unrelated individualairs during acquisition. This procedure was repeated twice, once with wordairs displayed for 13.25 s to allow enough time for initial encoding and againor 8.75 s to allow rehearsal. Word pairs were presented in the same order foroth of these learning trials. These intervals were chosen so that each word pairas presented for the same duration per pair as used in the study by Gais et

l. [26]. Participants were instructed to visually relate the words to one another

ith mental imagery to try to memorize the pairs. Participants were urged tose creative and unusual imagery during memorization. This mnemonic strat-gy was instructed to the participants so that a similar strategy would be usedetween individuals, and also because previous studies (for review see [66])ave not demonstrated a clear relationship between sleep and declarative learn-

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ng using typical instructions which simply instruct the participant to “memorizehe word pairs”. Without specific instruction, the use of multiple strategies may

ask the relationship. Recall was tested immediately after learning of the twolocks of acquisition sessions. During recall testing, the first word in the pairf study words was presented alone in a randomized order. The participant wasnstructed to respond with the other member of the pair from memory by typ-ng their response onto the computer keyboard. Participants were instructed toheck each response (displayed on screen) for typographical errors before pro-eeding to the next test item. Words with multiple spellings were excluded fromhe list of words to avoid spelling errors. Responses were saved to a text fileo that spelling errors could be checked, however, no typographical errors wereetected. Re-testing was the same as the testing procedure (including both theraining session and the testing session), and was repeated one week after initialesting. The number of correctly recalled words was used as a measure of taskerformance.

.3.4. Control TasksThe control group spent the equivalent amount of time as the learning groups

lling out sleep-related questionnaires and forms to collect demographic infor-ation. They also spent their time using a computer for various tasks such as

hecking email, or using a person-to-person chat program. Thus, the controlroup was engaged in equivalent types of motor activity as the Mirror Tracingask (writing), the Pursuit Rotor Task (using a computer mouse) and in reading,ecalling and typing words as in the Paired Associates Task, without a learningomponent.

.4. Polysomnographic recording

Physiological signals were recorded using a 64-channel Mizar SD32+ dig-tal amplifier and Sandman and Spyder software (Tyco Inc.), to measure brainave activity (Electroencephalogram, EEG), horizontal eye movement recorded

rom the left and right lower canthi (electro-oculogram, EOG), and submentaluscle activity (Electromyogram, EMG). EEG was recorded at 256 Hz with

ardware filters set to cut off frequencies below 0.099 and above 115.2 Hz (highrequency cut off = 0.45 × sampling rate). Bipolar EEG was recorded from A1,2, Fp1, Fp2, F3, Fz, F4, C3, Cz, C4, P3, Pz, P4, O1, Oz, and O2 according

o the International 10–20 system for electrode placement [55] and was refer-nced to Fpz with a ground placed at AFz. EEG channels were re-referencedffline to an average of A1 and A2, and a software filter was applied to cut offrequencies below 0.5 Hz and above 35 Hz. A 35 Hz (high frequency cut off)oftware filter was applied to the EOG channels, and a 0.1 Hz (low frequencyut off) filter was applied to the EMG. A Notch (60 Hz) filter was applied to allhannels to eliminate electrical noise. On the screening night, respiratory effortas measured from the chest and abdomen using respiratory effort belts, and legovements were measured using electrodes placed on the left and right anterior

ibialis muscle. Impedances were measured at the start and end of recording,nd were <5 Ohm for all EEG sites, <10 Ohm for EOG, and EMG.

.5. Procedure

All participants spent three consecutive uninterrupted nights in the Sleepesearch Laboratory, including an acclimatization/screening, baseline, and testight. Time in bed was fixed at 11:00 PM to 7:00 AM. The acclimatization nighterved to control for the “first night effect” [82], and as a screening night toxclude participants from further involvement in the study due to any symptomselated to sleep disorders including sleep apnea and periodic leg movementsreduced respiratory effort equivalent to a respiratory distress index above 5vents per hour, or periodic leg movements above 5 per hour). The baseline nightas used to collect baseline EEG for within subject comparisons to the test night.n the test night, at 9:00 PM, participants were randomly assigned to performne of the following memory tasks: the Mirror Tracing Task (MT), Pursuit Rotorask (PR), Paired-Associates Task (PA), or no learning task (C). One week

hereafter, they repeated the practice session at 9:00 PM. The participants andhe experimenter were blind to the experimental condition before the assignmentf participants to the experimental condition on the test night.

Recordings from the baseline and test night were sleep stage scored in 30-spochs according to standard criteria [58] by one independent judge who was

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n Research 180 (2007) 48–61 51

lind to experimental condition and to the night the recording was made. Scor-ng reliabilities had been established previously between the judge and otherndividuals above 90% agreement. Sleep spindles, K-complexes, and rapid eye

ovements were all scored with the experimenter blind to the experimentalondition and to the night the recording was made. Sleep spindles were visuallyounted from Cz with the aid of a filtered channel to display 12–16 Hz activitynly. Each sleep spindle was also measured in duration (seconds). Sleep spin-les were counted and measured in Stages 2, and SWS separately across thentire night. All spindles included in the count exceeded 0.5 s, and had typicalusiform spindle morphology (waxing and waning amplitude). Typically, theaximum amplitude exceeded 10 �V; however, there was no minimum ampli-

ude criteria set in accordance with standard sleep scoring procedures [58].-complexes were visually counted across Fz, Cz and Pz with the aid of an

dditional filtered channel at 12–16 Hz in order to identify K-complexes thatccurred overlapping in time with spindles. K-complexes were counted if theyad a typical morphology including a large negative peak followed by a positiveeflection, exceeded 75 �V, and were maximal in amplitude at frontal sites. K-omplexes were binned into three categories: K-complexes that occurred in theresence of spindles (spindle activity that occurred prior to, overlapping with,r following the K-complex), K-complexes that occurred in the absence of spin-les, and the total number of K-complexes (a combination of the two previousategories).

