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A systematic review of the neurophysiology of mindfulness on EEG oscillations

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Mindfulness meditation has been purported to be a beneficial practice for wellbeing. It would therefore be expected that the neurophysiology of mindfulness would reflect this impact on wellbeing. However, investigations of the effect of mindfulness have generated mixed reports of increases, decreases, as well as no differences in EEG oscillations in comparison with a resting state and a variety of tasks. We have performed systematic review of EEG studies of mindfulness meditation in order to determine any common effects and to identify factors which may impact on the effects. Databases were reviewed from 1966 to August 2015. Eligibility criteria included empirical quantitative analyses of mindfulness meditation practice and EEG measurements acquired in relation to practice. A total of 56 papers met the eligibility criteria and were included in the systematic review, consisting of a total 1,715 subjects: 1,358 healthy individuals and 357 individuals with psychiatric diagnoses. Studies were principally examined for power outcomes in each bandwidth, in particular the power differentials between mindfulness and the control state, as well as outcomes relating to hemispheric asymmetry and event-related potentials. The systematic review revealed that mindfulness was most commonly associated with enhanced alpha and theta power as compared to an eyes closed resting state, although such outcomes were not uniformly reported. No consistent patterns were observed with respect to beta, delta and gamma bandwidths. In summary, mindfulness is associated with increased alpha and theta power in both healthy individuals and in patient groups. This co-presence of elevated alpha and theta may signify a state of relaxed alertness which is conducive to mental health.
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Running title: The neurophysiology of mindfulness
1
Title
A systematic review of the neurophysiology of mindfulness on EEG oscillations
Authors
Tim Lomas 1, Itai Ivtzan 1, Cynthia H.Y. Fu 1,2
Affiliations:
1. School of Psychology, University of East London, London, UK
2. Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience,
King’s College London, London, UK
Author for Correspondence:
Dr. Tim Lomas, School of Psychology, University of East London, Arthur Edwards Building,
Water Lane, London, E15 4LZ, United Kingdom
Tel: +44 (0)20 8223 4465 Fax: +44 (0)20 8223 4937 Email: t.lomas@uel.ac.uk
Running title: The neurophysiology of mindfulness
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Abstract:
Mindfulness meditation has been purported as a beneficial practice for wellbeing. It would
be expected that the neurophysiology of mindfulness would reflect this impact on wellbeing.
However, investigations of the effect of mindfulness have generated mixed reports of
increases, decreases, as well as no differences in EEG oscillations in comparison with a
resting state and a variety of tasks. We have performed systematic review of EEG studies of
mindfulness meditation in order to determine any common effects and to identify factors
which may impact on the effects. Databases were reviewed from 1966 to August 2015.
Eligibility criteria included empirical quantitative analyses of mindfulness meditation practice
and EEG measurements acquired in relation to practice. A total of 56 papers met the
eligibility criteria and were included in the systematic review, consisting of a total 1,715
subjects: 1,358 healthy individuals and 357 individuals with psychiatric diagnoses. Studies
were principally examined for power outcomes in each bandwidth, in particular the power
differentials between mindfulness and the control state, as well as outcomes relating to
hemispheric asymmetry and event-related potentials. The systematic review revealed that
mindfulness was most commonly associated with enhanced alpha and theta power as
compared to an eyes closed resting state, although such outcomes were not uniformly
reported. No consistent patterns were observed with respect to beta, delta and gamma
bandwidths. In summary, mindfulness is associated with increased alpha and theta power in
both healthy individuals and in patient groups. This co-presence of elevated alpha and theta
may signify a state of relaxed alertness which is conducive to mental health.
Keywords: mindfulness; meditation; neurophysiology; EEG; systematic review.
Running title: The neurophysiology of mindfulness
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Introduction
Meditation refers to a diverse range of mental activities which share a common focus on the
regulation of attention and awareness (Cahn and Polich, 2006) in order to improve voluntary
control of mental processes which is purported to foster general wellbeing (Walsh and
Shapiro, 2006). Most world cultures have developed their own forms of meditation; for
example, Christianity has a long tradition of contemplative prayer (Egan, 1978). Much of the
recent scientific interest in meditation has centred on mindfulness meditation, which is a
practice that is believed to have originated with Buddhism around the fifth millennium B.C.
although its roots may stretch back further to the third millennium B.C. in Hindu culture
(Cousins, 1996).
The most common forms of meditation may be conceptualized as involving either focused
attention or an open-monitoring form of processes (Lutz et al., 2008). Focused attention
practices may be operationalized into their respective attention networks (Posner and
Petersen, 1990; Mirsky et al., 1991): sustained attention (e.g. towards a target, such as the
breath), executive attention (e.g. preventing one’s focus from ‘wandering’), attention
switching (e.g. disengaging from distractions), selective attention and attention re-orienting
(e.g. redirecting focus back to the breath), and working memory (Lutz et al., 2008; Vago and
Silbersweig, 2012). Open-monitoring refers to a broader receptive awareness or capacity to
detect events within an unrestricted awareness without a specific focus (Raffone and
Srinivasan, 2010), which can include a process of ‘meta-awareness’ (i.e., awareness of
awareness, in which practitioners are able to reflect on the process of consciousness itself).
Mindfulness has been described as the awareness that arises through purposeful attention
on the present moment with nonjudgmental experience (Kabat-Zinn, 2003). While
mindfulness has been commonly viewed as an example of open-monitoring, it has been
proposed to involve an admixture of focused attention and open-monitoring (Lutz et al.,
2008; Vago and Silbersweig, 2012) as most mindfulness practices begin with a period of
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focused attention on a target, such as the breath, in order to focus awareness, followed by
the more receptive state of open-monitoring (Cahn and Polich, 2006). In Vago and
Silbersweig’smodel (2012), the practice of mindfulness leads to three overarching self-
related capacities: meta self-awareness, self-regulation, and self-transcendence. These are
subserved by numerous subcomponent cognitive components, including motivation (which is
crucial in terms of people practicing meditation in the first place), attention regulation (via the
development of attention modalities), and de-centring (an ability, defined below, that arises
from enhanced attention regulation, and which facilitates self-awareness and
transcendence). It is further proposed that these three overarching capacities modulate
‘self-specifying and narrative self-networks’ through an integrative fronto-parietal control
network.
Mindfulness has been applied as a clinical intervention based on the notion that it is a
method for training attention and awareness. By developing the ability to observe one’s
thoughts and feelings, practitioners learn how to perceive them as temporary, objective
events in the mind as opposed to reflections of the self that are necessarily true, which has
been termed as the ability to “decentre” (Fresco et al., 2007). As a clinical intervention, it
involves the process to engage with negative experiences, such as pain or dysphoric
emotions, with more dispassion and less reactivity (Shapiro et al., 2005). Mindfulness was
initially applied as an intervention for chronic pain with Kabat-Zinn’s (1982) Mindfulness-
Based Stress Reduction (MBSR) program. The MBSR program has since been applied in
the treatment for number of conditions, including cancer (Ledesma & Kumano, 2009) and
migraine (Schmidt et al., 2010), and adapted as a treatment to prevent relapse in depression
(Mindfulness-Based Cognitive Therapy; Segal et al., 2002) and for the treatment of
substance abuse (Mindfulness-Based Relapse Prevention; Bowen et al., 2014, Mindfulness-
Oriented Recovery Enhancement; Garland et al., 2014).
The effectiveness of mindfulness has been assessed by measures for depression and
quality of life (Hofmann et al., 2010). As mindfulness may be considered to be a method of
Running title: The neurophysiology of mindfulness
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attention training and emotion regulation, we would expect that the corresponding
neurophysiological states should be observable. Electroencephalography (EEG) is a non-
invasive technique that analyzes spatiotemporal aspects of underlying brain activity, which
provides a measure of the large-scale synchronization of neural networks (Cacioppo et al.,
2007). Patterns of EEG activity to particular meditative states have been investigated. A
commonly reported feature of meditation has been theta and alpha event-related
synchronization (Fell et al., 2010), which are regarded as markers of internally-directed
attention processing (Shaw, 1996). Such synchronization has been observed across
different meditation practices, including mindfulness, as well as practices such as
transcendental meditation, which involves focused attention upon an internally-voiced
mantra. However, different types of meditation practice have been associated with unique
frequency patterns, reflecting the form of attention (Dunn et al., 1999). For example,
mindfulness has been associated with increase alpha power while focused attention has
been associated with increased gamma activity and idiosyncratic meditation with decreased
alpha and beta (Hinterberger et al., 2014).
Additionally, Event-Related Potentials (ERP) provide a measure of large number of time-
locked experimental trials, enabling the analysis of sensory, perceptual, and cognitive
processing (Light et al., 2010). Such studies involve the precision analysis of populations of
neuronal transients directly manifested via a stimulus/event, which is frequently a stimulus
connected to an attention-based task (e.g., listening to an auditory signal) (Schoenberg and
Speckens, 2014). The high temporal resolution of this approach, involving millisecond
precision, allows the investigation of early information processing stages and subsequent
transitions to higher-level cognitive operations. ERP studies have been used to corroborate
the idea of mindfulness as a system of attention training. For example, van Leeuwen et al.
(2012) examined the impact of mindfulness practice on hierarchical stimulus processing and
attentional selection, focusing on differences in early components of the evoked visual
response (e.g., P1 and N1 components) in meditators versus matched controls. Meditators
Running title: The neurophysiology of mindfulness
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exhibited faster attentional disengagement from a dominant global presentation in order to
focus in on specific stimuli, suggesting that meditation enhances speed of attention
allocation and relocation, thus increasing the depth of information processing.
In the present review, we have focused on mindfulness meditation. We have examined
factors which appear to impact upon EEG measures including the experience of the
meditator, being a novice or relative expert, as experience has been reported to accentuate
amplitude differences between meditation and the resting state (Hinterberger et al., 2014)
while the converse has also been observed (Cahn et al., 2010). An additional factor
includes the location of the brain activity. For example, increased alpha during mindfulness
has been localized to frontal regions (Takahashi et al., 2005) but has also been observed
increases in posterior regions (Lagopoulos et al., 2009; Cahn et al., 2010). Furthermore,
EEG analysis of meditation may be affected by whether the control task is a resting state or
a cognitive task as increased theta amplitude during meditation has been observed in
comparison to a resting state baseline, but was comparable in amplitude to an executive
attention task, which may be further modulated by the experience of the meditator (Lomas et
al., 2014).
We sought to perform a systematic review of patterns of electrophysiological activity
associated mindfulness in order to examine the impact on neurophysiology as assessed by
EEG bandwidth activation and other measures, including hemispheric asymmetry or event-
related potential, and the functional significance of these activities. If mindfulness is
expected to impact on functioning attentional networks as well as open-monitoring, then we
would expect to observe distinct neural features associated with its practice. We also
expected that the experience of the meditator, type of control task, and location of the EEG
oscillation would moderate the impact of mindfulness on neurophysiology.
Methods
Running title: The neurophysiology of mindfulness
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The literature search was conducted using the MEDLINE and Scopus electronic databases
with the criteria: EEG (AND) mindfulness OR meditation”, in all fields in MEDLINE and
limited to article title, abstract, and keywords in Scopus, with the dates: from 1966 to 1st
August 2015. The participants, interventions, comparisons, outcomes and study design
(PICOS) characteristics, the key criteria were interventions: mindfulness meditation or
functional equivalent; participants: adults; and outcomes: EEG analysis. Studies were
required to be published, or a manuscript in press, and to be in English. The review was
conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-
Analyses (PRISMA) guidelines (Moher et al., 2009). The review protocol was registered with
the International Prospective Register of Systematic Reviews (PROSPERO) database on
15th September 2014. Registration number: CRD42014013766
(http://www.crd.york.ac.uk/PROSPERO).
