Reflection

One page Reflection

Psychology 102: Paper Option to Substitute for Experimental Participation

You may earn experimental participation points by reading and summarizing research articles published in psychology journals. Each article that you read and summarize must be handed in to your instructor, who will determine whether or not your summary is acceptable. You need to turn in four acceptable summaries to fulfill your course research requirement. For this, you will earn 24 points. Each acceptable summary will earn you 6 points. You may choose to do up to four additional acceptable summaries for extra credit. Each additional summary will earn you 4 extra credit points.

Each summary must be typed, double spaced, and must be at least one page, but no longer than two pages in length. Your instructor will evaluate the content of the paper, how well you have summarized the article you read, and will also consider grammar, spelling, and punctuation in determining whether or not each paper is acceptable. Papers that have poor spelling, grammar, or organization will NOT be considered acceptable, even if the paper’s content is adequate. If you have doubts about your ability to write well, you might consider visiting the campus student writing center to get feedback on expository matters before you turn your papers in.

Copies of journal articles can be found on Blackboard. A list of articles is listed at the end of this document. Please refer to the list when choosing your articles. ONLY articles from that list are acceptable for summary.

If you plan to do this assignment to fulfill your research requirement, you MUST inform your TA before the 4th week of the semester ends. If you decide to do any research papers for extra credit, you may start them later in the semester. However, you still MUST inform your TA that you will be turning them in and set up a submission schedule. Your instructor will set specific dates when your work must be turned in, (see below) and, as with all experimental points, all work must be completed by the second to the last week of the semester.

Paper schedule:

To spread the workload out over the entire semester, 4 submission periods have been established for the papers. You can submit up to two papers in each period. Hence, if you want to do 8 papers, you would submit two in each period. Note that you cannot “make up” missed papers by doing additional papers in a later period – only a MAXIMUM of two papers can be submitted per period (unless approved by your TA). Hence, to be safe, it is to your benefit to start by doing 2 papers per period – that always leaves open the option of doing all 8 papers. If you decide that you don’t want to do all 8, you can always cut back later in the semester.

Deadlines:

1st paper set – by the end of the 5th week of the semester
due by: September 25 2020

2nd paper set – by the end of the 8th week

due by: October 16 2020

3rd paper set – by the end of the 11th week

due by: November 6 2020

4th paper set – by the end of the 14th week

due by: November 27 2020

Completing the papers is easy. Your papers should include answers to some of (but not necessarily all of) the following questions:

Introduction section:

What is the overall purpose of this study?

Why was the author conducting this experiment?

What theory is the author testing in this research?

What is the author’s hypothesis?

Method section:

How were subjects selected for this experiment?

What were the subjects’ ages, gender, and other characteristics?

What were the experimental conditions used in the experiment? Please describe with detail.

What were the control conditions used? Please describe in detail.

What were the independent and dependent variables?

Results section:

How did the researchers score the dependent variables?

What types of statistical analyses were used?

What did the statistical analyses indicate?

How significant were the results?

Discussion section:

Did the results of the experiment support the experimenter’s hypothesis?

How did the author summarize the outcome of their experiment?

Were there results of the experiment that were inconsistent with the researcher’s initial hypothesis or theory?

Can you relate this article to a section of the textbook, or explain why this is important to that particular area of psychology?

Remember that the main purpose of the assignment is for you to gain understanding of HOW psychological research is done and what it means. Hence, try to convey this information when writing your papers. In addition, you are reminded that papers that have poor spelling, grammar, or organization will NOT be considered acceptable, even if the paper’s content is adequate. If you have doubts about your ability to write well, you might consider visiting the campus student writing center to get feedback on expository matters before you turn your papers in.

Reading List for Psychology 102 (each article is uploaded on blackboard)

Topic 1: Memory

Cochran, K. J., Greenspan, R. L., Bogart, D. F., & Loftus, E. F. (2016). Memory blindness: Altered memory reports lead to distortion in eyewitness memory. Memory & Cognition, 44(5), 717-726.

Topic 2: Consciousness

Ferrarelli, F., Smith, R., Dentico, D., Riedner, B. A., Zennig, C., Benca, R. M., … & Tononi, G. (2013). Experienced mindfulness meditators exhibit higher parietal-occipital EEG gamma activity during NREM sleep. PLoS One, 8(8), e73417.

Topic 3: Learning

Myers, C. E., VanMeenen, K. M., Devin McAuley, J., Beck, K. D., Pang, K. C., & Servatius, R. J. (2012). Behaviorally inhibited temperament is associated with severity of post-traumatic stress disorder symptoms and faster eyeblink conditioning in veterans. Stress, 15(1), 31-44.

Topic 4: Lifespan Development

Amso, D., & Johnson, S. P. (2006). Learning by selection: Visual search and object perception in young infants. Developmental psychology, 42(6), 1236.

Topic 5: Motivation and Emotion

Porreca, F., & Navratilova, E. (2017). Reward, motivation and emotion of pain and its relief. Pain, 158(Suppl 1), S43.

Topic 6

Harmon-Jones, C., Schmeichel, B. J., Inzlicht, M., & Harmon-Jones, E. (2011). Trait approach motivation relates to dissonance reduction. Social Psychological and Personality Science, 2(1), 21-28.