Rapid eye movements were visually scored from left and right eye channelsdentified according to standard criteria and examples [58]. The only criterion inddition to this was a minimum amplitude criterion of 25 �V [71]. EOG channelsere filtered off-line at 0.5 Hz to eliminate any slow-rolling eye movements, and

o display a flat baseline. Thus, any deflections above the amplitude criterionould be counted as a rapid eye movement. This filtering technique aided in the

dentification of rapid eye movements, and eliminated only very slow rollingye movements. All phasic activity including sleep spindles, K-complexes, andapid eye movements were scored in the absence of movement artifact. Sincen increase in the duration of Stage 2 sleep following procedural learning wasbserved previously [21], the number of spindles and K-complexes per minuten Stages 2, 3 or 4 (i.e., density) were calculated to control for changes in sleeprchitecture. Similarly, the number of rapid eye movements per minute duringEM sleep (REM density) was used.

Power spectral analysis of the EEG was done using Fast Fourier Transfor-ation (FFT) techniques in each sleep stage separately including Stages 2, SWS

Stages 3 and 4) and REM sleep. FFT analysis was conducted on all recordedrtifact-free epochs of the sleep recordings across the entire night of sleep. TheEG was analyzed in 2 s Hanning windows with a 75% overlap, and was re-

eferenced to an average of A1 and A2. Low frequency EEG was filtered at.5 Hz using a software filter, and high frequency EEG cut-offs remained at theardware setting of 115.2 Hz described above. Stage 2 sleep was analyzed inwo separate halves to determine if time of night was an important factor forearning-dependent changes in sleep. The duration of the night from sleep onseto lights on was divided to mark the midpoint of the night for each participant onach night separately. Sleep onset was considered to begin after 5 min of unin-errupted Stage 2 sleep. To follow-up any time of night differences, for Stagesleep, the night was also divided by NREM period in a separate analysis. All

pochs scored as Stage 2 sleep were submitted for FFT analysis in each NREMeriod. Each NREM period was separated by at least 5 min of uninterruptedEM sleep. Only the first four NREM periods were analyzed due to the fact thatot all participants had five or more NREM periods. For all FFT analyses spectralower was binned into eight frequency bins including: delta (0.5–4 Hz), theta4–8 Hz), low frequency alpha (8–10 Hz), high frequency alpha (10–12 Hz), lowrequency sigma (12–14 Hz), high frequency sigma (14–16 Hz), beta (16–35 Hz)nd gamma (35–60 Hz). All FFT data was log transformed prior to statisticalnalysis to normalize the distribution of scores. The sigma band was split into lownd high frequency bins to further explore the topography of the two proposedigma generators [16,32,46].

.6. Data analyses

.6.1. Sleep architectureTo determine if new learning had an effect on sleep architecture, 2 × 4

night × learning group) mixed-design ANOVAs were used to analyze differ-nces in minutes spent in each of Stages 1, 2, SWS, REM and total sleep time

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tSii(gtuBalUgtdtest night. No significant night by group interaction was foundfor the duration of Stage 1, SWS, REM sleep or total sleeptime. Group means for baseline and test nights are presented inTable 2.

Table 1Means (M) and standard deviations (S.D.) for test and re-test on the PursuitRotor Task (PR), Mirror Tracing Task (MT), and Paired-Associates Task (PA)

Test Re-test

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eparately. If a significant group by night interaction was detected, a follow-upimple effects one-way ANOVA on baseline night data only was used to deter-ine if the four groups differed significantly at baseline. If it was found that

roups differed at baseline, a one-way ANCOVA was used to partial out baselineifferences to determine which groups differed on the test night. If a significantain effect for learning group was found using the one-way ANCOVA, inde-

endent t-tests with a Bonferroni correction were used. If groups did not differ ataseline, paired Bonferroni t-tests using the pooled error term from the overallnteraction were used to test changes from baseline to test night in each group30]. Due to the highly variable duration of Stages 3 and 4 sleep across individ-als, Stages 3 and 4 sleep were collapsed into a single category, slow wave sleepSWS). This was done for sleep scoring, spindle counts, K-complexes, rapid eyeovements and FFT analyses.

.6.2. Sleep spindles, K-complexes and rapid eye movementsThe same analytic strategy was used for sleep spindles, K-complexes and

apid eye movements as described above for the sleep architecture data. Usinghis strategy, the number of sleep spindles per minute (spindle density), averagepindle duration, and the number of K-complexes per minute were analyzed fortage 2 and SWS separately. The assumption of normality for the PA and thegroups was questionable for REM density on the test night. The number of

apid eye movements per minute in REM sleep (REM density) was analyzedsing the Sign test, which tests whether the probability of observing the numberf differences in a score is beyond chance.

.6.3. Spectral analysis of sleep EEG using FFT techniquesIn addition to the analytic strategy outlined above for the analysis of the

leep architecture data, a “top-down” approach was used to analyze the FFT datahat involved several steps. This was done to analyze the data in a systematicypothesis-driven fashion that controlled inflation of experimentwise Type-Irror rates and is similar in concept to the protected tests strategy recommendedy Howell [30]. First, each frequency band (delta, theta, low frequency alpha,igh frequency alpha, low frequency sigma, high frequency sigma, beta andamma) in each stage of sleep (2, SWS, REM) was analyzed separately at midlineites (Fz, Cz, Pz, Oz) to determine if there were any changes from baseline to testight as a function of learning condition, and at which site along the midline theffect was significant. Follow-up analyses were conducted only at the midlineite where the effect was found to be significant. To further investigate timef night differences in Stage 2 sleep sigma power, total sigma power for therst and second halves of the night were analyzed separately at the site where

he effect was significant along the midline. If changes in the total sigma bandaried as a function of learning condition in a particular part of the night, thenow and high frequency sigma were analyzed separately to determine if low origh frequency sigma power was of particular importance to sleep-dependentemory consolidation at the site where the effect was significant. Low and

igh frequency sigma bands were analyzed separately in order to differentiateetween the anterior and posterior generators. The same analytic strategy wassed to investigate learning-related changes in spectral power during SWS andEM sleep with the exception that they were not categorized into the first and

econd halves of the night due to the fact that SWS dominates the early portionf the sleep period while REM dominates the latter.

There was no EEG data from the Pz site for two participants on the baselineight due to an irresolvable signal problem with that channel. For these data,he power from the four nearest neighbors (Cz, P3, P4, Oz) was averaged andncluded in the data set. Two participants (one in the MT group and one in theA group) were excluded from all FFT data analyses due to poor impedances>100 K Ohm) on the recording reference electrodes (Fpz). In addition, two morearticipants were excluded based on unusually high power across all bands whichccurred only after re-referencing the active sites to an average of A1 and A2.fter the removal of these participants (for EEG spectral analysis only), the

emaining samples were eight in the PR group, seven in the MT group, nine inhe PA group, and eight in the C group.