Inclusion criteria were: 1) mindfulness meditation practice or functional equivalent, such as
Vipassana or Zen meditation; 2) EEG measurements acquired in relation to mindfulness
meditation practice (whether assessment during the practice itself or connected to its
practice, e.g., pre- and post-intervention); 3) quantitative analysis supported by appropriate
statistical methodology; and exclusion criteria; and 4) adult sample; and exclusion criteria: 1)
theoretical articles or commentaries without statistical analyses.
The following variables were extracted from each paper: experimental protocol (control
condition, meditation condition, and/or experimental task), experience of participants (novice
or expert), sample features (clinical or non-clinical), outcomes for each individual bandwidth
(alpha, beta, theta, delta, and gamma), hemispheric asymmetry, and any event-related
potential outcomes.
The primary summary measures were differences in levels of power in each of the
bandwidths. Neural activity generates electrical potentials which can be analyzed in terms of
parameters of amplitude, frequency, coherence and synchrony. Amplitude, or power, which
Running title: The neurophysiology of mindfulness
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is the square of the amplitude, reflects the magnitude of the electrical signal, representing
the level of synchronized activity in the underlying tissue, i.e. neurons discharging
simultaneously. Frequency is the number of oscillatory cycles per second and is divided into
the following bandwidths: Delta (1-4 Hz); Theta (4-8 Hz); Alpha (8-13 Hz); Beta (1330 Hz);
and Gamma (36-44 Hz) (Cacioppo et al., 2007). EEG connectivity is the functional
integration of spatially distributed neural populations which can be assessed in terms of
synchrony, the degree of leading or lagging in the relationship between signals from
electrode pairs, and coherence, the stability of that phase relationship.
The primary summary variable was principally the difference in power between a meditation
condition and a resting state condition. Secondary power differentials included longitudinal
pre- and post- differences, such as, in meditation and/or resting state and/or task conditions
before and after an intervention. If applicable, outcomes relating to coherence, synchrony,
asymmetry and event-related potentials were also noted.
Of note, there was considerable diversity in how the experience of the participant was
defined. In terms of years meditating, the range for which papers rated participants as being
‘experienced’ varied from 1 year (Kasamatsu and Hirai, 1966) to 9 years (Lagopoulos et al.,
2009). Likewise, in terms of hours meditating, the range for which papers rated participants
as being ‘experienced’ varied from 40 hours (Hinterberger et al., 2011) to 1740 hours
(Berkovich-Ohana et al., 2012). In the present systematic review, we have applied the
lowest of these cutoffs, such that an ‘experienced’ (i.e., non-novice) meditator was
considered to have been meditating for longer than 1 year or have completed more than 40
hours of meditation.
The Quality Assessment Tool for Quantitative Studies (QATQS; National Collaborating
Centre for Methods and Tools, 2008) was used to assess the quality of the studies. QATQS
assesses methodological rigor in six areas: (a) selection bias; (b) design; (c) confounders;
(d) blinding; (e) data collection method; and (f) withdrawals and drop-outs. Each area is
Running title: The neurophysiology of mindfulness
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assessed on a quality score of 1 to 3 (1 = strong; 2 = moderate; 3 = weak). Scores for each
area were collated, and a global score was assigned to each study. If there are no weak
ratings, the study is given a score of 1 (judged as strong); one weak rating leads to a score
of 2 (moderate); and two or more weak ratings generates a score of 3 (weak)
(Supplementary Materials). QATQS scoring was conducted (II) and checked independently
(TL). Any discrepancies were resolved by discussion with agreement reached in all cases.
The first authors of each paper were contacted for additional information as needed (Amihai
and Kozhevnikov, 2014; Arita, 2012; Cahn et al., 2010; Cahn et al., 2013; Hinterberger et al.,
2011; Hinterberger et al., Walach, 2014; Howells et al., 2012; Huang and Lo, 2009;
Lagopoulos et al., 2009; Lehmann et al., 2012; Lo et al., 2003; Milz et al., 2014; Murata et
al., 2004; Saggar et al., 2012; Stinson and Arthur, 2013; Tang et al., 2009; Xue et al., 2014).
Data were extracted (TL) and reviewed (II) with guidance and review (CF).
Results
Search results
Following removal of duplicate citations, 284 potentially relevant papers were identified (302
articles from Scopus, 291 articles from MEDLINE, and 12 from the reference lists of articles).
From the abstract review, 120 papers were excluded. From the full text reviews of 164
papers, 108 papers were excluded. Thus, a total of 56 papers were included in the
systematic analysis. Ten of these papers were identified as reporting on overlapping
samples: (Berkovich-Ohana et al., 2012; Berkovich-Ohana et al., 2013); (Cahn et al., 2010;
Cahn et al., 2013); (Slagter et al., 2007; Slagter et al., 2009); (Hinterberger et al., 2011;
Hinterberger et al., 2014); (Schoenberg and Speckens, 2014; Schoenberg and Speckens,
2015). As such, the 56 papers included in the systematic analysis represented results from
51 independent participant samples (n = 1,715 subjects; age range = 19-72 years) (Figure
1). 46 papers focused on healthy participants, representing results from 42 independent
Running title: The neurophysiology of mindfulness
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samples (n = 1,358 subjects; age range = 18-72 years)(Table 1), and 10 papers included
participants with a psychiatric disorder, representing results from 9 independent samples (n
= 357 subjects; age range = 22-64 years): 3 studies on depressed patients in remission (n =
157), 1 study of patients with suicidal depression (n = 22),1 study involving patients
diagnosed with major depressive disorder, reported across 2 papers (Schoenberg and
Speckens, 2014, 2015) (n = 51), 1 study of patients with bipolar disorder (n = 21), 1 study of
patients with chronic pain (n = 27), 1 study of patients with chronic pain with risk of opioid
abuse (n = 29), and 1 study of patients with attention-deficit/hyperactivity disorder (ADHD) (n
= 50) (Table 2).
The findings fall into two main types: (a) studies examining the effects of mindfulness in
comparison with a resting state; and (b) studies examining longitudinal changes in EEG
patterns relating to practicing mindfulness (Table 3, Supplementary Tables 3-9).
Effects of mindfulness on neurophysiology
Twenty-one studies examined the alpha bandwidth, reporting greater amplitude during
mindfulness in comparison with an eyes-closed resting state (n = 12), lower amplitude (n =
1), and no significant differences (n = 3) (Table 3). Most of the studies involved experienced
meditators; novice participants were involved in4 of the reports of greater amplitude and 1 of
the reports of no significant differences. Coherence was examined in 2 papers, with mixed
results, and more complex analyses in another 2 papers.
The beta bandwidth was examined in 12 studies which compared mindfulness with a resting
state, reporting greater amplitude during mindfulness (n = 3; including n = 1 with novice
meditators), lower amplitude (n = 1), and no significant differences (n = 5, including n = 2
novices). Coherence (n = 1, with no difference found), asynchrony (n = 1, finding higher
synchrony with meditation) and more complex analyses (n = 1) were also examined.
The theta bandwidth was examined in 19 studies, reporting greater amplitude during
mindfulness (n = 11; including n = 3 with novices), lower amplitude (n = 3; including n = 2
Running title: The neurophysiology of mindfulness
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with novices), and no significant differences (n = 2; n = 1 with novices). Coherence (n = 2,
with no difference found), and more complex analyses (n = 1) were also examined.
The delta bandwidth was examined in 5 studies, reporting greater amplitude during
mindfulness (n = 1, with novices, limited to frontal regions) as well as no significant
differences (n =3; n = 1 with novices). More complex analyses (n = 1) were also examined.
The gamma bandwidth was examined in 7 studies, reporting greater amplitude during
mindfulness (n = 3) and no significant differences (n = 2; n = 1 with novices). Gamma
amplitude during mindfulness also correlated with train mindfulness and years of practice (n
= 1). Coherence (n = 1, with no difference found) and asymmetry (n = 3, finding greater left-
sided activation (n = 2) and no differences (n = 1)) were also examined.
Event-related potentials were examined in 15 studies, with mindfulness found to have an
impact on attention processing measures including P300 (n = 5; n = 2 on P3b specifically),
Late Positive Potential (n = 2), Feedback Related Negativity (n = 1), Error Related Negativity
(n = 1), Readiness Potential (n = 1), pain-evoked ERPs (n = 2), Late Contingent Negative
Variation (n = 1), and a Go/NoGo task (n = 2).
Longitudinal neurophysiological changes associated with mindfulness practice
In healthy individuals, learning mindfulness was associated with decreased alpha amplitude
(n = 2 studies), increased (n = 1) as well as decreased (n = 1) theta amplitude, and changes
in asymmetry with an increase relative left-sided activation (n = 1).
In participants with chronic pain, a course of mindfulness was associated with a decrease in
beta amplitude (n = 1). In patients with depression and suicidal ideation, a relative increase
in left-sided activation following mindfulness training was observed, while the inverse pattern
with a relative decrease in left-sided activation was reported in patients in remission from
depression.
Running title: The neurophysiology of mindfulness
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Discussion
The main finding to emerge from the systematic review is an increase in alpha power
associated with mindfulness relative to a resting state. Additional effects have been reported
in the oscillation bandwidths, including a majority trend towards increased theta power
during meditation compared to resting state. The patterns of increased alpha and theta
amplitude associated with meditation were observed in both experienced and novice
meditators. Clinical studies of mindfulness-based interventions revealed a shift towards
greater relative left-sided activation which may be associated with increased positive affect.
However, these findings have been mixed with reports of increases, decreases as well as no
differences, particularly in other bandwidths, but also in alpha and theta bandwidths.
Alpha synchronization has been regarded as one of the ‘signatures’ of meditation as it has
been consistently observed across a range of different meditation practices relatively
independent of both technique and degree of practice (Fell et al., 2010). In the present
review, increased alpha synchronization during meditation as compared to a resting state
was reported 65% of papers that analyzed this outcome (12 out of 18), all of which involved
healthy participants, including both novice (Lo et al., 2003; Milz et al., 2014; Takahashiet al.,
2005; Yu et al., 2011) and experienced meditators (Ahani et al., 2014; Arita, 2012; Cahn et
al., 2013; Dunn et al., 1999; Hinterberger et al., 2014; Huang and Lo, 2009; Kasamatsu and
Hirai, 1966; Lagopoulos et al., 2009). Most of the studies had examined participants during
mindfulness in comparison to a resting state with eyes closed with a few exceptions (ex.
Takahashiet al., 2005). However, the findings have not been wholly consistent as a few
studies found no differences with mindfulness in novice (Kubota et al., 2001) or experienced
(Cahn et al., 2010; Lehmann et al., 2012) participants, as well as decreased alpha power
during mindfulness (Amihai and Kozhevnikov, 2014). It is of note that none of the studies
involving clinical populations had analyzed or reported findings on alpha power.
Comparisons of mindfulness with performance on attention tasks reported no differences in
alpha power with eyes closed while attending to auditory clicks (Becker and Shapiro, 1981);
Running title: The neurophysiology of mindfulness
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with a time production task (Berkovich-Ohana et al., 2013); and with an eyes-open session
watching a video about neurofeedback (Stinson and Arthur, 2013), although Ren et al.
(2011) found lower levels of alpha compared to a problem-solving task.
