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Biofeedback ÓAssociation for Applied Psychophysiology & Biofeedback
Volume 43, Issue 3, pp. 000–000 www.aapb.org
DOI: 10.5298/1081-5937-43.3.05
SPECIAL ISSUESPECIAL ISSUE
Neurofeedback from the Posterior Cingulate Cortex as
a Mental Mirror for Meditation
Remko van Lutterveld, PhD, and Judson Brewer, MD, PhD
Center for Mindfulness, University of Massachusetts School of Medicine, Shrewsbury, MA
Keywords: meditation, posterior cingulate cortex, PCC, neurofeedback, mindfulness
Meditation
?1 has several beneficial effects. However, learn-
ing how to
?2 meditate is not easy, as there are no clearly
visible outward signs of performance, making it difficult for
teachers to provide feedback. Neurofeedback from the
posterior cingulate cortex (PCC), a brain region that is
associated with both meditation and mind wandering, may
provide a valuable tool to help individuals learn to
meditate.
Introduction
Although meditation has positive psychological, biological
and neurobiological effects (Goyal et al., 2014; H ¨
olzel et al.,
2011; Schutte & Malouff, 2014), learning how to meditate
is not straightforward. Unlike activities like yoga or soccer,
where a teacher can see when a practitioner is performing
correctly or incorrectly, no immediate feedback to medita-
tion students is possible because there are no easily
discernable outward signs of performance. In addition, a
teacher’s feedback may be biased as it is based on students’
verbal descriptions, which are influenced by several factors
such as the ability of a student to describe internal states
and the interpretation by the teacher. A possible solution to
this issue would be to provide real-time neurofeedback, so
individuals have a ‘‘mental mirror’’ that informs them on
the quality of particular mental aspects of their experience
during meditation in real time.
A prime candidate for delivery of this type of neuro-
feedback would be electroencephalography (EEG), which is
relatively cheap and accessible, as well as able to track
changes in brain activity quickly (in the range of
milliseconds). For almost half a century, brain processes
related to meditation have been investigated using this
technique (Cahn & Polich, 2006). However, no definitive
finding with regard to frequency range or lead placement
has yet surfaced. For example, some studies reported a
decrease in alpha power during meditation (Jacobs & Lubar,
1989; Pagano & Warrenburg, 1983), some did not find any
change in alpha activity (Jacobs, Benson, & Friedman,1996),
others reported increases in alpha power in primarily
frontal regions (Takahashi et al., 2005), while still others
reported increases in alpha power in primarily posterior
regions (Dunn, Hartigan, & Mikulas, 1999). In addition to
these inconsistent results, most EEG studies analyzed the
data on the sensory level, which is at best a very coarse
marker of where in the brain activity changes during
meditation.
We and other researchers have taken a different
approach by first determining neural correlates of particular
cognitive states related to meditation. By investigating
those brain regions where activity changes when people
meditate, a theoretical and practical framework can be
developed, guiding the design of an EEG neurofeedback
paradigm that helps people meditate through objective
feedback.
Neural Correlates
Which brain regions change their activity during medita-
tion? The technique currently most frequently used to
localize brain activity is functional magnetic resonance
imaging (fMRI). With fMRI, the demand for oxygen
related to activity in the brain is measured. It is an excellent
technique to pinpoint location of brain activity changes. To
determine brain regions associated with meditation, we
invited 12 experienced meditators and 12 novice meditators
to meditate while their brains were scanned using fMRI.
In order to ensure generalizability of findings beyond
one specific meditation technique, participants performed
three different kinds of meditations within the Theravada
tradition: concentration, loving-kindness, and choiceless
awareness. Data were collapsed across these three catego-
ries. In the first analysis, the fMRI scans were analyzed
using functional connectivity analysis, i.e., looking at how
activity in different brain regions correlated over time. We
found an increased correlation in activity between the
posterior cingulate cortex (PCC) and the dorsal anterior
cingulate cortex (dACC) in the experienced meditators
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compared to the novice meditators (Brewer et al., 2011).
These brain regions are main nodes of the so-called default
mode network (DMN), which is a network of brain regions
that become active when one is wakefully resting and not
focused on the outside world.