.6.4. Topography of the learning-dependent changes in spectral powerStatistically significant findings from the spectral analysis of sleep using FFT

escribed in the previous section were investigated further to determine howhe learning-dependent changes in spectral power during sleep were distributed

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n Research 180 (2007) 48–61

cross the scalp. The previous analyses were used to establish where the effectsere maximal along the midline of the scalp. For the following analyses, a less

onservative approach was taken in order to gain a more descriptive picture ofhe topography of these effects at Fp1, Fp2, F3, Fz, F4, C3, Cz, C4, P3, Pz,4, O1, Oz, and O2. Each significant finding reported was followed up usingaired t-tests. To this end, in the groups that had a change in spectral power fromaseline to test night, both statistically significant results (p < .05) and trendsp < .10) were reported. Brain maps were computed for topographic display ofhe frequencies of interest using SpyderTM software (Tyco Inc.).

. Results

.1. Learning data

Paired t-tests were used to assess pre-post changes in perfor-ance for each memory group. It was found that all groups

mproved on memory task performance from test to re-testTable 1). Following the retention interval, the MT group madeewer errors on the Mirror Tracing Task (t(8) = 10.00 p < .001);he PA group recalled more word pairs (t(8) = 4.19, p = .003);nd the PR group had a higher percent time-on-target on theursuit Rotor task (t(7) = 4.62, p = .002).

.2. Sleep architecture data

To determine if sleep architecture was affected by learning,he duration of time spent in each stage of sleep (Stages 1, 2,WS, and REM) prior to and following learning was compared

n the four learning conditions. As predicted, there was a signif-cant night by group interaction for the duration of Stage 2 sleepF(3, 32) = 4.34, p = .01). A one-way ANOVA revealed that theroups had a significantly different duration of Stage 2 sleep onhe baseline night (F(3, 32) = 3.09, p = .04). However, follow-p tests did not reveal any significant group differences usingonferroni paired comparisons. A one-way ANCOVA revealedsignificant effect of learning on the test night after base-

ine differences have been removed (F(3, 31) = 5.93, p = .003).sing independent Bonferroni t-tests, it was found that the PRroup had more Stage 2 sleep on the test night than the con-rol group (t(8) = 3.83, p = .004). The MT and the PA groupid not differ from controls in the duration of Stage 2 sleep on

irror Tracing 116.56 38.71 45.00 24.37aired-Associatesc 116.44 35.40 149.67 13.42

a Time on target.b Number of errors.c Number of correctly recalled word pairs.

S.M. Fogel et al. / Behavioural Brai

Table 2Means (M) and Standard Deviations (S.D.) for minutes of sleep Stages 1, 2,SWS, REM and total sleep time (TST) for the Pursuit Rotor learning (PR)group, Mirror Trace learning (MT), Paired Associates learning (PA) and (C)control group on the baseline night and test night

Baseline night Test night

M S.D. M S.D.

PRStage 1 6.3 5.2 5.8 3.7Stage 2 250.8 18.6 268.5* 25.3SWS 74.2 18.1 62.4 15.0REM 111.5 27.2 111.5 16.0TST 436.6 13.3 442.4 9.3

MTStage 1 13.0 14.5 12.6 17.3Stage 2 251.7 25.7 238.1 33.2SWS 76.2 17.9 74.7 22.1REM 95.7 21.7 100.4 24.7TST 423.5 35.0 413.2 35.4

PAStage 1 12.2 18.3 10.1 5.7Stage 2 220.9 33.2 203.0 35.1SWS 89.6 29.0 87.8 27.9REM 106.3 31.1 118.0 21.8TST 416.8 22.6 408.8 14.7

ControlStage 1 9.4 7.3 20.1 12.4Stage 2 233.0 21.8 209.6 24.6SWS 78.6 23.7 79.3 16.9REM 102.7 22.9 95.1 18.5TST 414.3 15.9 384.0 34.1

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.3. Sleep spindles

.3.1. Stage 2 spindle densityAs predicted, a 2 × 4 (night × learning group) ANOVA

evealed a significant interaction between night and learning

roup (F(3, 32) = 3.02, p = .04) for sleep spindle density dur-ng Stage 2 sleep (Fig. 1A). A one-way ANOVA revealed thathe groups did not differ on the baseline night (F(3, 32) = 1.49,= .24). Bonferroni t-tests revealed that the PR group had

Md(i

ig. 1. Changes in sleep spindles during Stage 2 and SWS following Pursuit Rotor lesleep from baseline (night 1) to test night (night 2), p < .05. (B) The average duration), p < .01. (C) The number of sleep spindle per minute (spindle density) in SWS fromaired Associates (PA); (�) Mirror Tracing (MT); (�) control (C).

n Research 180 (2007) 48–61 53

n average increase of 0.89 spindles per minute from base-ine (M = 6.36, S.D. = 2.08) to test night (M = 7.25, S.D. = 2.09)t(32) = 3.14, p < .05). Spindle density did not change from base-ine to test night for the PA, MT or C groups.

.3.2. Stage 2 spindle durationA similar pattern of results was found for Stage 2 sleep spindle

uration. A 2 × 4 (night × learning group) ANOVA revealed aignificant night by group interaction (F(3, 32) = 5.65, p = .003)or Stage 2 sleep spindle duration (Fig. 1B). The groups didot differ at baseline (F(3, 32) = 1.35, p = .28). Bonferroni t-testsevealed that spindle duration increased from baseline (M = 1.42,.D. = 0.18) to test night (M = 1.68, S.D. = 0.12) following PR

earning (t(32) = 4.21, p < .01). Spindle duration in the MT, PAnd C groups did not change from baseline to test night.