Topic 7

Vrijsen, J. N., van Amen, C. T., Koekkoek, B., van Oostrom, I., Schene, A. H., & Tendolkar, I. (2017). Childhood trauma and negative memory bias as shared risk factors for psychopathology and comorbidity in a naturalistic psychiatric patient sample. Brain and behavior, 7(6), e00693.

Topic 8

Woike, K., Sim, E. J., Keller, F., Schönfeldt-Lecuona, C., Sosic-Vasic, Z., & Kiefer, M. (2019). Common factors of psychotherapy in inpatients with major depressive disorder: A Pilot Study. Frontiers in psychiatry, 10, 463.

Experienced Mindfulness Meditators Exhibit Higher
Parietal-Occipital EEG Gamma Activity during NREM
Sleep
Fabio Ferrarelli1, Richard Smith1, Daniela Dentico1, Brady A. Riedner1, Corinna Zennig1, Ruth M. Benca1,

Antoine Lutz2,4, Richard J. Davidson2,3, Giulio Tononi1*

1 Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin, United States of America, 2 Waisman Center for Brain Imaging and Behavior, University

of Wisconsin-Madison, Madison, Wisconsin, United States of America, 3 Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin, United States of

America, 4 Lyon Neuroscience Research Center, Lyon 1 University, Lyon, France

Abstract

Over the past several years meditation practice has gained increasing attention as a non-pharmacological intervention to
provide health related benefits, from promoting general wellness to alleviating the symptoms of a variety of medical
conditions. However, the effects of meditation training on brain activity still need to be fully characterized. Sleep provides a
unique approach to explore the meditation-related plastic changes in brain function. In this study we performed sleep high-
density electroencephalographic (hdEEG) recordings in long-term meditators (LTM) of Buddhist meditation practices
(approximately 8700 mean hours of life practice) and meditation naive individuals. We found that LTM had increased
parietal-occipital EEG gamma power during NREM sleep. This increase was specific for the gamma range (25–40 Hz), was
not related to the level of spontaneous arousal during NREM and was positively correlated with the length of lifetime daily
meditation practice. Altogether, these findings indicate that meditation practice produces measurable changes in
spontaneous brain activity, and suggest that EEG gamma activity during sleep represents a sensitive measure of the long-
lasting, plastic effects of meditative training on brain function.

Citation: Ferrarelli F, Smith R, Dentico D, Riedner BA, Zennig C, et al. (2013) Experienced Mindfulness Meditators Exhibit Higher Parietal-Occipital EEG Gamma
Activity during NREM Sleep. PLoS ONE 8(8): e73417. doi:10.1371/journal.pone.0073417

Editor: Wieslaw Nowinski, Biomedical Imaging Lab, Agency for Science, Singapore

Received February 27, 2013; Accepted July 22, 2013; Published August 28, 2013

Copyright: � 2013 Ferrarelli et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported by the National Center for Complementary and Alternative Medicine (NCCAM) P01AT004952 to TG, RJD and AL, grants from
the National Institute of Mental Health (NIMH) R01-MH43454, P50-MH084051 to RJD, grants from the Fetzer Institute and the John Templeton Foundation to RJD,
a core grant to the Waisman Center from the National Institute of Child Health and Human Development [P30 HD003352-449015], and a International Re-
integration Grants (IRG), FP7-PEOPLE-2009-RG for AL. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of
the manuscript.

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: gtononi@wisc.edu

Introduction

Meditation can be conceptualized as a set of regulatory and self-

inquiry mental training regimes cultivated for various ends,

including the training of well-being and psychological health

[1,2],[3]. Accumulating evidence suggests that meditation training

induces functional and anatomical neuronal changes [1],[2,3].

These neuroplastic changes are linked to changes in behavior

during cognitive and affective tasks [4],[3]. The investigation of

spontaneous brain activity, at rest or during practice, is a sensitive

approach to identify these neuroplastic changes. Some studies

found meditation-related increases in alpha and theta frequency

bands, particularly in frontal midline and prefrontal areas, which

were associated with increased relaxation (reviewed in [5]).

Another study showed that long-term meditators (LTM) had

high-amplitude gamma band (25–40 Hz) oscillations during

mental practice localized to lateral frontal and posterior parietal

electrodes bilaterally [6]. A gamma power increase in a parietal-

occipital region was also reported in another group of LTM during

Vipassana meditation [7], whereas in a recent EEG study

meditation experts showed higher parietal-occipital gamma power

compared to novices during resting wakefulness preceding

meditation practice [8].

An important question raised by these studies is whether

enhanced EEG activity is state dependent (e.g.– occurring only

during meditation and immediately after it), or trait dependent

(e.g. – occurring outside of formal meditation practice) [5]. The

latter would suggests that long-term meditation training causes

lasting neuro-plastic changes in cortico-thalamic circuits, which

should be detected in the spontaneous brain activity [9]. This

question is difficult to address because meditation training occurs

at multiple time-scales. For example it can happen intensively, as

during meditation retreats, or less intensively but across a longer

period, as during daily practice. Training also occurs, in different

contexts, either during formal practice, or when the practitioner

intentionally cultivates meditative qualities within her/his daily

activities. Another critical methodological issue is that resting state

in meditation experts may be an elusive concept. Indeed, it is

possible that during wakefulness a meditator may spontaneously

enter a meditative state while “at rest” as a consequence of his/her

level of training. However, during sleep brain activity is directed

by neither conscious effort nor attention, but rather reflects the

intrinsic function of cortico-cortical and cortico-thalamic circuits

PLOS ONE | www.plosone.org 1 August 2013 | Volume 8 | Issue 8 | e73417

[10]. Thus, the examination of sleep EEG is an effective means to

identify individual trait differences in the brain unconfounded by

the effect of meditation on waking mentation. [11].