The functional significance of alpha has been much debated. Alpha synchronization has
been understood as reflecting the ‘de-activation’ of cortical areas as a signifier of the brain
‘idling’ since it occurs during relaxed eyes closed wakefulness (Shaw, 1996; Pfurtscheller et
al., 1996). The increase in alpha synchronization with mindfulness as compared to an eyes
closed rest may indicate even greater levels of synchronization associated with mindfulness.
According to the ‘brain idling’ hypothesis, the effect suggests that meditation generates
greater cortical de-activation than during an eyes closed resting state. However, Shaw
(1996) proposes that there is a paradoxical response which distinguishes between ‘outer-
directed’ and ‘inner-directed’ attention. While ‘outer-directed’ attention is associated with
alpha desynchronisation, ‘inner-directed’ attention, which is also referred to as ‘intention,’ is
associated with increases in alpha power. In support, tasks requiring memory (Jensen et al.,
2002) and imagination (Cooper et al., 2006) lead to increases in alpha power. Mindfulness
improves the training and development of various attention networks (sustained, executive,
executive, selective, and re-orienting) in terms of its focused-attention aspects and
awareness in terms of its open-monitoring aspects (Lutz et al., 2008; Vago and Silbersweig,
2012). As such, it is possible to infer that increased alpha power associated with
mindfulness is evidence that alpha synchronization is indeed a signifier of increased
processing in these various attention modalities (e.g., as per Vago and Silbersweig’s (2012)
model) with respect to internally generated stimuli.
With regards to beta oscillations, of the 12 studies which compared beta activity in
meditation with eyes closed rest in healthy individuals, only 3 studies reported that beta
amplitude was higher in meditation, involving experienced meditators (Ahani et al., 2014;
Cahn et al., 2013) and novices (Dunn et al., 1999). Five studies found no significant
differences in experienced practitioners (Cahn et al., 2010; Lagopoulos et al., 2009;
Running title: The neurophysiology of mindfulness
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Lehmann et al., 2012) and in novices (Milz et al., 2014; Yu et al., 2011), while one study
observed lower beta amplitude in meditation in experienced practitioners (Amihai and
Kozhevnikov, 2014), and 5 studies found no significant differences (Cahn et al., 2010;
Lagopoulos et al., 2009; Lehmann et al., 2012; Milz et al., 2014; Yu et al., 2011). A
comparison of mindfulness with task performance, an eyes open session watching a video
about neurofeedback, reported lower amplitude in meditation relative to the task (Stinson
and Arthur, 2013). Only one paper reported on beta power in clinical populations, observing
pre-post longitudinal decreases in beta power during the resting state which was linked to
the practice of mindfulness (Howells et al., 2012).
Interpretations of the significance of beta are mixed because it has been proposed to reflect
a reduction in cortical activity as it is associated with barbiturates and benzodiazepines use
(Herning et al., 1994), but beta activity has also been attenuated with increasing cognitive
task demands (Ray and Cole, 1985) while around 20% of patients with deficit hyperactivity
disorder exhibit ‘excessive’ beta activity, which is associated with elevated behavioural
problems (Clarke et al., 2001).
Increased theta power has been considered to be another key feature of meditation
(Josipovic, 2010; Fell et al., 2010). This pattern was to some extent borne out in the present
review and was observed in both novice and experienced meditators, although there did
appear to be a slight weighting towards this effect being more prevalent in experienced
practitioners. Of the 19 studies that that compared theta activity in meditation with eyes
closed rest, a majority (n = 11) reported that theta power was higher in mindfulness,
including 8 with experienced practitioners (Ahani et al., 2014; Arita, 2012; Cahn et al., 2010;
Chan et al., 2008; Kasamatsu and Hirai, 1966; Lagopoulos et al., 2009; Lomas et al., 2014),
but only 2 with novices (Kubota et al., 2001; Takahashiet al., 2005), plus also Tanaka et al.
(2014), who found this effect with both novice and experienced practitioners. Against this, 3
studies reported that theta was lower during mindfulness compared to eyes-closed rest, 2 of
which involved novices (Dunn et al., 1999; Yu et al., 2011) and 1 involving experienced
Running title: The neurophysiology of mindfulness
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practitioners (Huang and Lo, 2009). Moreover, 2 studies found no significant differences in
experienced (Amihai and Kozhevnikov, 2014) and novice practitioners (Milz et al., 2014). An
additional 2 longitudinal studies also observed pre-post decreases in theta power during the
resting state which was linked to the practice of mindfulness (Saggar et al., 2012; Tang et
al., 2009). Only one paper reported on theta power in clinical populations, observing pre-
post longitudinal increases in theta power (during the resting state) linked to the practice of
mindfulness (Howells et al., 2012).
The presence of theta along with alpha synchronization during mindfulness lends support to
the hypothesis that increased alpha power during signifies internalized attention rather than
the brain ‘idling’ because theta synchronization is widely viewed as a marker of executive
functioning. Theta activity has been linked to various types of cognitive activity, including
switching and orienting attention (Dietl et al., 1999), processing of new information
(Grunwald et al., 1999), and memory in episodic encoding and retrieval (Klimesch et al.,
1997), and theta power increases as task demands increase (Klimesch et al., 1997). Taken
together, the finding suggests that mindfulness constitutes a state of enhanced internally-
directed attention. Theta oscillations during wakefulness generally occur maximally in the
frontal-midline regions of the brain, particularly in the prefrontal cortex (Asada et al., 1999)
and may be localized to the anterior cingulate cortex (Onton et al., 2005), in contrast to theta
activity during REM sleep, which is generated mainly by the hippocampus (Cantero et al.,
2003). These regions are centrally involved in the executive control of attention, as well as
other higher-level cognitive activities such as volition and planning (Posner and Dehaene,
1994; Miller and Cohen, 2001), and have been proposed as central to the development of
attention and awareness in meditation (Newberg and Iversen, 2003).
This interpretation is strengthened by the differences observed between experienced and
novice practitioners, in which the former were more reliably found across the studies to
exhibit higher levels of theta activation during meditation in comparison to a resting state,
suggesting that enhanced theta activation during meditation is to some extent a function of
Running title: The neurophysiology of mindfulness
16
training and practice in meditation by learning to maintain an inner-directed attention.
Furthermore, it has been suggested that the co-presence of theta and alpha in mindfulness
indicates a state of ‘relaxed alertness’ (Britton et al., 2014), which is corroborated by
qualitative self-reports of their experiences in mindfulness (Cahn and Polich, 2006).
Fewer studies have reported delta and gamma activity with mixed findings and have all been
limited to healthy individuals. Slow wave delta band activity is more commonly associated
with sleep, particularly during deep non-REM stages (Hofle et al., 1997). It has been
suggested though that an increase in delta activity during wakefulness reflects attention to
internal processing during the performance of cognitive tasks, such as difficult arithmetical
calculation tasks (Harmony et al., 1996). The reports of delta activity have generally found
no differences (Lagopoulos et al., 2009; Amihai and Kozhevnikov, 2014; Milz et al., 2014),
although reduced (Dunn et al., 1999) as well as increased amplitudes, which were localized
to frontal regions (Cahn et al., 2010), have been described in comparison to an eyes closed
resting state in novice (Dunn et al., 1999; Milz et al., 2014) and experienced (Lagopoulos et
al., 2009; Cahn et al., 2010; Amihai and Kozhevnikov, 2014) meditators. Stinson and Arthur
(2013) also found lower amplitude during meditation compared a control task of watching a
neurofeedback video.
Gamma synchronization is purported to reflect activity in the default mode network
(Berkovich-Ohana et al., 2012) which refers to the self-referential and reflective thoughts that
occur in the absence of requirements to respond to external stimuli (Buckner, Andrews-
Hanna, and Schacter, 2008). With mindfulness, gamma power has been reported as
increased (Berkovich-Ohana et al., 2012; Cahn et al., 2010; Lehmann et al., 2012) as well as
showing no differences (Amihai and Kozhevnikov, 2014; Milz et al., 2014) in comparison with
an eyes closed resting state. Of interest, increased gamma activity was observed in
experienced meditators (Berkovich-Ohana et al., 2012; Cahn et al., 2010; Lehmann et al.,
2012), although no differences were also found in both experienced (Amihai and
Kozhevnikov, 2014) and novice (Milz et al., 2014) meditators. In comparison with a control
Running title: The neurophysiology of mindfulness
17
task, lower amplitude was reported during mindfulness as compared to a neurofeedback
video task (Stinson and Arthur, 2013). In addition, studying experienced Zen meditators,
Hauswald et al. (2015) found that gamma power during meditation correlated both with
levels of trait mindfulness and years of meditation practice. Ferrarelli et al. (2013) also
reported a correlation between meditation experience and gamma power during non-REM
sleep, but Berkovich-Ohana et al. (2012) found no difference in coherence between
meditation and rest. Gamma oscillations have also been implicated in theories of
consciousness, in which the fast rhythmic synchronization of neural discharges provide the
necessary spatial and temporal links to bind processing across different brain areas, thereby
integrating disparate experiential qualia into a coherent state of moment-to-moment
awareness (Singer, 1993; Tallon-Baudry and Bertrand, 1999). Increased gamma power
during mindfulness thus might indicate a more unified and coherent mental state.
In addition to analysis of specific bandwidths, patterns of asymmetric brain activation have
been examined in which left prefrontal activity has been associated with positive affect and
‘approach-related’ behavior, and right prefrontal activity with negative affect and ‘withdrawal-
related’ behavior (Davidson, 1992). If mindfulness is associated with enhanced subjective
wellbeing, then its practice should be linked to greater left prefrontal activity. Such an
asymmetry has been observed during mindfulness in experienced meditators relative to an
eyes closed resting state (Amihai and Kozhevnikov, 2014; Chan et al., 2008). Following
mindfulness training, similar changes have been reported in novice participants who were
healthy volunteers (Davidson et al., 2003) as well as with a history of suicidal ideation
(Barnhofer et al., 2007). In novice participants with a history of depression, there have been
reports of no differences (Milz et al., 2014), increased (Barnhofer et al., 2010) and
decreased (Keune et al., 2011) left-sided activation.
Using event-related potentials, reduced P3b in response to distractor stimuli (Slagter et al.,
2007) and faster attentional disengagement from a dominant global presentation in order to
focus in on specific stimuli (van Leeuwen et al., 2012) was observed in experienced
Running title: The neurophysiology of mindfulness
18
meditators. Likewise, Delgado et al. (2013) found that experienced Vipassana meditators
demonstrated larger P3b amplitudes to a target tone after meditation than before meditation,
findings, which are interpreted as reflecting increased attentional engagement following
meditation, given that P3b is interpreted as reflecting allocation of attentional resources to
incoming stimulation to facilitate information processing, thus corroborating the notion of
mindfulness as a system of attention training. Moreover, anticipatory and pain-evoked ERPs
to acute pain were reduced in participants who received mindfulness training but not in
controls (Brown and Jones, 2013). Sobolewski et al. (2011) explored the impact of
meditation practice on late positive potential (LPP), the amplitude of which tends to be
greater in ERPs evoked by emotionally arousing images, particularly ones that are
negatively valenced. While control participants with no meditation experience showed an
increase in LPP amplitude in response to negative stimuli, no such increases were observed
in meditators, suggesting that the latter were less affected by negative emotional load than
control participants; in contrast, both groups responded equally to positively-valenced
stimuli. Teper and Inzlicht (2014) explored participants’ neuroaffective reaction to rewarding,
aversive and neutral feedback, as gauged by feedback-related negativity (FRN), a brain
response that differentiates positive from negative feedback, reporting that trait levels of
mindfulness in novice meditators predicted less differentiation of reward from neutral
feedback. Lakey et al. (2011) explored the impact of brief mindfulness training on
performance of a P300-based brain-compute interface task. Compared to non-meditating
control participants, the experimental subjects produced significantly larger P300 amplitudes
and were also more accurate at the task which was understood as suggesting that the
experimental participants were better able to harness present-moment attentional resources.