.3.3. SWS spindle densityThere was no significant interaction between night and learn-

ng condition for SWS spindle density. However, this analysisevealed a similar pattern of results as spindle density in Stage

sleep, and the interaction did approach significance (F(3,2) = 2.52, p = .075). Thus, follow-up analyses were conductedn the hypothesized differences. The groups did not differ signif-cantly at baseline (F(3, 32) = 0.528, p = .67). Bonferroni t-testsevealed that as with the Stage 2 sleep data, there was a signifi-ant increase from baseline (M = 7.68, S.D. = 3.00) to test nightM = 8.75, S.D. = 2.40) in sleep spindle density following PRearning during SWS (t(32) = 3.01, p < .05) but not in the MT,A or C groups (Table 3 and Fig. 1C).

.3.4. SWS spindle durationA similar pattern of results was found for spindle duration in

WS. A 2 × 4 (night × learning group) ANOVA revealed a sig-ificant interaction between night and learning group for spindleuration (F(3, 32) = 3.86, p = .018) and the four groups did notiffer at baseline (F(3, 32) = 0.42, p = .74). Post-hoc t-tests didot detect any significant change in spindle duration in the PR,

T, PA or C groups, however, it is worth noting that spindle

uration did change in the hypothesized direction from baselineM = 1.02, S.D. = 0.20) to the test night (M = 1.11, S.D. = 0.27)n the PR group.

arning. (A) The number of sleep spindles per minute (spindle density) in Stageof the sleep spindle in Stage 2 sleep from baseline (night 1) to test night (nightbaseline (night 1) to test night (night 2), p < .05. (©) Pursuit Rotor (PR); (�)

54 S.M. Fogel et al. / Behavioural Brain Research 180 (2007) 48–61

Table 3The number of sleep spindles per minute (spindle density) in SWS, number oftotal K-complexes, K-complexes in the absence of spindles, and K-complexesin the presence of spindles, are displayed separately per minute in Stage 2 sleep.Means (M) and Standard Deviations (S.D.) are displayed for the Pursuit Rotor(PR), Mirror Tracing (MT), Paired Associates (PA), and control (C) groups onthe baseline and test nights

Baseline night Test night

M S.D. M S.D.

SWS spindle densityPR 7.68 3.00 8.75* 2.40MT 6.79 1.84 6.50 1.22PA 7.26 2.23 7.45 2.54C 6.38 2.14 6.58 2.15

Total # of K-complexesPR 2.51 1.01 2.66 1.37MT 2.40 0.73 2.28 0.90PA 2.58 0.80 2.77 0.83C 2.73 0.93 2.74 0.70

K-complexes in the absence of spindlesPR 0.86 0.49 0.90 0.59MT 0.89 0.41 0.84 0.34PA 1.03 0.31 1.15 0.39C 1.08 0.47 0.97 0.45

K-complexes in the presence of spindlesPR 1.65 0.64 1.77 1.00MT 1.50 0.47 1.44 0.60PA 1.55 0.59 1.61 0.66

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.4. K-Complexes

Night by Group (2 × 4) ANOVAs revealed that there waso change from baseline to test night as a function of learningondition in the total number of K-complexes (F(3, 32) = 0.35,= .79), K-complexes that co-occurred with sleep spindles (F(3,2) = 0.17, p = .92), or K-complexes that did not co-occur withleep spindles (F(3, 32) = 0.81, p = .50) per minute of Stage 2leep. The results from K-complex analyses are displayed inable 3.

.5. Rapid eye movements

It was observed in these data, following learning on the Mir-or Tracing Task, that 8 of the 9 participants had an increase inEM density from baseline to test night as predicted, whereas

n the C, PA and PR groups there was no consistent change inEM density. The Sign test is used to test the null hypothe-

is that the number of instances from one set of observationso another remains unchanged. It is particularly useful whenhe assumption of normality is questionable. A Sign test washerefore used to determine if the number of positive increasesn REM density following Mirror Trace learning was beyond

hance. This analysis revealed that the number of subjects whoad an increase in REM density was significantly beyond chanceollowing learning on the Mirror Tracing Task from baseline toest night (p = .04). The number of subjects who had a change in

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aseline to the test night in each experimental condition. A significant numberf participants had increases in REM density following Mirror Trace learning,ndicated by *p < .05.

EM density in the control (p = 1.0), PA (p = 1.0) or PR (p = .51)roups was not beyond chance (Fig. 2).

.6. Spectral analysis of sleep using FFT

.6.1. Stage 2 sigma powerAt all midline sites, 2 × 4 (night × group) ANOVAs were

sed to test if total sigma power (12–16 Hz) for the entire nighthanged from baseline to test night as a function of learningondition during Stage 2 sleep. It was found that night andearning condition interacted significantly along the midline atz only (F(3, 28) = 4.03, p = .017) and did not differ at base-

ine (F(3, 28) = 1.07, p = .19). There were no significant nighty learning group interactions for any frequencies outside of theigma band at Oz. Follow-up analyses revealed that there wassignificant night by learning condition interaction in low fre-uency sigma (12–14 Hz) power at Oz in the second half of theight (F(3, 28) = 3.48, p = .03) and did not differ at baseline (F(3,8) = 2.82, p = .06). Bonferroni t-tests revealed that there was aignificant decrease in sigma following Paired Associates learn-ng (t(28) = −3.06, p < .05). Contrary to the predictions, thereas no increase from baseline to test night in the PR group dur-

ng Stage 2 sleep, however, it is worth noting that sigma powerid change in the hypothesized direction, although this effectas not statistically robust (t(28) = 1.54, p < .10). A summary of

hese results is displayed in Fig. 3A. The results from the follow-p analysis of Stage 2 sleep broken down into NREM periodsid not yield any additional findings.

