Distinct Neural Activity Associated with Focused-Attention Meditation and Loving-Kindness Meditation
This study examined the dissociable neural effects of ānāpānasati (focused-attention meditation, FAM) and mettā (loving-kindness meditation, LKM) on BOLD signals during cognitive (continuous performance test, CPT) and affective (emotion-processing task, EPT, in which participants viewed affective pictures) processing. Twenty-two male Chinese expert meditators (11 FAM experts, 11 LKM experts) and 22 male Chinese novice meditators (11 FAM novices, 11 LKM novices) had their brain activity monitored by a 3T MRI scanner while performing the cognitive and affective tasks in both meditation and baseline states. We examined the interaction between state (meditation vs. baseline) and expertise (expert vs. novice) separately during LKM and FAM, using a conjunction approach to reveal common regions sensitive to the expert meditative state. Additionally, exclusive masking techniques revealed distinct interactions between state and group during LKM and FAM. Specifically, we demonstrated that the practice of FAM was associated with expertise-related behavioral improvements and neural activation differences in attention task performance. However, the effect of state LKM meditation did not carry over to attention task performance. On the other hand, both FAM and LKM practice appeared to affect the neural responses to affective pictures. For viewing sad faces, the regions activated for FAM practitioners were consistent with attention-related processing; whereas responses of LKM experts to sad pictures were more in line with differentiating emotional contagion from compassion/emotional regulation processes. Our findings provide the first report of distinct neural activity associated with forms of meditation during sustained attention and emotion processing.
Distinct Neural Activity Associated with Focused-
Attention Meditation and Loving-Kindness Meditation
Tatia M. C. Lee
*, Mei-Kei Leung
, Wai-Kai Hou
, Joey C. Y. Tang
, Jing Yin
, Kwok-Fai So
, Chetwyn C. H. Chan
1Laboratory of Neuropsychology, The University of Hong Kong, Hong Kong, China, 2Laboratory of Cognitive Affective Neuroscience, The University of Hong Kong, Hong
Kong, China, 3The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China, 4Social Neuroscience Research Network, The
University of Hong Kong, Hong Kong, China, 5Number Laboratory, The University of Hong Kong, Hong Kong, China, 6Centre of Buddhist Studies, The University of Hong
Kong, Hong Kong, China, 7Department of Anatomy, The University of Hong Kong, Hong Kong, China, 8Applied Cognitive Neuroscience Laboratory, Department of
Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
This study examined the dissociable neural effects of a
¯nasati (focused-attention meditation, FAM) and metta
kindness meditation, LKM) on BOLD signals during cognitive (continuous performance test, CPT) and affective (emotion-
processing task, EPT, in which participants viewed affective pictures) processing. Twenty-two male Chinese expert
meditators (11 FAM experts, 11 LKM experts) and 22 male Chinese novice meditators (11 FAM novices, 11 LKM novices) had
their brain activity monitored by a 3T MRI scanner while performing the cognitive and affective tasks in both meditation and
baseline states. We examined the interaction between state (meditation vs. baseline) and expertise (expert vs. novice)
separately during LKM and FAM, using a conjunction approach to reveal common regions sensitive to the expert meditative
state. Additionally, exclusive masking techniques revealed distinct interactions between state and group during LKM and
FAM. Specifically, we demonstrated that the practice of FAM was associated with expertise-related behavioral
improvements and neural activation differences in attention task performance. However, the effect of state LKM
meditation did not carry over to attention task performance. On the other hand, both FAM and LKM practice appeared to
affect the neural responses to affective pictures. For viewing sad faces, the regions activated for FAM practitioners were
consistent with attention-related processing; whereas responses of LKM experts to sad pictures were more in line with
differentiating emotional contagion from compassion/emotional regulation processes. Our findings provide the first report
of distinct neural activity associated with forms of meditation during sustained attention and emotion processing.
Citation: Lee TMC, Leung M-K, Hou W-K, Tang JCY, Yin J, et al. (2012) Distinct Neural Activity Associated with Focused-Attention Meditation and Loving-Kindness
Meditation. PLoS ONE 7(8): e40054. doi:10.1371/journal.pone.0040054
Editor: Suliann Ben Hamed, CNRS - Universite
´Claude Bernard Lyon 1, France
Received June 29, 2011; Accepted June 5, 2012; Published August 15, 2012
Copyright: ß2012 Lee et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by research funding from the Centre of Buddhist Studies of The University of Hong Kong and the Research Grant Council
General Research Fund (Ref: HKU747612H). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com (TMCL); Chetwyn.Chan@inet.polyu.edu.hk (CCHC)
It has been widely speculated that longterm meditation training
has a significant positive impact on neuropsychological functioning
in both cognitive and affective domains [1,2]. Here we report a
study on the dissociable effects on neural activity of two forms of
meditation following the Therava
¯da school of Buddhism:
¯nasati (focused-attention meditation) and metta
Among the broad array of meditation practices, the most basic
and widely studied form is concentrative or focused-attention
meditation (FAM). FAM practitioners focus their entire attention
upon an object or a bodily sensation and, whenever they are
distracted by external stimuli or inner thoughts, they bring their
attention back to that object or sensation. The goal is to achieve a
clear (vivid) and unwavering (calm and stable) state free from
distraction. FAM’s reported major longterm benefit is cognitive—
attentional control. For example, expert meditators show larger
mismatch negativity amplitudes, a measure of attention. Mismatch
negativity is an event-related potential waveform reflecting the
involuntary attentional switching that can be elicited by the
appearance of an infrequent stimulus in a stream of frequent
stimuli. The larger mismatch negativity amplitudes observed in the
experts (with 3 to 7 years of daily practice) implies their higher
ability, relative to the matched novice meditators, in detecting
changes appeared in the auditory task—especially after medita-
tion—suggesting that they are more efficient at preattentive
detection of signal changes .
The relationship between the strength of BOLD signals and
meditation experience appears to follow an inverted u-shaped
function. Compared to novices, experts with at least 3 years of
experience actually had lower sustained activation in attention-
related brain areas, including the left superior frontal gyrus (SFG)
and cingulate cortex . However, Buddhist meditators with an
average of 7.9 years of meditative practice (equivalent to
5,767 hours, 2 hours daily) showed higher activation in the medial
prefrontal cortex (mPFC) and the anterior cingulate cortex (ACC)
during Vipassana (Buddhist mindfulness meditation) compared to
matched controls who had no previous experience with meditation
or similar practices . Also, experts with 10,000–24,000 hours of
PLOS ONE | www.plosone.org 1 August 2012 | Volume 7 | Issue 8 | e40054
practice (mean 19,000 hours) showed significantly more activation
in attention-related regions, compared to age-matched novices
who had no prior experience except in the week before the
experiment . On the other hand, longterm practitioners who
averaged more than twice as much meditation experience (mean
44,000 hours) showed less activation in attention-related regions.
The authors interpreted this inverted u-shaped function to reflect
skill acquisition—a pattern that has been observed in others
domains of expertise [7,8]. The findings are consistent with
meditation texts that describe concentration meditation as initially
requiring greater levels of effort but later becoming less effortful.
So experts seem to ‘‘settle’’ into meditative states with minimal
There has been a recent surge of research interest in the effect of
loving-kindness meditation (LKM) on brain functioning. LKM
emphasizes a state of universal love and compassion, equalizing
the self and others [9,10]. Compassion cultivates the desire to
relieve pain and suffering for the self and others, while loving-
kindness loads the mind with universal, nonreferential compassion
towards oneself and other beings . Practitioners of LKM
imagine a being—human or animal—and flow unconditional love
and benevolence towards that being; they extend this love to all
sentient beings and wish that all living beings are free from
suffering and its causes. Love and compassion eventually grow and
fill the entire mind, with no other consideration, reasoning, or
discursive thoughts. Disparate as they may seem, LKM and FAM
can be complimentary: a focused state enables people to sustain
universal, nonreferential love and compassion; conversely, the
feeling of love and kindness helps people achieve a peace of mind
useful for entering into a focused state .
Compared to FAM, relatively little is known about the neural
basis of LKM. Lutz and colleagues  played LKM experts
emotional sounds during meditation and baseline and found that
they had increased neural activity during meditation including the
anterior insula, postcentral gyrus, inferior parietal lobule (IPL),
amygdala, right temporal-parietal junction, and right posterior
and superior temporal sulcus. They interpreted the findings as
meaning that LKM experts have a higher level of integration of
sensory-perceptual processes and affective responses than novices.