The recent availability of high-density electroencephalography

(hdEEG) has greatly enhanced the spatial resolution of standard

EEG recordings, thus allowing for better characterization of local

plastic changes [12],[13],[14],[15]. HdEEG studies of sleep

rhythms, with good spatial and temporal resolution, are extremely

sensitive measures of changes in the activity in corticothalamic

circuits. For example, we previously showed that slow wave

parameters, such as amplitude and slope, display local changes in

the brain in response to learning [16],[17],[12]. Moreover, we

have demonstrated that sleep spindle activity is decreased in

schizophrenic subjects compared to controls [14]. Finally, gamma

power has been shown to increase during NREM sleep in human

subjects following declarative learning [18].

Here we performed whole night sleep hdEEG (256-channels) in

experienced meditators (LTM) and meditation naive individuals

matched for age and sex. Even if this is a correlative and not a

causal approach, it paves the way for studies on long term plastic

effects of meditation on brain activity in healthy humans.

Materials and Methods

Participants
Twenty-nine right-handed long-term meditators (LTM, mean

age = 50.7 6 10.4, 15 female) and a group of twenty-nine

meditation naive subjects matched for age and sex were recruited.

LTM had a history of daily meditation practice of at least 3 years

and had participated in at least 3 one-week intensive retreats.

Mean duration of meditation training was 15.6 years (6 7.8, SD).

Naive subjects had no previous experience with meditation. All

LTM participants were proficient in meditation practices, as

taught within the framework of either Theravada or Tibetan

Buddhisms. These practices included two attention-based medita-

tions, which we referred to as open monitoring (OM) and focused

attention (FA), as well as one compassion/loving kindness

meditation referred to as metta meditation [1] (Table 1). Briefly,

FA meditation involves directing and sustaining attention on a

selected object (e.g., breathing), detecting mind wandering and

distractors (e.g., thoughts), as well as disengagement of attention

from distractors and shifting of the focus of attention back to the

selected object. By contrast, OM meditation has no explicit focus

of attention, but rather requires nonreactive meta cognitive

monitoring of anything that is experienced, thus replacing the

“effortful” selection of an object as primary focus with an

“effortless” sustained awareness of the rich features of each

experience [1]. The practice of compassion/loving kindness

meditation is a form of concentration practice where the

practitioner focuses his/her mind on the suffering of oneself or

others and then on the wish that the individual(s) in question may

be happy and free from suffering. After an initial phone screening

to collect the medical and psychiatric history, each subject

underwent a thorough in-person screening, which included several

questionnaires (see below). Sleep-disordered breathing and sleep-

related movement disorders were also established/excluded with

in-laboratory polysomnography (see below). All subjects provided

written informed consent and were instructed to maintain regular

sleep-wake schedules in the week preceding EEG recordings. This

study was approved by the Institutional Review Board of the

University of Wisconsin-Madison.

Study design
All subjects underwent in-laboratory hdEEG polysomnography

(PSG) that utilized 256 channel hdEEG (Electrical Geodesics Inc.,

Eugene, OR), as well as standard sleep monitoring leads, including

electrooculogram, sub-mental electromyogram, electrocardio-

gram, bilateral tibial electromyogram, respiratory inductance

plethysmography, pulse oximetry, and a position sensor. Partic-

ipants arrived at the laboratory between 4:00 and 5:00 p.m. for

set-up that took approximately two hours. Baseline EEG and an

attention and a fear conditioning task were performed. After 9pm

the participants were allowed to sleep undisturbed in the

laboratory beginning within one hour of their usual bedtime.

Additional measures were collected the next day. In this report we

will focus only on the EEG data during sleep.

Self reported measures
The socioeconomic status (SES) measure (Socioeconomic status

was measured with the Hollingshead Index of Social Position,

[19]) was administered to assess the level of education. The Quick

Inventory of Depressive Symptoms [20] and the Symptom

Checklist-90-Revised [21] were collected to exclude current

depression and other mental health issues. The cut-off for

exclusion was a score ,2 for both questionnaires. Additionally,

each participant completed validated sleep rating scales, including

the Insomnia Severity Index (ISI) [22], the Fatigue Severity Scale

(FSS) [23], the Epworth Sleepiness Scale [24], a sleep history

questionnaire, and the Stanford Sleepiness Scale [25] to assess for

symptoms of common sleep disorders, such as restless legs

syndrome and obstructive sleep apnea. Thresholds for exclusion

were an ISI .10 and/or an FSS .4 and/or an ESS .9.

Meditation Practice
LTM had an average of 8762 lifetime hours of meditation

practice, ranging from 1,526 to 32,349 total hours. The

computation of lifetime hours of practice was based on subjects’

reports of their average hours of formal (sitting and walking)

meditation practice per week, including time spent on meditation

retreats (for details see Table 1).