.6.2. SWS sigma powerTotal sigma power (12–16 Hz) during SWS was analyzed

sing a 2 × 4 (night × learning group) ANOVA to determine theffect of learning during SWS at all midline sites. It was foundhat total sigma during SWS changed as a function of learning

ondition from baseline to test night at Oz only (F(3, 28) = 6.28,= .002) and was not significantly different at baseline (F(3,8) = 0.94, p = .44). There were no significant night by learningroup interactions for any frequencies outside of the sigma band

S.M. Fogel et al. / Behavioural Brai

Fig. 3. Learning-dependent changes in spectral power (log 10 �v2) displayed asmean differences so that positive scores reflect increases in the power spectra,whereas negative scores reflect decreases from baseline to test night. (A: lateStage 2 sleep) Following Paired Associates learning, a decrease in low frequencysigma power (12–16 Hz) from the baseline to the test night was observed at Ozduring Stage 2 sleep in the second half of the night, (*p < .05) and a statisticaltrend for increased low sigma power following Pursuit Rotor learning, (+p < .10).(B: Slow wave sleep) During SWS at Oz, a similar decrease in low sigma powerwas observed following Paired Associates learning where a statistical trend wasobserved, (+p < .10), and a significant increase following Pursuit Rotor learn-ing, (**p < .01). There was also a smaller, yet statistically significant decreasein the control group, (*p < .05). (C: REM sleep) Following Paired Associateslearning, an increase in theta power during REM sleep was observed at Cz,(**p < .01), whereas an increase in low frequency sigma power was observeda(

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t Cz, (**p < .01) during REM. Group legend: Control (C), Paired AssociatesPA), Pursuit Rotor (PR), Mirror Tracing (MT).

t Oz. Using a 2 × 4 (night × learning group) ANOVA to ana-

yze low frequency sigma power (12–14 Hz) during SWS it wasound that there was a significant night by learning conditionnteraction (F(3, 28) = 8.16, p = .00046). A one-way ANOVA

iih

n Research 180 (2007) 48–61 55

evealed that the four groups did not differ on the baseline nightn low frequency sigma power (F(3, 28) = 1.33, p = .28). Pairwiseonferroni t-tests revealed that low frequency sigma power dur-

ng SWS increased in the PR group (t(28) = 3.37, p < .01), andecreased in the control group (t(28) = −2.27, p < .05). Low fre-uency sigma power during SWS did not change from baselineo the test night in the MT group, although the decrease in sigmaower did approach significance in the PA group (t(28) = −2.66,< .10). A summary of these results is displayed in Fig. 3B.here was no significant change in the high frequency sigmaand during SWS.

.6.3. REM sleep theta powerA 2 × 4 (night × learning group) ANOVA was used at each

idline site to determine if theta changed during REM sleep asfunction of learning condition from baseline to test night. Itas found that there was a significant night by learning group

nteraction at Cz (F(3, 28) = 3.27, p = .036), but not at any otheridline sites and that the groups did not differ on the baseline

ight (F(3, 28) = .98, p = .42). As predicted, paired Bonferroni t-ests revealed that there was a significant increase in theta poweruring REM sleep from the baseline to test night for the PA groupt(28) = 4.52, p < .01), but no change in theta power for the PR,

T, or C groups (Fig. 3C).

.6.4. REM sleep sigma powerAdditional 2 × 4 (night × learning group) ANOVAs were

erformed at Cz for the delta, alpha, sigma, beta and gammaands to determine if the changes in power during REM sleepere isolated to the theta band. Interestingly, there was a signifi-

ant night by learning condition interaction in the low frequencyigma band (12–16 Hz) at Cz (F(3, 28) = 4.19, p = .014), butot in any other frequency bands. A follow-up simple effectsNOVA for sigma at Cz during REM sleep revealed that

he groups were not statistically different at baseline (F(3,8) = 0.92, p = .45), and paired Bonferroni t-tests revealed thathe PA group had an increase in the low frequency sigma banduring REM sleep at Cz from baseline to test night (t(28) = 4.17,< .01). There was no change from baseline to test night in theR, MT and C groups for sigma power during REM sleep at Cz.here was no significant change in high frequency sigma duringEM sleep. A summary of these results is displayed in Fig. 3C.

.7. Topography of the learning-dependent changes inpectral power

While the decrease in the sigma band during Stage 2 sleepor the PA group was not as hypothesized, in light of the find-ng that both theta and sigma power were affected by Pairedssociates learning during REM sleep, the topography of this

ffect was investigated further to provide a more complete pic-ure of the effect of Paired Associates learning on sleep. Toummarize from the previous section, it was found that the PA

ng the second half of the night during Stage 2 sleep, but notn high frequency sigma power, or sigma power during the firstalf of the night. There was no change in the sigma band dur-

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6 S.M. Fogel et al. / Behavioura

ng Stage 2 sleep for the PR, MT, or C groups. Paired t-testst all active sites for the PA group (Fp1, Fp2, F3, Fz, F4, C3,z, C4, P3, Pz, P4, O1, Oz, and O2) revealed that there wassignificant decrease in low frequency sigma power or trend

owards significance in Stage 2 sleep for the last half of theight at Cz (t(8) = 2.09, p = .07), Pz (t(8) = 1.94, p = .089), andz (t(8) = 6.59, p = .0002) where the effect was most robust.

t is also worth noting that low frequency sigma power wasaximal at Pz on baseline (M = 0.787, S.D. = 0.206) and test

ight (M = 0.741, S.D. = 0.219), however, the largest decrease inow frequency sigma power was statistically most robust at OzFig. 4A). Given the change in sleep spindles during stage 2 sleepnd the hypothesized, but non-significant increase in late Stagelow frequency sigma power, we investigated the topographic

ature of sigma power during Stage 2 sleep following Pursuitotor learning. It was found that there was a significant increase

rom baseline to test night in low frequency sigma power in the

R group during late Stage 2 sleep at F4 (t(7) = −2.53, p = .039)Fig. 4A). The distribution of low frequency sigma power wasaximal at Cz on the baseline (M = 0.655, S.D. = 0.289) and test

ight (M = 0.657, S.D. = 0.274).

ffw

ig. 4. Learning-dependent changes from baseline (top maps) to test night (bottomuring late stage 2 sleep in the second half of the night following Paired Associatesentral, and parietal regions. Sigma power was maximal at parietal regions on bothow frequency sigma (12–14 Hz) power during late stage 2 sleep (2nd half of the nigarmer colours at frontal regions. Sigma power was maximal at the vertex, howeve

ow frequency sigma (12–14 Hz) power during SWS following Pursuit Rotor (PR) lrontal, central, parietal and occipital regions. Sigma power was maximal at the vertC) Increase in theta (4–8 Hz) power during REM sleep following Paired Associatesrontal, central, parietal and occipital regions. Theta power was maximal at the verterequency sigma (12–14 Hz) power during REM sleep following Paired Associates (Pccipital regions. Sigma power was maximal at occipital regions, however, the largey open circles, and orientation of maps indicated by A: anterior, and R: right side.