In a follow-up study, Lutz and colleagues  further confirmed
that LKM experts—relative to novices—had more activity in the
left somatosensory cortex, IPL, ACC, and insula in response to
emotional sounds. These findings suggest that longterm LKM
practice may enhance sensitivity to the emotional experiences of
others, which may be similar to empathy . Thus far no study
has directly compared neural activity measured by BOLD signals
associated with FAM and LKM during cognitive and affective
tasks. This study fills this research gap by investigating the
overlapping and distinct neural correlates of FAM and LKM with
cognitive and affective processing. Cognitive performance with the
continuous performance test (CPT) and affective processing with
the emotion-processing task (EPT), which involves viewing happy,
sad, and neutral photos from the International Affective Picture
System (IAPS), were employed as the experimental tasks. For each
task, two voxel-wise analyses using a 262 factorial design were
conducted for the two forms of meditation respectively, with state
(meditation and baseline) as a within-subject factor and group
(experts and novices) as a between-subjects factor. We hypothe-
sized that the neural activity associated with different states and
groups during the CPT and the EPT would be distinct between
FAM and LKM. To test this hypothesis, we examined the
overlapping and distinct neural activity.
Since attention is the training goal of FAM, we predicted that
there would be differences in BOLD signals in attention-related
brain areas during the CPT, including the lateral PFC, IPL,
superior temporal gyrus (STG), cingulate cortex, and caudate,
regions that has previously been reported to have connected to
attention [4,14–16]. We predicted that FAM experts would have
stronger signals in these regions during meditation versus baseline
and that this effect would be even stronger for FAM experts over
novices. On the other hand, because LKM focuses on emotional
training, we predicted that LKM mediators would have stronger
BOLD signals in emotion-related brain areas while they viewed
affective pictures. Those areas include the PFC (dorsomedial PFC,
lateral PFC, and orbitofrontal cortex), parietal and temporal
cortical regions, and limbic regions, including the insula, cingulate
regions, amygdala, hippocampal region, and the caudate [17–21].
We hypothesized that LKM experts would have stronger signals in
these regions during meditation versus baseline and that this effect
would be stronger for LKM experts than novices. We also sought
to verify whether or not activity in these brain regions correlated
with performance on the CPT and ratings of the IAPS. Self-
reported affect measures were administered in order to verify if our
LKM experts, with their years of LKM practice, were better able
to reduce negative affect, relative to novices who had only one
week of LKM experience.
Materials and Methods
After approval from The University of Hong Kong’s Ethics
Committee, we recruited experts from a Buddhist meditation
network in Hong Kong. Meditators were included if they were
male and had practiced FAM or LKM for 2 hours each day for at
least 5 years. Exclusion criteria were: history of traumatic brain
injury, medical conditions, or any psychiatric disorder that could
affect neural activity and brain functioning. A total of 22 Chinese
experts were recruited, 11 for FAM and 11 for LKM. They had
been practicing either FAM (a
¯nasati) or LKM (metta
¯) based on
¯da tradition, the oldest Buddhist practice. Therava
is still prevalent in India, Sri Lanka, and other Southeast Asian
countries. The forms of meditation reported by the experts were
verified by the teacher of these meditators as well as by Venerable
Jing Yin. The FAM experts ranged in age from 39 to 68 years
(mean = 52.7269.69 years), with an average of 14.0963.21 years
of education. The LKM experts ranged in age from 31 to 68 years
(mean = 51.82611.28 years), with an average of 14.2763.95 years
of education. Participants reported having at least 5 years of
practice of FAM (mean = 5,248.9566,191.94 hours; range = 810
to 17,850 hours) or LKM (mean = 7,491.9866,681.43 hours;
range = 588 to 17,850 hours). All practitioners commenced
meditation practice in FAM first and then afterwards they chose
the form of practice that they wanted to pursue, which in this study
was either LKM or FAM. Hence, it was impossible to recruit
participants who solely practiced LKM. Nevertheless, since this
study examined the state effects of FAM and LKM, the fact that
our two groups of meditators had clearly demonstrated expertise
in their respective forms of meditation, FAM or LKM, was
considered sufficient for verifying the different state effects of the
two forms of meditation examined in this study.
Matched healthy volunteers were recruited from the commu-
nity. Inclusion criteria were: male ages 30 to 65, Chinese ethnicity,
interested in meditative training, and no prior meditative practice.
A total of 22 novices were recruited and randomly assigned as
controls (FAM novices; n = 11) of the FAM experts or the controls
(LKM novices; n = 11) of the LKM experts. They were given
written instructions for a 1-week, home-based meditation practice,
based on their group membership. The Chinese instructions for
Neural Activity during Meditation
PLOS ONE | www.plosone.org 2 August 2012 | Volume 7 | Issue 8 | e40054
the home practice of FAM and LKM were designed by one of our
co-authors, Venerable Jing Yin, who himself has more than 40
years of experience in meditation. These instructions are very
similar to those offered by Venerable Dr. M. Ricard, who has
much experience practicing and teaching meditation. The
participants were instructed to perform the home practice for
1 hour per day for 7 days consecutively. Specifically, they were
asked to separate the 1-hour practice into three 20-minute sessions
in order to achieve better effects. After the 1-week practice, the
experimenter solicited from the novices self-reports of changes in
their meditation experience. The experimenter then sought
confirmation from Venerable Jing Yin that the novices were on
the right track. The FAM novices ranged in age from 31 to 63
years (mean = 47.1669.67), with an average of 18.4562.11 years
of education. The LKM novices ranged in age from 36 to 59 years
(mean = 47.3468.95), with an average of 16.7365.10 years of
education. There were no significant differences in terms of age
between experts and novices F(1, 40) = 2.81, p..10. All partici-
pants (experts and novices) were right-handed according to the
Edinburgh Handedness Inventory  and with at least high
All participants also completed two mindfulness questionnaires,
namely the Toronto Mindfulness Scale (TMS)  and Cognitive
and Affective Mindfulness Scale Revised (CAMSR) , prior to
the study (i.e. after the 1-week practice for novices). For both
scores, no significant group by form-of-meditation interactions
[TMS: F(1,40) = 0.67, p..1; CAMSR: F(1,40) = 0.28, p..5] or
main effects for form-of-meditation [TMS: F(1,40) = 2.75, p..1;
CAMSR: F(1,40) = 2.10, p..1] were detected. Only the main
effects for group were significant [TMS: F(1,40) = 14.49, p,.0005;
CAMSR: F(1,40) = 13.49, p,.001], indicating that experts had in
general much higher mindfulness scores than novices prior to the
In the CPT, numbers zero to nine were displayed in white, 18-
point Arial font for 50 milliseconds (ms) at the center of a dark
background once every second. The grayness of the numbers
varied randomly on 5 levels. In the experimental condition,
participants pressed a button once if the number zero appeared,
which happened on one-third of the 450 total trials. Performance
is summarized into four scores: commission errors, omission
errors, reaction time, and its variability. In the control condition,
stimulus presentation was identical, except that participants were
instructed to simply look at the screen without attending to or
pressing the button for the number zero. The control condition
was used to account for the visual components of watching flashing
numbers; it represents baseline attention level. All stimuli were
generated by E-Prime on a control computer located outside the
MR room and displayed in the room using a back projection
The EPT included 20 happy, 20 sad, and 20 neutral pictures
from the IAPS with the highest valence and arousal ratings in
published norms . Each emotion valence had equal propor-
tions of pictures with human and nonhuman images (i.e., animals,
objects, and scenes). All stimuli appeared once on a dark
background randomly in two 30-trial runs. Each trial had a 3-
second stimulus presentation, separated by a white central fixation
cross with varying durations from 500 to 2,500 ms in steps of
500 ms (i.e., 500; 1,000; 1,500; 2,000; and 2,500 ms). Participants
also rated the valence from 1 (very negative)to9(very positive) and
arousal (1 = not arousing,9=very arousing) of each happy and sad
picture. Figure 1 illustrates the CPT and EPT. All behavioral data
of the CPT and the EPT were analyzed using Statistical Package
for the Social Sciences (SPSS, v.16).
