Enhanced Memory Consolidation Via Automatic Sound Stimulation

pii: zsx003http://dx.doi.org/10.1093/sleep/zsx003
Enhanced Memory Consolidation Via Automatic Sound Stimulation During
Non-REM Sleep
Miika M. Leminen, MPsych1–3; Jussi Virkkala, PhD1; Emma Saure, MPsych1; Teemu Paajanen, MPsych1; Phyllis C. Zee, MD, PhD4; Giovanni Santostasi, PhD4;
Christer Hublin, MD, PhD1; Kiti Müller, MD, PhD1; Tarja Porkka-Heiskanen, MD, PhD5; Minna Huotilainen, DSc1,2,6; Tiina Paunio, MD, PhD1,7,8
Finnish Institute of Occupational Health, Helsinki, Finland; 2Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki,
Helsinki, Finland; 3Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; 4Department of Neurology, Northwestern
University, Feinberg School of Medicine, Chicago, IL; 5Department of Physiology, University of Helsinki, Helsinki, Finland; 6Cicero Learning Network, University of Helsinki, Helsinki,
Finland; 7Department of Psychiatry, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland; 8Department of Health, National Institute for Health and
Welfare, Helsinki, Finland
Introduction: Slow-wave sleep (SWS) slow waves and sleep spindle activity have been shown to be crucial for memory consolidation. Recently, memory consolidation has been causally facilitated in human participants via auditory stimuli phase-locked to SWS slow waves.
Aims: Here, we aimed to develop a new acoustic stimulus protocol to facilitate learning and to validate it using different memory tasks. Most importantly, the
stimulation setup was automated to be applicable for ambulatory home use.
Methods: Fifteen healthy participants slept 3 nights in the laboratory. Learning was tested with 4 memory tasks (word pairs, serial finger tapping, picture recognition, and face-name association). Additional questionnaires addressed subjective sleep quality and overnight changes in mood. During the stimulus night,
auditory stimuli were adjusted and targeted by an unsupervised algorithm to be phase-locked to the negative peak of slow waves in SWS. During the control
night no sounds were presented.
Results: Results showed that the sound stimulation increased both slow wave (p = .002) and sleep spindle activity (p < .001). When overnight improvement
of memory performance was compared between stimulus and control nights, we found a significant effect in word pair task but not in other memory tasks. The
stimulation did not affect sleep structure or subjective sleep quality.
Conclusions: We showed that the memory effect of the SWS-targeted individually triggered single-sound stimulation is specific to verbal associative memory.
Moreover, the ambulatory and automated sound stimulus setup was promising and allows for a broad range of potential follow-up studies in the future.
Keywords: EEG, memory, acoustic stimulation, slow-wave sleep, auditory-evoked K-complex.
Statement of Significance
We developed an automated acoustic stimulation system that can facilitate learning. Precisely timed sounds were targeted to single slow waves recorded
by EEG while healthy human participants were sleeping. The acoustic stimulation was able to increase the number of correctly recalled word pairs after the
night sleep. Using several different memory tasks, we found that the memory effect is specific to verbal associative memory. The results provide important
insights into the neural underpinnings of memory consolidation in sleep. In addition, our ambulatory and automated stimulus protocol opens new possibilities for clinical studies and further validation of the method.
Recent evidence stresses the critical role of sleep in learning,
in particular for memory consolidation.1 Being awake after the
initial learning increases the risk of forgetting,2 whereas even
a short nap protects memories against new conflicting experiences.3,4 However, the causal relationship between different
sleep-related neural activities and different types of memory
mechanisms is still being debated.
Current evidence supports the model of consolidation, where
three different types of neural activities are hierarchically
linked to each other.5 The memory material is replayed from
the hippocampus to the neocortex via fast spindle activity,1 seen
as minimum 500 ms bursts of 13–15 Hz activity in electroencephalography (EEG).6,7 In the hippocampus, the sleep spindles are phase-locked with high-frequency bursts (~80–100 Hz
in humans), called hippocampal ripples.1,5 The sleep spindles
are temporally grouped by large-scale cortical slow waves, the
markers of slow-wave sleep (SWS).5,8 Support for this model
has been found in both rodents and humans. Repetition of neural activity patterns related to awake experiences were shown
to be replayed during SWS in the sensory cortical areas and
SLEEP, Vol. 40, No. 3, 2017
hippocampus.9 In a combined EEG-fMRI study performed
in humans, Bergmann et al.10 observed sleep spindle-related
hemodynamic activity in SWS both in the hippocampus and
in the task-specific cortical area, and reported that the strength
of this network activity was related to memory recall performance. Larger scale dynamics of the consolidation mechanism,
shown by Takashima et al.,11 revealed that memory consolidation-related hippocampal activity was systematically reduced
at 1, 30, and 90 days after learning, while the cortical activity
pattern was enhanced. The cortical activity pattern representing
the memorized material not only strengthens over time, but is
also distributed to wider neural networks.12,13 Thus, it seems that
during the consolidation process the engram becomes gradually
less dependent on the hippocampus, while storage of memorized material is distributed to the sensory areas of the neocortex (for another hypothesized model for sleep-related memory
consolidation, see the study of Tononi and Cirelli14).