n Research 180 (2007) 48–61

To summarize from the previous section, it was found thaturing SWS, low frequency sigma power increased at Oz inhe PR group, but not in any other groups. For the followingests, the data was excluded from one participant, due to pooruality recordings from O2 (impedances > 100 K Ohm). Mul-iple paired t-tests at all active sites revealed that there was aignificant increase from baseline to test night in low frequencyigma power or trend towards significance in the PR group dur-ng SWS sleep at Fp1 (t(7) = −2.82, p = .026), Fp2 (t(7) = −2.49,= .042), F4 (t(7) = −2.58, p = .036), Cz (t(7) = −3.41, p = .011),z (t(7) = −1.96, p = .091), and Oz (t(7) = −3.11, p = .017). Theistribution of low frequency sigma power was maximal at Czn baseline (M = 0.566, S.D. = 0.289) and test night (M = 0.588,.D. = 0.299); and the increase in low frequency sigma poweras most statistically robust at Cz. Interestingly, it is also worthoting that there was a robust increase in low frequency sigmaower at Oz (Fig. 4B).

From the analyses reported in the previous section it wasound that there was an increase in theta at Cz during REM sleepor the PA group only. Multiple paired t-tests revealed that thereas a significant increase or trend towards significance in theta

maps) in sleep EEG. (A) Decrease in low frequency sigma (12–14 Hz) powerlearning (PA; left) as indicated by a decrease in warmer colours at occipital,nights, however, the decrease was maximal at occipital regions. Increase in

ht) following Pursuit Rotor learning (PR; right) as indicated by an increase inr, the largest increase was maximal over right frontal regions. (B) Increase inearning as indicated by an increase in warm colours widely distributed acrossex, however, the largest increase was maximal at central and occipital regions.(PA) learning as indicated by an increase in warmer colours distributed acrossx, and the largest increase was maximal at central regions. (D) Increase in lowA) learning as indicated by an increase in warmer colours at frontal, central andst increase was maximal at central regions. Electrode scalp locations indicated

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rom baseline to test night during REM sleep at Fz (t(8) = −2.29,= .051), F4 (t(8) = −4.97, p = .001), C3 (t(8) = −2.33, p = .048),z (t(8) = −5.32, p = .001), C4 (t(8) = −4.28, p = .003), Pz

t(8) = −3.56, p = .007), and O1 (t(8) = −4.22, p = .003). Thetaas maximal at Cz on baseline (M = 1.010, S.D. = 0.131) and

est night (M = 1.064, S.D. = 0.116); the increase from base-ine to test night in the PA group was most statistically robustt Cz (t(8) = −5.315, p = .001) (Fig. 4C). Changes in spec-ral power during REM sleep following Paired Associatesearning were not limited to the theta band. Multiple paired-tests revealed that there was a significant increase or trendowards significance in sigma from baseline to test night dur-ng REM sleep at F3 (t(8) = −2.21, p = .058), Fz (t(8) = −2.26,= .033), F4 (t(8) = −2.51, p = .037), C3 (t(8) = −2.93, p = .019),z (t(8) = −3.42, p = .009), C4 (t(8) = −2.05, p = .074) and O1

t(8) = −2.07, p = .072). Low frequency sigma was maximalt O1 on baseline (M = 0.056, S.D. = 0.197) and the test nightM = 0.111, S.D. = 0.202); however, the increase in power wasost statistically robust at Cz (Fig. 4D).

. Discussion

In the on-going debate among scientists over whether orot memory consolidation is one of the functions of sleep42,62,76,77,78,79], recent attempts have been made to identifyhich sleep stages are important for particular types of memory

onsolidation [for review see: 66]. An important issue that haseen debated in the sleep and memory literature centers aroundhat type of learning is sleep dependent, and what specific brain

ctivity during sleep is important for memory consolidation.his has been done by Smith’s group using selective sleep depri-ation techniques [2,36,68,69] and sleep recording techniques5,6,21,26]. Generally, it has been found that simple motor pro-edural learning is Stage 2 sleep dependent [2,21,68,69], whileearning involving procedural rules and strategies such as the

irror Tracing Task is REM dependent [2,5,6,36,71]. Relation-hips have been found using declarative learning tasks, however,he findings have been less consistent with respect to a partic-lar sleep stage [3,19,22,26,56,66,83]. It appears that memoryonsolidation during sleep is not a uniform process. Rather, dif-erent types of procedural learning produce dissociable changesn brain activity during sleep. This suggests that there are differ-nt subtypes of procedural tasks across a continuum of cognitiveomplexity, or according to the level of familiarity with the taskemands. A recent formulation of this hypothesis [67] and sub-equent preliminary findings [54] suggest that it is not the typef learning that affects sleep; rather, it is the individual’s initialkill level that determines the nature of the learning-dependenthanges to sleep. In low-skill individuals, changes to REM sleepre observed, whereas in high-skill individuals, changes to Stagesleep are observed. These findings indicate that REM sleep is

nvolved in the consolidation of newly learned skills, whereastage 2 sleep is involved in the refinement of existing skills.

n experimental design using Pursuit Rotor, Mirror Trace, andaired Associates learning tasks was used in the current study to

nvestigate the learning-dependent changes to subsequent sleep.he findings from the present study have demonstrated that there

tmia

n Research 180 (2007) 48–61 57

re dissociable learning-dependent changes in REM and non-EM sleep in declarative and procedural memory systems. Asypothesized, the duration of Stage 2 sleep and sleep spindlectivity (in both Stage 2 sleep and SWS) increased followingursuit Rotor learning, REMs increased following Mirror Trace

earning, and theta power increased following Paired Associatesearning. There was also an unexpected increase in sigma powerollowing Paired Associates learning during REM sleep, and aecrease in sigma power during Stage 2 sleep in the second halff the night.