Sleep PSG assessment and hdEEG data analysis
Sleep staging, which was based on six mastoid-referenced

channels (F3, F4, C3, C4, O1, and O2), a sub-mental electro-

myogram and an electrooculogram, was performed by a registered

polysomnographic technologist in 30-second epochs according to

standard criteria [26] using AliceH Sleepware (Philips Respironics,
Murrysville, PA) The sleep technician was blind to group

assignment. PSG recordings were reviewed by a board certified

sleep medicine physician, who was able to confirm the absence of

sleep disorders in 26 out of 29 LTM. Two LTM met the clinical

criteria for sleep-related movement disorders (periodic limb

movement arousal index .10/h), while one had sleep-disordered

breathing (apnea–hypopnea index .10/h). As a result, data from

these three LTM (as well as from the age- and sex- matched

meditation naives) were not further analyzed. All-night sleep

hdEEG recordings were collected with vertex-referencing, using a

NetAmps 300 amplifier and NetStation software (Electrical

Geodesics Inc., Eugene, OR). EEG data were sampled at

500 Hz, and a first-order high-pass filter (Kaiser type, 0.1 Hz)

was applied to eliminate the DC shift. Data were then band-pass

filtered (1–50 Hz), down-sampled to 128 Hz and average-refer-

enced to the mean power in all channels. Spectral analysis was

performed for each channel in six-second epochs (Welch’s

averaged modified periodogram with a Hamming window). For

Local NREM Sleep Gamma Increase in Meditators

PLOS ONE | www.plosone.org 2 August 2013 | Volume 8 | Issue 8 | e73417

NREM sleep data, a semi-automatic artifact rejection was

conducted to remove six-second sleep epochs which exceeded a

threshold based on the mean power at either a low (1–4 Hz) or

high (20–30 Hz) frequency band [27],[28]. EEG channels in

which artifacts affected most of the recording were excluded. REM

sleep epochs were visually analyzed and divided in tonic and

phasic (i.e., characterized by rapid eye movements). To eliminate

artifacts distinctively observed during REM sleep (i.e., muscle

twitches, eye movements, heartbeats), independent component

analysis (ICA) was performed [29]. After removal of ICA

components, power spectral of tonic and phasic REM epochs

was computed. We computed six frequency ranges (delta: 1–

4.5 Hz, theta: 4.5–8 Hz, alpha: 8–12 Hz, sigma; 12–15 Hz, beta:

15–25 Hz, and gamma: 25–40 Hz) consistent with previous

studies from our lab [30]. In order to determine whether group

differences in NREM gamma activity were related to increased

muscle tone or variation in saccades, we compared the power in

the gamma band in neck EMG and EOG derivations [30]

between LTM and meditation naive participants.

Sleep cycles were defined according to the modified criteria [31]

of Feinberg and Floyd [32] also consistent with our previous

studies [33].

Statistics
Differences in clinical as well as sleep architecture variables

were examined using 2-tailed, unpaired t-tests. Topographical

analysis was computed after spatially normalizing each subject’s

topography (z-score across all channels) within the frequency

bands of interest as a means to reduce between-subject variance.

Group differences in topographical NREM and REM sleep

hdEEG power were assessed with statistical non-parametric

mapping (SnPM) using a suprathreshold cluster test to identify

significant groups of electrodes after accounting for the multiple

comparisons due to the numerous electrodes [34]. Briefly, an

appropriate threshold t-value was chosen (t = 2, corresponding to

a = 0.05 for the given degrees of freedom) before topographic
power maps were randomly shuffled between groups (LTM and

meditation naive individuals). The size of the largest cluster above

the threshold for each reshuffling was then used to create a cluster

size distribution. Given the impracticality of computing all possible

combinations (4.9661014), 50000 unique combinations were run
for each comparison in order to approximate the actual cluster

distribution. The suprathreshold cluster p-value was then deter-

mined by comparison of the true cluster size against the

approximate maximal cluster size distribution. Bonferroni correc-

tion of the p value was performed to account for the 18 tests

deriving from separately evaluating 6 different frequency bands

over the course of 3 distinct phases of sleep.

The magnitude of NREM sleep EEG gamma power differences

between LTM and meditation naive individuals was assessed with

the Cohen’s d, a measure of the effect size [35]. We also performed

correlation analysis between duration of meditation practice in

LTM and NREM sleep EEG gamma power, as well as TST and

WASO. Statistical analyses were performed using MATLAB (The

MathWorks Inc., Natick, MA) and STATISTICA (StatSoft Inc.,

Tulsa, OK). Non-parametric statistics were used to assess group

differences in EMG, EOG, and global EEG gamma power

(Wilcoxon rank sum test), as well as to correlate gamma power

with variables of interest (Spearman rank correlation). An outlier

subject was excluded from the correlation between EEG gamma

power and hours of daily practice in LTM based on a suspected

over inflation of daily practice (value above 1.5 interquartile ranges

from the 75th percentile). Specifically, this LTM had the least

amount of retreat time compared with all the LTMs, but the

highest amount of non-retreat hours.

Results

Demographics and sleep variables
The LTM and the meditation naive groups were matched for

age and sex (see Table 2). Additionally, the groups did not differ in

education level, as assessed by the socioeconomic status question-

naire (Table 2). LTM had a significantly reduced total sleep time

(TST) as well as an increased wake after sleep onset (WASO)

compared to meditation naive subjects (Table 2). These sleep

parameters did not show a correlation with daily meditation

practice. By contrast, the two groups did not differ in sleep onset

latency or in the relative time (% of TST) spent in each sleep stage

and did not differ in the number of arousals (Table 2).