The neural memory consolidation process is sensitive to several types of sensory interference. During deep sleep, repetition
of the learned material has been found to enhance the consolidation process.15,16 Similarly, memory consolidation can be excited
Enhanced Memory Consolidation—Leminen et al.
by exposing a sleeping person to stimuli that is otherwise associated with the learning event, e.g., a smell.17,18 An alternative
approach is to use sensory stimuli, such as acoustic stimulation,
that are not directly associated with the learned material or learning event. Their possible influence on the mechanism is likely
related to emergence of a K-complex (KC), which can both rise
spontaneously and be evoked by a sensory stimulus (for review,
see the study of Colrain19). An auditory stimulus presented during non-REM (NREM) sleep is the most common way to trigger
a KC wave, but largely similar KCs can be triggered also by
other stimulus modalities (visual, somatosensory).20
Ngo et al.21 used acoustic stimulation during SWS by presenting a train of sounds with a repetition rate at typical slow-wave
activity (SWA) frequency. The results showed an increase in
SWA, but no memory tasks were tested to show specific interference with a memory consolidation mechanism. Next Ngo
et al.22 used a similar stimulus, now time-locked to spontaneous
slow-wave onsets during SWS, and the acoustic stimulus was
found to evoke additional slow waves as well as sleep spindle
activity during the positive phases of slow waves. In addition,
recall performance in a recall memory task was strikingly
improved and correlated with sleep spindle activity enhancement. This finding of improving the memory consolidation
process by slow wave-triggered acoustic stimulation has been
replicated recently23 and even more advanced methods have
been developed for accurate triggering.24
The idea of improving memory by a simple procedure during
sleep is intriguing. However, in addition to safety issues, the
procedure itself needs to be simplified and further validated.
In the current study, we aimed at developing a simplified and
automated protocol which could accurately target sound stimuli
to single spontaneous slow waves during NREM sleep without
the need for manual control. We elucidated the memory type
specificity of sound stimulation by performing different memory tasks. Finally, we also evaluated the effects of sound stimulation on subjective sleep quality and mood.
hair with EC2 paste (Grass Technologies, USA). These EEG
electrodes were placed in the standard locations of Fz, Cz, CPz,
and Oz based on the 10/20 system. In the online recording, the
Cz electrode acted as a common reference and the amplifier’s
common mode feedback was connected to the electrode located
at FCz. Additionally, single-use adhesive electrodes (Ambu,
Denmark) were placed at Fpz (forehead), left and right mastoids, lower corner of the left eye (LOC), and upper corner
of the right eye (ROC). The online algorithm controlling for
acoustic stimulation utilized only these single-use electrodes
attached to hair-free area. Other electrodes were used only to
validate the data. Finally, a combined electrocardiogram/electromyograph electrode was attached to the left front shoulder.
The data were hardware filtered to DC-250 Hz and sampled
with a frequency of 500 Hz. The EEG data were transferred in
real-time via Bluetooth from the bioamplifier to a tablet computer (Surface Pro 1, Microsoft Inc., USA), where the data were
received by a custom-made C++ program and shared with the
Matlab (Mathworks Inc., USA) program.
The auditory stimulus was generated by Matlab and comprised 50 ms long 1/f noise burst with 5 ms onset and offset
fading (sampling frequency 44.1 kHz). Sounds were played
via USB soundcard (Nuforce Icon uDAC2, Nuforce Inc.,
USA) and an active loudspeaker (Genelec 2029A, Genelec
Oy, Finland) located 125 cm above the participants’ head. The
stimulus timing was controlled by the C++ program running
on the tablet computer and the triggering delay was calibrated
and automatically compensated to zero before every recording. To detect the onsets of the slow waves the EEG signal
on mastoid-referenced Fpz channel was bandpass filtered to
0.3–35 Hz. The algorithm then detected negative peaks of at
least −50 µV whose next zero crossing was 125–500 ms ahead
(corresponding to a wave frequency of 0.5–2 Hz, as the distance between negative peak and the next zero crossing is ¼
of a full cycle of pure sine wave; see Figure 1C). The sound
onset was accurately 600 ms after the detected negative peak
of slow wave (see Figures 1C and 4). After the detection of
a slow wave, the algorithm waited for 2000 ms before being
ready for a new detection.