.1. Pursuit Rotor learning-dependent changes in sleep

Pursuit Rotor learning on the Pursuit Rotor Task affectedleep in a number of ways. As hypothesized, it was found that theuration of Stage 2 sleep increased from baseline to test night fol-owing Pursuit Rotor learning, but not following Mirror Tracingr Paired Associates learning. It was also found that the numberf sleep spindles per minute increased during Stage 2 and SWS,nd that the average duration of sleep spindles increased duringtage 2 sleep following Pursuit Rotor learning. Together (Stageduration, spindle density and spindle duration) on average,

his amounts to an additional 13.7 min of spindle activity or anverage increase of 35% more spindle time following Pursuitotor learning. These findings suggest that simple proceduralemory consolidation may require Stage 2 sleep, with a higher

ensity of spindles that are longer in duration. In addition, SWSay be necessary where the changes in spindle generation per-

ist. The overall learning group by night interaction effect forncreased spindle density during SWS was less robust than thaturing Stage 2 sleep. Future research could further investigatehe function of the sleep spindle during SWS, to identify whetherhe sleep spindle has a uniform function across NREM sleeptages. Consistent with the change in spindles, power spectralnalyses revealed changes in sigma EEG power in both Stage 2nd SWS. This indicates that it is not only Stage 2 sleep per se,ut the state of NREM sleep in general that may be important forrocedural memory consolidation. The changes in sigma poweruring Stage 2 sleep were observed during the second half of theight. It is unclear whether the changes in sigma power duringWS were isolated to the first or second half of the night. Timef night differences in SWS sigma power could be investigatedn future research. The changes to the duration of Stage 2 sleepnd spindles may be required to efficiently encode new learn-ng into a more permanent form. These findings provide strongvidence that sleep spindles may be a mechanism for synapticlasticity of simple motor procedural memory traces in the neo-ortex. The finding that low frequency sigma power (12–14 Hz),ut not high frequency sigma power (14–16 Hz), increased fol-owing Pursuit Rotor learning may indicate that low frequencyigma is particularly well suited for synaptic plasticity, andmportantly, that there may be a functional dissociation betweenow and high frequency spindles. Alternatively, visual identifica-

ion of sleep spindles (which includes characteristics like shape)

ay be a more reliable way to detect spindles because spectran the 12–16 Hz range can include activity other than spindlectivity.

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8 S.M. Fogel et al. / Behavioura

Analysis of the topographic changes following learningevealed a number of interesting findings, and supported a num-er of hypotheses. Previous research has shown that fast spindlesre localized to posterior regions whereas slow spindles areocalized to anterior regions [16,32]. It has been suggested thatlow and fast spindles have separate generators [46], and further,hat these frequencies may be functionally dissociable. Consis-ent with the previous literature, sigma power was maximal atz, which is typical for slower spindle frequencies [32]. Low

requency sigma activity was maximal at Cz, and the largestncrease in low frequency sigma power from baseline to testight was at Cz. In addition, there was an increase in lowrequency sigma activity over occipital regions. One intrigu-ng hypothesis is that this learning-dependent change in sigmaeflects reactivation of the brain areas involved in the acqui-ition of motor tasks involving eye-hand coordination. Usingeuroimaging techniques, it has been found elsewhere that therain regions active during learning are reactivated during sleep53]. Thus, the increase in sigma power at centro-parietal andccipital regions following Pursuit Rotor learning suggests thathese areas are not only reactivated during post-learning sleep,ut that sleep spindles may be a mechanism for that reactivationnd be involved in synaptic plasticity.

Since Stage 2 sleep in general has been implicated in the con-olidation of simple motor procedural learning, it was importanto investigate other phasic activity that characterizes Stage 2leep such as K-complexes. K-complexes may occur sponta-eously in sleep or they may be reliably evoked by externaltimuli [15]. While disagreement remains as to whether the-complex is sleep protective, or an arousal [1,11,81], its sensi-

ivity to stimulus salience (e.g., the sleeper’s own name) suggestshat it plays a role in information processing in some way. Inhe current study, there was no change in K-complex densityhether K-complexes occurred in the presence of sleep spindles,

n the absence of sleep spindles, or both combined. It may behat K-complexes and sleep spindles are functionally unrelatedhasic events in Stage 2 sleep, even when they occur simultane-usly. The possibility remains that K-complexes are related toome aspects of memory consolidation during sleep for types ofemory that were not tested in this experiment. Alternatively, K-

omplexes may not be related to consolidation of new memories,ut rather they may be involved in memory retrieval processese.g., comparing a stimulus with information stored in memory,n order to determine whether to remain asleep or wake up toake action).

The increase in sigma power during Stage 2 sleep followingursuit Rotor learning was not as strong as would be expectediven the robust changes observed in sleep spindles. FFT proce-ures may have not been sensitive to detecting changes in sleeppindles since sleep spindles are not the only source of 12–16 Hzctivity in Stage 2 sleep. It is possible that other frequencies notunctionally associated with the spindle, such as high frequencylpha may have contaminated the user-selected 12–16 Hz fre-

uency band. Alternatively, if the association with learning andpindles was more robust at a particular time of night, the rela-ionship may have been blurred by collapsing NREM periodsnto gross comparisons of early and late halves of the night.

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n Research 180 (2007) 48–61

n attempt was made to further investigate changes in spectralower during Stage 2 sleep by dividing the night into NREMeriods. This method of analysis did not improve the associa-ion between spindle counts and FTT measures; however, oneroblem with looking at the data in this way is that participantsid not have the same duration of NREM within the periods,nd thus any changes in spectral power isolated to the end ofhe night could not be tested. This period has been suggestedo be of particular importance for the consolidation of proce-ural memory [80]. Nonetheless, you can expect some level ofisagreement between visual spindle counts, and sigma powerue to tonic 12–16 Hz EEG that is perhaps unrelated to spindlectivity or unaffected by learning.

.2. Paired Associates learning-dependent changes in sleep

Paired Associates learning also had a number of dissocia-le effects on post-learning sleep. As predicted, there was anncrease in theta power following Paired Associates learning,ut not after Pursuit Rotor or Mirror Trace learning duringEM sleep. While it is known that theta is typically higheruring REM sleep versus NREM sleep [7], this study pro-ides the first evidence to indicate that REM sleep theta isnvolved in the consolidation of declarative memory. Declar-tive memory is dependent on the hippocampus [59,51]. Thetarequency activity is associated with LTP in the hippocampus37], and predominates during REM sleep [7]. In addition tohe increase in theta power, there were also unexpected changesn the sigma band following Paired Associates learning. Thereas a decrease in sigma power at Oz during Stage 2 sleep thatas limited to the low frequency sigma band during the sec-nd half of the night. On the other hand, during REM sleep,here was a significant increase in Sigma power at Cz fol-owing Paired Associates learning that was limited to the lowrequency sigma band. These findings suggest that EEG in theigma band may be a marker for declarative memory consoli-ation during Stage 2 in the last half of the night and in REMleep. The Paired Associates learning-dependent increase in theigma band appears to be independent of the sleep spindle.hree sources of information are consistent with this explana-

ion: there was no change in sleep spindles following Pairedssociates learning during Stage 2 sleep, and by definition,

here is an absence of sleep spindles during REM sleep [58].urthermore, the topographic distribution of sigma power dur-

ng REM sleep was circumscribed to occipital regions, unlikehe typical centro-parietal distribution observed during Stage 2leep.