Working with patients with ADHD, Schoenberg et al. (2014) explored the impact of
Mindfulness-Based Cognitive Therapy (MBCT) on error processing (ERN, Pe), conflict
monitoring (NoGo-N2), and inhibitory control (NoGo-P3) in relation to a continuous
performance task (CPT-X). Compared to matched controls, MBCT was linked to increased
Running title: The neurophysiology of mindfulness
19
Pe and NoGo-P3 amplitudes, which coincided with reduced ‘hyperactivity/impulsivity’ and
‘inattention’ symptomatology. In a trial involving patients currently diagnosed with major
depressive disorder, Schoenberg and Speckens (2014) found that an MBCT intervention
had a modulating effect on evoked FM-theta power during a Go/NoGo task: enhanced
event-related synchronization (ERS) in the late temporal window was observed pre-to-post
for the experimental group, with the reverse pattern found in control participants. It was
suggested that these findings were reflective of optimized allocation of attentional resources
as a result of the intervention. Moreover, these modulated ERS dynamics were also found to
correlate with ameliorated depressive and rumination symptoms in the MBCT group.
Studying patients with chronic pain at risk of opioid abuse, Garland et al. (2015) found that a
Mindfulness-Oriented Recovery Enhancement intervention was able to enhance natural
reward processing. In particular, the intervention was associated with increases in LPP in
response to natural reward stimuli relative to neutral stimuli, which also correlated with
reduced opioid craving from pre- to post-treatment. Jo et al. (2014) explored the Readiness
Potential correlates of the intentional binding effect, and found that early neural activity
correlates with the participants’ reports of initiating a voluntary action; however, there were
no differences between experienced Zen meditators and matched controls in this regard.
A significant limitation of the present systematic review has been the variability of the
measures which were acquired and reported such that a meta-analysis was not feasible for
any of the measures because there were no more than 3 studies which used the same
measure at the same site. The quality was assessed for each of the studies using the
Quality Assessment Tool for Quantitative Studies (National Collaborating Centre for Methods
and Tools, 2008), revealing considerable variation. Clinical studies were generally of higher
quality as they tended to keep track of withdrawal and attrition rates and used standardized
meditation protocols. Furthermore, a key issue was limited reporting on participants’ prior
level of meditation experience. Some studies reported this in terms of years, some in terms
of total number of hours, and a few omitted to specify this. Moreover, there was variation in
Running title: The neurophysiology of mindfulness
20
the criteria for which studies rated participants as ‘experienced’; in terms of years, this
ranged from 1 year (Kasamatsu and Hirai, 1966) to 9 years (Lagopoulos et al., 2009), while
in terms of hours this ranged from 40 hours (Hinterberger et al., 2011) to 1740 hours
(Berkovich-Ohana et al., 2012). We applied the lowest of these cutoffs such that an
‘experienced’ (i.e., non-novice) meditator was considered to have been meditating for longer
than 1 year or have completed more than 40 hours of meditation. Arguably hours would be
a better metric than years since it better reflects a person’s general amount of practice;
however, it is recommended that future studies report both hours and years which would
provide some indication of the ‘intensity’ of participants’ practice. Another issue was key
poor and/or inconsistent reporting on the nature of participants’ meditation practice.
Although all the studies included in the review featured mindfulness specifically (or a
functional equivalent), even this is a somewhat generic label, with nuances and differences
among practices that can be classified as such mindfulness prior level of meditation
experience. Many studies had not described in detail the form and type of mindfulness
practice engaged in by participants.
In conclusion, the burgeoning literature on EEG investigations of mindfulness is beginning to
highlight some consistent trends, most notably with respect to increased amplitude in the
alpha and theta bandwidths. The co-presence of elevated alpha and theta waves may
reflect a state of ‘relaxed alertness as alpha and theta can both be interpreted as signifiers of
increased attention with alpha specifically representing internalized attention and both have
also been identified as indexing states of relaxation. Further work will be needed to explore
the nuances of brain states associated with mindfulness, particularly with respect to the
other bandwidths and measures such as ERP and asymmetry, to elucidate the differences
between mindfulness and other meditation practices, and to further explore the impact of
factors such as degree of meditation practice.
Running title: The neurophysiology of mindfulness
21
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Running title: The neurophysiology of mindfulness
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Figure Legends
Figure 1.The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Flow
Diagram
Running title: The neurophysiology of mindfulness
36
Figure 1.
Records identified through
database search
(n = 551)
Records identified through
additional sources
(n = 12)
Records after duplicates removed
(n = 284)
Records screened
(n = 284)
Records excluded
(n = 120)
Reason: Not an empirical
study of meditation and EEG
Full-text articles assessed
for eligibility
(n = 164)
Full-text articles excluded
(n = 108)
Reason: Form of meditation
not mindfulness
Papers included
(n = 56)
Running title: The neurophysiology of mindfulness
37
Table 1.Demographics of healthy participants
First author
Year
Meditators
Meditators (male)
Controls
Mean age meditators
Mean years meditating
Meditation type
Study type
Davidson
2003
25 (from 32)
6
16
36
0
MBCT
Pre-post
Dunn
1999
9 (from 10)
NR
-
NR
0
FA& MM
Pre-post
Kerr
2011
12 (from 16)
1
6
31
0
MBCT
Pre-post
Lomas
2014
30
30
-
42.3
10.1
Various (inc. MM)
Pre-post
Moore
2012
12 (from 19)
NR
16 (from 23)
36.9
0
MM
Pre-post
Saggar
2012
22 (from 30)
12
22 (from 30)
49.5
Experienced (yrs NR)
MM (retreat)
Pre-post
Slagter
2007
17
17
23
NR
Experienced (yrs NR)
Vipassana (retreat)
Pre-post
Slagter
2009
17
17
23
NR
Experienced (yrs NR)
Vipassana (retreat)
Pre-post
Tang
2009
40
NR
40
NR
0
Mind-body training
Pre-post
Xue
2014
45
29
24
22.9
0
Mind-body training
Pre-post
Ahani
2014
34
6
-
61
0 (6 weeks training)
MM
Non pre-post
Amihai
2014
19
16
-
44.4
7.7
Vipassana
Non pre-post
Arita
2012
15
NA
-
NA
NA
Zen
Non pre-post
Becker
1981
30 (10 Zen)
17 (Zen=8)
10
32.7 (Zen=37.8)
6.5 (Zen=7.5)
Zen, TM & Yoga
Non pre-post
Berkovich-Ohana
2012
36
NR
12
41.7
3,673 (hrs)
MM
Non pre-post
Berkovich-Ohana
2013
36
NR
12
41.7
3,673 (hrs)
MM
Non pre-post
Brown
2010
12
6
15
34
NR
Various (inc. MM)
Non pre-post
Cahn
2010
16
11
-
45.5
20
Vipassana
Non pre-post
Cahn
2013
16
11
-
45.5
20
Vipassana
Non pre-post
Chan
2008
19
8
-
19-22 (range)
NR
Triarchic
Non pre-post
Delgado
2013
10
10
0
20-61 (range)
7.5
Vipassana
Non pre-post
Ferrarelli
2013
29
14
29
50.7
15.6
MM
Non pre-post
Hauswald
2015
11
5
-
50
12
Zen
Non pre-post
Hinterberger
2011
49
33
-
45
40-1000 (hrs; range)
Various (inc. MM)
Non pre-post
Hinterberger
2014
49
33
-
45
40-1000 (hrs; range)
Various (inc. MM)
Non pre-post
Huang
2009
23
16
23
31.5
8.4
Zen
Non pre-post
Jo
2014
20
7
19 (from 20)
40.7
3 (minimum)
Zen
Non pre-post
Kasamatsu
1966
48
48
18
24-72 (range)
1-20 (range)
Zen
Non pre-post
Kubota
2001
25
11
-
23.1
0
Zen
Non pre-post
Lakey
2011
18
7
-
18-33 (range)
0
MM
Non pre-post
Lagopoulos
2009
18
13
-
52
9-14 (range)
Acem
Non pre-post
Lehmann
2012
71 (15 Zen)
NR
-
41.4 (Zen=42)
11.3 (Zen=12.3)
Various (inc. Zen)
Non pre-post
Lo
2003
20
NR
10
NR
NR
Zen
Non pre-post
Lo
2013
10
7
10
28
5.8
Zen
Non pre-post
Milz
2014
23
23
2
23.2
0
MM
Non pre-post
Murata
2004
22
22
-
23.3
0
Su-soku
Non pre-post
Pasquini
2015
17
9
14
44.6
2 (minimum)
Zen
Non pre-post
Ren
2011
32
23
16
23.3
0
Su-soku
Non pre-post
Sobolewski
2011
13
7
13
38.7
5 (minimum)
MM
Non pre-post
Stinson
2013
13
NR
-
NR
0
Neurofeedback
Non pre-post
Takahashi
2005
20
20
-
28.6
0
Zen
Non pre-post
Tanaka
2014
10
4
10
49.2
11.6
MM
Non pre-post
Teper
2013
20
9
18
33
3.19
MM
Non pre-post
Running title: The neurophysiology of mindfulness
38
Teper
2014
45 (from 47)
27
-
19.26
0
Trait mindfulness
Non pre-post
van Leeuwen
2012
8
5
8
29
5
MM
Non pre-post
Yu
2011
15
14
-
38
0
Zen
Non pre-post
Note: MBCT = mindfulness-based cognitive therapy; MM = mindfulness meditation; NCC = neural correlates of consciousness; NR = not recorded; RCT = randomized controlled trial; TM = transcendental meditation;
Number of meditators is presented in column headed by Meditators.
Running title: The neurophysiology of mindfulness
39
Table 2.Demographics of participants with a clinical history
First author
Year
n meditators
n males
meditators
n controls
Mean age
meditators
Meditation type
Psychiatric Disorder
Barnhofer
2007
10 (from 16)
5
12 (from 18)
48
MBCT
Suicidal depression
Barnhofer
2010
8
1
8
31.6
MM
Previously depressed
Bostanov
2012
32 (from 45)
9
32 (from 46)
50.9
MBCT
Depressed (remission)
Brown
2013
12
NA
15
NA
MM pain manage.
Chronic pain
Garland
2015
11
NA
18
NA
MORE
Chronic pain
Howells
2012
12
2
9
37
MBCT
Bipolar disorder
Keune
2013
40 (from 53)
10
37 (from 50)
48.9
MBCT
Depressed (remission)
Schoenberg (et al.)
2014
26 (from 32)
NA
24 (from 29)
NA
MBCT
ADHD
Schoenberg (& S.)
2014
26
6
25
47.8
MBCT
Depression (current)
Schoenberg (& S.)
2015
26
6
25
47.8
MBCT
Depression (current)
Note: MBCT = mindfulness-based cognitive therapy; MM = mindfulness meditation; MORE = mindfulness-oriented recovery enhancement. All studies featured pre-post designs, and all
except Howells were RCTs. All subjects participating had no previous experience with meditation.