The 20-item Chinese Affect Scale (CAS ) assessed positive
affect (CAS-PA) and negative affect (CAS-NA). The CAS is
culturally adapted to be linguistically and structurally equivalent to
the Positive and Negative Affect Schedule (PANAS) .
Participants rated the frequency of 10 positive and 10 negative
affective states in the previous 2 weeks on a 5-point scale (0 = not at
all,1=rarely,2=sometimes,3=often,4=all the time). Separate
summation scores were calculated for PA and NA (range = 0–40).
The reliability of the CAS has been demonstrated in Chinese
young and middle-aged adults (a..85) . In the current study,
the alphas of the CAS-PA and CAS-NA were .87 and .96,
respectively. The CAS scores were analyzed using SPSS.
Figure 1. Schematic diagrams of the fMRI experimental tasks.
Neural Activity during Meditation
PLOS ONE | www.plosone.org 3 August 2012 | Volume 7 | Issue 8 | e40054
After being fully informed about the study, participants gave
their written informed consent and then completed the self-report
measures in a face-to-face interview. Prior to scanning, they were
given about 30 minutes to practice FAM or LKM. It was
considered that this amount of time would be sufficient for them
to enter the meditation state. All the participants then completed
the CPT and the EPT during a meditation session (FAM/LKM)
and then again during a baseline session (Baseline) separated by a
15-minute break. Both tasks were conducted during scanning.
Task order was counterbalanced across participants. After each
scan, all participants were asked to rate the clarity (1 = very unclear,
9=very clear) and stability (1 = very unstable,9=very stable) of their
mental state during the task.
fMRI Data Acquisition
Whole-brain axial scanning was performed with a 3.0 Tesla
Philips Medical Systems Achieva scanner equipped with an 8-
channel SENSE head coil. The imaging session involved two
acquisitions (FAM/LKM, Baseline) of a series of functional
images. Thirty-two functional slices were acquired using a T2*-
weighted gradient echo planar imaging sequence (slice thick-
ness = 4 mm, TR = 1,800 ms, TE = 30 ms, flip angle = 90u, ma-
trix = 64664, field of view [FOV] = 23062306128 mm, voxel
size = 3.5963.5964mm
). The axial slices were adjusted to be
parallel to the AC-PC plane. The first 6 volumes were discarded to
allow for T1 equilibration effects. The acquisitions of FAM/LKM
functional scans and Baseline functional scans were separated by a
T1-weighted high-resolution anatomical scan (MPRAGE, 164
sagittal slices; TR = 7 ms, TE = 3.2 ms, flip angle = 8u, ma-
trix = 2566240, FOV = 25662406164 mm; voxel size = 1 mm
fMRI Data Analysis
Pre-processing. The fMRI data were preprocessed and
analyzed using Statistical Parametric Mapping (SPM5; Wellcome
Department of Cognitive Neurology, London, UK) in MATLAB
7.7 (Mathworks Inc., Natick, MA, USA). The functional scans
were spatially realigned to adjust for head movement and
corrected for slice-acquisition timing. Each functional scan was
then registered with the anatomical image, warped to the
Montreal Neurological Institute (MNI) brain template using a
12-parameter affine transformation and spatially smoothed with
an isotropic 8 mm full width at half maximum (FWHM) Gaussian
filter. Motion parameters for each session were saved and
subsequently included as covariates in the generalized linear
model (GLM) in the first-level analyses.
First-level single-subject analysis. For the CPT, first-level,
single-subject analyses were conducted to derive parameter
estimates of four types of block-related activity at each voxel
within the brain for the two experimental conditions (FAM/LKM-
exp, Baseline-exp) and the two control conditions (FAM/LKM-
control, Baseline-control). The control conditions were subtracted
from the corresponding experimental conditions (i.e., FAM/
LKM-exp minus FAM/LKM-control; Baseline-exp minus Base-
line-control) by applying appropriate linear contrasts to the
parameter estimates of each block to remove the neural activity
for basic visual processing. As a result, two sets of contrast images
reflecting only the neural correlates of sustained attention were
obtained for both meditation (FAM/LKM) and Baseline states in
For the EPT, first-level, single-subject analyses were conducted
using an event-related model, with six trial types derived
respectively for happy, sad, and neutral pictures in FAM/LKM
and Baseline sessions: FAM/LKM-hap, Baseline-hap; FAM/
LKM-sad, Baseline-sad; FAM/LKM-neu, Baseline-neu. The
reason for separating the trials of happy and sad pictures is that
the processing of happy and sad emotions may involve different
neural networks . The onset of each trial was modeled with a
canonical hemodynamic response function, becoming a neural
event that represented the association between neuronal activation
and blood-flow changes. Each trial type included separate
regressors, which served as parameter estimates for the average
hemodynamic responses evoked in each trial. These models were
used to construct a set of within-subject contrast images to
represent the estimated amplitude of the hemodynamic responses
for viewing happy, sad, and neutral pictures. The experimental
conditions of EPT were the trials viewing happy (FAM/LKM-hap,
Baseline-hap) and sad pictures (FAM/LKM-sad, Baseline-sad),
whereas the trials viewing neutral pictures represented the baseline
condition (FAM/LKM-neu, Baseline-neu). Similarly, using the
subtraction method used with the CPT (e.g., FAM-hap minus
FAM-neu, Baseline-sad minus Baseline-neu, and so on), four sets
of contrast images reflecting the neural correlates of happy and sad
emotion processing were obtained for both meditation (FAM/
LKM) and Baseline states in each subject.
Second-level group analysis: One-sample t-test. The
validity of the fMRI paradigms (the CPT and the EPT) was
confirmed by comparing regions of activation observed in this
study with those reported in previous neuroimaging studies on
attention (captured by the CPT) and emotion processing. The
contrast images of all novices when they were performing the task
during baseline (Baseline-exp minus Baseline-control) were
grouped for one-sample t-tests, with the threshold at p,.001
(uncorrected) and a cluster extent of 10 contiguous voxels
, unless otherwise specified). This combination of p
value and extent thresholding reduced the effective per-voxel false
positive rate to a corrected p,.05 .
Second-level group analysis: Analysis of variance. The
contrast images of each subject were entered into second-level,
voxel-wise, two-way ANOVA tests (262 factorial design), with
state (meditation and baseline) as a within-subject factor, and
group (experts and novices) as a between-subjects factor. For each
task (CPT, EPT-hap, and EPT-sad), one ANOVA model was
constructed for one form of meditation practice (FAM and LKM).
As a result, 6 two-way ANOVA models were built in all.
Interaction effects—obtained by applying appropriate linear
contrasts—are reported and discussed here to identify the distinct
neural activity associated with different states and groups when
performing the CPT and the EPT. The main effects of state were
reported in the supporting information (Table S1). The statistical
maps (SPM [F]) generated respectively were then thresholded at
p,.001 (uncorrected) with a cluster extent of 10 contiguous voxels.
A series of post hoc pairwise t-contrasts were conducted to identify
the levels of the two factors at which the effects detected by the F-
contrasts (if any) could be explained. All the t-statistical maps were
also thresholded at p,.001 (uncorrected) with a cluster extent of
10 contiguous voxels.
Conjunction and exclusive masking analyses. To address
the a priori hypothesis of dissociability between the task-related
activation patterns in FAM and LKM, conjunction and exclusive
masking techniques were used to identify significant activations
that were common and unique to FAM and LKM. For
conjunction analysis, the interaction effects for FAM and LKM
were thresholded at p,.001 (uncorrected) and then saved as two
individual image outputs (i1 for FAM and i2 for LKM). They were
then tested with a logical AND statement [‘‘(i1.0) & (i2.0)’’] in
the image calculator (ImCalc) implemented in SPM to examine for
neuronal overlap between FAM and LKM (with ‘‘conjunction
Neural Activity during Meditation
PLOS ONE | www.plosone.org 4 August 2012 | Volume 7 | Issue 8 | e40054
null’’ as the null hypothesis). For exclusive masking analysis, one
interaction effect was saved at p,.001 (uncorrected), while the
effect to be used as the mask was saved at p,.05 (uncorrected).