Independently of the stimulus-triggering mechanism, the
stimulus loudness was controlled automatically to avoid
waking the participants and to target the stimuli to SWS (see
Figure 1A). The algorithm used mastoid-referenced LOC
and ROC channels, in which beta activity (power in 18–45
Hz, where power increase indicates lighter sleep or awakening), eye movements (indicating REM sleep or awaking), and
SWA (power in 0.5–6 Hz) were calculated.25 All indices were
updated at 0.5-second time intervals and calculated in 2 s
Hann windowed epochs. SWA-dominated sleep pattern (correlation coefficient larger than 0.5 and peak-to-peak difference more than 20 µV in SWA of LOC and ROC channels25,26)
increased the sound volume from a minimum of 5 dB below
the hearing threshold until the maximum level of 15 dB above
the hearing threshold was reached (0.25 dB step after every
0.5 s interval). Eye movements (less than −0.75 correlation
between LOC and ROC) and increase in beta activity (at least
5 times the median beta activity over previous 30 time windows) caused an immediate lowering of the sound level to the
Fifteen healthy subjects (7 women, 8 men) with a mean age of
30.5 (range 23–42) years participated in the study. None of the
participants had been diagnosed with sleep disorders or had medication that interferes significantly with sleep patterns. The participants were instructed to follow their regular circadian rhythm
until all laboratory test nights were completed and to avoid alcohol for 3 days preceding every laboratory test night. All subjects
gave their written informed consent before participation. The
experiments were performed in accordance with the Declaration
of Helsinki, and the study protocol was approved by the Research
Ethics Committee of the Helsinki and Uusimaa Hospital District.
Biosignal recording, stimulus material, and the
stimulus system
The EEG, electrooculography, and electrocardiogram data
were recorded by a wireless Enobio system (Neuroelectrics,
Spain). Electrode contacts were prepared with abrasive paste
(Abralyt2000, Easycap, Germany or Elektrodipasta, Berner,
Finland). Ag/AgCl cup electrodes were attached beneath the
SLEEP, Vol. 40, No. 3, 2017
Enhanced Memory Consolidation—Leminen et al.
B) Experimental procedure
A) Stimulus setup
1 week
Auditory stimulation
50 ms
noise burst
EEG amplifier
Tablet computer, with an automatic
algorithm, controlling for...
Stimulus timing
SW peak
Play sound
600 ms later
night 1
night 2
Group 1
Group 2
C) SW detection procedure
peak over
-50 µV
Sleep maintenance
Sound level
by 0.25 dB
up to
HL+15 dB
1 week
2000 ms
gap before
allowed to detect
a new SW event
0 µV
-50 µV
Sound level
HL-5 dB
Next zero
crossing within
125-500 ms
time point
“zero” for
the sound onset
600 ms after the
negative peak of SW
Figure 1—Visualization of the research procedure. In Figure 1C, the signal is the single trial data from the Fpz channel from one subject during
the control night, during which no sounds were presented. HL = hearing level; SW = slow wave; SWS = slow-wave sleep.
Memory tests
To compare different memory systems, we used recall tests for
semantically associated items, procedural sequences, and pictures with emotionally different contents. The memory tasks
were semantic word pair task, face-name association task, finger tapping, and picture recognition task. All memory tests were
performed by using the tablet computer and USB keyboard.
Picture recognition and word pair tests were programmed in C#
programming language, finger tapping task in Visual Basic, and
face-name test was running on a PsychoPy version 1.80 programming environment.27
The word pair test included 240 semantically related word
pairs (e.g., “muscle—tendon”), which were divided into two
lists. Word pairs were translated and adapted from German, and
the same stimuli have been used in several studies related to
memory consolidation in sleep.22,28,29 Order of word pairs in the
list was randomized for each subject, and both lists were equally
often used on the stimulus night and on the control night across
participants. In the learning phase, every word pair was visible for 4 s on the tablet screen, while the interstimulus interval
(ISI) was 1 s. The first recall test was performed immediately
after the learning phase. The first member of each word pair (in
SLEEP, Vol. 40, No. 3, 2017
random order) was displayed, while the participants were asked
to write its pair on the tablet. After the response, the correct
answer was visible for 2 s independent of the correctness of
the participant’s answer. The delayed recall task took place the
next morning. The procedure was the same except that the correct answer was not presented. The memory performance was
scored by counting together all correctly recalled words. The
correct answers included responses that were clearly typos or
inflectional errors (e.g., a plural form). Derivational mistakes
were counted as errors (e.g., crime → criminality).
The face pictures for face-name associations test were chosen
from The Center for Vital Longevity Face Database.30 The faces
were for 18- to 29-year-old adults equally from both genders.