Running title: The neurophysiology of mindfulness
40
Table 3. Results synthesis of papers according to principle findings and according to bandwidth or asymmetry
Outcome
MED > RS
MED = RS
MED < RS
Pre < post (linked to MED)
Pre > post (linked to MED)
Alpha power
Ahani (2014); Arita (2012);
Cahn (2013); Dunn (1999);
Hinterberger (2014); Huang
(2009); Kasamatsu (1966);
Lagopoulos (2009); (2003);
Milz (2014); Murata (2004;
coherence); Takahashi (2005);
Yu (2011)
Berkovich-Ohana (2013;
coherence); Cahn (2010);
Kubota (2001); Lehmann
(2012)
Amihai (2014)
Saggar (2012)
Beta power
Ahani (2014); Cahn (2013);
Dunn (1999); Lo (2003;
synchrony)
Cahn (2010); Lagopoulos
(2009); Lehmann (2012); Milz
(2014); Murata (2004;
coherence); Yu (2011)
Amihai (2014)
Howells (2012)
Saggar (2012)
Theta power
Ahani (2014); Arita (2012);
Cahn (2010); Chan (2008);
Kasamatsu (1966); Kubota
(2001); Lagopoulos (2009);
Lehmann (2012); Lomas
(2014); Takahashi (2005);
Tanaka (2014)
Amihai (2014); Berkovich-
Ohana (2013; coherence); Milz
(2014); Murata (2004;
coherence)
Dunn (1999); Huang (2009); Yu
(2011)
Howells (2012); Xue (2014)
Saggar(2012; in RS); Tang
(2009)
Delta power
Cahn (2010; at frontal brain
regions)
Amihai (2014); Cahn (2010; at
central and parietal brain
regions); Lagopoulos (2009);
Milz (2014)
Dunn (1999)
Gamma power
Berkovich-Ohana (2012); Cahn
(2010); Hauswald (2015);
Lehmann (2012)
Amihai (2014); Berkovich-
Ohana (2012; coherence); Milz
(2014)
Greater relative left-sided
activation
Amihai (2014); Chan (2008)
Milz et al. (2014)
Barnhofer (2007); Barnhofer
(2010);Davidson (2003)
Keune (2011; CNT also
decreased)
Note:> = significantly greater than; < =significantly lower than; = = no significant differences; CNT = control group; MED = meditation; RS = resting state. Studies featuring novice participants are indicated by the
author/year being italicized in bold. All findings refer to amplitude, unless stated otherwise (e.g., coherence).
Running title: The neurophysiology of mindfulness
41
Supplementary Table 1. Quality rating of papers with healthy participant samples
First author
Year
Selection bias
Design
Confounders
Blinding
Data collection
Attrition
Global
Ahani
2014
1
1
3
3
1
1
1
Amihai
2014
1
2
2
2
1
2
2
Arita
2012
NA
NA
NA
NA
NA
NA
NA
Becker
1981
1
3
3
3
2
3
3
Berkovich-Ohana
2012
1
1
1
1
1
2
1
Berkovich-Ohana
2013
1
1
1
1
1
2
1
Brown
2010
2
1
3
2
1
2
2
Cahn
2010
1
3
3
3
1
3
3
Cahn
2013
1
3
3
3
1
3
3
Chan
2008
3
3
3
3
1
3
3
Davidson
2003
1
1
1
2
1
1
1
Delgado
2013
2
2
2
3
1
2
2
Dunn
1999
2
3
2
2
3
3
3
Ferrarelli
2013
1
1
1
2
1
2
1
Hauswald
2015
2
2
2
2
1
2
2
Hinterberger
2011
1
2
2
2
1
2
2
Hinterberger
2014
1
2
2
2
1
2
2
Huang
2009
1
1
2
2
1
2
2
Jo
2014
2
1
2
2
1
1
1
Kasamatsu
1966
1
2
2
2
2
2
2
Kerr
2011
1
1
1
2
1
1
1
Kubota
2001
2
2
2
2
1
2
2
Lakey
2011
1
2
2
2
1
2
2
Lagopoulos
2009
1
1
1
2
2
1
1
Lehmann
2012
2
1
2
2
1
2
2
Lo
2003
2
3
3
2
1
2
3
Lo
2013
1
2
2
2
2
2
2
Lomas
2014
1
2
2
2
1
2
2
Milz
2014
1
1
1
2
1
2
1
Moore
2012
1
1
1
1
1
1
1
Murata
2004
1
2
2
2
1
2
2
Pasquini
2015
2
1
2
2
1
2
2
Ren
2011
1
1
2
2
2
2
2
Saggar
2012
1
1
1
2
1
1
1
Slagter
2007
3
3
3
2
1
2
3
Slagter
2009
2
1
1
1
1
2
1
Sobolewski
2011
1
1
1
2
1
2
1
Stinson
2013
2
3
3
1
1
2
3
Takahashi
2005
2
2
2
2
1
2
2
Tanaka
2014
1
1
2
2
1
1
1
Tang
2009
1
1
2
2
1
1
1
Teper
2013
1
1
1
2
1
1
1
Teper
2014
1
2
1
1
1
1
1
Running title: The neurophysiology of mindfulness
42
van Leeuwen
2012
1
1
1
2
1
2
1
Xue
2014
1
1
2
2
1
2
2
Yu
2011
2
3
2
3
2
2
3
Note: NA = full pdf not available. Studies featuring novice participants are indicated by the author being italicized in bold.
Running title: The neurophysiology of mindfulness
43
Supplementary Table 2. Quality rating of papers with clinical samples
First author
Year
Selection bias
Design
Confounders
Blinding
Data collection
Attrition
Global
Barnhofer
2007
1
1
2
1
1
1
1
Barnhofer
2010
1
1
1
1
1
1
1
Bostanov
2012
1
2
2
2
1
2
2
Brown
2013
NA
NA
NA
NA
NA
NA
NA
Garland
2015
1
1
1
1
1
2
1
Howells
2012
1
1
1
2
1
2
1
Keune
2013
1
1
1
1
1
1
1
Schoenberg (et al.)
2014
1
1
2
1
1
1
1
Schoenberg (& S.)
2014
1
1
1
1
1
1
1
Schoenberg (& S.)
2015
1
1
1
1
1
1
1
Note: NA = not available. Studies featuring novice participants are indicated by the author being italicized in bold.
Running title: The neurophysiology of mindfulness
44
Supplementary Table 3. Alpha bandwidth
Meditation vs resting state
Author
Year
Meditation
Protocol: Resting
Findings
Higher amplitude during meditation
Ahani
2014
15 mins MM (E-C)
15 mins audio listening (E-C)
ME > CNT (F(1,33) = 10.58, p ≤ 0.0011)
Arita
2012
NA
NA
MED > RS (stats NA)
Cahn
2013
21 mins (Vipassana)
21 mins (E-C)
MED > RS (F(1, 15) = 6.64, p < .05)
Dunn
1999
15 mins (FA) then 15 mins (MM)
15 mins (E-C)
MED (MM) > RS & FA. Stats NR
Hinterberger
2014
15 mins (self-chosen), 2 mins (MM), 2 mins (thoughtless emptiness), 2
mins (FA on ‘third eye’), 2 mins (FA on body axis)
5 mins (E-O), 5 mins (E-C), 5
mins (reading)
MED (MM) > RS (t = 2.7, p < .05)
Huang
2009
40 mins (Zen) (vs 40 mins rest for CNT)
EXP (MED) > CNT (rest) (F(1, 45) = 31.57, P < .0001)
Kasamatsu
1966
Time NR (Zen)
Time NR (E-C)
MED > RS. Stats NR.
Lagopoulos
2009
20 mins (Acem)
20 mins (E-C)
MED > RS (F(1, 17) = 7.19, p = .02)
Lo
2003
40 mins (Zen)
15 mins (E-C)
MED > RS. Stats NR.
Milz
2014
2 x 5 mins (breath counting)
3 x 5 mins (E-C)
Power: MED > RS (t = 3.02, p = .036); Coherence: MED =RS (t(22)= 1.29, p = .20);
Murata
2004
15 mins (Su-soku; E-O)
15 mins (E-O)
MED > RS (coherence) (t = 3.03, p< .01)
Takahashi
2005
15 mins (Su-soku)
15 mins (E-O)
MED > RS (F(1, 19) = 29.47, p < .001)
Yu
2011
20 mins (Zen: tanden breathing)
2 mins (E-C)
MED > RS (F = 9.31, p < .001)
Lower amplitude during meditation
Amihai
2014
Therevada = 15 mins(Samatha) & 15 mins (Vipassna); V=Vajrayana = 15
mins (deity) & 15 mins (Rig-pa)
10 mins (E-C)
MED (Therevada) < RS (F(2,18) = 6.84, p < 0.01);
MED (Vajrayana) = RS (p > 0.8)
No significant differences (or no clear reportable patterns)
Berkovich-Ohana
2013
15 mins (MM)
2.5 mins (E-C) + 2.5 mins (E-O)
MED = RS (coherence); Stats NR. RS: EXP = CNT (coherence); Stats NR.
Cahn
2010
21 mins (Vipassana)
21 mins (E-C)
MED = RS (F(1, 15) = 0.096, p = .76)
Hinterberger
2011
15 mins (self-chosen), 2 mins (MM), 2 mins (thoughtless emptiness), 2
mins (FA on ‘third eye’), 2 mins (FA on body axis)
5 mins (E-O), 5 mins (E-C), 5
mins (reading)
Complex analyses. Stats NU.
Kubota
2001
25 mins (Su-soku)
2.5 mins (cued-breathing)
MED = RS (t = 0.68, p NR)
Lehmann
2012
60 mins (self-chosen)
4 mins (20 sec E-O, 40 sec E-C;
x 4)
MED (Zen) = RS. T = 1.81, p = 0.09
Lo
2013
40 mins (Zen)
15 mins (E-C)
Complex analyses. Stats NU
Pre-post changes
Saggar
2012
12 mins (MM: pre, mid, & post retreat)
Group x time interaction: (F(2,41) = 23.26, p < .001): pre-post decrease for EXP
(t(21) = 6.59, p<.001), not CNT
Meditation vs task
Author
Year
Meditation
Protocol: task
Findings
Becker
1981
30 mins (self-chosen MED: Zen, TM or Yoga), then 30 mins (self-chosen
MED) concurrent with task
Auditory clicks (30 mins)
Task: EXP = CNT (alpha suppression). Stats NR
Berkovich-Ohana
2013
15 mins (MM)
Time-production task (2-3
mins)
EXP = CNT (coherence); Stats NR
Kerr
2011
Cued attention-detection runs
EXP (vs CNT): Enhanced alpha modulation in early (600-800ms period) (Mann-
Whitney, p < .01)
Pasquini
2015
Focused attention task
EXP in task: Negative correlation between alpha powerand both meditation
practice time (r = -0.52, p = .003) and meditation weekly frequency (r = -0.41, p
= .021).
Running title: The neurophysiology of mindfulness
45
Ren
2011
Time NR (Su-soku)
Problem solving (Time NR)
EXP (MED) < CNT (cognitive task) (F(2, 45) = 4.14, p = .05)
Schoenberg (& S.)
2015
Go/NoGo task
Task: pre-post power increase for negative stimuli (t(24) = 2.58, p = .02) for CNT
only (EXT: no significant increase).
Stinson
2013
Time NR (relaxation 'Alpha brain state exercise')
Neurofeedback video (Time NR)
MED = task. Stats NR.