In the second analysis, we investigated whether certain
brain regions become more or less active during meditation
compared to wakeful resting. This so-called activation
analysis also implicated DMN areas. During meditation,
the main nodes of the DMN including the PCC became
relatively less active in experienced meditators.
Importantly, these results were corroborated by another
study that observed decreases in activity of the same DMN
regions in meditators from a different tradition (i.e., Zen
meditation) while viewing emotionally evocative pictures
(Taylor et al., 2011). An association between the activation
of the PCC, meditation, and behavior was observed in
another group of Zen meditators by Pagnoni et al. (2012),
who demonstrated that PCC deactivation during meditation
correlated with improved performance on a sustained
attention task. Although these studies all implicated
decreases in PCC activity to be associated with meditation,
they used fairly small groups of up to 12 participants, which
limits their interpretation.
In a study addressing this issue by employing larger
groups, meditation-related brain activity was assessed in 20
experienced meditators from the Theravada tradition and 26
novice meditators. Again, participants performed three
different kinds of meditation: concentration, loving-kind-
ness and choiceless awareness. Data were collapsed across
these three techniques. Compared to resting baseline,
experienced meditators showed reduced activity in two
main nodes of the DMN, including the PCC, during
meditation compared to nonmeditators (Garrison, Zeffiro,
Scheinost, Constable, & Brewer, 2015).
In sum, these results indicate that decreases in activity in
the PCC are associated with meditation, across different
meditation techniques (concentration, loving-kindness and
choiceless awareness), and across different meditation
traditions (Therevada and Zen). Interestingly, as decreases
in PCC activity have been found to be associated with
meditation, increases in activity in this brain region have
been suggested to be associated with ‘‘ getting caught up’’ in
one’s experience, such as getting caught up in mind
wandering, a particular viewpoint, or drug craving (Brewer,
Garrison, & Whitfield-Gabrieli, 2013). As being caught up
can be regarded as the opposite of meditation, the PCC may
provide a potentially suitable target for neurofeedback in
learning how to meditate, as activity in this brain region
may inform when one is in the meditative state, but also
when one is not in the meditative state.
Neurofeedback
The activation studies described above provided valuable
information about changes in brain activity associated with
meditation. However, they did not inform us about the
temporal association between changes in brain activity and
meditation. That is, are deeper subjective experiences of
meditation associated with relatively greater decreases of
PCC activity? And are spontaneous short episodes of mind
wandering during meditation associated with increases in
PCC activity? To test this, Garrison et al. (2013) provided
real-time neurofeedback from the PCC during a focused
attention meditation task. ?3
Participants were told that the
neurofeedback signal was related to a brain region involved
in self-related processing. Furthermore, participants were
told that while they meditated with eyes open, the
neurofeedback graph would show an upward red signal
when they engaged in self-related processes such as mind
wandering, and a downward blue signal if fully in
meditation. Both novice and experienced meditators re-
ported a significant correspondence between PCC activity
and their subjective experiences of meditation and self-
related processes. This shows that the neurofeedback signal
from the PCC is indicative of the depth of meditation, as
well as the presence of mind wandering, supporting the
suitability of neurofeedback from the PCC in assisting both
novice and experienced meditators.
However, could participants control the signal? In the
same study, the same participants were asked to volitionally
decrease PCC activity. Garrison and colleagues (2013)
found that the experienced meditators showed significant
PCC deactivation compared to the novice meditators. This
shows that the experienced meditators were better in
manipulating their PCC activity. However, it could be that
the results were influenced by confounding factors such as
confirmation bias or other expectancy effects. To exclude
this possibility, a series of experiments was performed with
a group of experienced meditators that were not involved in
the previous studies. In this study, participants followed a
blinded discovery protocol: (1) meditation, (2) meditation
with mock PCC feedback, (3) meditation with real-time
PCC feedback, and (4) volitional manipulation of the graph.
This protocol, progressing from the easiest setting of
meditation without any feedback to volitional manipula-
tion, allowed participants to discover how the neurofeed-
back graph corresponded with their subjective experience of
meditation. Importantly, participants were not provided
with any information regarding the brain region from
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which the neurofeedback signal originated, or what process
(i.e., meditation) the neurofeedback signal could represent.
