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Effects of mindful-attention and compassion meditation training on amygdala response to emotional stimuli in an ordinary, Nonmeditative State

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The amygdala has been repeatedly implicated in emotional processing of both positive and negative-valence stimuli. Previous studies suggest that the amygdala response to emotional stimuli is lower when the subject is in a meditative state of mindful-attention, both in beginner meditators after an 8-week meditation intervention and in expert meditators. However, the longitudinal effects of meditation training on amygdala responses have not been reported when participants are in an ordinary, non-meditative state. In this study, we investigated how 8 weeks of training in meditation affects amygdala responses to emotional stimuli in subjects when in a non-meditative state. Healthy adults with no prior meditation experience took part in 8 weeks of either Mindful Attention Training (MAT), Cognitively-Based Compassion Training (CBCT; a program based on Tibetan Buddhist compassion meditation practices), or an active control intervention. Before and after the intervention, participants underwent an fMRI experiment during which they were presented images with positive, negative, and neutral emotional valences from the IAPS database while remaining in an ordinary, non-meditative state. Using a region-of-interest analysis, we found a longitudinal decrease in right amygdala activation in the Mindful Attention group in response to positive images, and in response to images of all valences overall. In the CBCT group, we found a trend increase in right amygdala response to negative images, which was significantly correlated with a decrease in depression score. No effects or trends were observed in the control group. This finding suggests that the effects of meditation training on emotional processing might transfer to non-meditative states. This is consistent with the hypothesis that meditation training may induce learning that is not stimulus- or task-specific, but process-specific, and thereby may result in enduring changes in mental function.
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IN HUMAN NEUROSCIENCE
Effects of mindful-attention and compassion meditation training on
amygdala response to emotional stimuli in an ordinary, non-meditative
state
Gaelle Desbordes, Lobsang Tenzin Negi, Thaddeus W. W. Pace, B. Alan Wallace, Charles L. Raison and Eric L. Schwartz
Journal Name: Frontiers in Human Neuroscience
ISSN: 1662-5161
Article type: Original Research Article
Received on: 01 Feb 2012
Accepted on: 03 Oct 2012
Provisional PDF published on: 03 Oct 2012
Frontiers website link: www.frontiersin.org
Citation: Desbordes G, Negi LT, Pace TW, Wallace BA, Raison CL and
Schwartz EL(2012) Effects of mindful-attention and compassion
meditation training on amygdala response to emotional stimuli in
an ordinary, non-meditative state. Front. Hum. Neurosci. 6:292.
doi:10.3389/fnhum.2012.00292
Article URL: http://www.frontiersin.org/Journal/Abstract.aspx?s=537&
name=human%20neuroscience&ART_DOI=10.3389
/fnhum.2012.00292
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1
Effects of mindful-attention and compassion meditation training on
amygdala response to emotional stimuli in an ordinary, non-
meditative state
Authors: Gaëlle Desbordes*
1,2
, Lobsang Tenzin Negi
3
,
Thaddeus W. W. Pace
3
, B. Alan Wallace
4
, Charles L. Raison
5
, Eric L. Schwartz
2
Institutions:
1
Massachusetts General Hospital,
2
Boston University,
3
Emory University,
4
Santa Barbara Institute for Consciousness Studies,
5
University of Arizona
Correspondence:
Dr. Gaëlle Desbordes
Athinoula A. Martinos Center for Biomedical Imaging
Massachusetts General Hospital
149 Thirteenth St Suite 2301
Boston, MA 02129 USA
(+1)(617) 480-2605
desbordes@gmail.com
Running title: Meditation training longitudinally affects amygdala response to emotional stimuli
Keywords: meditation, mindfulness, attention, compassion, amygdala, emotion, fMRI
Abstract:
The amygdala has been repeatedly implicated in emotional processing of both positive and
negative valence stimuli. Previous studies suggest that the amygdala response to emotional
stimuli is lower when the subject is in a meditative state of mindful attention, both in beginner
meditators after an eight-week meditation intervention and in expert meditators. However, the
longitudinal effects of meditation training on amygdala responses have not been reported when
participants are in an ordinary, non-meditative state. In this study, we investigated how eight
weeks of training in meditation affects amygdala responses to emotional stimuli in subjects when
in a non-meditative state. Healthy adults with no prior meditation experience took part in eight
weeks of either Mindful Attention Training, Cognitively-Based Compassion Training (CBCT; a
program based on Tibetan Buddhist compassion meditation practices), or an active control
intervention. Before and after the intervention, participants underwent an fMRI experiment
during which they were presented images with positive, negative, and neutral emotional valences
from the IAPS database while remaining in an ordinary, non-meditative state. Using a region-of-
interest analysis, we found a longitudinal decrease in right amygdala activation in the Mindful
Attention group in response to positive images, and in response to images of all valences overall.
2
In the CBCT group, we found a trend increase in right amygdala response to negative images,
which was significantly correlated with a decrease in depression score. No effects or trends were
observed in the control group. This finding suggests that the effects of meditation training on
emotional processing might transfer to non-meditative states. This is consistent with the
hypothesis that meditation training may induce learning that is not stimulus- or task-specific, but
process-specific, and thereby may result in enduring changes in mental function.
Introduction
Meditative practices have generated much interest in the scientific community, in particular with
regards to how meditation affects brain function (Austin, 2009; Lutz, Dunne, & Davidson, 2007;
Slagter, Davidson, & Lutz, 2011). While meditative states are interesting to study per se, perhaps
more intriguing is the possibility that meditation training leads to enduring changes in brain
function, even outside meditation sessions (Slagter et al., 2011).
Contemplative practices purportedly lead to increased well-being (e.g., Dalai Lama & Cutler,
1998), a claim supported by subjective reports of participants in mindfulness-based interventions
(reviewed in Chambers, Gullone, & Allen, 2009; Grossman, 2004; Rubia, 2009). It has been
proposed that these beneficial effects of meditation training may be due to improvements in
attentional skills, which are themselves associated with better emotion regulation skills
(Chambers et al., 2009; Wadlinger & Isaacowitz, 2011). Accumulating evidence suggests that
meditation training yields improved emotional regulation, both in clinical and nonclinical
populations. In nonclinical populations mindfulness-based interventions have been associated
with lowered intensity and frequency of negative affect (K. W. Brown & Ryan, 2003; Chambers,
Lo, & Allen, 2008), reduced anxiety (Shapiro, Schwartz, & Bonner, 1998), more adaptive
responding to stress (Davidson et al., 2003), decreased ego-defensive responsivity under threat
(K. W. Brown, Ryan, Creswell, & Niemiec, 2008), decreases in difficulties regulating emotions
(Robins, Keng, Ekblad, & Brantley, 2012), reduced emotional interference from unpleasant
stimuli (Ortner, Kilner, & Zelazo, 2007), and less prolonged physiological reactivity to
emotional stimuli, in the form of decreased autonomic arousal (skin conductance response)
(Ortner et al., 2007).
The interactions between attention and emotion regulation are complex, and likely involve
several interrelated brain networks. One brain region that is centrally involved in emotional
processing and the interactions between attention and emotion is the amygdala (Davis & Whalen,
2001; Pessoa, 2008; Phelps, 2006). The amygdala facilitates attention toward emotionally
significant, or relevant, stimuli (Sander, Grafman, & Zalla, 2003; Vuilleumier, 2005; Whalen,
1998; Whalen & Phelps, 2009). It is involved with attending to and encoding emotional stimuli,
learning about the emotional significance of potentially ambiguous stimuli, distinguishing threat
from safety, and appraising and responding to emotionally-significant events—including stimuli
of both positive and negative valence (Baxter & Murray, 2002; Haas & Canli, 2008; Phan,
Wager, Taylor, & Liberzon, 2002; D. Sander et al., 2003; Sergerie, Chochol, & Armony, 2008;
Zald, 2003; reviewed in Whalen & Phelps, 2009). Interestingly, Schaefer et al. (2002) found that
amygdala activation could be voluntarily increased when subjects were asked to ‘‘maintain’’ the
emotional response to negative-valence stimuli, and the amount of amygdala activation increase
was correlated with subjects’ self-reported dispositional levels of negative affect. Conversely,
3
decreased amygdala activation was observed during the application of emotional regulation
strategies such as cognitive distancing and reappraisal (Beauregard, Lévesque, & Bourgouin,
2001; Lévesque et al., 2003; Ochsner et al., 2004; Ochsner, Bunge, Gross, & Gabrieli, 2002).