LTM had higher NREM sleep gamma power compared to
meditation naives in a parietal-occipital brain region

We first checked for global, frequency non-specific differences,

by comparing the absolute average power across the spectrum

between the groups. This analysis revealed no difference between

LTM and meditation naive individuals (p = 0.094). We then

focused on local, frequency specific effects. Whole night NREM

sleep EEG power was topographically compared between LTM

and meditation naives by spatially normalizing each subject’s map

to the average power in six frequency ranges (delta: 1–4.5 Hz,

theta: 4–8 Hz, alpha: 8–12 Hz, sigma; 12–15 Hz, beta: 15–

25 Hz, and gamma: 25–40 Hz). A frequency specific increase in

EEG gamma power was found in LTM. Whereas NREM sleep

Table 1. Information about long-term meditators’ (LTM) practice history: In our convention, Focused Attention meditation
encompasses concentrative practices such as Theravada Jhana, or breath awareness meditation.

Total amount of meditation practice in life (in hours) Mean, 8126; SD, 6066; Min, 1439; Max, 32613

Daily practice (Mean 50.5%, SD 28.6%) vs retreat practice (Mean 49.5%, SD 72.6%)

LTM’s meditation lineage type (N = 26) Theravada (N = 21)

Tibetan Buddhism (N = 2)

Mixed of the above, or of the above with Zen (N = 3)

Meditation practices (%) Focused Attention meditation
(Mean, 33.0; SD, 22.6)

Open Monitoring meditation
(Mean, 51.4; SD, 23.6)

Compassion/loving kindness meditationOther meditations

(Mean, 14.0; SD, 10.8) (Mean, 1.6; SD, 6.1)

Open Monitoring meditation encompasses practices such as Vipassana meditation.
doi:10.1371/journal.pone.0073417.t001

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PLOS ONE | www.plosone.org 3 August 2013 | Volume 8 | Issue 8 | e73417

gamma power in both LTM and meditation naives was strongest

in frontal/prefrontal areas, and weakest in the temporal regions

bilaterally (Figure 1, top row), LTM showed higher relative EEG

gamma activity in a parietal-occipital region compared to

meditation naives (Figure 1, bottom left). Suprathreshold cluster

analysis, a statistical non parametric mapping test (SnPM) which

corrects for the multiple comparison problem resulting from the

numerous individual electrode tests inherent in hdEEG analysis,

confirmed that a parieto-occipital cluster (N = 39 electrodes)

showed significantly higher relative gamma power in LTM

compared to meditation naive subjects (SnPM, p = 0.002,

Figure 1, bottom right). This result survived Bonferroni correction

for the 18 tests derived from separately evaluating 6 different

frequency bands over the course of 3 distinct phases of sleep

(p = 0.05/18 = 0.003). Therefore, we wanted to check whether this

topographical increase in gamma power was paralleled by a

difference in the non-normalized gamma power between groups.

We found a parieto-occipital cluster (N = 46 electrodes) largely

overlapping the normalized cluster, that showed significantly

higher absolute gamma power in LTM compared to meditation

naive subjects (SnPM, p = 0.036). We also examined whether this

result was consistent across the night and found similar results in

the first three NREM cycles, although the magnitude of the result

in the first NREM cycle did not survive multiple comparison

corrections after normalization (SnPM, normalized data, cycle 2:

N = 20, p = 0.012, cycle 3: N = 30, p = 0.002; absolute data, cycle

1: N = 37, p = 0.0250, cycle 2: N = 44, p = 0.021, cycle 3: N = 54,

p = 0.020). Similar results were obtained when breaking down

NREM by stages into N2 and N3, with only N2 surviving multiple

comparisons corrections after normalization (SnPM, normalized

data, N2: N = 23, p = 0.010; absolute data, N2: N = 49, p = 0.019,

N3, N = 51, p = 0.014). We suspect that the low values of gamma

at the beginning of the night as well as during deep NREM sleep

make it difficult to appreciate a difference between groups and

therefore the effect is evident but less robust during these times.

We found no gamma power difference in either the electro-

myographic (EMG) or electrooculographic (EOG) derivations

between the two groups (p = 0.589 and p = 0.493 respectively)

(Table 2), suggesting that the local gamma increase was not

artifactual. SnPM topographic analysis found no significant

differences between the two groups for the other five frequency

ranges.

LTM NREM sleep gamma power correlated with
meditation training

To further characterize the gamma increase at the individual

subject level, we calculated the average NREM gamma power of

the parietal-occipital cluster for each subject. The average cluster

difference in gamma power between groups was 35% (Figure 2,

left panel) and the between group Cohen’s d was 0.8,

corresponding to a large effect size (more than 50% separation

between LTM and meditation naives). We next investigated

whether the cluster gamma power was significantly correlated with

the overall duration of meditation practice. We found a significant

correlation between parietal-occipital NREM gamma power and

daily meditation practice, but not retreat time (rho = 0.475,

p = 0.017, and rho = 0.029, p = 0.887, for daily practice and

retreat time respectively). As gamma power in LTM was

correlated with age (rho = 0.487, p = 0.012), we further tested

whether the age of meditation naive individuals predicted also

EEG gamma power, but no correlation was found (rho = 20.035,

p = 0.866). For each group there was also no correlation between

gamma power in the cluster and the significantly different sleep

architecture variables (TST, rho = 20.195, p = 0.339, rho = 0.287,

p = 0.156; WASO, rho = 0.170, p = 0.408, rho = 20.319,

p = 0.112, for LTM and meditation naive individuals, respective-

ly). Thus, daily practice was the most sensitive predictor of the

correlation with gamma power activity.