The only information provided to the participants was that
an upward red signal was related to increased activity in a
particular brain region and a downward blue signal in the
graph was related to decreased activity in the same brain
region. Participants reported a significant correspondence
rating between their subjective experience and the neuro-
feedback graph and were able to volitionally deactivate PCC
activity, replicating the earlier results without the bias of
being provided experiential anchors.
Neurophenomenology
The neurofeedback study described above linked PCC
activity to the subjective experience of meditation and
mind wandering. However, the PCC has been associated
with numerous cognitive states other than meditation and
mind wandering (Andrews-Hanna, Reidler, Sepulcre,
Poulin, & Buckner, 2010). As such, the exact processes
that the neurofeedback signal represented were still
unknown. To investigate this issue, Garrison et al. (2013)
analyzed subjective reports of participants in the blinded
discovery protocol described above, in which participants
described their subjective experiences after each meditation.
In a data-driven manner, these reports were coded by
content and grouped into different concepts. For example,
focus on the body was categorized in the concept
‘‘concentration.’’ These concepts were then categorized as
relating to either PCC activation or PCC deactivation. As
expected, it was found that undistracted awareness, such as
concentration, observing sensory experience and, in partic-
ular, an effortless quality of the awareness, corresponded
with PCC deactivation. Furthermore, the experience of
distracted awareness, such as distraction, interpreting, and
‘‘efforting’’ corresponded with PCC activation. These
findings refined our understanding of the PCC correlates
of subjective experience (Brewer & Garrison, 2014).
PCC Neurofeedback with EEG Source
Localization
The neurofeedback studies described above used fMRI to
investigate brain activity related to meditation and mind
wandering. However, although fMRI is an excellent
technique to pinpoint where brain activity changes, it not
the best technique to pinpoint when brain activity changes,
as there is a lag of 4–8 seconds between the peaking of the
actual brain activity and the peaking of the fMRI signal.
This delay makes it more challenging for participants to
interpret the feedback signal. In addition, for fMRI one
needs an MRI machine, and these devices are scarce and
expensive. Because of these reasons, fMRI is not the most
practical solution to provide neurofeedback. In contrast,
EEG allows investigators to track brain activity on a
millisecond time-scale, and it is relatively inexpensive and
portable, making it a potentially suitable method to deliver
neurofeedback to help individuals meditate.
Although EEG does not have the same accuracy to
pinpoint where brain activity changes as fMRI, recent
innovations have made it possible to use source localization
with EEG (van Veen, van Drongelen, Yuchtman, & Suzuki,
1997). Based on the theoretical framework developed with
the fMRI findings described above, logical next steps for the
field would be to use source-localized EEG neurofeedback
from the PCC to guide people to track neural correlates of
subjective states associated with meditation and, eventually,
provide augmentation strategies for typical teacher-based
guidance as individuals learn how to meditate. At our lab,
we are now investigating the usefulness of this paradigm.
Later this year, we will be starting a large randomized
controlled trial to investigate whether source-localized EEG
from the PCC as add-on treatment to a regular 8-week
mindfulness-based stress reduction (MBSR) course, could
help people learn how to meditate. If successful, this will
open a new avenue to help people learn mindfulness and
receive its associated salutary effects (Goyal et al., 2014;
Tang et al., 2007).
References
Andrews-Hanna, J. R., Reidler, J. S., Sepulcre, J., Poulin, R., &
Buckner, R. L. (2010). Functional-anatomic fractionation of the
brain’s default network. Neuron, 65, 550–562
Brewer, J. A., & Garrison, K. A. (2014). The posterior cingulate
cortex as a plausible mechanistic target of meditation: Findings
from neuroimaging. Annals of the New York Academy of
Sciences, 1307, 19–27
Brewer, J. A., Worhunsky, P. D., Gray, J. R., Tang, Y-Y, Weber, J.,
& Kober, H. (2011) Meditation experience is associated with
differences in default mode network activity and connectivity.
Proceedings of the National Academy of Sciences of the United
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