Then, other logical statements [‘‘(i1.0).(i2.0)’’ when i2 was the
mask; ‘‘(i2.0).(i1.0)’’ when i1 was the mask] were used to look
for neuronal distinction between FAM and LKM. The final image
outputs resulting from both analyses were thresholded with a
cluster extent of 10 contiguous voxels, so that all the resultant
clusters were greater than 10 voxels. The resultant images were
overlaid onto a high-resolution anatomical image in MNI space
(Colin27_T1_seg_MNI.nii; courtesy of Simon Eickhoff) to identify
the anatomical name of the results, with frequent consultation of
Region-of-interest analysis. To further explore the under-
lying pattern of neural activity behind the significant interaction
effect(s) found from the above analysis (if any), mean percent signal
change in each of these suprathreshold clusters was examined
across different conditions. The signal change data was extracted
using region-of-interest (ROI) analysis with the MarsBar toolbox
(release 0.42, http://marsbar.sourceforge.net/) . To do so, the
voxels in a suprathreshold cluster were used to build an ROI mask,
and the anatomical confines of the reported brain region for that
suprathreshold cluster were used to modify the ROI mask (in other
words, the significantly activated voxels that were spatially outside
the anatomical brain region were not included in the ROI mask).
This allowed us to examine only the evoked activity that was
anatomically sensible and plausible, which should minimize the
problems of imperfect voxel-wise correspondence across different
individuals. The mean percent signal changes were then obtained
by averaging the signal changes within the modified ROI masks
for the corresponding conditions in each subject. After that, the
net mean percent signal changes representing the neural activity of
sustained attention in CPT and emotion processing in EPT were
obtained by subtracting the mean percent signal changes in the
baseline condition from the experimental condition. Hence, the
same 262 factorial analyses were conducted on the net mean
percent signal change data in SPSS to further delineate the
interaction effects. A series of post-hoc (paired and independent) t-
tests were then conducted.
Correlation analysis. We used correlations to measure the
relationship between behavioral performance and its respective
neural activity (measured by the percent signal change) in the
experts. By considering the signals in all voxels in the modified
ROI masks, we could avoid the ‘‘nonindependence error’’ of fMRI
correlations . Hence, we could get an unbiased measure of the
association between evoked activity and individual difference(s) in
behavioral performance. CPT performance included commission
errors, omission errors, reaction time, and its variability. For the
EPT, we examined the correlations of brain activity with ratings of
valence and arousal.
Table 1 summarizes all of the behavioral results: performance
on the CPT, ratings of IAPS pictures, and self-report measures of
affect. Although there were no significant state-by-group interac-
tions in all four behavioral measures of CPT for both the FAM
and LKM practitioners, there were some significant and trend-
level main effects. For the FAM practitioners, the main effect of
group difference (FAM experts vs. novices) on omission errors was
significant [F(1,40) = 12.5, p,.005], while the main effects of
group difference on commission errors [F(1,40) = 3.22, p,.1] and
variability of RT [F(1,40) = 3.78, p,.1] both reached trend-level
significances. For the LKM practitioners, only the main effect of
group difference (LKM experts vs. novices) on omission errors was
significant [F(1,40) = 9.68, p,.005]. Since there were no signifi-
cant differences in omission errors (or other performance
measures) between the two groups of novices for FAM and
LKM in both states (data not shown), we can rule out the
possibility that the FAM novices were just particularly weak on the
CPT. Furthermore, the FAM experts seemed to make fewer
commission errors than the FAM novices during the meditation
(p = .062) but not the baseline state (p = .721). This means that the
FAM experts may be better able to withhold making responses to
non-target stimuli during meditation.
There was no significant group difference between experts and
novices in the ratings of valence and arousal of happy and sad
pictures (Table 1c) and positive affect (Table 1d) for both forms of
meditation. LKM experts had significantly lower negative affect
than LKM novices t(22) = 22.97, p,.01. This was not the case
between FAM experts and novices (Table 1d).
Validity of the experimental paradigms. The activation
maps of the CPT and EPT in our novices resembled the findings
of previous studies. In the CPT experimental condition (compared
to the control condition), novices had stronger BOLD signals in
the right middle frontal gyrus (MFG), left IPL, bilateral STG,
bilateral middle temporal gyrus (MTG), right inferior temporal
gyrus, and left middle occipital gyrus (MOG; see Figure S1a).
While viewing happy pictures, novices had significant activity in
the right ACC, bilateral posterior cingulate cortex (PCC), right
insula, right MTG, right precuneus, left caudate, left IPL, left
MOG, and visual cortex (the bilateral cuneus; see Figure S1b).
While viewing sad pictures, novices had significant BOLD signals
in the left SFG, left MOG, right MTG, right thalamus, right ACC,
and visual cortex (the left cuneus; see Figure S1c).
Whole-brain voxel-wise ANOVA. On the CPT, there was a
significant state-by-group interaction (p,.001, k = 10) in the right
MTG, thalamus, and precuneus for FAM, but not for LKM
(Figure 2a, Table 2a). Post hoc t-tests showed that FAM experts had
weaker BOLD signals than the FAM novices during baseline in
the right thalamus and stronger BOLD signals during meditation
in the right MTG and the right precuneus.
While viewing happy pictures, there was a significant state-by-
group interaction (p,.001, k = 10) in the left insula only for FAM
and in the left ventral ACC, right inferior frontal gyrus (IFG), and
right precuneus for LKM (Figure 2b, Table 2b). Post hoc t-tests
revealed stronger BOLD signals in the left insula in the FAM
experts during meditation than baseline. For the LKM, post hoc t-
tests showed stronger activity in the experts during meditation
than baseline in the left ventral ACC and right precuneus, and
stronger activity in the experts than novices during meditation in
the left ventral ACC and right IFG.
While viewing sad pictures, the FAM had a significant state-by-
group interaction (p,.001, k = 10) in the left SFG and right IFG,
and the LKM had a significant interaction in the left MFG and
bilateral caudate (Figure 2c, Table 2c). For FAM, post hoc t-tests
revealed stronger activity in the left SFG in both states of experts
than the baseline state of novices and stronger activity in the right
IFG in the novices during meditation compared to baseline. For
LKM, post hoc t-tests showed stronger BOLD signals in the experts
than novices during meditation in the left MFG and left caudate,
and stronger BOLD signals in the novices during baseline
compared to meditation in the right caudate [see Fig. S2 for
results of the 3-way (state6group6form-of-meditation) interaction
Neural Activity during Meditation
PLOS ONE | www.plosone.org 5 August 2012 | Volume 7 | Issue 8 | e40054
Conjunction analysis. Conjunction analysis did not reveal
any neuronal overlap between the state-by-group interactions of
FAM and LKM for any of the three tasks (CPT or viewing happy
and sad pictures in EPT). The same was true even if a more lenient
threshold (p,.005) was initially applied to the interaction effects
(i.e., when saving i1 and i2 as individual image outputs). In other
words, the significant differences between FAM experts and
novices in their meditation and baseline states were neither
identical nor similar to that between LKM experts and novices
with respect to their meditation and baseline states. Therefore, the
two forms of meditation—FAM and LKM—do not share the
same neural mechanism for attention and emotion processing.
Exclusive masking analysis. For the CPT, when the
interaction effect of FAM was exclusively masked with that of
LKM, the significant activations in the right thalamus and
precuneus remained. Activation of the right MTG was also
marginally significant (9 contiguous voxels). Alternatively, when
the interaction effect of LKM was exclusively masked with that of
FAM, no suprathreshold clusters remained.
For viewing happy pictures, when the interaction effect of FAM
was exclusively masked with that of LKM, significant activation in
the left insula remained. Alternatively, when the interaction effect
of LKM was exclusively masked with that of FAM, significant
activations in the left ventral ACC, right IFG, and precuneus
For viewing sad pictures, when the interaction effect of FAM
was exclusively masked with that of LKM, significant activation in
the right IFG and left SFG remained. Alternatively, when the
interaction effect of LKM was exclusively masked with that of
FAM, significant activations in the left MFG and bilateral caudate
To further support our claim that FAM and LKM do not share
the same neural mechanism, the conjunction and exclusive
masking of the main effect of state between FAM and LKM
experts were performed and the results of which were reported
separately in Additional Results S1.