Note: > = significantly greater than; < =significantly lower than; = = no significant differences; CNT = control group; E-C = eyes-closed; E-O =eyes-open; EXP =experimental group; FA= focussed-attention (concentrative)
meditation; (from …) = initial number of participants in a pre-post study; MED = meditation; MM = Mindfulness meditation; NA = not available; NR = not reported; NU = not usable (here); NCC = neural correlates of
consciousness (i.e., EEG measurement during MED vs RS); ROI = region of interest; RS = resting state. All bandwidth outcomes pertain to power, unless otherwise stated in parentheses (e.g., coherence). Most entries are
comparing the experimental group (EXT; i.e., meditators) under different conditions (e.g., RS vs MED): significantly higher power levels during meditation are written as MED > RS; significantly lower levels as MED < RS;
and no significant differences as MED = RS. Some entries are comparing two groups (i.e., EXP vs CNT) on a particular condition (e.g., RS): this will be indicated as RS: EXT >/=/< CNT. Studies featuring novice participants
are indicated by the author being italicized in bold.
Running title: The neurophysiology of mindfulness
46
Supplementary Table 4. Beta bandwidth
Meditation vs resting state
Author
Year
Meditation
Protocol: Resting
Findings
Higher amplitude during meditation
Ahani
2014
15 mins MM (E-C)
15 mins audio listening (E-C)
ME > CNT (F(1,33) = 142.03, p ≤ 0.004)
Cahn
2013
21 mins (Vipassana)
21 mins (E-C)
MED > RS (synchrony) (F(1, 15) = 9.01, p < .01)
Dunn
1999
15 mins (FA) then 15 mins (MM)
15 mins (E-C)
MM > RS & FA. Stats NR
Lo
2003
40 mins (Zen)
15 mins (E-C)
MED > RS. Stats NR.
Lower amplitude during meditation
Amihai
2014
Therevada = 15 mins(Samatha) & 15 mins (Vipassna);
V=Vajrayana = 15 mins (deity) & 15 mins (Rig-pa)
10 mins (E-C)
MED (Therevada) < RS (F(2,18) = 3.68, p < 0.05); MED (Vajrayana)< RS
(F(2,18) = 8.42, p < 0.01)
No significant differences (or no clear reportable patterns)
Cahn
2010
21 mins (Vipassana)
21 mins (E-C)
MED = RS (F(1, 15) = 0.62, p = .44)
Hinterberger
2011
15 mins (self-chosen), 2 mins (MM), 2 mins (thoughtless
emptiness), 2 mins (FA on ‘third eye’), 2 mins (FA on body axis)
5 mins (E-O), 5 mins (E-C), 5
mins (reading)
Complex analyses. Stats NU.
Lagopoulos
2009
20 mins (Acem)
20 mins (E-C)
MED = RS (F(1, 17) = 0.57, p = .46)
Lehmann
2012
60 mins (self-chosen)
4 mins (20 sec E-O, 40 sec E-
C; x 4)
MED (Zen) = RS. T = 0.48, p = 0.63
Milz
2014
2 x 5 mins (breath counting)
3 x 5 mins (E-C)
Power: MED = RS; Stats NR. Coherence: MED =RS (t(22)= 0.11, p = .91)
Murata
2004
15 mins (Su-soku; E-O)
15 mins (E-O)
EXP: MED = RS (coherence). Stats NR.
Yu
2011
20 mins (Zen: tanden breathing)
2 mins (E-C)
MED = RS (F = 0.96, p = .44)
Pre-post changes
Saggar
2012
12 mins (MM: pre, mid, & post retreat)
Group x time interaction: (F(2,41) = 7.11, p < .01): pre-post decrease for
EXP (t(21) = 8.65, p<.001), not CNT
Howells
2012
3 mins (E-O), 3 mins (E-C),
RS (EXP only): post < pre (t = 2.23, p < .05)
Meditation vs task
Author
Year
Meditation
Protocol: task
Findings
Stinson
2013
Time NR (relaxation 'Alpha brain state exercise')
Neurofeedback video (Time
NR)
MED < task. Stats NR.
Note: > = significantly greater than; < =significantly lower than; = = no significant differences; CNT = control group; E-C = eyes-closed; E-O =eyes-open; EXP =experimental group; FA= focussed-attention (concentrative)
meditation; (from …) = initial number of participants in a pre-post study; MED = meditation; MM = Mindfulness meditation; NA = not available; NR = not reported; NU = not usable (here); NCC = neural correlates of
consciousness (i.e., EEG measurement during MED vs RS); ROI = region of interest; RS = resting state. All bandwidth outcomes pertain to power, unless otherwise stated in parentheses (e.g., coherence). Most entries are
comparing the experimental group (EXT; i.e., meditators) under different conditions (e.g., RS vs MED): significantly higher power levels during meditation are written as MED > RS; significantly lower levels as MED < RS;
and no significant differences as MED = RS. Some entries are comparing two groups (i.e., EXP vs CNT) on a particular condition (e.g., RS): this will be indicated as RS: EXT >/=/< CNT. Studies featuring novice participants
are indicated by the author being italicized in bold.
Running title: The neurophysiology of mindfulness
47
Supplementary Table 5. Theta bandwidth
Meditation vs resting state
Author
Year
Meditation
Protocol: Resting
Findings
Higher amplitude during meditation
Ahani
2014
15 mins MM (E-C)
15 mins audio listening (E-C)
ME > CNT (F(1,33) = 118.79, p ≤ 0.001)
Arita
2012
NA
NA
MED > RS (stats NA)
Cahn
2010
21 mins (Vipassana)
21 mins (E-C)
MED > RS (condition x ROI interaction only at specific sites (F(2, 30) =
7.75, p = .002). Increase in MED at Fz (p= .006), but not Cz (p= .99) or Pz
(p=.85)
Chan
2008
12 mins(Triarchic Body Relaxation Technique)
5 mins (E-C)
MED > RS (a range of t-tests; t = -3.73 - -4.82, p < .02.
Kasamatsu
1966
Time NR (Zen)
Time NR (E-C)
MED > RS. Stats NR.
Kubota
2001
25 mins (Su-soku)
2.5 mins (cued-breathing)
MED > RS (t = 6.14, p< .0001)
Lagopoulos
2009
20 mins (Acem)
20 mins (E-C)
MED > RS (F(1, 17) = 4.99, p = .04)
Lehmann
2012
60 mins (self-chosen)
4 mins (20 sec E-O, 40 sec E-
C; x 4)
MED (Zen) > RS. T = 4.95, p < 0.001.
Takahashi
2005
15 mins (Su-soku)
15 mins (E-O)
MED > RS (F(1, 19) = 5.5, p = .031)
Tanaka
2014
40 mins (MM)
8 mins (E-C)
EXP: MED > RS (stats NA). MED: EXP > CNT (p < .0001). RS: EXP < CNT (p <
.0001).
Lower amplitude during meditation
Dunn
1999
15 mins (FA) then 15 mins (MM)
15 mins (E-C)
MM < RS; Stats NR; MM > FA; Stats NR
Huang
2009
40 mins (Zen) (vs 40 mins rest for CNT)
EXP (MED) < CNT (rest) (F(1, 45) = 28.68, P < .0001)
Yu
2011
20 mins (Zen: tanden breathing)
2 mins (E-C)
MED < RS (F = 9.85, p < .001)
No significant differences (or no clear reportable patterns)
Amihai
2014
Therevada = 15 mins (Samatha) & 15 mins (Vipassna); Vajrayana
= 15 mins (deity) & 15 mins (Rig-pa)
10 mins (E-C)
MED (Therevada) = RS (F(2,18) = 1.11, p>0.3); MED (Vajrayana) = RS
(F(2,16) = 2.5, p>0.1)
Berkovich-
Ohana
2013
15 mins (MM)
2.5 mins (E-C) + 2.5 mins (E-
O)
MED = RS (coherence); Stats NR. RS: EXP = CNT (coherence); Stats NR.
Hinterberger
2011
15 mins (self-chosen), 2 mins (MM), 2 mins (thoughtless
emptiness), 2 mins (FA on ‘third eye’), 2 mins (FA on body axis)
5 mins (E-O), 5 mins (E-C), 5
mins (reading)
Complex analyses. Stats NU.
Milz
2014
2 x 5 mins (breath counting)
3 x 5 mins (E-C)
Power: MED = RS; Stats NR. Coherence: MED =RS (t(22) = 0.62, p = .53)
Murata
2004
15 mins (Su-soku; E-O)
15 mins (E-O)
EXP: MED = RS (coherence). Stats NR.
Pre-post changes
Lomas
2014
10 mins (MM)
5 mins (E-C)
Pre: MED > RS (F(1, 27) = 7.14, p = .013); Post: MED > RS (F(1, 27) = 5.74,
p = .024)
Tang
2009
Time NR (E-C)
Group x time interaction (F(1, 32) = 4.92, p < .05): pre-post decrease for
EXP (p < .05), not CNT
Xue
2014
5 mins (E-C)
Group x time interaction (in connectivity) (F(1,43) = 2.93; p = 0.09). Pre:
EXP = CNT (p > .05). Post: decreased path-length in EXP (t(23) = 3.72,p =
.001), not CNT.
Meditation vs task
Author
Year
Meditation
Protocol: task
Findings
Berkovich-
Ohana
2013
15 mins (MM)
Time-production task (2-3
mins)
EXP = CNT (coherence); Stats NR
Howells
2012
Resting state: 3 mins (E-O), 3 mins (E-C)
Sustained attention (visual A-
RS (EXP only): post < pre (t = 2.29, p < .05)
Running title: The neurophysiology of mindfulness
48
X continuous performance)
(10 mins)
Pasquini
2015
Focused attention task
EXP in task: Correlation between theta power and meditation weekly
frequency (r = 0.42, p = .02).
Slagter
2009
Attentional blink (time NR)
Group x time interaction: MED-related changes in the phase of target
induced EEG responses. Stats NU
Stinson
2013
Time NR (relaxation 'Alpha brain state exercise')
Watching video explaining
neurofeedback (Time NR)
MED < task. Stats NR.
Note: > = significantly greater than; < =significantly lower than; = = no significant differences; CNT = control group; E-C = eyes-closed; E-O =eyes-open; EXP =experimental group; FA= focussed-attention (concentrative)
meditation; (from …) = initial number of participants in a pre-post study; MED = meditation; MM = Mindfulness meditation; NA = not available; NR = not reported; NU = not usable (here); NCC = neural correlates of
consciousness (i.e., EEG measurement during MED vs RS); ROI = region of interest; RS = resting state. All bandwidth outcomes pertain to power, unless otherwise stated in parentheses (e.g., coherence). Most entries are
comparing the experimental group (EXT; i.e., meditators) under different conditions (e.g., RS vs MED): significantly higher power levels during meditation are written as MED > RS; significantly lower levels as MED < RS;
and no significant differences as MED = RS. Some entries are comparing two groups (i.e., EXP vs CNT) on a particular condition (e.g., RS): this will be indicated as RS: EXT >/=/< CNT. Studies featuring novice participants
are indicated by the author being italicized in bold.
Running title: The neurophysiology of mindfulness
49
Supplementary Table 6. Delta bandwidth
Meditation vs resting state
Author
Year
Meditation
Protocol: Resting
Findings
Higher amplitude during meditation
Cahn
2013
21 mins (Vipassana)
21 mins (E-C)
Expertise x state x ROI interaction(synchrony) (F(2, 2, 28) = 5.83, p < .01):
long- term EXP higher synchrony during MED (vs RS) at frontal sites, but
not central or parietal sites; for short term meds, MED = RS
Lower amplitude during meditation
Dunn
1999
15 mins (FA) then 15 mins (MM)
15 mins (E-C)
MM < RS; Stats NR
No significant differences (or no clear reportable patterns)
Amihai
2014
Therevada = 15 mins (Samatha) & 15 mins (Vipassna); Vajrayana
= 15 mins (deity) & 15 mins (Rig-pa)
10 mins (E-C)
Med < RS (F(2,18) = 8.37, p < 0.01). Post-hoc: no diff between RS and
Therevada MED (only Vajrayana MED)
Cahn
2010
21 mins (Vipassana)
21 mins (E-C)
MED = RS (F(1, 15) = 1.85, p = .19)
Hinterberger
2011
15 mins (self-chosen), 2 mins (MM), 2 mins (thoughtless
emptiness), 2 mins (FA on ‘third eye’), 2 mins (FA on body axis)
5 mins (E-O), 5 mins (E-C), 5
mins (reading)
Complex analyses. Stats NU.