While it is well known that amygdala function is impaired in a number of disorders including
depression, anxiety, and post-traumatic stress disorder, amygdala activation also differs across
healthy individuals according to their personality traits (Davidson, 1998; Davidson & Irwin,
1999; Lapate et al., 2012). Individuals differ in how they attend to, process, and remember
emotional stimuli. Individual differences in personality traits can be traced to a brain attentional
network driven primarily by amygdala reactivity during the encoding of emotional stimuli (Haas
& Canli, 2008). For example, Fischer, Tillfors, Furmark, & Fredrikson (2001) found that
amygdala activation while viewing fear-eliciting stimuli was correlated with dispositional
pessimism. Canli and colleagues found that amygdala response to positive and negative valence
stimuli was correlated with the personality traits of extraversion and neuroticism (Canli et al.,
2001; Canli, Sivers, Whitfield, Gotlib, & Gabrieli, 2002).
Individuals also vary in their propensity to engage in spontaneous emotion regulation strategies,
such as reappraisal or suppression. Spontaneous reappraisal tendencies have been associated with
lower levels of negative affect, greater interpersonal functioning, and greater psychological and
physical well-being, and the opposite was found for spontaneous tendencies for emotional
suppression (Gross & John, 2003). Individual differences in self-reported reappraisal tendencies
were associated with decreased amygdala activity during the processing of negative emotional
facial expressions (Drabant, McRae, Manuck, Hariri, & Gross, 2009), pointing again at a crucial
role of the amygdala in trait-like emotion regulation skills.
Given the association between amygdala activation and trait emotion regulation and attention,
and given the hypothesis that meditation training may contribute to the development of such
traits (Slagter et al., 2011), the question naturally arises as to whether amygdala activation may
be modified by meditation training. Previous studies indicate that this might be the case. Several
neuroimaging studies have implicated the amygdala in the effects of meditation training on the
brain. In participants without prior meditation experience, mindfulness meditation training was
associated with lower amygdala response to emotional stimuli when the subject entered a
meditative state of mindful attention, both in patients with social anxiety disorder (Goldin &
Gross, 2010) and in healthy subjects (Taylor et al., 2011). Similar results have been reported in
highly experienced meditation practitioners (Brefczynski-Lewis, Lutz, Schaefer, Levinson, &
Davidson, 2007; but see Taylor et al., 2011). While the above studies investigated meditative
states, it has also been proposed that meditation training may induce learning that is not stimulus-
or task-specific, but process-specific, and thereby may result in enduring changes in mental
function (Lutz et al., 2007; Lutz, Slagter, Dunne, & Davidson, 2008). These changes should
correspond to changes in the brain that outlast the functional changes measured during
meditation, and would be more indicative of a change in trait. Some support for this hypothesis
can be found in structural differences in the brain which have been reported in relation to
meditation training, both cross-sectionally in comparing experienced meditators with matched,
meditation-naïve controls (Grant, Courtemanche, Duerden, Duncan, & Rainville, 2010; Lazar et
al., 2005; Luders, Clark, Narr, & Toga, 2011; Luders, Toga, Lepore, & Gaser, 2009; Pagnoni &
Cekic, 2007), and longitudinally as measured before and after a meditation-based intervention
(Hölzel et al., 2010; Hölzel, Carmody, et al., 2011; Tang et al., 2010). In addition, two recent
4
cross-sectional studies found differences in functional connectivity in the “resting state” which
indicated stronger coupling between brain regions implicated in self-monitoring and cognitive
control in experienced meditators compared to meditation-naïve controls (Brewer et al., 2011;
Jang et al., 2011). However, little is known on the longitudinal effects of meditation training on
emotional reactivity when participants are in an ordinary, non-meditative state.
In the present study, based on the extensive literature implicating the amygdala in emotion
regulation, we tested the hypothesis that the amygdala response to emotional stimuli, as
measured with functional magnetic resonance imaging (fMRI), would longitudinally decrease
after an eight-week training in mindful-attention meditation. We also investigated how the effect
might differ in another type of meditation training that has received less scientific attention so
far, namely compassion meditation. The rationale for choosing these two types of meditation
training is explained below.
Attention training is considered the foundation of meditation practices, as emphasized in the
traditional texts (reviewed in Austin, 2009; Lutz et al., 2007). Meditation training demonstrably
improves attentional skills (Baijal, Jha, Kiyonaga, Singh, & Srinivasan, 2011; Chambers, Lo, &
Allen, 2008; Jha, Krompinger, & Baime, 2007; Lutz, Slagter, et al., 2009; MacLean et al., 2010;
Tang et al., 2007; Valentine & Sweet, 1999; van den Hurk, Giommi, Gielen, Speckens, &
Barendregt, 2010; reviewed in Lutz, Slagter, et al., 2008; Wadlinger & Isaacowitz, 2011), and
theoretical accounts emphasize the role of attention regulation as one of the core components of
mindfulness meditation (K. W. Brown & Ryan, 2003; Carmody, 2009; Hölzel, Lazar, et al.,
2011; Lutz, Slagter, et al., 2008). Substantial evidence exists that attentional skills are a critical
component of the emotion regulatory process (reviewed in Wadlinger & Isaacowitz, 2011), and
it has been suggested that meditative interventions may be one of the most effective attention-
based training methods available to improve emotional regulation (Wadlinger & Isaacowitz,
2011).
In this study, we implemented an eight-week program of mindful-attention training, in which
subjects practice meditative techniques for enhancing focused attention and mindful awareness
of one’s internal state and external environment (Wallace, 2006). This program has been used
principally in the form of three-month intensive retreats, as was the case in the Shamatha
Project—a longitudinal study aimed at investigating a broad range of health-related outcomes
and effects on basic physiology and brain function (Jacobs et al., 2011; MacLean et al., 2010;
Saggar et al., 2012; Sahdra et al., 2011). The training includes two components of attention,
which have been called focused attention (FA) and open monitoring (OM) (Lutz, Slagter, et al.,
2008), also known as concentrative attention and receptive attention, respectively (Austin, 2009;
D. P. Brown, 1977; Jha et al., 2007; Valentine & Sweet, 1999). Three main meditative
techniques are taught: mindfulness of breathing (i.e., cultivating awareness of one’s breathing),
mindfulness of mental events (i.e., cultivating awareness of the contents of one’s mind, such as
thoughts, emotions, etc.), and awareness of awareness (in which awareness itself becomes the
focus of meditation).