Region-of-interest analysis. On the CPT, the complemen-
tary two-way factorial analysis using the ROI results obtained with
the modified ROI masks replicated the interaction result for FAM
(Figure 3). For the interaction in the right thalamus F(1,18) = 10.5,
p,.005, both groups had similar activation during meditation, but
FAM experts had significantly lower activity than FAM novices
during baseline. For the interaction in the right MTG
F(1,18) = 26.3, p,.001, FAM experts had significantly higher
activity than FAM novices during meditation, but similar
activation during baseline. For the interaction in the right
precuneus F(1,18) = 9.6, p,.01, FAM experts had significantly
higher activity than FAM novices during meditation, but similar
activation during baseline.
Correlation analysis. For the CPT, there was only one
significant correlation between performance and neural activity.
Table 1. Descriptive statistics of all behavioral tasks.
Focused-Attention Meditation Loving-Kindness Meditation
-value Experts Novices
Mean (SD) Mean (SD) Mean (SD) Mean (SD)
a. CPT (Meditation state)
Commission errors (%) 0.7 (0.7) 2.0 (1.8) .062 4.7 (11.5) 2.0 (2.9) .458
Omission errors (%) 2.4 (4.4) 13.8 (11.7) .010* 3.1 (3.8) 13.0 (15.1) .059
Reaction time (RT) (ms) 620.5 (68.7) 637 (50.3) .544 626.1 (62.6) 634 (64.1) .777
Variability of RT (ms) 65.6 (19.6) 82.3 (25.6) .125 72.4 (28.4) 73.9 (17.3) .883
b. CPT (Baseline state)
Commission errors (%) 1.0 (1.1) 1.2 (1.1) .721 4.1 (9.1) 1.8 (2.2) .435
Omission errors (%) 3.7 (4.4) 11.6 (10.0) .032* 3.9 (3.8) 13.8 (15.8) .068
Reaction time (RT) (ms) 625 (64.3) 625.9 (54.0) .974 626.5 (70.4) 619.8 (60.0) .817
Variability of RT (ms) 66.3 (20.9) 78.6 (25.7) .266 66.8 (23.0) 64.2 (18.7) .777
c. Ratings of IAPS pictures
Happy: valence 6.8 (1.0) 6.8 (0.8) .991 6.8 (1.0) 6.6 (0.9) .568
Happy: arousal 6.3 (1.1) 5.6 (0.6) .076 6.2 (1.2) 6.0 (0.7) .585
Sad: valence 3.0 (0.6) 2.7 (0.7) .251 3.1 (0.7) 2.9 (0.8) .727
Sad: arousal 6.9 (0.9) 6.4 (0.7) .192 6.7 (1.1) 6.2 (0.8) .243
d. Chinese Affect Scale
Positive affect 23.5 (5.2) 21.3 (3.7) .253 23.5 (5.6) 23.1 (5.5) .850
Negative affect 11.8 (10.0) 11.0 (3.3) .799 7.4 (5.2) 15.3 (7.1) .008**
Note: The p-value represents the significance of group differences between experts and novices of FAM and LKM using independent-samples t-tests. (a–b): For the CPT,
commission errors were measured as the percentage of trials that participants still responded on when the target stimulus was not present. Omission errors were
measured as the percentage of trials that participants did not respond on when the target stimulus was present. Reaction time (RT) is the amount of time that
participants took to press the button after the presentation of target stimulus (for trials that they should respond to and did respond). The variability of RT was
measured by its standard deviation. Only the omission errors of FAM experts were significantly fewer than those of FAM novices in both meditation and baseline states
(*p,.05, two-tailed). (c): Ratings of valence and arousal of happy and sad pictures adopted from the International Affective Picture System (IAPS). (d): Positive and
negative affect were measured by the Chinese Affect Scale. Only the negative affect of LKM experts was significantly lower than that of LKM novices (**p,.01, two-
Neural Activity during Meditation
PLOS ONE | www.plosone.org 6 August 2012 | Volume 7 | Issue 8 | e40054
For the FAM experts at baseline, activity in the right thalamus
correlated positively with commission errors (r= .711, p= .03). For
the LKM, there were no significant correlations between neural
activity and performance.
For the EPT, there were two significant negative correlations
between ratings of the IAPS pictures and neural activity. When the
FAM experts were viewing happy pictures at baseline, activity in
the left insula correlated negatively with both valence (r=2.618,
p= .04) and arousal (r=2.655, p= .03).
This study investigated whether there was a dissociable effect on
neural activity associated with attentional control and emotion
processing between longterm FAM and LKM. Neural activity
associated with performing on the experimental tasks was largely
consistent with that reported in the literature (CPT [6,31]; EPT
[17,32]). This observation provides reassuring evidence of the
validity of the experimental paradigms used in this study.
Findings from conjunction and exclusive masking analyses offer
evidence for a dissociable pattern of activation associated with
FAM and LKM as evoked by the CPT and the EPT. This finding
constitutes the first report that different forms of meditation have
meditation-specific effects on neural activity, rather than a
common neural mechanism. It clearly points to the idea that
different forms of meditation practice create domain-specific
plastic changes in neural activity . Exclusive masking analyses
gave further support to the prediction set forth in the a priori
hypothesis that each form of meditation is associated with a
dissociable pattern of neural activity during performing cognitive
(CPT) and emotion (EPT) tasks. During the CPT, the FAM group
(both experts and novices) showed a stronger state-by-group
interaction of BOLD signals in attention-related regions. The
LKM group did not show a significant state-by-group interaction.
On the other hand, when processing affective pictures, both the
FAM and the LKM groups showed distinct state-by-group
interactions of BOLD signals in the emotion-processing neural
Focused-attention meditation. Behaviorally, the FAM ex-
perts made significantly fewer omission errors than the FAM
novices during both the meditation and baseline states. This
suggests that long-term practice of FAM is helpful with vigilance
during tasks that require sustained attention. In terms of neural
activity, the distinct patterns of BOLD signals associated with
performing CPT in the FAM suggest that practicing FAM may be
associated with enhancing attention-specific brain areas. We can
differentiate FAM experts and novices by activity in the right
thalamus, right MTG, and right precuneus, as shown by the
similar results from the post hoc t-tests and ROI analyses. Experts
had weaker BOLD signals in the right thalamus than the novices
during baseline. Furthermore, activity of the right thalamus was
positively associated with the rates of commission errors in experts
during baseline. Previous research has suggested that the right
thalamus is involved in the attention processes measured by CPT
and similar paradigms [14–16]. Furthermore, lower thalamic
activity appears to be associated with high arousal, as well as a
subjective feeling of needing less mental load to complete attention
tasks . Hence, the weaker BOLD signals in the right thalamus
among the experts is consistent with the notion that longterm
practice of FAM may bring about a clear and unwarvering mind
to increase attentional stability and reduce task effort [6,35].
Experts also had stronger BOLD signals in the right MTG during
meditation, compared to the novices. Researchers have suggested
that the temporal cortex is associated with maintaining attention
 and controlling bottom-up, stimulus-driven attention, espe-
cially during tasks like the CPT that involve contingencies (i.e.,
Figure 2. State-by-group interaction of the whole-brain voxel-wise ANOVA. Notes: The state-by-group interaction was thresholded at
p,.001 with a cluster extent of 10 contiguous voxels. L is left, and R is right. All subjects (group: experts and novices) completed the (a) continuous
performance task (CPT) and emotion-processing test (EPT), which was subdivided into two parts including viewing of (b) happy pictures (EPT-happy)
and (c) sad pictures (EPT-sad) during meditation and baseline states.
Neural Activity during Meditation
PLOS ONE | www.plosone.org 7 August 2012 | Volume 7 | Issue 8 | e40054
responding to randomly presented target stimulus; . Indeed, a
previous study found that activity in the MTG was negatively
correlated with reaction time during CPT . Thus, we
interpreted the stronger BOLD signals in the MTG during
meditation as implying that FAM facilitates accurate and efficient
responses to target stimuli.
Experts had stronger BOLD signals in the right precuneus
during meditation, compared to the novices. In a sustained
attention study, healthy volunteers demonstrated reduced precu-
neus activity during later task trials, suggesting that the reduced
precuneus activity could serve as an index of task familiarity .