Lagopoulos
2009
20 mins (Acem)
20 mins (E-C)
MED = RS (F(1, 17) = 0.99, p = .34)
Milz
2014
2 x 5 mins (breath counting)
3 x 5 mins (E-C)
Power: MED = RS. Stats NR
Meditation vs task
Author
Year
Meditation
Protocol: task
Findings
Stinson
2013
Time NR (relaxation 'Alpha brain state exercise')
Watching video explaining
neurofeedback (Time NR)
MED < task. Stats NR.
Note: > = significantly greater than; < =significantly lower than; = = no significant differences; CNT = control group; E-C = eyes-closed; E-O =eyes-open; EXP =experimental group; FA= focussed-attention (concentrative)
meditation; (from …) = initial number of participants in a pre-post study; MED = meditation; MM = Mindfulness meditation; NA = not available; NR = not reported; NU = not usable (here); NCC = neural correlates of
consciousness (i.e., EEG measurement during MED vs RS); ROI = region of interest; RS = resting state. All bandwidth outcomes pertain to power, unless otherwise stated in parentheses (e.g., coherence). Most entries are
comparing the experimental group (EXT; i.e., meditators) under different conditions (e.g., RS vs MED): significantly higher power levels during meditation are written as MED > RS; significantly lower levels as MED < RS;
and no significant differences as MED = RS. Some entries are comparing two groups (i.e., EXP vs CNT) on a particular condition (e.g., RS): this will be indicated as RS: EXT >/=/< CNT. Studies featuring novice participants
are indicated by the author being italicized in bold.
Running title: The neurophysiology of mindfulness
50
Supplementary Table 7. Gamma bandwidth
Meditation vs resting state
Author
Year
Meditation
Protocol: Resting
Findings
Higher amplitude during meditation
Berkovich-Ohana
2012
15 mins (MM)
2.5 mins (E-C) + 2.5 mins (E-
O)
MED > RS (F(1, 34)= 17.00, p < 0.0001).
Cahn
2010
21 mins (Vipassana)
21 mins (E-C)
MED > RS (F(1, 15) = 9.32, p = .008).
Hauswald
2015
20 mins (Zen, E-O)
5 mins (E-O)
Increased gamma power in MED correlated both with trait mindfulness (p
= .015) and years of practice (p = .036).
Lehmann
2012
60 mins (self-chosen)
4 mins (20 sec E-O, 40 sec E-
C; x 4)
MED (Zen) > RS. T = 2.66, p = 0.019.
No significant differences (or no clear reportable patterns)
Amihai
2014
Therevada = 15 mins (Samatha) & 15 mins (Vipassna); Vajrayana
= 15 mins (deity) & 15 mins (Rig-pa)
10 mins (E-C)
MED (Therevada) = RS; Stats NR. MED (Vajrayana) < RS (F(2,16) = 6.16,
p>0.01)
Berkovich-Ohana
2013
15 mins (MM)
2.5 mins (E-C) + 2.5 mins (E-
O)
MED = RS (coherence); Stats NR. RS: EXP = CNT (coherence); Stats NR.
Hinterberger
2011
15 mins (self-chosen), 2 mins (MM), 2 mins (thoughtless
emptiness), 2 mins (FA on ‘third eye’), 2 mins (FA on body axis)
5 mins (E-O), 5 mins (E-C), 5
mins (reading)
Complex analyses. Stats NU.
Milz
2014
2 x 5 mins (breath counting)
3 x 5 mins (E-C)
Power; MED = RS. Stats NR
Meditation vs task
Author
Year
Meditation
Protocol: task
Findings
Berkovich-Ohana
2013
15 mins (MM)
Time-production task (2-3
mins)
EXP = CNT (coherence); Stats NR
Ferarelli
2013
Sleep
Correlation: MED experience & NREM gamma (r = 0.47, p =.017). No
correlation with REM gamma
Schoenberg (& S.)
2015
Go/NoGo task
Task: Interaction (time x group x site x epoch) (F(1, 47) = 4.12, p = .05)
pre-post power increase for CNT only (EXT: no increase).
Stinson
2013
Time NR (relaxation 'Alpha brain state exercise')
Watching video explaining
neurofeedback (Time NR)
MED < task. Stats NR.
Note: > = significantly greater than; < =significantly lower than; = = no significant differences; CNT = control group; E-C = eyes-closed; E-O =eyes-open; EXP =experimental group; FA= focussed-attention (concentrative)
meditation; (from …) = initial number of participants in a pre-post study; MED = meditation; MM = Mindfulness meditation; NA = not available; NR = not reported; NU = not usable (here); NCC = neural correlates of
consciousness (i.e., EEG measurement during MED vs RS); ROI = region of interest; RS = resting state. All bandwidth outcomes pertain to power, unless otherwise stated in parentheses (e.g., coherence). Most entries are
comparing the experimental group (EXT; i.e., meditators) under different conditions (e.g., RS vs MED): significantly higher power levels during meditation are written as MED > RS; significantly lower levels as MED < RS;
and no significant differences as MED = RS. Some entries are comparing two groups (i.e., EXP vs CNT) on a particular condition (e.g., RS): this will be indicated as RS: EXT >/=/< CNT. Studies featuring novice participants
are indicated by the author being italicized in bold.
Running title: The neurophysiology of mindfulness
51
Supplementary Table 8. Asymmetry Findings
Meditation vs resting state
Author
Year
Meditation
Protocol
Findings
Increase in relative left-frontal activation linked to meditation
Amihai
2014
Therevada = 15 mins (Samatha) & 15 mins (Vipassna); Vajrayana
= 15 mins (deity) & 15 mins (Rig-pa)
10 mins resting state (E-C)
MED (Therevada): Condition x location interaction (F(4,36) = 3.09,
p<0.05). MED (Vipassana) > RS at left location (p<0.05), but not right or
center (p>0.2)
Barnhofer
2007
8 x 1min (4 = E-O, 4 = E-C)
8 x 1min (4 = E-O, 4 = E-C)
Group x time interaction (F(2, 19) = 3.7, p = .044): pre-post decreases in
relative left prefrontal asymmetry for CNT (p = .003), but not EXP (p =
.918)
Barnhofer
2010
EXP = 15 mins (MM); CNT = 15 mins (LKM)
2 mins (E-C): EEG assessed
pre and post MED
Pre-post increase (EXP & CNT) in relative left prefrontal activation (F(1,
13) = 5.06, p = .04)
Chan
2008
Triarchic Body Relaxation Technique (12 mins) 5 mins (E-C)
5 mins resting state (E-C
MED > RS (left-sided activation; F(1, 18) = 5.42, p = .032)
Davidson
2003
8 x 1min (4 = E-O, 4 = E-C)
Writing about experiences
(EEG recorded 1 min before
& 3 mins after)
Group x time interaction in RS (F(1, 37) = 5.14, p < .05): pre-post
increase in relative left- activation for EXP (not CNT)
Decrease in relative left-frontal activation linked to meditation
Keune
2011
Sad mood induction (sad
music, and neg. experience
recall). Time NR.
Pre-post decrease (EXP & CNT) in relative left prefrontal activation (F(4,
64) = 3.38, p < .05)
No change in relative left-frontal activation linked to meditation
Milz
2014
2 x 5 mins (breath counting)
3 x 5 mins resting state (E-C)
Power: MED = RS. Stats NR
Note: > = significantly greater than; < =significantly lower than; = = no significant differences; CNT = control group; E-C = eyes-closed; E-O =eyes-open; EXP =experimental group; FA= focussed-attention (concentrative)
meditation; (from …) = initial number of participants in a pre-post study; MED = meditation; MM = Mindfulness meditation; NA = not available; NR = not reported; NU = not usable (here); NCC = neural correlates of
consciousness (i.e., EEG measurement during MED vs RS); ROI = region of interest; RS = resting state. All bandwidth outcomes pertain to power, unless otherwise stated in parentheses (e.g., coherence). Most entries are
comparing the experimental group (EXT; i.e., meditators) under different conditions (e.g., RS vs MED): significantly higher power levels during meditation are written as MED > RS; significantly lower levels as MED < RS;
and no significant differences as MED = RS. Some entries are comparing two groups (i.e., EXP vs CNT) on a particular condition (e.g., RS): this will be indicated as RS: EXT >/=/< CNT. Studies featuring novice participants
are indicated by the author being italicized in bold.
Running title: The neurophysiology of mindfulness
52
Supplementary Table 9. ERP Findings
Meditation vs task
Author
Year
Meditation
Protocol: task
Findings
Bostanov
2012
20 mins (MM)
Auditory stimuli during MM
Increased pre-post ‘Late contingent negative variation for EXP. but not
CNT (F = 7.7, p < .01)
Brown
2010
Stimulation of pain (5 mins)
Lower activation for EXP than CNT in S2 and insula during pain stimulus, t
= 2.51, p < .05
Brown
2013
-
Anticipation and simulation
of pain (time NR)
Anticipatory and pain-evoked ERPs to acute pain reduced in EXP but not
CNT (stats NA)
Delgado
2013
Auditory oddball task
Two-way P3b interaction (task × oddball order): significant effects of
meditation after the meditation/control task (p = 0.01).
Garland
2015
Event-related affective
picture viewing task
Tim x group x cue interaction (F(1, 25) = 4.99, p = ..035). EXP group (vs
CNT) = pre-post increases in LPP activation to natural reward cues across
400 1000 ms window.
Jo
2014
Performing voluntary finger
movement (time NR)
MED = CNT (Readiness Potential amplitude prior to voluntary action) (p =
.26).
Lakey
2011
EXP = 6 mins (MM); CNT = 6 mins (non-MM-task)
P300-based braincomputer
interface (BCI) task
Task: EXP > CNT (P300 amplitude peaks) (t(16) = 2.10, P < .05)
Moore
2012
Stroop (time NR)
Group x time interaction: pre-post increase in focusing attention (EXP
only). Stats NU.
Schoenberg (et al.)
2014
Go/NoGo task
Time x condition x group interaction: significant pre-post increases for
EXP in Go-P3 (t(23) = -2.986, p = .007) and NoGo-P3 (t(23) = -2.502, p =
.02) amplitude at Pz, contrary to pre-post parietal decreases for CNT in
Go-P3 (p = .42) and NoGo-P3 (p = .40).
Schoenberg (& S.)
2014
Go/NoGo task
Pre-post increase in event-related theta synchronization during the late
time window (400-800 ms) for EXT (F(1, 49) = 10.933, p = .002), vs pre-
post decrease for CNT
Slagter
2007
Attentional blink (time NR)
Group x time interaction (F(1, 20) = 5.4, p = .03): pre-post decrease in
elicited P3B amplitude for EXP, not CNT
Sobolewski
2011
Looking at emotional pictures
(Time NR)
Group x valence interaction (p = .03): EXP less affected by negative
emotional load.