In contrast to mindful-attention practices aimed at improving attentional skills, compassion
meditation is a distinct form of contemplative practice aimed at cultivating higher levels of
compassion. Compassion can be defined as the feeling that arises in witnessing another’s
suffering and that motivates a subsequent desire to help (Goetz, Keltner, & Simon-Thomas,
2010). In the Mahayana Buddhist tradition, compassion is considered the ultimate source of
5
well-being and happiness (Davidson & Harrington, 2001). Buddhist-inspired practices for
cultivating compassion for self and others have been proposed by a number of authors as
accessible methods to help alleviate psychological problems and improve well-being (Germer,
2009; Gilbert, 2005; Hofmann, Grossman, & Hinton, 2011; Jazaieri et al., 2012; Makransky,
2007; Ozawa-de Silva & Dodson-Lavelle, 2011; Salzberg, 1995; Wallmark, Safarzadeh,
Daukantaitė, & Maddux, 2012). Emerging scientific evidence suggests that these interventions
may be beneficial on multiple levels. A pilot study indicated that compassionate mind training
could lead to significant reductions in depression, anxiety, self-criticism, and shame (Gilbert &
Procter, 2006). Another study suggested that compassion meditation may offer health-related
benefits such as reduced immune and behavioral response to psychosocial stress (Pace et al.,
2009, 2010). In a pilot study of loving-kindness meditation, a practice related to compassion
meditation, chronic low back pain patients showed significant improvements in pain and
psychological distress (Carson et al., 2005). Remarkably, Hutcherson, Seppala, & Gross (2008)
found that even only a few minutes of loving-kindness meditation could increase feelings of
social connection and positivity toward novel individuals. A few hours of training over the
course of several days increased positive affective experiences and elicited activity in brain
regions previously associated with positive affect and social affiliation (Klimecki, Leiberg,
Lamm, & Singer, 2012). In a larger field experiment, Fredrickson, Cohn, Coffey, Pek, & Finkel
(2008) found that loving-kindness meditation produced increases over a two-month period in
daily experiences of positive emotions, which promoted increases in a wide range of personal
resources (e.g., increased mindfulness, purpose in life, social support, decreased illness
symptoms), which, in turn, predicted increased life satisfaction and reduced depressive
symptoms. In a recent randomized controlled trial, an intensive meditation/emotion regulation
intervention that included multiple elements of compassion training yielded reduced trait
negative affect, rumination, depression, and anxiety, increased trait positive affect and
mindfulness, and improved recognition of subtle facial expressions of emotion (Kemeny et al.,
2012). Taken together, these recent studies support the hypothesis that compassion meditation
contributes to improved emotion regulation. However, a direct comparison of mindful-attention
training and compassion meditation training has been lacking.
In this study, we investigated how eight weeks of training in either mindful-attention meditation
or compassion meditation affected amygdala responses to emotional stimuli. Since we were
interested in putative changes in affective trait, study participants were not instructed to enter a
meditative state, so that changes in brain activity would reflect uncontrived emotional responses
without being influenced by a purposeful manipulation of brain state.
Materials and methods
Study participants
Study participants were a subset of the subjects enrolled in a parent study being conducted at
Emory University in Atlanta, GA, called the Compassion and Attention Longitudinal Meditation
(CALM) study. All procedures were approved by the Institutional Review Boards at Emory
University and Boston University. Healthy, medication-free adults (25–55 year-old) with no
prior meditation experience were recruited in the Atlanta metropolitan area. Study participants
gave written informed consent with Emory University to participate in the parent study, and
6
additionally with Boston University to participate in the brain imaging study reported here.
Participants in the parent study were randomized to eight weeks of training in either Mindful
Attention Training (MAT), or Cognitively-Based Compassion Training (CBCT), or an active
control intervention consisting of a health discussion group (CTRL). All interventions are
described below. Fifty-one subjects (31 females, 20 males; age 34.1 ± 7.7 years, mean ± standard
deviation) volunteered to participate in the brain imaging study. They underwent the pre-
intervention scan before their randomization to any of the three groups. Of those who completed
baseline assessments, five subjects dropped out of the study, and 10 either fell asleep or showed
excessive motion in the scanner in at least one of their two scanning sessions. The final subject
population in the present brain imaging study was N = 12 (8 females, 4 males; age 34.3 ± 9.6
years, mean ± standard deviation) in the MAT group, N = 12 (9 females, 3 males; age 32.0 ± 5.4
years) in the CBCT group, and N = 12 (5 females, 7 males; age 36.0 ± 7.6 years) in the CTRL
group.
Interventions and meditation training
All subjects participated in two hours of class time per week for eight weeks, for a total of 16 hours
during the study. During these eight weeks subjects in the MAT and CBCT groups were asked to
meditate for an average of 20 minutes a day outside of the class. Each intervention is described in
detail below. The meditation interventions were designed to be primarily experiential, with
theoretical background presented only to the extent that it facilitated the meditative experience.
Complete protocols for MAT, CBCT, and the active control intervention are provided in Table 1.
Mindful Attention Training (MAT). In this program, subjects are trained in a set of meditation
techniques for enhancing focused attention and mindful awareness of one’s internal state and
external environment. The program taught in this study was a simplified version of a
comprehensive meditation training program fully described in (Wallace, 2006). The latter
program is regularly taught in the form of three-month intensive retreats, as was the case in the
Shamatha Project—a longitudinal study aimed at investigating a broad range of health-related
outcomes and effects on basic physiology and brain function (Jacobs et al., 2011; MacLean et al.,
2010; Saggar et al., 2012; Sahdra et al., 2011). It should be noted that, while the three-month
retreat program introduces elements of compassion training as well as Buddhist ethics, these
components were not taught in the MAT program in this study to avoid possible confounds with
the CBCT program described below.
Each MAT class included a 50-min didactic session that introduced the meditative technique to be
practiced during the week, a 30-min discussion period, and a 40-min meditation practice session.
The full protocol for the MAT program is detailed in Table 1. In essence, the training includes
two components of attention, which have been called focused attention (FA) and open
monitoring (OM) (Lutz, Slagter, et al., 2008), also known as concentrative attention and
receptive attention, respectively (Austin, 2009; D. P. Brown, 1977; Jha et al., 2007; Valentine &
Sweet, 1999). Attention is trained by developing two complementary mental functions. One
consists in attending, without forgetfulness, to the meditative object of focus (e.g., one’s
breathing); this faculty is called sati in Pali, which has been translated as awareness, bare
attention, or mindfulness (Wallace, 2006). The second mental function, called introspection, is a
type of metacognition that operates as the ‘quality control’ by monitoring the meditative process
7
and swiftly detecting the occurrence of either excitation or laxity, which are both impediments to
the practice (Wallace, 1999). Three main meditative techniques are taught: mindfulness of
breathing (in which the object of focused attention is one’s own breath), “settling the mind in its
natural state”, i.e., mindfulness of mental events (in which the object of focused attention is
one’s own mind and mental activity, such as thoughts, emotions, etc.), and awareness of
awareness (in which awareness itself becomes the focus of meditation, without a specific object,
so that one is simply aware of being aware). The purpose of having a meditation object that is
more and more subtle or elusive as the training progresses is to increase the quality of attention.
This training format closely follows standard presentations in Tibetan Buddhism and is
traditionally considered appropriate for novices (Wallace, 2006). Of note, meditation practices in
the MAT program bear many similarities with the practices included in the sitting meditation
component of Mindfulness-Based Stress Reduction (MBSR; Kabat-Zinn, 1990). We chose to use
MAT rather than MBSR because MBSR is a heterogeneous technique that involves training in
mindful awareness across different practices, including not only sitting meditation but also some
yoga movements and a lying-down practice called the body scan (Kabat-Zinn, 1990), which we
felt would be difficult to match in the CBCT program and could potentially confound our
comparison of both trainings.