REM sleep gamma power did not differ between LTM
and naives

To assess whether the gamma increase of LTM was specific for

NREM sleep, topographic EEG analysis of REM sleep was also

performed. As previous literature has suggested functional

differences between tonic and phasic REM, we separated these

two REM sleep patterns in our analysis [36],[37],[38]. Both tonic

Table 2. Clinical and sleep variables of subject groups.

Long Term Meditators Meditation Naive

(N = 26) (n = 26)

Clinical Variables p values

Age 49.4 6 10.7 47.0 6 10.8 n.s

Gender (F/M) 14/12 14/12 n.s.

Sleep Variables

Total sleep time (min) 368.56 51.5 404.8 6 25.3 0.001

Sleep onset latency
a

(min) 11.06 15.35 9.6 6 10.7 n.s.

REM onset latencyb (min) 120.566 63.82 107.836 47.44 n.s.

WASO
c

(min) 74.16 47.1 43.5 6 18.4 0.003

NREM N1 (%) 11.06 5.7 9.7 6 5.4 n.s.

NREM N2 (%) 61.36 7.6 64.36 10.0 n.s.

NREM N3 (%) 10.76 6.8 9.0 6 9.8 n.s.

REM (%) 17.06 5.3 17.0 6 4.9 n.s.

a.
Sleep onset latency is defined as the time from the beginning of lights out until the first staged epoch other than wake.

b.REM onset latency is defined as the time from Sleep onset until the first staged REM sleep epoch.
c.

WASO = wake after sleep onset.
doi:10.1371/journal.pone.0073417.t002

Local NREM Sleep Gamma Increase in Meditators

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Figure 1. Long-term practitioners (LTM) had higher NREM gamma power (25–40 Hz) compared to meditation naives in a parietal-
occipital region. As shown in topographic color plots (colorbar in mV2), both groups had maximal EEG gamma power in frontal/prefrontal regions.
Furthermore, LTM showed a 35% gamma power increase in a parietal-occipital region compared to meditation naives. The pink area in the white
topographic plot depicts the parietal-occipital electrode cluster (N = 39) with a significant power increase in LTM (p = 0.002, Statistical non Parametric
Mapping, SnPM).
doi:10.1371/journal.pone.0073417.g001

Figure 2. NREM Gamma increase in LTM compared to meditation naives had a large effect size (ES = 0.8, Panel A), and was
significantly correlated with the length of meditation daily practice (rho = 0.475, p = 0.017, Panel B).
doi:10.1371/journal.pone.0073417.g002

Local NREM Sleep Gamma Increase in Meditators

PLOS ONE | www.plosone.org 5 August 2013 | Volume 8 | Issue 8 | e73417

and phasic REM gamma power peaked in a frontal/prefrontal as

well as in a parieto-occipital region in LTM and meditation naives

(Figure S1). SNPM analyses revealed no topographical differences

in any frequency band in REM sleep between the two groups.

Discussion

By performing sleep hdEEG (256 channels) recordings in this

study we found that LTM had increased parietal-occipital EEG

gamma power during NREM sleep compared to meditation

naives. This increase was specific for the gamma frequency range,

was not found during REM sleep, and was positively correlated

with the length of daily meditation

practice.

The LTMs recorded in this study were experienced in Focused

Attention (FA), Open Monitoring (OM), as well as loving

kindness/compassion meditations. An increase in gamma activity

was recently demonstrated in expert Buddhist practitioners

(.10.000 hours of practice) during a style of meditation which

contains features of both loving kindness/compassion and OM

meditations [39]. Compared to a group of novices, the expert

practitioners showed self-induced, higher-amplitude, sustained

EEG gamma-band oscillations, especially over lateral frontal-

parietal electrodes, while meditating as well as in the resting state

immediately preceding and following meditation [6]. Notably, a

link between higher gamma-band activity and stronger cognitive

control has been reported by a variety of human electrophysio-

logical techniques, including magnetoencephalography (MEG)

[40], scalp EEG [41], and direct cortical recordings [42].

Furthermore, a recent EEG study has found that LTM had

increased gamma power in a parietal-occipital area compared to

meditation naive individuals during resting state, as well as during

meditation practice as compared to baseline [8]. Based on these

findings, the authors concluded that enhanced posterior EEG

gamma power was a state (meditation-related), and to some same

extent trait (resting state associated) feature of meditation practice.

Here we established that LTM had higher EEG gamma power

over an extended period of spontaneous brain activity (i.e., whole

night NREM sleep) compared to meditation naive individuals.

This parietal-occipital gamma increase corresponded to a large

effect size (ES = 0.8, allowing more than 50% separation between

meditation experts and naives). These findings strongly suggest

that changes in EEG gamma activity related to meditation

practice are trait (in addition to state) related. Comparing the

activity of meditation experts and novices during an unambiguous

resting condition is a challenging task [5]. This is because

experienced long-term meditation experts are usually able to

blend formal meditation session with daily life, and that a

meditator might spontaneously generate a meditative state while

at rest in the lab out of demand characteristic. In this regard, sleep

provides an exquisite window to explore the spontaneous brain

activity as well as the function of neural circuits at rest. Brain

activity during sleep does not require conscious effort or attention,

and a condition of physical immobility is obtained for several

hours. Moreover, the recently available combination of standard

polysomnography with hdEEG, which provides enhanced spatial

and temporal resolution, offers the opportunity to analyze in

greater details NREM/REM sleep activity as well as to observe

local changes in brain function due to neuroplasticity [43],[16].