The names were chosen from the most common first names in
Finland for people born in 1970–1990. For each test night, 30
individually randomized face-name pairs were constituted in a
way that gender of the face and the name matched. In the learning phase, each face was visible for 500 ms. After the face image,
the written name was shown under the face image and also heard
at a comfortable loudness. In the immediate recall, the face
images were presented in random order, and participants were
asked to write the recalled name on the table computer. After
Enhanced Memory Consolidation—Leminen et al.
the response, the correct answer was heard. The morning recall
was similar except that the correct answer was not heard. The
answers were classified as correct, wrong, and missing.
In the finger tapping task, the participants placed four fingers
(other than thumb) of the nondominant hand on the standard keyboard’s number buttons 1–4. Then they were given 5-unit-long
digit series (e.g., “4-1-3-2-4”) that they were allowed to practice
for 10 loops. Thereafter, they were instructed to repeat the series
as many times as they could, as quickly and correctly as they
could. The instruction was visible on the screen all the time. The
task consisted of 6 blocks, each 30 s long with a 5 s break in
between. The performance was scored by counting together all
correctly repeated series in all 6 blocks. The test was repeated in
the morning with the same digit series, but without the practicing
loops. The task included 2 different sets of digit series, which
were counterbalanced between the participants and test nights.
Pictures of the picture recognition task were obtained from
the picture library of the International Affective Picture System
(IAPS).31 For both nights, 119 pictures were used. Randomly
presented images were visible for 4 s and ISI was 1 s. In an
immediate recall, the participants answered with two response
buttons whether or not they had seen the picture in the previous
list of images. In addition to the original 119 learned images,
the list included 119 new images. The order of pictures was
randomized. The memory performance was scored by counting
together correctly recognized pictures. Each picture had been
rated in valence and arousal scales by 100 subjects.31 Thus, pictures were also divided into high versus low valence and into
high versus low arousal categories based on the median value.
POMS partly due to Finnish translation. Also two additional scales
were created: inattentivity [3 adjectives] and inefficiency [3 adjectives]. However, their relevance in light of the results of the current
study is minimal.) The sleep effect was calculated by subtracting
the evening score from the morning score of each sum scale.
The participants visited the sleep laboratory (Brain and Work
Research Center, Finnish Institute of Occupational Health,
Helsinki, Finland) for three nights, all of them separated by one
week (see Figure 1B). The facilities allowed three participants to
be recorded simultaneously in separate isolated sleeping rooms
with identical equipment. The purpose of the first night was to
get participants familiarized with sleeping with equipment in
the sleep laboratory. The other 2 nights were experimental test
nights, and the order of them was randomized and balanced
across the participants but also within the group of three simultaneously measured participants. During the stimulus night the
sound stimuli were presented by the protocol described above.
During the control night the stimulus algorithm only marked the
detected slow waves in the data, but no sound was presented.
Participants arrived at the laboratory 3–4 hours before their
typical sleep onset. Completion of memory tests and questionnaires took one hour and was typically performed between 9
and 10 pm. Tests were performed in acoustically isolated rooms
without external distractors. Participants did not use any electronic equipment or watch displays (e.g., mobile phones, tablet
computers, or television) in bed or just before sleeping. The
hearing threshold was tested every evening with the stimulus
sounds individually for each participant. It allowed us to control for the sound pressure level against the hearing level, but it
also made the participants believe that the sounds were potentially played every night. Participants did not know beforehand
that some of the nights would be without sound stimulus. When
subjects went to the bed and lights were switched off, the algorithm controlling the detection of slow-wave events and sound
stimulus was activated. Thereafter, the automatic sound level
control took care of sound levels also during short awakenings. Subjects were monitored by research personnel for ethical safety reasons, but they did not interact with the algorithm,
which autonomously determined deep sleep and detected slowwave events. The wake-up time depended on the participant’s
own sleeping rhythm, but the total sleeping time was at least
7 hours if possible. Usually waking up took place between 7
and 8 am. About 20–30 minutes after waking, the delayed recall
memory tests were performed and the morning questionnaires
completed. The morning test lasted typically about 30 minutes.
If the sound stimulus causes a measurable difference in sleep quality, the memory performance in the morning might be affected
by overall difference in cognitive ability (performing the task is
more difficult after a poorly slept night). To be able to investigate
this possibility, we used the NASA-TLX test for subjective cognitive load.32 Participants marked on a six-dimension continuous
scale how much effort the completion of memory tasks required.
The effect of each test night was calculated by subtracting the
evening value from the corresponding morning measure.
Subjective sleep quality was also directly assessed with a
questionnaire every morning. The variable was formulated by
calculating together the answers to two questions: (1) How did
you sleep last night (1 = well, …, 4 = badly) and (2) Did you
sleep worse or better than usually (1 = better, …, 5 = worse). We
also asked if the participants had heard sounds during the night.
This question was used to find out if the sound level adaptation worked and if the subjects correctly guess which night the
sound stimulus was present compared with the control night.