This observation may reflect how alertness decreases upon
prolonged exposure—habituation. This is consistent with the
observation that people undergo a profound deactivation in the
precuneus and the adjacent posteromedial cortex during altered
states of consciousness, such as sleep . Therefore, increased
BOLD signals in the right precuneus of the experts during
meditation may help them sustain their attention without being
affected by the reduced novelty of the CPT. On the other hand, it
seems there was a trend that they made fewer commission errors
than the FAM novices during the meditation state but not during
the baseline state. Hence, among the FAM experts, there may be a
trait-like effect on controlling for omission errors but a state-like
effect on inhibiting commission errors.
Table 2. State-by-group interaction of the whole-brain voxel-wise ANOVA.
(Brodmann Area) Coordinates
x y z Meditation Baseline Expert Novice
(a) CPT FAM R Middle Temporal
44 258 20 18.79 14 X 4.40
R Thalamus 2 212 24 17.6 14 5.99
R Precuneus (7) 4 266 24 15.44 29 3.09
LKM no suprathreshold voxels
(b) EPT-happy FAM L Insula (13) 234 22 26 23.99 35 X X 4.14
232 14 22 14.78
LKM L ventral Anterior
Cingulate Cortex (24)
24 34 16 27.23 57 4.40
R Precuneus (7) 26 260 20 21.7 39 X X 4.40
R Inferior Frontal
42 34 14 20.88 18 4.37
(c) EPT-sad FAM L Superior Frontal
210 40 42 25.2 28 X 5.03
R Inferior Frontal
38 34 12 17.31 11 X X X 5.17
LKM L Middle Frontal
234 46 18 23.1 86 4.32
226 40 20 16.5
R Caudate 14 24 22 18.01 19 X X X 4.37
L Caudate 212 6 20 16.44 11 4.03
Note: The state-by-group interaction was thresholded at p,.001 with a cluster extent of 10 contiguous voxels, with state (meditation and baseline) as a within-subject
factor and group (experts and novices) as a between-subjects factor. FAM is focused-attention meditation, and LKM is loving-kindness meditation. L is left, and R is right.
(a) CPT: continuous performance task, (b) EPT-happy: viewing happy pictures in the emotion-processing task (EPT), and (c) EPT-sad: viewing sad pictures in the EPT. X
Figure 3. The percent signal change in brain regions showing
significant interactions for CPT in FAM group. Notes: R: right;
MTG: Middle temporal gyrus.
Neural Activity during Meditation
PLOS ONE | www.plosone.org 8 August 2012 | Volume 7 | Issue 8 | e40054
Loving-kindness meditation. During the CPT, no signifi-
cant findings on state-by-group interaction in any neural regions
were observed. This suggests that longterm LKM may not be
associated with change in attention-related regions.
The effects of FAM and LKM on brain activity when processing
affective pictures were very different. The results of the conjunc-
tion and exclusive masking analyses corroborate the hypothesized
dissociable neural activation pattern associated with the processing
of affective photos between the FAM and LKM experts.
Focused-attention meditation. Consistent with previous
findings, our findings suggest that the impact of longterm FAM
on brain activity extends from attentional control to emotion
processing . Attention-related meditation might involve a more
elaborative processing of the features of emotional stimuli, leading
to higher activity in the ventral neural system . Our findings
add to the current literature by showing the impact of FAM on
emotion processing, adding to the previous evidence for the
cognitive impact of attention-related meditation [2,41,42].
While viewing happy pictures, experts had significantly stronger
BOLD signals in the left (anterior) insula during meditation versus
baseline. Activity in the left anterior insula is related to the
interaction between arousal and valence when viewing affective
pictures . In this study, our FAM experts, relative to the
novices, gave marginally higher arousal ratings to the happy
pictures (p= .08). Furthermore, their arousal and valence ratings
were negatively correlated with activity in the left insula. These
findings are unexpected because literature has suggested a positive
relationship between insular activity and emotion intensity
[43,44]. We thought that the insular activity in the FAM expert
may relate to the possiblity that these experts, different from the
novices who do not usually require to regulate the positive emotion
associated with viewing happy pictures, might have attempted to
suppress the affective impact of these positive pictures, for arriving
at the state of tranquillity. Hence, there was greater insular activity
but lower arousal and valence ratings for these FAM experts.
There were two findings worth noting for viewing sad pictures.
The experts had significantly stronger BOLD signals in the left
SFG, both in meditation and at baseline, compared with the
novices’ baseline state. Furthermore, novices had stronger BOLD
signals in the right IFG during meditation versus baseline. FAM is
believed to be associated with remarkably lower emotional
reactivity, which is important for maintaining emotional stability
and a focused state . Activity in the dorsolateral frontal regions
is associated with monitoring and attentional orienting in FAM
(meditation vs. baseline state) . Therefore, when the experts and
novices were in a focused meditative state, they may have
recruited these two regions more than the novices did during
baseline in order to maintain emotional stability. We suggest that
the lower emotional reactivity of experts may come from—at least
partly—a generalized effect of longterm practice, even when the
form of meditation practice does not emphasize emotion
Despite the absence of behavioral difference on valence and
arousal ratings between the experts and novices, their different
neural activities when performing the EPT could be interpreted as
reflecting the different neural mechanisms and pathways for
processing affective stimuli adopted by these two groups. This
speculation was supported by previous literature on sex-related
difference in neural processing of affective stimuli, which also
reported similar levels of behavioral outputs between men and
Loving-kindness meditation. Our findings add to the base
of evidence from the recent surge of LKM research and practice.
LKM cultivates a generalized feeling of love and compassion
towards all humankind and living creatures without causing
significant distress to practitioners.
While viewing happy pictures, activity in the left ventral ACC
showed both state difference (meditation.baseline) within the
experts and group difference (experts.novices). Activity in the
right IFG showed only group difference in activation level
(experts.novices) during meditation. Also, experts had stronger
BOLD signals in the right precuneus during meditation versus
The left ventral ACC in the ventral neural system is important
for identifying the emotional value of stimuli and producing the
corresponding affective state. In contrast, the right IFG in the
dorsal affective processing system is important for regulating
emotional responses . Cavanna and Trimble (2006) suggest
that the precuneus forms a classic network with the right PFC
through the ACC, which is implicated in episodic memory
retrieval and self-referential processing. Therefore, the cultivation
of love and kindness in the practice of LKM may allow experts to
be more capable of sharing the positive emotions of others by
feeling the happiness of others as their own and further wishing for
others’ happiness . It seems that LKM practice is associated
with activity in these emotion-processing regions, which may have
an impact on emotion regulation and the subsequent production
of positive emotions.
While viewing sad pictures, the experts, relative to the novices,
showed stronger BOLD signals in the left caudate and MFG
during meditation. On the other hand, novices showed stronger
BOLD signals in the right caudate during baseline than meditation
state. Previous literature has reported that the striatum (which
includes the caudate) was related to processing positive affect, such
as reward. Some recent literature, however, has shown that the
caudate is also involved in aversive affective processing, such as of
processing negative words  and unpleasant pictures .
Activity in the left caudate seems to be associated with the arousal
level of emotions . Furthermore, in the framework of appraisal
theory, activity of the dorsolateral prefrontal cortex is indicated in
voluntary and effortful regulation of emotional responses .
Since activity of the left caudate and MFG of LKM experts were
negatively correlated with the arousal level of the sad pictures seen
in this study, taken into the consideration of the roles played by the
caudate and the lateral prefrontal cortex as discussed above, we
suggest that LKM might be related to higher emotion reactivity in
conjunction with more efficient voluntary emotion regulation,
which would be consistent with the idea that emotion regulation
helps distinguish empathy from emotional contagion and distress
. Since LKM cultivates the feelings of love, kindness, and
compassion towards affective stimuli, it might automatically
engage purposive emotion regulation. These strategies could be
cognitive reappraisal, distraction, or expressive suppression [50–
52]; future studies are needed to determine which strategy (or
strategies) is used.
According to a recent study of the neural correlates of exposure
to death-related thoughts, activity in the right caudate increased
strongly when subjects answered death-related questions . It
was speculated that the right caudate is involved in the automatic
psychological defense against mortality threat because of its role in
habitual behaviors . Approximately 85% of our sad pictures
are related to the sorrow of death (e.g., scenes of graves, dying
patients or animals, and war), which may explain the strong right
caudate activity in the novices when they were viewing the sad
pictures in the baseline state. The basal ganglia, including the
Neural Activity during Meditation
PLOS ONE | www.plosone.org 9 August 2012 | Volume 7 | Issue 8 | e40054
caudate and the putamen, are involved in the production of
negative emotions [55,56]. An increase in negative affect has been
associated with increases in right-sided activation in the orbito-
frontal and dorsolateral prefrontal cortices , regions that are
closely connected to the right caudate . According to
literature, people with a strong right-sided activation appear to
be slower in recovering from negative affect or stress than people
with a strong left-sided activation .