Daily practice and meditation retreat could contribute differ-

ently to the neuroplastic changes induced by meditation. For

instance, meditation frequency (days per week with meditation

practice) has recently been shown to reliably predict both higher

mindfulness and psychological well-being [44]. In this study we

showed that the daily practice, but not the retreat time, predicted

the parietal-occipital gamma activity during NREM sleep in

LTM. The differential effect of the amount of daily practice and of

retreat time on localized NREM gamma activity is to our

knowledge the first indication of a specific effect of constant

meditation daily practice, but not of intensive retreat practice, on

brain neuroplasticity. On one side, this finding raises some

methodological issues about how to quantify meditation practice,

suggesting the potential usefulness of differentially investigating the

contribution of retreat time and daily practice on behavioral and

physiological measures. On the other side, it enhances the

effectiveness of our approach in revealing stable (trait-like) effects

on brain functioning induced by prolonged training during

waking.

In the present study LTM had a significantly reduced total sleep

time (TST) and increased wake after sleep onset (WASO)

compared to meditation naive individuals. A reduction in TST

has been recently reported by another sleep study in LTM [45],

and it indicates that meditation practice may decrease sleep needs.

However other studies [46,47] investigating sleep architecture

didn’t find a reduction of TST, suggesting that the sleep

architecture is not the most reliable parameter to study the effect

of meditation on neuronal plasticity during sleep. Consistent with

this idea, we did not find a correlation between the changes in any

traditional polysomnographic sleep parameters and meditation

practice.

Only a handful of studies so far have investigated the sleep EEG

activity of meditation experts beyond sleep architecture. One of

these studies explored EEG differences in thirteen individuals with

at least 2 years of meditation experience during Transcendental

meditation (TM; a form of meditation different from that explored

in the current study), resting wakefulness, drowsiness, and sleep

and found a progressive slowing of the main EEG frequency from

wakefulness to sleep, with no appreciable change in power

between wakefulness and meditation EEG [48]. The authors also

analyzed the EEG activity of meditation practitioners and of

meditation naive control subjects during resting wakefulness, and

found no difference in power but a slight slowing in the mean EEG

frequency of the practitioners; however, they did not compare the

sleep EEG of these two groups [48]. Another study found that

eleven long term TM practitioners had increased theta-alpha

power during slow wave sleep compared to nine short term

practitioners as well as eleven experienced practitioners [49]. Here

we found no difference in theta-alpha EEG power between LTM

and meditation naive individuals during NREM sleep. Differences

in style of meditation practices may account for the discrepancy of

these findings. Furthermore, whereas we screened participants for

sleep disorders and performed PSG recordings during the first

hdEEG night, the LTM recorded by Mason et al. did not undergo

such screening. An EEG pattern of alpha wave in delta wave sleep

(alpha-delta sleep) is commonly reported in individuals with sleep

disturbances, including restless leg syndrome [50] and sleep apnea

[51], and is associated with increased arousability and lighter sleep

[52]. Notably, their LTM spent a significantly higher amount of

time in light (N1) NREM sleep, while we found no difference in

N1 between LTM and meditation naive individuals. [45]And

finally, the type of meditation practice examined in the current

study differs from the practice studied in these other studies.

What is the functional meaning of the gamma increase in LTM

that was found here during NREM sleep? A large body of

evidence from animal and human recordings have suggested that

gamma-frequency activity is implicated during wakefulness in

plasticity-related processes, including attention, learning, as well as

both working and long-term memory [53]. For instance, an

increase in gamma activity occurs when sensory stimuli are

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attended [54], as well as during the active maintenance of

representations during working memory tasks [55],[56]. Several

studies employing EEG, MEG and intracranial EEG recordings

have also shown that gamma-frequency activity during encoding

predicts successful formation of long-term memory [57],[58].

Little is still known about the functional significance of gamma

activity during sleep. In this study we found a gamma power

increase in meditation experts during NREM sleep in a scalp

region overlying posterior parietal and occipital cortical areas. Our

finding is in line with the recent report of Valderrama et al., which

suggested a functional significance for gamma oscillations during

NREM sleep in humans [59]. A possible confound in the study of

gamma activity is the presence of muscular or ocular artifacts in

the scalp EEG. However, we found no difference in neck EMG

and EOG gamma power between LTM and meditation naive

individuals. This result therefore strongly indicates that such

artifacts do not contribute significantly to our finding.