To control for the mood-related variables and to measure possible qualitative changes in sleep, participants filled out a shortened version of the Profile of Mood States (POMS) questionnaire33
every evening and morning. The questionnaire includes 38 questions with a 5-point scale (0 = not at all, …, 4 = very much). The
mood variables were calculated as a sum of 3–7 questions each
(some of them reversed); tension-anxiety, fatigue, inattentivity,
vigor-activity, depression, anger-hostility, inefficiency, and confusion. (Scales are slightly modified from the original short form of
SLEEP, Vol. 40, No. 3, 2017
Data analysis
Data analyses were performed on data from the Cz channel,
which was referenced digitally to the average of the mastoid
electrodes. The evoked response analysis was time-locked to
the spontaneous slow-wave negative peak detected by the online
algorithm described above. Thus, for the control night, the averaged responses showed an average of spontaneous slow waves.
Correspondingly, the stimulus night response included the average
of the spontaneous slow waves and evoked activity caused by the
sound stimulus, whose onset was 600 ms later than the negative
Enhanced Memory Consolidation—Leminen et al.
Supplementary Table S1]. On average, participants correctly
recalled 21.1 (standard deviation [SD] 7.7) word pairs more on
the morning after the sound stimulus night than in the evening,
while during the sham stimulus night the overnight improvement was 15.6 (SD 8.1) words (large overall improvement was
likely partly due to feedback during the evening measure). In
the face-name association task, picture recognition task (for all
pictures together), or serial finger tapping task, the improvement difference between test nights was not statistically significant (see Table 1). Also the emotional content in pictures did
not modulate the effect of sound stimulus (interaction of sound
stimulus and arousal: F(1,14) = 2.12, p = .17; interaction of
sound stimulus and valence: F(1,14) = 0.39, p = .54).
In this table, results of memory tests are shown for test night
(sound) and control night (no sound) along with t-test results.
The unit of retention is the number of correctly recalled words
in the word pair task, the number of correctly recalled names
in the names and faces task, the number of correctly tapped
sequences in the finger tapping test, and the number of correctly
recognized pictures in the picture recognition test. Test scores
were calculated by subtracting the number of correctly recalled
items on the evening test from the morning performance.
Retention increase
peak of a spontaneous slow wave. For clearer presentation, all of
the time axes in this paper have a zero at the time of sound stimulus onset or corresponding moment in the control night data. For
statistical testing, the evoked response was quantified by calculating an average in the time window from 800 to 1200 ms. The
time window of interest was chosen based on previous literature to
cover the positive component of expected KC evoked by a sound
stimulus (see also Figure 4). The sleep spindle activity was measured with wavelet transformation (3 cycles, Morlet base function),
which was done using the EEGLAB toolbox in Matlab.34 The preliminary analysis was first performed to find an individual local
maximum in the wavelet spectrum between 12 and 15 Hz. All
subjects had a local spectrum maximum at approximately 14 Hz,
and thus, this frequency point was chosen for the sleep spindle
analysis. Spontaneous slow-wave event-related synchronization
of sleep spindle activity was quantified by calculating the average
within the time window of 800–1200 ms. To analyze the accuracy
of the slow-wave event detection algorithm, the phase of SWA at
the time of tone onset (or corresponding time in the control condition) was calculated. First EEG signal was band pass filtered to
SWA frequency band (0.5–2 Hz), and then Hilbert transformed.
Phase data were extracted at time point zero (tone onset) and circular means, standard deviations, and distributions were calculated in CircStat toolbox35 in Matlab.
Statistical analyses of different variables were performed with
a paired t-test, with stimulus night and control night acting as a
repeated factor. The normality of the variables was confirmed
by the Kolmogorov–Smirnov test. In a picture recognition test,
the effect of emotional components was tested with repeated
measures ANOVA with the factors of Arousal (two levels, low
and high) and Valence (two levels, low and high). All of the
statistical testing was done using Sigmaplot software (version
11.0, Systat Software, Germany). The significance threshold of
0.05 was used and all the accurate p values were reported.
To validate the accuracy of an automatic sleep staging algorithm, an experienced professional sleep technologist manually
scored the sleep data in 30 s segments to awake, REM, N1, N2,
or N3, and marked all the arousals. The analysis was blinded so
that the technologist did not know which data sets were which
experimental conditions.
and faces
Sound stimulus night
Control night
Figure 2.—Memory performance test results. The unit of retention
is the number of correctly recalled words in the word pair task, the
number of correctly recalled names in the names and faces test,
the number of correctly tapped sequences in the finger tapping
test, and the number of correctly recognized pictures in the picture
recognition test. In all tests, the test scores were calculated by
subtracting the number of correctly recalled items on the evening
test from the morning performance. *p < .05.
Memory tests
Cued recall performance in the word pair test improved during the sound stimulus night significantly more than during
the control night [t(14) = 2.93; p = .01; see also Figure 2 and
Table 1—Results of Memory Tests.