Cognitively-Based Compassion Training (CBCT). The CBCT program was designed by
Lobsang Tenzin Negi, PhD, Geshe-Lharampa, Director of the Emory-Tibet Partnership at Emory
University and Spiritual Director of Drepung Loseling Monastery, Atlanta, GA. The CBCT
program is based on traditional Buddhist methods for cultivating compassion (see Pace et al.,
2009, 2010; Reddy et al., 2012). The CBCT program includes several meditation practices that
were adapted from a set of the Mind-Training techniques (“lo-jong”) in the Tibetan Buddhist
tradition, which derive largely from writings ascribed to the Indian Buddhist masters Shantideva
(8
th
century) and Atisha (11
th
century) (Dalai Lama, 2001; Jinpa, 2005; Santideva, 1997;
Wallace, 2001). The goal of these techniques is to reverse thoughts, emotions, and behaviors that
are harmful to oneself and others and to transform them into thoughts, emotions, and behaviors
that are beneficial to oneself and others (Ozawa-de Silva & Dodson-Lavelle, 2011). The full
protocol for the CBCT program is detailed in Table 2. The CBCT program is taught in weekly
stages and includes the standard meditative practices for developing focused attention as
preludes to deploying meditative concentration to the purposeful cultivation of specific mind
states. These mind states include equanimity towards all beings, appreciation and affection for
others (also known as maitrī in Sanskrit or metta in Pali, often translated as “loving-kindness” or
simply “love”), and compassion for all including oneself (karuā in Sanskrit). The training
culminates with the cultivation of “active compassion”, in which meditators develop a
determination to work actively to alleviate the suffering of others. The training protocol is highly
iterative, and techniques introduced early in the program are practiced throughout the entire
training period. As in MAT, each CBCT meditation class includes a 50-min didactic session that
describes the meditative technique introduced during the week, a 30-min discussion period, and a
40-min meditation practice session.
In both meditation interventions, participants received detailed instructions pertaining to the
meditative technique that they were to practice in class and then at home for the following week.
Our meditation instructors employed various pedagogical methods for making these practices
accessible, for example using various metaphors, real-life examples, and stories. A considerable
amount of time was spent explaining the potential applications of these practices in everyday life.
8
A fair portion of the discussion periods was also spent reviewing the meditation practice to make
sure that the participants had a grasp of the practice, skills, and concepts at hand.
Health Discussion Control Intervention. This active control intervention was adapted from a
university-level health education class designed by Daniel D. Adame, MSPH, PhD, CHES,
Associate Professor of Health Education at Emory University (retired). This course (PE101) was
mandatory for all Emory University freshmen for many years until Dr. Adame’s retirement. The
intervention used in the present study was designed and taught by two MPH students of Dr.
Adame’s based on material from discussion sections that were key components of the PE101
course. Both teachers were fully convinced of the utility of this health education intervention,
which was deemed worthy of university credit at Emory University. The active control group
met for two hours per week for eight weeks, exactly matching the class time commitment
required for both MAT and CBCT training. Each discussion class focused on a topic of direct
relevance to emotional and/or physical health in adults (see Table 3). After a didactic talk on the
topic of the week, subjects actively participated in small group discussions, role-playing
scenarios, and friendly debates designed to make the health topics covered personally relevant.
At the start of the first class of each week, subjects completed a brief questionnaire querying
which aspects of the health topic from the previous week (if any) they had started implementing
in their daily lives.
Self-report assessments
As part of the CALM study, participants filled the Beck Depression Inventory (BDI-II) and the
Beck Anxiety Inventory (BAI) before and after the eight-week intervention. In addition, those in
the CBCT and MAT groups were asked to log on a daily basis the amount of time that they spent
practicing meditation at home.
Brain imaging (fMRI) experiment
Volunteers for the brain imaging study took part in two scanning sessions, one within the three
weeks preceding the intervention (PRE) and one within three weeks after the intervention
(POST). All scanning took place at the Athinoula A. Martinos Center for Biomedical Imaging, a
joint center of the Harvard-MIT Division of Health Sciences & Technology and the
Massachusetts General Hospital Radiology Department. The MRI scanner was a Siemens 3T
Tim-Trio scanner with vendor-supplied 32-channel head array coil. Each scanning session
included a high-resolution (1-mm
3
voxel) T
1
-weighted anatomical scan using multi-echo
magnetizationprepared rapid gradientecho (MEMPRAGE) imaging (van der Kouwe, Benner,
Salat, & Fischl, 2008), and a 35-min T
2
*-weighted blood-oxygenation level dependent (BOLD)
fMRI experiment. Functional images (108 volumes per functional run) were obtained with
gradient-echo Echo-Planar Imaging (EPI) using the following parameter values: TE = 30 ms, TR
= 3 s, voxel size = 3 × 3 × 3 mm, bandwidth = 2240 Hz/px, matrix size 72 × 72, and 47 slices
with no gap.
During the fMRI experiment participants were presented photographs from the IAPS database
(Lang, Bradley, & Cuthbert, 2005). All selected images depicted people in various settings, with
equally distributed positive, negative, or neutral emotional valences (with 36 images in each
9
category for a total of 108 images). The instructions given to the subjects were as follows:
“Please press the button every time you see a picture appear on the screen. You'll notice that
these images have various emotional contents. Just watch the images and let yourself react to
them naturally.” The simple button press task, without rating or labeling the image, was chosen
to keep participants engaged while minimizing cognitive load, which is known to interfere with
neural activation in emotion-associated brain regions (Critchley et al., 2000; Hariri, Bookheimer,
& Mazziotta, 2000; Liberzon et al., 2000; S. F. Taylor, Phan, Decker, & Liberzon, 2003). The
order of the images was randomized across sessions and across subjects, such that each subject
viewed all 108 images once over the two scanning sessions (PRE and POST), but in a different
order. Each session consisted of six runs; during each run 18 images were presented, for a total
of 54 images during the whole session. Each image was presented for 5 s followed by a 13 s
blank (gray) screen of similar overall luminance. This mixed block/event-related design was
chosen to allow the BOLD signal to return to baseline between images, which increases
functional signal-to-noise ratio, statistical power, and robustness of the results (Amaro & Barker,
2006). The duration of each block was short enough to prevent habituation in the amygdala
(Haas, Constable, & Canli, 2009).
To prevent unintentional influences on the study participants, which may have confounded our
results, the experimenters (GD and ELS) were blinded with respect to subjects’ group
assignment until after the end of the post-intervention fMRI experiment.
We attempted to assure that the participants did not enter a meditative state during the fMRI
experiment. During the pre-intervention scan, all subjects were meditation-naïve, and therefore
did not have any training that would allow them to enter such a state. During the post-
intervention scan, the use of the word “meditation” was carefully avoided by the experimenters
during all interactions, and the participants were never primed about their meditation training (or
lack thereof in the case of subjects in the control group). During the debriefing at the end of the
scan, we confirmed that the subjects did not enter a meditative state by asking them: “Were you
meditating during the image presentation?” to which all of them replied in the negative. Some
subjects then asked, “Was I supposed to meditate?” to which we replied that they were not
supposed to meditate, and that in fact we expected them not to meditate.
Data analysis
Our approach is based on a region-of-interest (ROI) analysis (Nieto-Castanon, Ghosh, Tourville,
& Guenther, 2003; Poldrack, 2007). Two anatomically-defined ROIs, the left and right
amygdalae, were automatically segmented in each subject’s individual anatomical brain scan
with the FreeSurfer (v. 5.1) image analysis suite, which is documented and freely available for
download online (http://surfer.nmr.mgh.harvard.edu/) (Fischl et al., 2002, 2004). This
segmentation is shown on one of our subjects in Figure 1. One advantage of using anatomically-
defined ROIs is that it eliminates the potential problem of circular analysis that exists for
functionally-defined ROIs (Kriegeskorte, Simmons, Bellgowan, & Baker, 2009; Poldrack, 2007;
Poldrack & Mumford, 2009; Vul, Harris, Winkielman, & Pashler, 2009). There are other
advantages of using an ROI-based approach. First, it removes some variability due to noise by
averaging over all voxels in the ROI. Second, it is more statistically powerful than other
methods, since it controls for Type I errors by limiting the number of statistical tests. Third, it
10
enables precise spatial correspondence of the region of interest across subjects, since it does not
involve normalizing different brains to a common atlas (Poldrack, 2007).
