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Measuring the Neural Correlates
of Mindfulness with
Functional Near-Infrared Spectroscopy
Leanne M. Hirshfield
1
*, Dessa Bergen-Cico2, Mark Costa1,
Robert J.K. Jacob3, Sam Hincks3, Matthew Russell3
1Department of Mass Communication, Syracuse University, Syracuse, NY
2Department of Public Health, Syracuse University, Syracuse, NY
3Department of Computer Science, Tufts University, Medford, MA
ABSTRACT
Although a good deal of research has explored clinical intervention studies to
evaluate the efficacy of mindfulness-based interventions, little is known about how
mindfulness manifests itself in the mind and body of practitioners. In particular, real-
time, objective measurements of state mindfulness would be a valuable tool for
researchers to learn more about the mechanisms of mindfulness. In this chapter we
describe prior theoretical definitions of mindfulness, and we demonstrate the utility
of the non-invasive, lightweight, and highly practical functional near-infrared
spectroscopy (fNIRS) device for measuring the neural correlates of state mindfulness
in the brain in ecologically valid environments.
Keywords: functional near-infrared spectroscopy, mindfulness, meditation, brain
measurement, brain-computer interfaces
INTRODUCTION
Rigorous research has demonstrated that meditation and mindfulness-based practices
can improve psychological and physical well-being through cognitive, behavioral,
and neurological changes that reduce the physiological stress response and reshape
the neural landscape. With respect to effects of meditation, an emerging literature
argues that training the ability to regulate attention can improve well-being and
cognitive functions as well as ameliorate psychiatric disorders (Austin, 2010; Harris,
2015; Y.-Y. Tang, Posner, & Rothbart, 2014). Sustained practice has been shown to
reduce schizophrenia (Chien & Thompson, 2014), depression (Teasdale et al., 2000),
anxiety (Paul. Grossman, Niemann, Schmidt, & Walach, 2004), ADHD (Smalley et
al., 2009), posttraumatic stress (Possemato et al., 2016), as well as improve emotional
regulation (Lutz et al., 2014), memory (Davidson et al., 2003; P. Grossman, Ludger,
Schmidt, & Walach, 2004; Zeidan, Johnson, Diamond, David, & Goolkasian, 2010),
self regulation (Y.-Y. Tang et al., 2014), and self awareness (Holzel et al., 2011).
Mindfulness-based stress reduction (MBSR) and mindfulness-meditation have also
been shown to improve a number of chronic health problems such as asthma,
diabetes, high blood pressure, chronic pain and underlying neural dysregaultion that
1
* Corresponding Author. Newhouse MIND Lab, 100 Madison Street, Syracuse NY
13202. lmhirshf@syr.edu
Measuring the Neural Correlates of Mindfulness with Functional Near-Infrared Spectroscopy
affects the hypothalamic pituitary (HPA) axis and cortisol output (Bergen-Cico,
Possemato, & Pigeon, 2014; Davidson et al., 2003; P. Grossman et al., 2004; Loucks,
Britton, Howe, Eaton, & Buka, 2015; Parswani, Mahendra P., & Iyengar, 2013; Paul-
Labrador et al., 2006). Beyond the context of meditation, the cultivation of
mindfulness can be seen more generally as a compassionate open attitude with which
to face situations that involves a focus on the current experience (Bishop et al., 2004).
As outlined above, a good deal of research has explored clinical intervention
studies to evaluate the efficacy of mindfulness-based interventions. We have seen
repeatedly that mindfulness results in a number of positive outcomes. Although much
is known about why we should engage in mindfulness, far less is known about what
mindfulness is and how mindfulness actually works (Bishop et al., 2004; Shapiro,
Carlson, Astin, & Freedman, 2006). Kabat-Zinn operationalized mindfulness as
nonjudgmentally paying attention, on purpose, in the present moment (Kabat-Zinn,
2003). This was further expanded upon by Bishop et al (Bishop et al., 2004) and
Shapiro et al (Shapiro et al., 2006), and ultimately helped to strengthen and inform
many investigations into mindfulness in the past decade. This includes the creation
of several survey instruments that have been used to measure both trait (Baer et al.,
2008; N. Medvedev, Krägeloh, Narayanan, & Siegert, 2017) and state (Galia &
Bernstein, 2013) facets of mindfulness. However, mindfulness is a complex
construct that is difficult to measure (P. Grossman, 2008), and self-report surveys are
known to have validity problems and to lack insight into users’ changing experiences
throughout a task. The idea of stopping a mindfulness session to measure how
mindful you are is counter to the practice of mindfulness. To compensate for the
shortcomings of survey measures, researchers have made progress in objectively
measuring the neural correlates of mindfulness in real-time using EEG and
functional magnetic resonance imaging (fMRI) (Boccia, Piccardi, & Guariglia, 2015;
Lutz et al., 2014; M. Posner & Tang, 2013), but both devices have limitations with
regard to their efficacy for measuring mindfulness. EEG takes a long time to set-up,
has a noisy signal, and has low spatial resolution, making it difficult to measure
specific regions of the brain recruited during mindfulness (Parasuraman & Rizzo,
2008). Although fMRI provides high quality spatial information about the functional
human brain (Dimoka, 2012; Lutz et al., 2014), the device is limited for mindfulness-
based research as participants are constrained inside a loud magnet with their
movements heavily restricted, which can seriously inhibit one’s ability to reach a
mindful state while in the magnet.