Sound mean
Word pairs
Names & faces
Serial finger tapping
No sound mean
*p < .05. SEM = standard error of mean; SD = standard deviation.
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Enhanced Memory Consolidation—Leminen et al.
Evoked and induced responses in EEG
EEG data showed a response evoked by the slow wave-locked
sound stimulation [see Figure 4, t(14) = 3.82, p = .002]. Also the
increase of activity in the sleep spindle frequency band was significant [t(14) = 6.32, p < .001]. The linear correlation between
sleep spindle activity increase and word pair recall performance
did not significantly explain the variance [F(1,13) = 0.68,
p = .42].
Stimulus targeting and sleep staging
The sleep characteristics did not differ between the test night
and the control night (see Figure 3A and Supplementary
Table S3). The correlation analyses between the sleep parameters and memory retention (28 correlation tests) found only
one significant correlation with the amount of N2 sleep during the stimulus night (p = .04; see Supplementary Table
S5). The automatic algorithm aimed at triggering individual stimulus sounds according to the detected slow-wave
events in SWS. The comparison with manually scored sleep
stages shows that about 83% of detected slow-wave events
were found in N3, about 15% in N2, and a few single false
alarms in some subjects in N1 or awake (see Figure 3B and
Supplementary Table S4). On average, about 1400 tones
were delivered over night for each subject. Based on the
actual data, the minimum interval between two separate
sounds was 2127 ms and the full distribution of interevent
intervals for detected slow wave events is in Figure 3D. The
average phase of SWA at the tone onset (or corresponding
time point in control condition) over all the subjects was
−18.0 (SD 67.4) degrees and 2.4 (SD 71.1) degrees for
stimulus and control night, respectively. The distribution of
phases is shown in Figure 3C (for all the subjects individually, see Supplementary Figure S1).
Sleep architecture
Sound stimulus
Detected slow wave events
The results showed no difference in subjective cognitive load
caused by memory tasks (see Table 2) or in subjective sleep
quality [t(14) = 0.49, p = .63]. Furthermore, participants
could not reliably remember afterwards if they heard sounds
between the hearing threshold test in the evening and awakening [t(14) = 1.00, p = .34]. All mood-related scales improved
during the night regardless of the condition, but for the tension-anxiety scale the change was smaller after the sound
stimulus night [t(14) = 2.56, p = .02; see Table 3]. However,
already the baseline of the tension-anxiety scale differed
between the nights (p = .05, see Supplementary Table S2),
whereas the same score in the morning did not differ between
the nights. Further, the tension-anxiety baseline scores
did not correlate with word pair memory task performance
Awake REM N1
Sound stimulus night
Slow wave activity phase at sound onset
Sound stimulus
Control night
Distribution of intervals between
detected slow wave events
Event interval [s]
Figure 3—Sleep architecture, accuracy of stimulation, and stimulation pattern. A: Manually scored mean amounts of different sleep stages in
minutes during sound stimulus and control nights. B: Mean amount of automatically detected slow-wave events in each manually scored sleep
stage. C: The distribution and circular mean phase of slow-wave activity over all the subjects at the time of sound onset or corresponding time in
control night. D: Distribution of intervals between detected slow-wave events over all the subjects separately for sound stimulus and control nights.
SLEEP, Vol. 40, No. 3, 2017
Enhanced Memory Consolidation—Leminen et al.
improvement (r = .052, p = .86, and r = .043, p = .88 for stimulus and control nights, respectively).
In this table, results of experienced cognitive and physical
task load caused by the memory tests are shown for test night
(sound) and control night (no sound) along with t-test results.
The effect of each night was calculated by subtracting the
evening value from the corresponding morning measure.
In this table, results of POMS mood questionnaire are shown
for test night (sound) and control night (no sound) along with
t-test results. Test scores were calculated by subtracting the
evening score from the morning score of each sum scale.
Sound stimulus night
Control night
In this study, we aimed at developing an automated auditory
stimulation protocol for SWS. Starting with the basic parameters (short pink noise bursts as stimulus triggered by negative
peaks of slow waves during SWS and semantically associated
word pairs as memory task) adopted from the study of Ngo
et al.,22 the protocol was refined: the stimulus protocol was
simplified by using individually triggered single sounds and the
procedure was automated. These steps will be necessary for the
application of the procedure outside laboratory conditions.
The improvement in the cued memory recall performance after
the sound stimulus was observed also in our study, thereby supporting the previous findings22 and further showing that slow
wave-locked sound stimulus during SWS can interact with the
memory consolidation process.