The fMRI data were analyzed using FSL (www.fmrib.ox.ac.uk/fsl) (Smith et al., 2004; Woolrich
et al., 2009) and Matlab 7.11 (The MathWorks, Inc., Natick, MA). Preprocessing included brain
extraction, slice-timing correction, motion correction, B
0
unwarping, temporal high-pass
filtering, and registration to anatomical scan for each individual subject. The first 9 s of each run
(before the first image presentation) were discarded to eliminate any transverse magnetization
equilibration effects. For each run, the three categories of pictures (negative, neutral, and
positive) were included as separate explanatory variables in the model. In addition, the global
BOLD signal intensity (averaged across all within-brain voxels) was included as a nuisance
variable. ROI analysis was performed in each subject’s native space by averaging contrasts of
parameter estimate (COPE) values in the left and right amygdala separately. These contrast maps
were used as input for further statistical analyses in Matlab. In addition, for data exploration
purposes a whole-brain statistical parametric mapping analysis was also conducted in FSL.
11
Table 1: MAT protocol.
Week Mindful Attention Training (MAT)
1
Settling the Body and Respiration in their Natural State
Introduction of basic techniques for relaxing the body and settling the respiration in its
natural rhythm.
2
Mindfulness of the Breathing with Relaxation
Introduction and elaboration of practices for learning to calm the conceptually
discursive mind for the purpose of attenuating involuntary thoughts. Stability of
attention is practiced with the goal of sustaining attention for longer periods.
3
Mindfulness of the Breathing with Relaxation, and Stability
Continuing practice of techniques designed to instill a deepening sense of physical and
mental relaxation, stillness and vigilance. When successful, involuntary thoughts subside
and vividness of attention gradually increases. This gives rise to an overall sense of
greater presence, calm, and equilibrium.
4
Mindfulness of the Breathing, with Relaxation, Stability and Vividness
Continuing practice of techniques designed to instill a deepening sense of physical and
mental relaxation, stillness and vigilance. When successful, involuntary thoughts subside
and vividness of attention gradually increases. This gives rise to an overall sense of
greater presence, calm, and equilibrium.
5
Settling the Mind in its Natural State (i. e., Mindfulness of Mental Events) (I)
Introduction of practices for further refining the meditator’s metacognitive abilities,
with the goal of attenuating the immediate and habitual absorption in one’s thoughts that
characterize most mental functioning. When successful, insight into the nature of the
mind and its activities is achieved.
6
Settling the Mind in its Natural State (II)
Continued practice with the goal of developing increased relaxation, stillness of
awareness in the midst of mental activities, and vividness, together with heightened
metacognitive abilities to observe mental states and processes without identifying with
them.
7
Awareness of Awareness (I)
In this final technique, relaxation, stillness, and vividness of attention continue to be
enhanced, leading to a perception of the process of becoming aware, as opposed to only
perceiving the contents of awareness.
8
Awareness of Awareness (II)
As the meditator develops greater facility with this practice, the mind rests in its own
luminosity and awareness. When successful, this practice leads to insight into the nature
of consciousness itself.
12
13
Table 2: CBCT protocol.
Week Compassion-Based Compassion Training (CBCT)
1
Developing Attention and Stability of Mind
Introduction of basic meditation techniques for focusing attention for increasingly longer
periods of time. These techniques are included in the practice of all subsequent
compassion meditation components.
2
Awareness of Sensations, Feelings and Emotions
Often we are aware of only our reactions to feelings and sensations, rather than the
feelings and sensations themselves. This practice hones our attention to subjective
experience, and provides the meditator with practice in separating emotions and
reactions.
3
Developing Compassion for Oneself through the Wish to Emerge from Suffering
Introduction of techniques to develop awareness of how thoughts and actions contribute to
subjective experiences of happiness or suffering, and techniques to increase identification
of habitual, conditioned reactions.
4
Cultivating Equanimity
Introducing practices designed to challenge unexamined thoughts and feelings
determining categories of friend, enemy and stranger; introducing the perspective that all
persons are alike in wanting to be happy.
5
Developing Appreciation and Affection
It is common to feel appreciation only for a few close others whose actions on our
behalf are easy to observe and comprehend. Yet every day we reap the benefits of the
actions of countless others. We practice becoming aware of those others, and become
grateful to them.
6
Empathy
Techniques will be presented for developing undifferentiated affection for others, based
on the many ways that others benefit us each day. The meditators will be introduced to the
concept of empathy for others: identifying with their happiness and suffering alike.
7
Wishing and Aspirational Compassion
Using the concepts of appreciation and empathy as a starting point, the meditator will be
guided toward the first stages of compassion: the wish that all beings might be happy and
free of suffering, and the aspiration to help them achieve that.
8
Active Compassion for Others
The meditation training culminates in the generation of active compassion: practices
introduced to develop a determination to work actively to alleviate the suffering of others.
When this training is successful, this state of mind becomes ingrained and spontaneous.
14
Table 3: Health Discussion Control Intervention protocol.
Week Health Discussion Control Intervention
1
Interacting with our Environment
After introducing the students to each other and to the class, we will introduce the first of
the top 10 things we can do to improve our health: interact with our environment, which
improves mood and fosters a sense of well-being (1).
2
The Things We Put in our Bodies
The second item on the list related to hydration, for proper physical and mental function
(2). We will introduce the importance of small changes in diet for nutrition and long-
term health, particularly eating breakfast (3) and eating more fruits and vegetables (4).
3
Interacting with the Healthcare System
This module will help participants to better understand health-related information, and to
interact with healthcare providers most effectively (5).
4
Maintaining Healthy Relationships
Strong social ties create better health, by improving immune function, protecting heart
health, and warding off depression and anxiety (6).
5
The Role of Exercise in Physical Health
Regular exercise reduces the risk of heart disease and helps attain and maintain a
healthy weight (7).
6
The Role of Exercise in Emotional Health
Regular exercise can decrease depression and anxiety and improve overall mood (8).
7
Time Out
Regular and sufficient sleep, as well as quiet relaxation time, are essential to physical and
mental health (9).
8
Stop Stressing
Stress is unavoidable; the key is to recognize it. One component of stress management is
learning and implementing healthier emotional expression (10).
15
Results
Amount of meditation practice
The total reported duration of meditation practice in the MAT group was 645 ± 340 min (mean ±
standard deviation, N = 12), ranging from 210 to 1491 min. In the CBCT group it was 454 ± 205
min (N = 12), ranging from 190 to 905 min. There was no significant difference between the two
groups (two-sample t-test, p = 0.27, t = -1.13).
BAI and BDI
Before the interventions, the BAI scores of all participants who completed the Boston study and
the questionnaires were 2.6 ± 3.1 in the MAT group (N = 13), 4.6 ± 4.1 in the CBCT group (N =
17), and 4.0 ± 4.2 in the control group (N = 11), and BDI scores were 2.8 ± 3.8 in the MAT
group, 5.8 ± 7.9 in the CBCT group, and 3.4 ± 4.4 in the control group. After the interventions,
BAI scores were 1.8 ± 2.5 in the MAT group, 3.8 ± 4.2 in the CBCT group, and 1.3 ± 2.0 in the
control group, and BDI scores were 2.8 ± 5.2 in the MAT group, 2.7 ± 4.1 in the CBCT group,
and 2.4 ± 3.1 in the CTRL group. A single-factor repeated-measure ANOVA revealed a
significant effect of time on BAI scores (ANOVA F(1,38) = 13.23, p = 0.0008) and on BDI
scores (ANOVA F(1,38) = 6.33, p = 0.016), but no effect of group. The group × time interaction
was not statistically significant for BAI scores (F(2,38) = 2.41, p = 0.10), nor for BDI scores
(F(2,38) = 2.42, p = 0.10). Nonetheless, an exploratory analysis suggested that BDI scores were
significantly reduced from pre- to post-training in the CBCT group (mean difference –3.1 ± 4.7,
two-tailed paired t-test, p = 0.015, t = 2.72, df = 16), but not in the other groups.
In the two meditation groups, the amount of time that participants reported practicing meditation
was not correlated with pre- or post-intervention BDI or BAI scores, nor with their pre-post
differences (r
2
< 0.1, p > 0.2 in all cases).