A number of limitations should be discussed. First, the
participants were a convenience sample of Chinese meditation
practitioners in a Buddhist network in Hong Kong. This
subculture and ethnicity may affect the psychological and
physiological correlates of meditation. However, it is hard to
know anything about ethnic differences because most previous
studies used samples with mixed ethnicities [4,59]. Caution is
therefore warranted before generalizing these findings to other
populations. Also, all the participants were men, which limits the
generalization of the findings to female populations.
Due to time constraints, we were unable to directly measure
participants’ attention to and processing of the stimuli while they
performed the task. This makes it more difficult to interpret the
findings on emotion processing: some participants might be more
adept at reducing emotional reactivity by redirecting their
attention away from emotional stimuli consciously or automati-
cally without awareness . In the postexperiment debriefing
session, we asked participants whether they were able to maintain
the clarity and stability of their mental states while performing the
two experimental tasks. Future studies should consider incorpo-
rating objective measures of visual attentiveness to the experi-
mental stimuli, such as eletrooculography and infrared video
These limitations notwithstanding, this study helps advance
neuroscientific research on meditation. Our findings suggest that
FAM and LKM have dissociable effects on the neural activity
associated with attention and emotion processing. Specifically, we
demonstrated that the practice of FAM was associated with
expertise-related behavioral improvements and neural activation
differences in attention task performance. However, the effect of
state LKM meditation did not carry over to attention task
performance. On the other hand, both FAM and LKM practice
appeared to affect the neural responses to affective pictures. For
viewing sad faces, the regions activated for FAM practitioners
were consistent with attention-related processing; whereas re-
sponses of LKM experts to sad pictures were more in line with
differentiating emotional contagion from compassion/emotional
regulation processes. These observations contribute to the
literature on neuroplasticity by adding evidence that practice is
associated with specific effects on brain activity. Meditation does
influence emotion processing, regardless of whether the practice
focuses on cognition (a
¯nasati) or emotion (metta
¯). Finally, the
neural pathways underlying emotion processing associated with
LKM are likely to be different from those associated with FAM.
Figure S1 BOLD signals associated with performing the
Continuous Performance Test (CPT) and Emotion
Processing Task (EPT) by novices at the baseline state.
Notes: (a) Neural activity of all novices performing the CPT
(experimental.control condition) at a resting state (p,.001,
k = 10); (b) Neural activity of all novices while viewing happy
(p,.001, k = 10), and (c) sad picture (p,.005‘, k = 10) (comparing
with viewing neutral pictures) in the EPT during the resting state.
L: left, R: Right, STG: Superior Temporal Gyrus, MTG: Middle
Temporal Gyrus, ITG: Inferior Temporal Gyrus, MOG: Middle
Occipital Gyrus, SFG: Superior Frontal Gyrus, MFG: Middle
Frontal Gyrus, ACC: Anterior Cingulate Cortex, PCC: Posterior
Cingulate Cortex, IPL: Inferior Parietal Lobe. ‘The threshold was
relaxed because no suprathreshold clusters were detected at p,.001.
Figure S2 The whole-brain results of significant 3-way
interaction effects (state by group by form-of-medita-
tion) for each condition were shown below. A threshold of
p,0.001, k.10 was used, which is the same as that used for the 2-
way analyses in the main text. The differences between these 3-
way results and the 2-way results reported in the main text were
likely due to insufficient power because of a small sample size.
Notes: (a) CPT: Continuous Performing Task; (b) and (c) EPT:
Emotion Processing Test. L: left, R: Right, MFG: Middle Frontal
Gyrus, IFG: Inferior Frontal Gyrus, ACC: Anterior Cingulate
Table S1 Main effect of state of the whole-brain voxel-
wise ANOVA for different types of meditation in the
Additional Results S1.
The authors would like to thank Richard Davidson and Matthieu Ricard
for their support and advice on the project.
Conceived and designed the experiments: TMCL JY KFS CFL CCHC.
Performed the experiments: TMCL WKH. Analyzed the data: TMCL
MKL WKH JCYT. Contributed reagents/materials/analysis tools: WKH
JCYT JY. Wrote the paper: TMCL MKL WKH CCHC.
1. Cahn BR, Polich J (2006) Meditation states and traits: EEG, ERP, and
neuroimaging studies. Psychol Bull 132: 180–211.
2. Lutz A, Slagter HA, Dunne JD, Davidson RJ (2008) Attention regulation and
monitoring in meditation. Trends Cogn Sci 12: 163–169.
3. Srinivasan N, Baijal S (2007) Concentrative meditation enhances preattentive
processing: A mismatch negativity study. Neuroreport 18: 1709–1712.
4. Pagnoni G, Cekic M, Guo Y (2008) ‘‘Thinking about not-thinking’’: Neural
correlates of conceptual processing during Zen meditation. PLoS ONE 3: e3083.
5. Ho¨ lzel BK, Ott U, Hempel H, Hackl A, Wolf K, et al. (2007) Differential
engagement of anterior cingulate and adjacent medial frontal cortex in adept
meditators and non-meditators. Neurosci Lett 421: 16–21.
6. Brefczynsk i-Lewis JA, Lutz A, Schaefer HS, Levinson DB, Davidson RJ (2007)
Neural correlates of attentional expertise in long-term meditation practitioners.
Proc Natl Acad Sci U S A 104: 11483–11488.
7. Sakai KL (2005) Language acquisition and brain development. Science 310:
8. Doyon J, Song AW, Karni A, Lalonde F, Adams MM, et al. (2002) Experience-
dependent changes in cerebellar contributions to motor sequence learning. Proc
Natl Acad Sci USA 99: 1017–1022.
9. Kabat-Z inn J (2003) Mindfulness-based interventions in context: Past, present,
and future. Clinical Psychology: Science and Practice 10: 144–156.
10. Salzberg S (1995) Loving-kindness: The revolutionary art of happiness. Boston:
Shambhala. 7 p.
Neural Activity during Meditation
PLOS ONE | www.plosone.org 10 August 2012 | Volume 7 | Issue 8 | e40054
11. Lutz A, Brefczynski-Lewis J, Johnstone T, Davidson RJ (2008) Regulation of the
neural circuitry of emotion by compassion meditation: Effects of meditative
expertise. PLoS ONE 3: e1897.
12. Lutz A, Greischar LL, Perlman DM, Davidson RJ (2009) BOLD signal in insula
is differentially related to cardiac function during compassion meditation in
experts vs. novices. NeuroImage 47: 1038–1046.
13. de Vignemont F, Singer T (2006) The empathic brain: how, when and why?
Trends Cogn Sci 10: 435–441.
14. Hager F, Volz HP, Gaser C, Mentzel HJ, Kaiser WA, et al. (1998) Challenging
the anterior attentional system with a continuous performance task: a functional
magnetic resonance imaging approach. Eur Arch Psychiatry Clin Neurosci 248:
15. Ogg RJ, Zou P, Allen DN, Hutchins SB, Dutkiewicz RM, et al. (2008) Neural
correlates of a clinical continuous performance test. Magn Reson Imaging 26:
16. Salgado-Pineda P, Junque C, Vendrell P, Baeza I, Bargallo N, et al. (2004)
Decreased cerebral activation during CPT performance: structural and
functional deficits in schizophrenic patients. Neuroimage 21: 840–847.
17. Britton JC, Taylor SF, Sudheimer KD, Liberzon I (2006) Facial expressions and
complex IAPS pictures: common and differential networks. NeuroImage 31:
18. Mitterschiffthaler MT, Kumari V, Malhi GS, Brown RG, Giampietro VP, et al.
(2003) Neural response to pleasant stimuli in anhedonia: an fMRI study.
Neuroreport 14: 177–182.
19. Phan KL, Fitzgerald DA, Nathan PJ, Moore GJ, Uhde TW, et al. (2005) Neural
substrates for voluntary suppression of negative affect: a functional magnetic
resonance imaging study. Biol Psychiatry 57: 210–219.