The single-sound protocol used in this study had two important advantages. First, as we found that the single-sound alone
was able to produce memory consolidation effects, we may
hypothesize, that also in the previous studies the first sound
after the spontaneous slow wave was mainly causing the memory effects. This may explain also the recent finding23 in which
no differences in memory consolidation between trains of two
or more stimulation sounds were identified. However, although
a single stimulation sound improved the memory consolidation, it remains unresolved if two or more sounds in a sequence
increase refractoriness and if more precise targeting of the stimulus to EEG phase could result in more robust effects.24
Second, the single-sound stimulus method enabled more
precise analysis of the evoked EEG responses in the absence
of overlapping responses from consecutive sound stimuli. The
neurophysiological data showed large EEG responses evoked
by the sound stimulus, and a burst of activity at the sleep spindle
frequency range during the scalp-positive phase of the evoked
slow wave. The simplest explanation could be that the evoked
slow wave can naturally trigger sleep spindles (see the study of
Steriade8 for review), which then induce memory consolidation.
Slow waves and KCs have often been thought to reflect different phenomena, but recent evidence suggests that their neural
origins may be related.36 Bellesi et al.36 proposed a hypothetical
model in which nonlemniscal thalamic networks are able to give
rise to a large-scale neural chain reaction, which is recorded as
a slow wave in EEG. Via corticothalamic connections, this slow
wave is able to trigger sleep spindles,8 which are further temporally clustering hippocampal ripples.5
The memory task of semantically associated item pairs has
also previously been used in studying memory consolidation in
Sound onset
at 0 ms
Change in signal power @14Hz
Figure 4—Electroencephalography (EEG) results. Upper two
curves show the group average of the EEG response in Fpz and
Cz channels time locked to the negative peak of spontaneous
slow wave. An automatic online algorithm was using the channel
Fpz to detect slow waves and the channel Cz was used in group
level offline statistics. Below the curves the time points of significant difference between conditions are shown based on paired
samples t-test with noncorrected alpha level of 0.01. During the
stimulus night the sound stimulus was presented at 600 ms after
the slow-wave peak (time zero in this image), whereas during the
control night the sound stimulus was missing. Lowest curve shows
change in the 14 Hz oscillatory power from baseline (from −2 seconds to zero) in decibels. The grey boxes show the time window
(width), mean (horizontal line in the middle), and its standard error
(height of the box) of values extracted from the curves for statistical analyses. **p < .01, ***p < .001.
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Enhanced Memory Consolidation—Leminen et al.
Table 2—Results of Questionnaire for Experienced Cognitive and Physical Task Load.
Sound mean
No sound mean
Physical load
Temporal load
Mental load
SEM = standard error of mean; SD = standard deviation.
Table 3—Results of Mood Questionnaire.
Sound mean
No sound mean
Tension anxiety
Vigor activity
Anger hostility
*p < .05. SEM = standard error of mean; SD = standard deviation.
five-item motor sequences. These sequences were indeed consolidated by sleep, and the average overnight increase in performance was 113 sequences. However, the sound stimulus did
not facilitate this consolidation. This is in line with previous
findings, which have stressed the role of REM sleep29 as well as
stage 2 sleep38 and slow-frequency spindle activity39 in consolidation of procedural schemas. The second control task involved
remembering pictures. For the emotional content in pictures,
the result was expected. REM sleep has been shown to be more
important for emotional content consolidation than NREM
sleep, and thus, the acoustic stimulation targeted to the SWS
only did not affect neural processes during REM. However,
we found no improvement in overall picture recognition performance. This is somewhat surprising, as both word pairs and
pictures are thought to be stored within the declarative memory
system. The lack of effect in picture recognition task could be
explained by three important differences between the picture
recognition task and the word pair task. First, picture memory
overall has been demonstrated to be less dependent on consolidation in sleep.40,41 Second, the word pair learning sequence
included feedback, which allowed participants to rememorize
the correct word pair, and it is likely that repetition is critical
when the memory consolidation system values different memory items depending on their estimated importance. Additional
evidence supporting this view was given by our pilot study, in
which the word pair consolidation effect disappeared when the
sleep (see e.g., the study of Payne et al.37), and is potentially sensitive to changes in the hippocampus-dependent consolidation
process. In this study, we demonstrated a memory effect that
was specific to the word pair task (verbal associative memory):
the performance in the other memory tasks including semantically associated items of human faces and names were not
improved by sound stimulation during SWS. Lack of effect in
the latter task may be explained by the difference in the learned
material. Face-name task required integration of both verbal and
visual stimulus material, whereas the word pair test included
only verbal material. It is also possible that in the face-name
test task difficulty and the floor effect affected results, while
the trend of improvement in the recall performance suggests
that the optimized version of the faces and names task should
be further tested in future studies (see Figure 2 and Table 1).