Brain activation in the amygdala
The different activation levels in the right amygdala, across image valences and across groups,
are summarized in Figure 2. No effects or trends were found in the left amygdala.
The three groups did not differ significantly from each other before the intervention. Although
Figure 2A seems to show a difference in right amygdala activation across the three groups
before the training (PRE)—with the CTRL group seemingly showing lower activation than the
other two groups—this difference did not reach statistical significance (one-way ANOVA:
F(2,33) = 2.22, p = 0.12, df = 35). This trend may be explained by the higher proportion of male
subjects in the CTRL group. Indeed, regrouping all subjects by gender in PRE revealed a
statistically significant group difference with respect to gender, with females showing higher
activation in the right amygdala in response to all images (0.24 ± 0.19, mean ± standard
deviation in units of percentage change in BOLD signal, N = 22) compared to males (0.13 ±
0.11, N = 14; group difference: two-sample t-test, p = 0.038, t = 2.17).
16
When comparing right amygdala activation before the intervention (PRE) and after the
intervention (POST), we found a significant Group × Time interaction in response to images of
all valences (repeated-measure ANOVA F(2,33) = 3.73, p < 0.035). The PRE-POST difference
was significantly greater in MAT than in CTRL, as revealed by Tukey’s Honestly Significant
Difference test for multiple comparisons (estimated difference between MAT and CTRL: 17.5,
confidence interval [33.2, 1.8]), whereas the CBCT group did not differ significantly from the
other two groups.
In within-group analyses, no significant effects or non-significant trends were observed in the
CTRL group (who did not practice meditation). In the MAT group, we found a longitudinal
(PRE to POST) decrease in right amygdala activation in response to images of all valences
overall (two-tailed paired t-test, p = 0.012, t = 3.00, df = 11, Figure 2A), and in response to
positive-valence images (two-tailed paired t-test, p = 0.011, t = 3.06, df = 11, Figure 2B).
While the response to negative-valence images decreased as well, this trend was not statistically
significant (two-tailed paired t-test, p > 0.1, Figure 2C). The response to neutral-valence images
did not vary (two-tailed paired t-test, p > 0.1, Figure 2D).
The CBCT group also exhibited a decrease in right amygdala activation in response to positive-
valence images, by a similar amount as the MAT group on average (mean: 0.112 in CBCT,
0.127 in MAT), but it did not reach statistical significance (two-tailed paired t-test, p = 0.085, t
= 1.89, df = 11, Figure 2B). The response to neutral-valence images did not vary (two-tailed
paired t-test, p > 0.1, Figure 2D). Interestingly, we found a trend increase in right amygdala
activation in response to negative-valence images. While this increase was not significant at the
group level (two-tailed paired t-test, p > 0.1, Figure 2C), a correlation analysis with the amount
of meditation practice time indicated that increased amygdala activation occurred in the subjects
who had reported the most hours of practice, while those who had practiced less showed a small
decrease in amygdala activation. However, this correlation between practice time and PRE-
POST difference in amygdala activation did not reach statistical significance (correlation
coefficient r = 0.46, p = 0.13, N = 12, Figure 3). For completeness, we performed the same
analysis in the MAT group but found no effect of practice time (correlation coefficient r = 0.07,
p = 0.8, Figure 3). In both groups, there was no evidence of an effect of practice time on the
PRE-POST difference in amygdala response in the case of positive or neutral images.
To further investigate this trend increase in amygdala activation in response to negative images
after CBCT training, we performed a correlation analysis between the PRE-POST difference in
amygdala activation and the PRE-POST difference in depression (BDI) scores. We found a
statistically significant negative correlation in the CBCT group (r = 0.58, p = 0.048, N = 12,
Figure 4). In other words, a greater increase in amygdala activation in response to negative
images was associated with a greater decrease in depression score after CBCT training. No such
correlation was found after MAT training (r = 0.06, p = 0.9, N = 12, Figure 4). No correlation
was found in the case of images of positive of neutral valence in either the MAT or CBCT group
(p > 0.5 in all cases). Neither MAT nor CBCT group showed correlations between the difference
in amygdala activation and the difference in anxiety (BAI) scores (p > 0.3 in all cases).
Whole-brain analysis
17
For data exploration purposes, a whole-brain statistical parametric mapping analysis was also
conducted. It did not reveal any significant PRE-POST differences at the whole-brain level in
any of the three groups.
Discussion
The longitudinal effects of meditation training in beginners, in terms of brain function, are only
beginning to be elucidated. Based on the literature showing that the amygdala plays a prominent
role in emotional processing and attention, and that amygdala activation in response to emotional
stimuli varies with personality trait and with different meditative states, we hypothesized that
amygdala response to emotional stimuli when subjects were in a non-meditative state would
decrease longitudinally after eight weeks of training in mindful-attention meditation. We found a
longitudinal decrease in right amygdala activation in response to positive images, and in
response to images of all valences overall, after training in mindful-attention meditation. No
difference or trends were found in the control group between the pre- and post-intervention
scans. Because of the longitudinal, controlled design of our study, our results support our
hypothesis that participation in an eight-week mindful-attention meditation training would cause
a reduction in amygdala response to emotional stimuli while participants were not meditating.
However, the case of compassion meditation training was less straightforward, as we discuss
below.
Previous studies have implicated the amygdala in the effects of meditation training, both in
beginner and expert meditators. In beginner meditators after only one week of practice, V. A.
Taylor et al. (2011) reported a down-regulation of the amygdala during viewing emotional
images when the subjects were instructed to enter a “mindful” meditative state, compared to a
baseline, non-meditative state. In a longitudinal study of MBSR for patients with social anxiety
disorder, Goldin & Gross (2010) reported that after MBSR training, patients exhibited a faster
return to baseline in their right amygdala activation while viewing phrases of negative self-
beliefs, which in these patients can be considered a form of emotional stimulus with negative
valence. These subjects also showed decreased negative emotion ratings and increased activity in
brain regions implicated in attentional deployment (Goldin & Gross, 2010). These two studies
point to a decrease in amygdala activation after mindfulness meditation training in beginners.
Studies involving individuals with extensive meditation practice depict a more complex picture.
In one study, experienced meditators showed lower amygdala activation in response to emotional
distracters when in a meditative state of mindful attention (“one-pointed concentration”)
compared to a non-meditative, baseline state (Brefczynski-Lewis et al., 2007). The authors also
found a negative correlation between the number of hours of meditation training and right
amygdala activation during concentration meditation in a group of experienced meditators while
hearing negative-valence emotional sounds, which might indicate that more experience with
meditation leads to improved ability to down-regulate the amygdala. However, another study
found no effect in the amygdala when experienced meditators were instructed to pay mindful
attention to emotional images compared to a non-meditative, baseline state (V. A. Taylor et al.,
2011). A possible explanation for the discrepancy between these two studies is that subjects may
have deployed attention differently. In the Brefczynski-Lewis et al. (2007) study, the emotional
sounds were distracters, a condition which may have required some down-regulation. In the
18
Taylor et al. (2011) study, however, the subjects were instructed to take the emotional pictures as
the focus of their mindful-attention meditation. Commenting on the apparent lack of amygdala
down-regulation that they found in experienced meditators when comparing the Mindful
condition with the Baseline (non-meditative) condition, Taylor et al. suggested that the long-term
practice of meditation may lead to emotional stability by promoting acceptance of emotional
states and enhanced present-moment awareness, rather than by eliciting control over low-level
affective cerebral systems from higher-order cortical brain regions. We propose that the lack of
change in amygdala activation across conditions (Mindful vs. Baseline) that Taylor et al. found
in experienced meditators may also indicate that the Baseline state in these subjects is more
similar to the Mindful state. Indeed, the only differences between Mindful and Baseline states
reported by Taylor et al. in experienced meditators consisted in deactivations in regions of the
default-mode network, with no difference in brain areas associated with emotional processing. In
this light, the results from Taylor et al. may be an indication that the Baseline state in
experienced meditators is rather similar to their Mindful state and differs from the Baseline state
in non-meditators, consistent with other neuroimaging studies which found differences in resting
state (Brewer et al., 2011; Jang et al., 2011) and in brain morphometry (Grant et al., 2010; Lazar
et al., 2005; Luders et al., 2011, 2009; Pagnoni & Cekic, 2007) in meditators versus non-
meditators.