Previous studies have reported an increase in fast-frequency

activity during sleep in pathological conditions such as schizo-

phrenia, depression and insomnia [60],[61],[62]. This increase has

been associated with abnormalities in the arousal mechanisms

[62]. However, this functional interpretation of gamma activity is

not warranted for a number of reasons. First, we did not find a

group difference in arousal. Our finding was specific to spatially

normalized gamma activity. Furthermore, the topography of the

current finding over parietal-occipital electrodes differed from

those earlier findings. Finally inter-individual variability in gamma

activity among meditators predicted daily meditation experience

in life, which has been found to predict positive mental health

outcomes [44],[1]. Instead, we speculate here the specific gamma

increase is not pathological, but reflects the lasting, plastic effect on

specific neuronal circuits of long-term meditation practice. For

instance, the parietal cortex has been implicated in directing the

focus of attention on a specific object [63], a cognitive skill

perfected by attention-based meditation [1]. The finding of higher

whole night NREM gamma power in LTM compared to

meditation naives could therefore reflect an increase in activity

and connectivity within these neuronal circuits due to extensive

meditation training. Consistent with this assumption, here we

established that NREM sleep gamma band power correlated with

the duration of daily meditation practice in LTM. This finding not

only confirms a previous report from our group that the amount of

EEG gamma power generated during meditation by LTM

correlated with the length of their meditation training [6], but

also suggests that EEG gamma activity during sleep may represent

a trait-like sensitive measure for the neuronal plastic changes

determined over time by meditative training.

The increase in NREM sleep parieto-occipital gamma power

reported here could also reflect an enhanced activity of the

underlying cortical areas in LTM compared to meditation naives.

Higher EEG gamma power reflects higher firing rates of the

underlying neuronal populations [64], and local changes in

gamma oscillations closely mirror underlying activity in both

visual [65],[66] and parietal default network-associated cortical

regions [67],[68]. During NREM sleep both neuronal firing and

gamma power tend to decrease, as does the ability to process

sensory information [69],[70]. Thus, a higher gamma activity in

LTM could reflect a partially maintained capacity of parieto-

occipital sensory and default network-associated areas to process

information and maintain some level of awareness, even during a

state when usually these cognitive functions are greatly impaired.

Consistent with this idea, a higher incidence of dream reports has

been found in meditation experts compared to meditation naives

even during the deepest stages of NREM sleep [71]. If experienced

meditators retain a higher capacity for internal information

processing and awareness during NREM sleep compared to

meditation naives, such advantage should be reduced during REM

sleep, when these functions are partially restored and spontaneous

neuronal firing/gamma activity is enhanced compared to NREM

sleep [72]. In this study we found that LTM had only a slight, non

significant increase in REM sleep EEG gamma power compared

to meditation naives in the same parietal-occipital regions (Figure

S1).

Limitations of the study include a lack of an adaptation night,

which could account for the truncated sleep time (, 7 hrs) in all

participants, but it is unlikely to explain the observed group

difference in the sleep EEG. Future work will also need to address

some of the questions left unanswered in the present study. For

example, the relationship found here between higher EEG gamma

activity and longer meditation daily practice suggests that gamma

power is a good correlate of meditation training. This correlation

should be confirmed in longitudinal studies performing EEG

recordings in meditation naive individuals before and after

meditation training, ideally using only one style of meditation

practice. It will also be important to investigate whether the

observed gamma increase may be affected by pre-existing

“baseline” gamma activity differences between groups (i.e.,

meditation experts and naives), as previously suggested [6].

Gamma activity has been shown to be influenced by several

factors, including age [73], sex [74], and cognitive abilities [75].

However, these factors are unlikely to have contributed to the

present findings, given that LTM and meditation naive subjects

were matched for age, sex, and did not differ in education level.

Future studies should investigate whether the group difference in

NREM gamma activity in meditators is associated to a specific

meditation practice (e.g. mindfulness meditation vs. compassion

meditation) or style of meditation training (e.g. Tibetan Buddhism

vs. Theravada Buddhism). Specifically, investigating the acute

effect of an intense meditation session in LTM on sleep EEG

patterns could help in establishing a causal relationship between

meditation training and specific changes in EEG activity. Finally,

future experiments combining fMRI with simultaneous hdEEG

will be critical to fully characterize the cortical (and possibly sub-

cortical) networks underlying the enhanced NREM sleep EEG

gamma activity found in this study in meditation experts, whereas

studies investigating the healing effects of meditation interventions

could explore the ability of EEG gamma power to predict a

beneficial effect of such interventions. This work would contribute

to identify the neural circuits underlying the EEG correlates of

meditation training. It will also help to establish whether EEG

gamma activity represents a sensitive and objective measure of the

effects of meditative practice on brain function in both healthy

subjects and brain disordered patients.

Supporting Information

Figure S1 REM tonic as well as phasic gamma power
did not differ between LTM and meditation naives.
Topographic color plots showed maximal REM tonic as well as

phasic gamma power in frontal/prefrontal regions in both groups.

Compared to meditation naives, LTM had a slightly higher power

in the same parieto-occipital region significantly more active

during NREM sleep, which however failed to reach significance in

both tonic and phasic REM (white topographic plots, p = 0.975,

and p = 0.810, SnPM). Only the tonic REM topographies are

shown.

(TIF)

Local NREM Sleep Gamma Increase in Meditators

PLOS ONE | www.plosone.org 7 August 2013 | Volume 8 | Issue 8 | e73417

Acknowledgments

We would like to thank Mélanie Boly for providing helpful comments on

this manuscript and for helpful suggestion on EEG analysis and Donal

MacCoon and Brianna Schuyler for helping analyzing the self-report

questionnaires in this study.

Author Contributions

Conceived and designed the experiments: FF AL RJD GT. Performed the

experiments: FF RS DD BR CZ. Analyzed the data: FF RS DD CZ RB.

Contributed reagents/materials/analysis tools: FF BR RS DD. Wrote the

paper: FF BR AL RJD DD GT.

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