We also found a positive correlation between the amount of
N2 sleep and memory retention in word pair task (for stimulus night only; see Supplementary Table S5), but as the amount
of N2 sleep did not differ between the nights (Supplementary
Table S3), it is difficult to assess its relevance to the memory
To investigate the memory type specificity of the current
(memory consolidation facilitation) protocol, we used two
memory tasks that were not linked to associative pairs of
semantic items. The first control task was the procedural finger tapping task, which included continuous repeating of short
SLEEP, Vol. 40, No. 3, 2017
Enhanced Memory Consolidation—Leminen et al.
feedback in the learning phase was missing.42 The third important difference between the picture recognition and word pair
tasks was in the recalling test. In the word pair task, the participants saw a cue (the first member of the word pair), which they
used to recall the other word that was paired with it. In contrast, the picture recognition task was simply to answer whether
the picture had been seen before or not. In these two memory
tasks, the recall strategy may involve different neural strategies,
and thus, it is possible that only the cued recall task benefits
from the consolidation boost given by sound stimulation. This
hypothesis requires closer scrutiny.
In addition to exploring the effects of sound stimulation on
memory consolidation, we used questionnaires to examine its
possible effects on metacognitive ratings, subjective sleep quality, and mood. We did not find significant effects on cognitive
load or subjective sleep quality, but there was a difference in a
mood measure of the tension-anxiety scale; on the night with
acoustic stimulation the decrease in the tension-anxiety factor was smaller than on the control night. This may indicate
that the regulation mechanism of sleep for this specific mood
component was disturbed with putative input from serotonergic
neurons in raphe nuclei, which are involved in memory consolidation, sleep cycle regulation, and mood disorders (for review,
see the study by Zhao et al.43). However, by chance, the baseline
value of tension-anxiety scale was lower before the stimulus
night than before the control night, which may contribute to
the result (Supplementary Table S1). Taking this into account,
and that none of the other mood measures were sensitive to
sound stimulation, dispute the reliability of the result on acoustic stimulation’s capability to disrupt the mood regulation during sleep. To ensure that the observed baseline difference in the
tension-anxiety scale was unrelated to memory test results, we
used correlation analysis, which did not show any connection
between these two effects.
Finally, an important aim was to improve the reliability of
the sleep stimulation procedure by increasing its automatization and relieving the procedure of manual overnight monitoring of sleep stages. To this end, we successfully demonstrated
that the automated method was as efficient as the manual procedure used in the previous studies. The algorithm automatically (1) detected the SWS phases, (2) faded the sound stimulus
smoothly in, thus minimizing the possibility of waking the
subject, (3) detected single slow-wave event onsets for accurate timing of stimulation, and (4) disabled the stimulation
immediately following arousal, awakening, lightening of sleep,
or shifts in REM sleep. The automatic stimulation mechanism
ensured that subjects did not accidentally hear sounds, and
thus, increased the reliability by reducing possible placebo bias.
Indeed, subjects did not know which night they were presented
with sounds, despite the fact that many of them woke naturally
at some point during the night. The stimulation setup consisted
of a wireless mini-sized EEG amplifier, a tablet computer controlling the online analysis and stimulation, and a loudspeaker,
thus being maximally ambulatory and compatible with out-oflab use. Most importantly, the algorithm used the EEG signal
only from disposable adhesive electrodes which were attached
to hair-free areas (forehead, mastoids, and corners of eyes).
These electrodes can be easily attached by a person to her/himself and the signal is still relatively reliable without EEG caps
SLEEP, Vol. 40, No. 3, 2017
or other additional attaching techniques. In addition, based on
visual inspection of the EEG curves of Cz and Fpz channels
in Figure 4, it is possible to note that the negative peak seems
enhanced in Fpz, whereas sound evoked slow-wave amplitudes
look quite similar in both channels. This suggests that the forehead channel is not only more practical, but also detection of
the negative peak of SW might be more reliable (due to higher
signal to noise ratio). The most critical limitation of our study
is the relatively low number of subjects investigated. While a
group size of 15 subjects is common in cognitive neuroscience
studies, generalization of the results would benefit from a larger
subject pool. The automatic and ambulatory stimulation setup
developed in this study can be used in achieving this goal.
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Supplementary materials are available at SLEEP online.
Research was funded by the Finnish Funding Agency for Innovation (Tekes),
project 2310/31/2012.
Research site was Finnish Institute of Occupational Health, Helsinki, Finland.
We thank sleep technologists Riitta Velin and Nina Lapveteläinen for crucial
help in data collection.
Submitted for publication June, 2016
Submitted in final revised form December, 2016
Accepted for publication December, 2016
Address correspondence to: Tiina Paunio, MD, PhD, University of Helsinki,
P.O. Box 22, FIN-00014 Helsinki, Finland. Telephone: +358-9-47163734; Fax:
+358-9-47163735; Email: tiina.paunio@helsinki.fi
None declared.
Enhanced Memory Consolidation—Leminen et al.
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