Our results reported here are also consistent with the hypothesis that meditators display
differences with non-meditators in terms of brain function, in particular in how the amygdala is
activated in response to a passive emotional challenge. In addition, our results demonstrate that
changes in brain function, in a non-meditative state, after eight weeks of meditation training can
be measured longitudinally in a nonclinical population. These results support the more general
hypothesis that meditation training can promote enduring changes in mental function, i.e., in the
development of certain traits (Lutz et al., 2007; Lutz, Slagter, et al., 2008; Slagter et al., 2011).
Putative mechanisms
Emotion regulation relies on attentional capabilities (Gross, 1998; Ochsner & Gross, 2005,
2008), and it has been proposed that emotion regulation can be improved by attention training,
especially in the context of meditation training (Wadlinger & Isaacowitz, 2011). Given that the
amygdala plays an essential role in both attention and emotion regulation, it is possible that our
finding of a reduced amygdala response to emotional stimuli after mindful-attention training can
be explained by an improvement in attentional skills. Indeed, a growing number of studies
supports the view that meditation training improves attentional skills (Baijal, Jha, Kiyonaga,
Singh, & Srinivasan, 2011; Chambers, Lo, & Allen, 2008; Jha, Krompinger, & Baime, 2007;
Lutz, Slagter, et al., 2009; MacLean et al., 2010; Tang et al., 2007; Valentine & Sweet, 1999; van
den Hurk, Giommi, Gielen, Speckens, & Barendregt, 2010; reviewed in Lutz, Slagter, et al.,
2008; Wadlinger & Isaacowitz, 2011), and theoretical accounts emphasize the role of attention
regulation as one of the core components of mindfulness meditation (K. W. Brown & Ryan,
2003; Carmody, 2009; Hölzel, Lazar, et al., 2011; Lutz, Slagter, et al., 2008).
It was difficult to experimentally assess the level of attention that subjects deployed in our task,
because we did not want to include an attention-demanding cognitive task which may have
confounded the emotional response as it would normally occur outside the laboratory. Indeed, it
is well established that an experimental task consisting in rating or labeling emotions reduces
activation in the amygdala compared to passive viewing or to a match-to-sample task (Hariri et
19
al., 2000; Hutcherson, Goldin, Ramel, McRae, & Gross, 2008; S. F. Taylor et al., 2003), which
has led to the suggestion that attention alters the salience of some aspects of the emotional events
with which the amygdala is concerned (Hutcherson, Goldin, et al., 2008). Nevertheless, our
results are consistent with the possibility that MAT participants dedicated more attentional
resources to the images after meditation training than before training.
Another possibility is that mindful-attention training enhanced participants’ baseline positive
affect, which would make the effect of positive-valence stimuli on the amygdala comparatively
smaller. This would be consistent with previous findings that mindfulness-based interventions
were associated with lowered intensity and frequency of negative affect (K. W. Brown & Ryan,
2003; Chambers et al., 2008), and that heightened states of mindfulness were associated with
both higher positive affect and lower negative affect (K. W. Brown & Ryan, 2003). In addition,
dispositional mindfulness has been associated with less alarming stress appraisals, more
approach-oriented coping, and less avoidant coping (Heppner, 2007; Weinstein, Brown, & Ryan,
2009). If indeed our study participants showed higher positive affect after mindful-attention
training (which we did not measure directly), this would also be consistent with our finding that
amygdala reactivity was overall lower in response to images of all emotional valences.
Case of compassion meditation
One aspect of our study consisted in exploring the effects of compassion meditation training on
amygdala activation. As cited above, compassion can be defined as the feeling that arises in
witnessing another’s suffering and that motivates a subsequent desire to help (Goetz et al., 2010)
(for similar definitions, see (Halifax, 2012; Lazarus, 1991; Nussbaum, 1996, 2001). In this view,
compassion is an affective state defined by a specific subjective feeling which is related to
empathy or empathic concern (reviewed in Goetz et al., 2010). Compassion requires the ability
to understand the feelings or emotional states of others, which includes two major components:
affective empathy, which is the ability to experientially (i.e., emotionally, “viscerally”) share the
affective states of others; and cognitive empathy, or the ability to take the mental perspective of
others and make inferences about their mental or emotional states (Cox et al., 2012; Shamay-
Tsoory, 2011). Both components were included in the compassion meditation training that we
used in this study.
There exist important differences between compassion meditation and mindful-attention
meditation as meditative states. For example, in contrast to the previous studies discussed above,
experienced meditators showed higher amygdala activation in response to emotionally-charged
sounds of human vocalizations (i.e., a baby cooing, a woman screaming) when in a meditative
state of non-referential compassion, compared to when they were in a non-meditative, baseline
state (Lutz, Brefczynski-Lewis, Johnstone, & Davidson, 2008). The meditative state of non-
referential compassion was also accompanied by an increase in heart rate which was
significantly associated with brain activation in several brain areas involved with the modulation
of bodily arousal states, suggesting that the compassion meditative state was one of higher
arousal (Lutz, Greischar, Perlman, & Davidson, 2009).
Currently, the role of the amygdala in empathy and compassion is still not clear. While the
amygdala is usually not considered part of the core brain network for empathy (Fan, Duncan, de
Greck, & Northoff, 2011; Shamay-Tsoory, 2011), several neuroimaging studies of empathy and
compassion have implicated the amygdala. Increased amygdala activation was reported in
20
several empathy-related tasks, especially in females (Derntl et al., 2010; Klimecki et al., 2012),
and was also reported in response to hearing people or infants crying, stimuli that presumably
elicit compassion (K. Sander, Frome, & Scheich, 2007; K. Sander & Scheich, 2001, 2005). Some
evidence suggests that bilateral amygdala damage disrupts affective empathy, but not cognitive
empathy, indicating that the amygdala may play an important role in affective empathy
(Hurlemann et al., 2010). In a recent study, dominance of affective empathy compared to
cognitive empathy was associated with stronger functional connectivity among social-emotional
brain regions which included the amygdala (Cox et al., 2012).
Together, these previous studies suggest that compassion (as an affective state) is associated with
higher amygdala activation. Our finding of a trend increase in amygdala activation after
compassion meditation training in response to negative-valence images is consistent with those
previous results—especially in light of the fact that all photographs that we used as visual stimuli
depicted human beings, such that the negative-valence images typically showed people in
various situations of suffering. One might speculate that these images of suffering inspired more
compassion in the participants after compassion training, which may itself be related to an
increase in amygdala activation, as seen in experts (Lutz, Brefczynski-Lewis, et al., 2008). We
propose two reasons why this increase in amygdala response did not reach statistical significance
at the group level in our study. First, unlike in previous studies, in our task the subjects were not
instructed to specifically cultivate compassion or empathy, nor to enter a meditative state. While
this experimental condition was chosen because it is more relevant to everyday life and could
reveal changes in trait rather than mere changes in brain states, it has the disadvantage of
yielding a smaller effect size. This raises the possibility that our study simply did not have
sufficient statistical power, with our relatively small sample size of twelve subjects per group.