20. Mak AK, Hu ZG, Zhang JX, Xiao ZW, Lee TM (2009) Neural correlates of
regulation of positive and negative emotions: an fmri study. Neurosci Lett 457:
21. Lee TM, Liu HL, Chan CC, Fang SY, Gao JH (2005) Neural activities
associated with emotion recognition observed in men and women. Mol
Psychiatry 10: 450–455.
22. Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh
inventory. Neuropsychologia 9: 97–113.
23. Lau MA, Bishop SR, Segal ZV, Buis T, Anderson ND, et al. (2006) The
Toronto Mindfulness Scale: development and validation. J Clin Psychol 62:
24. Feldman G, Hayes A, Kumar S, Greeson J, Laurenceau JP (2007) The
development and initial validation of the cognitive and affective mindfulness
scale-revised (CAMS-R). J Psychopathol Behav Assess 29: 177–190.
25. Bradley MM, Lang PJ (2007) Handbook of emotion elicitation and assessment.
Series in affective science; Coan JA, Allen JJB, editors. New York: Oxford
University Press. 29–46 p.
26. Hamid PN, Cheng ST (1996) The development and validation of an index of
emotional disposition and mood state: the Chinese Affect Scale. Edu Psychol
Measure 56: 995–1014.
27. Watson D, Clark LA, Tellegen A (1988) Development and validation of brief
measures of positive and negative affect: the PANAS scales. J Pers Soc Psychol
28. Forman SD, Cohen JD, Fitzgerald M, Eddy WF, Mintun MA, et al. (1995)
Improved assessment of significant activation in functional magnetic resonance
imaging (fMRI): use of a cluster-size threshold. Magn Reson Med 33: 636–647.
29. Brett M, Johnsrude IS, Owen AM (2002) The problem of functional localization
in the human brain. Nat Rev Neurosci 3: 243–249.
30. Vul E, Harris C, Winkielman P, Pashler H (2009) Puzzlingly high correlations in
fMRI studies of emotion, personality, and social cognition. Perspectives on
psychological science 4: 274–290.
31. Ogg RJ, Zou P, Allen DN, Hutchins SB, Dutkiewicz RM, et al. (2008) Neural
correlates of a clinical continuous performance test. Magnetic resonance imaging
32. Phillips ML, Drevets WC, Rauch SL, Lane R (2003) Neurobiology of emotion
perception I: The neural basis of normal emotion perception. Biological
psychiatry 54: 504–514.
33. Ho¨ lzel BK, Ott U, Gard T, Hempel H, Weygandt M, et al. (2008) Investigation
of mindfulness meditation practitioners with voxel-based morphometry. Soc
Cogn Affect Neurosci 3: 55–61.
34. Portas CM, Rees G, Howseman AM, Josephs O, Turner R, et al. (199 8) A
specific role for the thalamus in mediating the interaction of attention and
arousal in humans. J Neurosci 18: 8979–8989.
35. Lutz A, Slagter HA, Rawlings NB, Francis AD, Greischar LL, et al. (2009)
Mental training enhances attentional stability: Neural and behavioral evidence.
J Neurosci 29: 13418–13427.
36. Karnath HO, Ferber S, Himmelbach M (2001) Spatial awareness is a function of
the temporal not the posterior parietal lobe. Nature 411: 950–953.
37. Corbetta M, Shulman GL (2002) Control of goal-directed and stimulus-driven
attention in the brain. Nat Rev Neurosci 3: 201–215.
38. Goldstein RZ, Tomasi D, Alia-Klein N, Zhang L, Telang F, et al. (2007) The
effect of practice on a sustained attention task in cocaine abusers. Neuroimage
39. Cavanna AE, Trimble MR (2006) The precuneus: a review of its functional
anatomy and behavioural correlates. Brain 129: 564–583.
40. Phillips ML, Drevets WC, Rauch SL, Lane R (2003) Neurobiology of emotion
perception I: The neural basis of normal emotion perception. Biol Psychiatry 54:
41. Chiesa A, Serretti A (2009) A systematic review of neurobiological and clinical
features of mindfulness meditations. Psychol Med 40: 1239–1252.
42. Rubia K (2009) The neurobiology of meditation and its clinical effectiveness in
psychiatric disorders. Biol Psychiatry 82: 1–11.
43. Nielen MM, Heslenfeld DJ, Heinen K, Van Strien JW, Witter MP, et al. (2009)
Distinct brain systems underlie the processing of valence and arousal of affective
pictures. Brain Cogn 71: 387–396.
44. Anders S, Lotze M, Erb M, Grodd W, Birbaumer N (2004) Brain activity
underlying emotional valence and arousal: a response-related fMRI study. Hum
Brain Mapp 23: 200–209.
45. Lee TM, Liu HL, Hoosain R, Liao WT, Wu CT, et al. (2002) Gender
differences in neural correlates of recognition of happy and sad faces in humans
assessed by functional magnetic resonance imaging. Neurosci Lett 333: 13–16.
46. Roiser JP, Levy J, Fromm SJ, Wang H, Hasler G, et al. (2008) The effect of acute
tryptophan depletion on the neural correlates of emotional processing in healthy
volunteers. Neuropsychopharmacology 33: 1992–2006.
47. Carretie L, Rios M, de la Gandara BS, Tapia M, Albert J, et al. (2009) The
striatum beyond reward: caudate responds intensely to unpleasant pictures.
Neuroscience 164: 1615–1622.
48. Colibazzi T, Posner J, Wang Z, Gorman D, Gerber A, et al. (2010) Neural
systems subserving valence and arousal during the experience of induced
emotions. Emotion 10: 377–389.
49. Decety J, Jackson PL (2004) The functional architecture of human empathy.
Behav Cogn Neurosci Rev 3: 71–100.
50. Ochsner KN, Bunge SA, Gross JJ, Gabrieli JDE (2002) Rethinking feelings: An
fMRI study of the cognitive regulation of emotion. J Cogn Neurosci 14: 1215–
51. Ochsner KN, Knierim K, Ludlow DH, Hanelin J, Ramachandran T, et al.
(2004) Reflecting upon feelings: An fMRI study of neural systems supporting the
attribution of emotion to self and other. J Cogn Neurosci 16: 1746–1772.
52. McRae K, Hughes B, Chopra S, Gabrieli JDE, Gross JJ, et al. (2010) The neural
bases of distraction and reappraisal. J Cogn Neurosci 22: 248–262.
53. Quirin M, Loktyushin A, Arndt J, Kustermann E, Lo YY, et al. (2011)
Existential neuroscience: a functional magnetic resonance imaging investigation
of neural responses to reminders of one’s mortality. Soc Cogn Affect Neurosci
Epub Jan 25.
54. Packard MG, McGaugh JL (1992) Double dissociation of fornix and caudate
nucleus lesions on acquisition of two water maze tasks: further evidence for
multiple memory systems. Behav Neurosci 106: 439–446.
55. Lee BT, Lee HY, Park SA, Lim JY, Tae WS, et al. (2011) Neural substrates of
affective face recognition in alexithymia: a functional magnetic resonance
imaging study. Neuropsychobiology 63: 119–124.
56. Phan KL, Wager T, Taylor SF, Liberzon I (2002) Functional neuroanatomy of
emotion: a meta-analysis of emotion activation studies in PET and fMRI.
NeuroImage 16: 331–348.
57. Davidson RJ, Jackson DC, Kalin NH (2000) Emotion, plasticity, context, and
regulation: perspectives from affective neuroscience. Psychological bulletin 126:
58. Alexander GE, DeLong MR, Str ick PL (1986) Paral lel organization of
functionally segregated circuits linking basal ganglia and cortex. Annual review
of neuroscience 9: 357–381.
59. Lutz A, Dunne JD, Davidso n RJ (2007) The Cambridge handbook of
consciousness; Zelazo PD, Moscovitch M, Thompson E, editors. New York:
Cambridge University Press. 499–551 p.
60. Phillips ML, Ladouceur CD, Drevets WC (2008) A neural model of voluntary
and automatic emotion regulation: implications for understanding the
pathophysiology and neurodevelopment of bipolar disorder. Mol Psychiatry
Neural Activity during Meditation
PLOS ONE | www.plosone.org 11 August 2012 | Volume 7 | Issue 8 | e40054