Teper
2013
Stroop (time NR)
Task: EXP > CMT (higher amplitude error-related negativity) (F(1, 36) =
3.32, p < .04)
Teper
2014
Performance feedback
(neutral, aversive, and
rewarding)
(time NR)
Trait MM: predicts less differentiation of rewarding from neutral
feedback. Stats NU
Van Leeuwen
2012
Target detection (time NR)
Task: MED > CNT (enhanced attentional processing). Stats NU.
Note: > = significantly greater than; < =significantly lower than; = = no significant differences; CNT = control group; E-C = eyes-closed; E-O =eyes-open; EXP =experimental group; FA= focussed-attention (concentrative)
meditation; (from …) = initial number of participants in a pre-post study; MED = meditation; MM = Mindfulness meditation; NA = not available; NR = not reported; NU = not usable (here); NCC = neural correlates of
consciousness (i.e., EEG measurement during MED vs RS); ROI = region of interest; RS = resting state. All bandwidth outcomes pertain to power, unless otherwise stated in parentheses (e.g., coherence). Most entries are
comparing the experimental group (EXT; i.e., meditators) under different conditions (e.g., RS vs MED): significantly higher power levels during meditation are written as MED > RS; significantly lower levels as MED < RS;
and no significant differences as MED = RS. Some entries are comparing two groups (i.e., EXP vs CNT) on a particular condition (e.g., RS): this will be indicated as RS: EXT >/=/< CNT. Studies featuring novice participants
are indicated by the author being italicized in bold.

Supplementary resource (1)

... Increased stress is associated with increased beta but reduced alpha frequencies [72], and with green exposure reducing beta frequency including when associated with traffic [73,74]. EEG changes in alpha, beta and gamma frequencies have been linked to changes in relaxation [59] and EEG has been proposed as an ideal measure of relaxation 'R-state' by Zhang et al. [58], with increases in relaxation reflecting restorative environments, such as increased alpha in response to passive viewing of rural images [73] and increased frontal and occipital alpha when viewing green rather than urban spaces [75]. Similarly, relaxed attentional states, as opposed to loss of attention and vigilance, have been associated with increased alpha and theta [61]. ...
... Whilst the subjective ratings of stress (UWIST MACL) and anxiety (STAI-S) did not result in significant differences between presentation formats (FS contrasted with VR), these EEG changes were aligned with some qualitative descriptors reflecting greater experiences of immersion, presence and realism for the VR presentations resulting in greater levels of cortical brain activity (EEG power). Increased alpha and theta with VR exposure may also reflect greater 'relaxed attentional states' as proposed by Lomas et al. [75]. ...
... Conversely, where the EEG results suggested there were differences in restorative effect between the environments (theta and alpha), it was the historic environment that appeared to have the largest effect, an apparent contradiction in the findings for research question 1. It is possible that historic settings can support other forms of mental well-being benefits, such as triggering soft attention stimulation or cognitive engagement [39] again reflecting relaxed yet attentional states [75], rather than stress reduction and relaxation which are properties of natural environments, and that these were detected by the EEG measurements. ...
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Improving the mental health of urban residents is a global public health priority. This study builds on existing work that demonstrates the ability of virtual exposure to restorative environments to improve population mental health. It compares the restorative effects of green, blue and historic environments delivered by both flat screen and immersive virtual reality technology, and triangulates data from psychological, physiological and qualitative sources. Results from the subjective measure analyses showed that exposures to all the experimental videos were associated with self-reported reduced anxiety and improved mood, although the historic environment was associated with a smaller reduction of anxiety (p < 0.01). These results were supported by the qualitative accounts. For two of the electroencephalography (EEG) frequency bands, higher levels of activity were observed for historic environments. In relation to the mode of delivery, the subjective measures did not suggest any effect, while for the EEG analyses there was evidence of a significant effect of technology across three out of four frequency bands. In conclusion, this study adds to the evidence that the benefits of restorative environments can be delivered through virtual exposure and suggests that virtual reality may provide greater levels of immersion than flat screen viewing.
... In particular, there is ample evidence of the effects of meditation on EEG activity (Fell et al., 2010;Lee et al., 2018;Lomas et al., 2015;Schoenberg & Vago, 2019). Among the range of EEG frequency bands (from alpha to gamma), the role of the gamma band has been recently highlighted, with reports evidencing increased activity in this band during meditation, mostly in parieto-occipital regions (Berkovich-Ohana et al., 2012;Berman & Stevens, 2015;Braboszcz et al., 2017;Cahn et al., 2010). ...
... Regarding study 2, a first limitation is related to the sample size, although our size is comparable to that of similar neuroimaging studies (see meta-analysis: Lomas et al., 2015). A further limitation of this study refers to the fact that data were collected for a single type of meditative exercise (breath focus), even though the different meditation practices share a great part of their phenomenological content Schoenberg & Vago, 2019). ...
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Objectives The present research was intended to validate a new psychometric instrument—the Meditative State Scale (MSS)—designed from a novel approach that integrates knowledge from the foundational pillars in which meditation practices were grounded with recent neuroscientific and psychological findings. Methods The research was divided into two studies. Study 1 (n = 241) comprised the development and validation of the MSS. Its factor structure was evaluated through the conduction of exploratory and confirmatory factor analyses. Measurement invariance of the MSS across groups of naïve and experienced meditators was also tested. A selection of additional instruments were used to further assess its convergent and discriminant validity. In study 2, additional validity of the MSS was investigated with an experimental design (n = 12) in which the electroencephalographic (EEG) signal was recorded while the participants were meditating. Next, the correlations between EEG activity and reported MSS scores were explored. Results Study 1 established psychometric reliability and validity of the MSS, supporting a three-factor structure encompassing a first factor of “transcendence,” a second factor of “difficulties,” and a third factor of “mental quietening.” The MSS also shows configural, metric, and partial scalar invariance across beginners and experienced meditators. In study 2, we found associations between reported MSS scores and changes in EEG gamma activity in parietal and occipital areas while engaging in meditation practice. Conclusions We expect that the MSS can contribute to synergistically explore meditative states, combining reliable psychometric measures of the meditative state with neurophysiological data. Thus, it may be possible to reach a better understanding of the complex mechanisms that are involved in meditation practice and a more grounded and rigorous application of meditation-based programs in research, educational, and clinical contexts.
... [16] evaluated pre-post-therapy changes in event-related brain potentials (ERPs) recorded during a MF meditation task and found a correlation between the ERPs and other self-report measures of MF and meditation practice. Furthermore, a review paper from 2015 [19] collected data from 56 papers on EEG and MF meditation and found that prolonged MF practice was associated with increased alpha and theta power in healthy individuals [20] as well as patient populations. ...
... In addition, total beta band power and relative theta band power and total power contributed significantly to the classification of before and after MF meditation exercise. This aligns with previous research and shows consistency between the effects of MF in previous research on typically developing individuals and other psychiatric conditions [19,22]. Previous research showed that MF resulted in increases in the alpha power band and theta band for typically developing populations. ...
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Mindfulness has growing empirical support for improving emotion regulation in individuals with Autism Spectrum Disorder (ASD). Mindfulness is cultivated through meditation practices. Assessing the role of mindfulness in improving emotion regulation is challenging given the reliance on self-report tools. Electroencephalography (EEG) has successfully quantified neural responses to emotional arousal and meditation in other populations, making it ideal to objectively measure neural responses before and after mindfulness (MF) practice among individuals with ASD. We performed an EEG-based analysis during a resting state paradigm in 35 youth with ASD. Specifically, we developed a machine learning classifier and a feature and channel selection approach that separates resting states preceding (Pre-MF) and following (Post-MF) a mindfulness meditation exercise within participants. Across individuals, frontal and temporal channels were most informative. Total power in the beta band (16-30 Hz), Total power (4-30 Hz), relative power in alpha band (8-12 Hz) were the most informative EEG features. A classifier using a non-linear combination of selected EEG features from selected channel locations separated Pre-MF and Post-MF resting states with an average accuracy, sensitivity, and specificity of 80.76%, 78.24%, and 82.14% respectively. Finally, we validated that separation between Pre-MF and Post-MF is due to the MF prime rather than linear-temporal drift. This work underscores machine learning as a critical tool for separating distinct resting states within youth with ASD and will enable better classification of underlying neural responses following brief MF meditation.
... Much of the neuroscientific literature on meditation has focused on investigations of brain activity during formal meditation practice (for reviews, see Cahn & Polich, 2006;Lee et al., 2018;Lomas et al., 2015). During formal meditation practice, practitioners engage with a specified set of mental activities for a given period of time. ...
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... Previous findings of increased alpha in post-drawing compared to pre-drawing states (Belkofer et al., 2014), would also be consistent with this interpretation, given the post-drawing state would have been the result of a period of focused attention. Increased alpha has been consistently associated with mindfulness in several studies, further supporting its correlation with attention (Lomas et al., 2015). Our results in the drawing task differed from what we expected. ...
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... Further, brain activity in the form of error-related negativity (ERN) or spectral power are thought to represent executive functioning processes, such as inhibitory control and working memory [23,24]. Those exposed to mindfulness training in the context of mental health show greater ERN magnitude and spectral power than controls [25,26]. Thus, in this pilot study, we hypothesized that the intervention group would show better executive function, as measured by the accuracy and reaction time on the Flanker task, Stroop test, and N-back test, compared to a control group over time. ...
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Executive functioning is a key component involved in many of the processes necessary for effective weight management behavior change (e.g., setting goals). Cognitive behavioral therapy (CBT) and third-wave CBT (e.g., mindfulness) are considered first-line treatments for obesity, but it is unknown to what extent they can improve or sustain executive functioning in a generalized weight management intervention. This pilot randomized controlled trial examined if a CBT-based generalized weight management intervention would affect executive functioning and executive function-related brain activity in individuals with obesity or overweight. Participants were randomized to an intervention condition (N = 24) that received the Noom Weight program or to a control group (N = 26) receiving weekly educational newsletters. EEG measurements were taken during Flanker, Stroop, and N-back tasks at baseline and months 1 through 4. After 4 months, the intervention condition evidenced greater accuracy over time on the Flanker and Stroop tasks and, to a lesser extent, neural markers of executive function compared to the control group. The intervention condition also lost more weight than controls (−7.1 pounds vs. +1.0 pounds). Given mixed evidence on whether weight management interventions, particularly CBT-based weight management interventions, are associated with changes in markers of executive function, this pilot study contributes preliminary evidence that a multicomponent CBT-based weight management intervention (i.e., that which provides both support for weight management and is based on CBT) can help individuals sustain executive function over 4 months compared to controls.
... Further light has been shed on the neurophysiological dynamics of mind states like meditation by paradigms such as electroencephalography (EEG), which reflect large-scale synchronisation of neural networks. With meditation, a key 'signature' across different practices is increased amplitude and coherence in alpha and theta bandwidths (Lomas et al., 2015). Although interpreting this pattern can be difficult, it is thought to reflect a state of 'relaxed alertness' (Fell et al., 2010). ...
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Background This study investigates measures of mindfulness meditation (MM) as a mental practice, in which a resting but alert state of mind is maintained. A population of older people with high stress level participated in this study, while electroencephalographic (EEG) and respiration signals were recorded during a MM intervention. The physiological signals during meditation and control conditions were analyzed with signal processing. Methods EEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis, phase analysis and classification to evaluate an objective marker for meditation. Results Different frequency bands showed differences in meditation and control conditions. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy (85%) at discriminating between meditation and control conditions than a classifier using the EEG signal only (78%). Conclusion Support vector machine (SVM) classifier with EEG and respiration feature vector is a viable objective marker for meditation ability. This classifier should be able to quantify different levels of meditation depth and meditation experience in future studies.