Further analyses were conducted on REM sleep theta powern the Paired Associates learning condition to characterize theopography of the learning-dependent changes in EEG. It wasound that theta increased only during REM sleep, and onlyollowing Paired Associates learning. It was found that thetaas maximal at Cz on the baseline and test nights, and the

argest increase in theta was observed at Cz. The change in thetaower during REM sleep was largest at the vertex followingaired Associates learning which indicates that the areas of theortex underlying central regions (for example, somatosensory

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nd supplementary motor cortex, or projections from subcorti-al structures such as the hippocampus) may be involved in theonsolidation of new declarative information during REM sleep.dditional studies involving source localization and imaging

echniques are needed to determine the source of this activity.espite being maximal at occipital regions on the baseline and

est night, sigma power had the largest increase at Cz duringEM sleep. It is possible that both theta and sigma frequenciesre involved in the consolidation of declarative memory, androvide further support that central regions of the cortex maye involved in declarative memory consolidation during REMleep.

Re-testing on procedural tasks requires the task to beepeated, as performance is used to measure improvement. Nor-ally, re-testing on Paired Associates tasks involves only recall.owever, for this study re-testing involved both training and

ecall so that the memory scores would reflect similar changes inerformance as those measured on the procedural tasks. Thus theemory scores reflect the amount of information retained from

he initial training, and the amount of additional informationearned at re-test.

.3. Mirror Trace learning-dependent changes in sleep

Procedural learning on tasks such as the Mirror Tracing Taskas been memory linked to REM sleep in a number of studies.n the current study, a Sign Test revealed that there was a signif-cantly consistent increase in the density of REMs during REMleep, but no significantly consistent change in REM density inhe Pursuit Rotor, Paired Associates or control groups. It haseen found that skills that are learned implicitly which requirelogical set of rules, such as the Wff’n Proof task [36,64] areEM dependent. Moreover, improved performance on the Mir-

or Tracing Task has been observed following intervals of sleepn the second half of the night during which REM predominates56], and performance on the Mirror Tracing Task is impairedollowing REM sleep deprivation, but not Stage 2 sleep disrup-ion [2]. More recently, Smith et al. [70] investigated changes inEM sleep following learning on the Mirror Tracing Task and

he Tower of Hanoi. It was found that the number of REMs andEM density increased following learning on these two tasksompared to controls, and that the increase was most robust inndividuals with a high IQ. The results from the current investi-ation support these findings and suggest that when the trainings less varied (including only one of the two tasks, the Mirrorrace) the effect is less pronounced, however, very consistentin 8 of the 9 participants).

One limitation of the present research was the over-epresentation of female participants in the sample, which didot allow for gender comparisons. There are gender differences39] in abilities such as spatial rotation that may be relevant tohe acquisition of tasks such as the Mirror Tracing Task. Fur-hermore, there are also gender differences in REM, Stage 2

leep and sigma [18,31], but little variation in slow wave activ-ty or other measures of sleep homeostasis [18]. Other studiesave found that women have more slow wave activity thanen [8,38,48]. It has been hypothesized that changes in REM

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n Research 180 (2007) 48–61 59

leep across the menstrual cycle may be due to fluctuations inore body temperature [17]. Importantly, some of the findingseported here may be specific to women, or quantifiably differ-nt in men. Future studies including a more even distribution ofen and women could investigate if the gender differences in

leep and in cognitive abilities are related.

. Conclusion

One of the longstanding debates in the memory literatureas been over how long it takes memory consolidation to occur.n the current study, learning-dependent changes in sleep werebserved over the course of one night of sleep, while otheresearch has indicated that even a small amount of sleep duringdaytime nap is sufficient to facilitate memory consolidation

45,47]. It is not known, however, how many nights memoryonsolidation will continue. Future research could address thisssue by measuring parameters such as sleep spindles, rapid eye

ovements, sigma and theta power over several nights follow-ng an intense period of learning. In addition, this paradigmould be used to determine if phasic and tonic markers ofleep-related memory consolidation change across the lifespan.hese parameters may serve as indicators for brain devel-pment in children, and of memory deficits associated withging. Indeed, age-related changes in sleep quality may ben important factor in age-related changes in memory perfor-ance. Furthermore, events in sleep may mark important stages

n development, or indicate developmental disorders such assperger’s syndrome [27]. Memory deficits and reduced cog-itive capacity associated with aging may be related to sleepragmentation, age-related decreases in sigma power [35], sleeppindles [50] and possibly other phasic and tonic markers suchs rapid eye movements and theta power. The electrophysiolog-cal markers of learning identified in this experiment could alsoe used to identify specific memory deficits associated brainamage from stroke, head injury, Alzheimer’s and Parkinson’sisease.

In conclusion, the current study has identified learning-ependent changes in sleep architecture, sleep-stage specifichasic EEG events, tonic EEG frequencies, and has charac-erized the topography of these changes. These findings showhat different types of learning (i.e., Pursuit Rotor, Mirror Trac-ng, Paired Associates) affect different sleep states (i.e., NREM,EM) in different EEG frequency bands (i.e., low frequency

igma, theta) in dissociable brain regions (i.e., occipital, central),nd have unique phasic markers (spindles, REMs). These find-ngs suggest that brain plasticity during sleep does not involve anitary process. In other words, different types of learning havenique sleep-related memory consolidation mechanisms that actn dissociable brain regions at different times throughout theight.

cknowledgements

The Brock University Sleep Research Laboratory is fundedy the Natural Science and Engineering Research CouncilN.S.E.R.C.) of Canada.

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