Future studies with larger cohorts are needed to address this possibility. Second, the training in
compassion meditation also included a substantial amount of practice in mindful-attention
meditation, which is considered foundational to meditation in general (see Table 2). Therefore,
we might expect the subjects in the compassion meditation group to show some of the same
effects from this training as the subjects in the MAT group. If indeed mindful-attention
meditation has the effect of reducing amygdala response to negative stimuli while compassion
meditation has the effect of enhancing it (as suggested by the previous studies mentioned above),
these two effects may counteract each other in subjects who practiced both types of meditation,
i.e., in the CBCT group. Interestingly, the CBCT participants who reported the least amount of
meditation practice showed a trend decrease in the amygdala response to images of human
suffering, similar to the trend decrease found in the MAT group (see Figure 3). This raises the
possibility that the CBCT participants who practiced less showed only the effect of the mindful-
attention aspect of the training, and that the specific effects of compassion training only appeared
in the subjects who practiced more.
In the case of compassion meditation training, we also found that a greater increase in amygdala
response to images of suffering was associated with a greater decrease in depression score (see
Figure 4
). This finding is especially intriguing as it seems to contradict the well-known
association between clinical depression and enhanced amygdala response to negative-valence
stimuli (reviewed in Drevets, Price, & Furey, 2008). However, these previous studies typically
used non-contextual images of sad or angry faces; such stimuli may be less likely to elicit
compassion than the more situated images from the IAPS database used in the present study.
While only few studies to date have directly investigated compassion or empathy in conjunction
21
with depression, they seem to suggest that acute depression is associated with impaired empathic
abilities and that empathy improves with remission (Cusi, Macqueen, Spreng, & McKinnon,
2011; Donges et al., 2005). If so, our finding of a greater increase in amygdala response
associated with a greater decrease in depression score may be explained by an increased capacity
for compassion after compassion training.
Possible gender effects
Although our study was not designed to investigate the effects of gender on meditation training
(our sample being too small in size and not balanced across gender), we found a trend which was
almost statistically significant (p = 0.059) suggesting a gender difference in the amygdala
response to all images at baseline (i.e., in the pre-intervention scan), with females showing
higher activation in the right amygdala in response to emotional images compared to males. This
is consistent with previous studies that reported a significant interaction of emotional valence
and gender of participants on brain activation, particularly affecting the amygdala (Killgore &
Yurgelun-Todd, 2001; Proverbio, Adorni, Zani, & Trestianu, 2009; Wager, Phan, Liberzon, &
Taylor, 2003; Wrase, 2003). A recent study also found that females showed stronger brain
activation in three different empathy tasks (emotion recognition, perspective taking, and
affective responsiveness) in several emotion-related areas, including the amygdala (Derntl et al.,
2010). More generally, some studies reported differences across gender in the engagement of
emotion regulation strategies (Matud, 2004; Thayer, Rossy, Ruiz-Padial, & Johnsen, 2003).
Overall, these gender differences raise the possibility that gender may act as a moderator on the
longitudinal effects of meditation training on amygdala activation. Future studies will be needed
to directly investigate this question.
Concluding remarks
In this study, eight weeks of training in two different forms of meditation yielded distinct
changes in amygdala activation in response to emotionally-valenced images while the subjects
were in an ordinary, non-meditative state. This finding suggests that meditation training may
affect emotional processing in everyday life, and not just during meditation. This is consistent
with the hypothesis that the cultivation of specific meditative states, which are relatively short-
term, can result in enduring changes in mental function, i.e., in the long-term development of
certain traits (Slagter et al., 2011). Future research is needed to investigate the longitudinal
impact of meditation training on other brain areas involved with affective response, emotion
regulation, and attention.
Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial or
financial relationships that could be construed as a potential conflict of interest. In the interest of
full disclosure, we would like to report that, in the previous 12 months, CLR has consulted for
Bristol Myers Squibb and Pamlab, and has prepared and presented disease-state promotional
material for Pamlab.
22
Acknowledgements
This study was funded by the National Institutes of Health National Center for Complementary
and Alternative Medicine (R01AT004698 and R01AT004698-01A1S1, P.I. Raison; ARRA
RC1AT005728, P.I. Schwartz). The authors thank Bruce Rosen, Willa Miller, Sara Lazar, and
Britta Hölzel for helpful discussions, Jonathan Polimeni, Thomas Benner, and Vitaly Napadow
for help with the fMRI experimental design, Doug Greve and Jonathan Polimeni for helpful
suggestions regarding data analysis, Teri Sivilli for valuable help with study coordination, and
meditation instructors Brendan Ozawa-de Silva, Brooke Dodson-Lavelle, Tom Comstock, and
Bryan Price
for providing the training to the study participants in the CBCT and MAT groups.
Figure legends
Figure 1. Coronal, sagittal, and horizontal views of the brain of one study participant. The right
amygdala is marked by a red crosshair and colored in blue. The other colors indicate different
brain regions as automatically segmented by the FreeSurfer software.
Figure 2. Percentage BOLD signal change in right amygdala for all three groups of subjects
(CBCT, MAT, CTRL), in the pre-intervention scan (PRE) and in the post-intervention scan
(POST), (A) for images of all valences (ALL), (B) for images with positive valence (POS), (C)
for images with negative valence (NEG), and (D) images with neutral valence (NEU). The
asterisks indicate statistically significant differences between PRE and POST (two-tailed paired
t-tests). Bars represent mean ± standard error.
Figure 3. PRE-POST difference in percentage BOLD signal change in right amygdala as a
function of total meditation practice time. Each data point corresponds to an individual subject.
The CBCT group is shown in blue, the MAT group in red. Linear regression lines are shown in
corresponding colors.
Figure 4. PRE-POST difference in depression score as a function of PRE-POST difference in
percentage BOLD signal change in right amygdala. Each data point corresponds to an individual
subject. The CBCT group is shown in blue, the MAT group in red. Linear regression lines are
shown in corresponding colors.
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... Yang et al. 2016;Zhao et al. 2019;Goldin and Gross 2010). The results have not always been consistent, but some studies have shown reduced activation of the amygdala when viewing negative images after training in compassion (Desbordes et al. 2012;Weng et al. 2018). However, regular compassionate meditation practitioners showed increased amygdala activation in a reaction to emotionally negative sounds (Lutz, Brefczynski-Lewis, et al. 2008). ...
... Dette MBSR-studium (ibid., 2011) viste forøget koncentration af grå substans i venstre hippocampus samt i andre regioner i eksplorative analyser, men "maengden af hjemmebaseret meditationspraksis og forandringer i mindfulness-scores (…) var ikke relateret til forandringer i de identificerede regioner" (ibid., 2011, p. 39, vores oversaettelse). Flere MBSR-studier har på lignende vis ikke demonstreret relationer mellem antal minutters meditationsaktivitet og de neurale fund (Desbordes et al., 2012;Goldin & Gross, 2010). ...
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This book examines how Western behavioral science - which has generally focused on negative aspects of human nature - holds up to cross-cultural scrutiny, in particular the Tibetan Buddhist celebration of the human potential for altruism, empathy, and compassion. Resulting from a meeting between the Dalai Lama, leading Western scholars, and a group of Tibetan monks, this volume includes excerpts from these dialogues as well as engaging chapters exploring points of difference and overlap between the two perspectives.
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Classical Tibetan meditation texts are used to specify the most important variables in meditation that can be subjected to empirical test. There are 3 kinds of variables: (a) nonspecific variables, common to all meditation systems; (b) specific variables, limited to spec & types of meditation practice; and (c) timedependent variables, changing over the course of meditation practice. The latter, time-dependent variables, comprise the majority of meditation variables. One set of time-dependent variables for classical concentrative meditation is explored. Using the semantic-field method of translating, technical terms most important in each level of the entire phenomenology of concentrative meditation are discussed. These terms are translated into hypotheses, which are worded in terms of traditional constructs from cognitive psychology. Supporting empirical research is presented and suggestions for further research are made. Certain similarities are noted between the Yogic texts and the constructivist theories of perception, information-processing, and affect. The overall direction of change in concentrative meditation follows an invariant sequence of levels of consciousness.