In this chapter we build on the definition of mindfulness from Kabat-Zinn
and expanded upon by Bishop (Bishop et al., 2004), and then Shapiro (Shapiro et al.,
2006), and we demonstrate the utility of the non-invasive, lightweight, and highly
practical functional near-infrared spectroscopy (fNIRS) device for measuring the
neural correlates of state mindfulness in the brain. As detailed below, the fNIRS
device provides researchers with a tool to complement and build upon prior fMRI
research, by allowing for similar spatially accurate measurements of blood flow in
the brain in more comfortable, quiet, and less restrictive settings (Chance, Zhuang,
Chu, Alter, & Lipton, 1993; L. Hirshfield et al., 2011; Izzetoglu, Bunce, Onaral,
Pourrezaei, & Chance, 2004; Solovey, Afergan, Peck, Hincks, & Jacob, 2015;
Solovey et al., 2009 ). An example of an 8-channel fNIRS device is shown in Figure
1.
Hirshfield, L., Ber gen-Cico, D., Costa, M., Jacob, R., Hincks, S., Russell, M.
Figure 1: The ISS Oxyplex is an 8-channel non-invasive fNIRS
device.
The rest of this chapter proceeds as follows: First, we describe Bishop and Shapiro’s
prior theoretical model on the construct of mindfulness, and we note elements of the
model that can be measured using cognitive and physiological sensors. We then
describe the fNIRS device in detail and we highlight prior fNIRS research (both our
own and from other researchers in the field) that dovetails with the model of
mindfulness. Lastly, we describe a research agenda for future studies using fNIRS to
directly measure and help empirically validate models of mindfulness.
AN OPERATIONAL MODEL OF MINDFULNESS
In order to define and measure mindfulness, it is important to distinguish between
one’s general tendency to be mindful (a trait) and an individual’s degree of
mindfulness at any particular point in time (a state) (N. Medvedev et al., 2017). Our
focus in this chapter is on measuring the state of mindfulness, but we do touch briefly
upon trait measures of mindfulness as well. We adopt Kabat-Zinn’s definition of
state mindfulness as “paying attention in a particular way: on purpose, in the present
moment, and non-judgmentally (Kabat-Zinn, 2003), which was used to outline the
theoretical definition of mindfulness proposed by Bishop (Bishop et al., 2004), and
added to by Shapiro (Shapiro et al., 2006), whereby experiencing the state of
mindfulness arises through the simultaneous cultivation of the three axioms of i)
intention, ii) attention, and iii) attitude. Intention includes the “on purpose” element
in Kabat-Zinn’s definition. Intention involves having a personal vision, which is
often dynamic and evolving, that includes a self-understanding of why one is
practicing mindfulness. Attention includes “paying attention” to the moment-by-
moment experience of mindfulness, and it is an essential element of mindfulness.
Attitude refers to the personal qualities that one brings to the process of attention, as
one can approach attention with a cold, critical quality, or with an affectionate,
compassionate, and curious quality (Shapiro et al., 2006). Figure 2 provides our
adaptation of Shapiro’s model of mindfulness.
Shapiro calls the result of the mindfulness process ‘reperceiving’, which is
a gradual shift in perspective that results from mindfulness. Reperceiving helps to
foster the positive outcomes produced by mindfulness and it involves self-regulation,
values clarification, cognitive and emotional flexibility, and exposure (Shapiro et al.,
2006). Self-regulation refers to one’s ability to self-regulate their emotional and
behavioral states. Related to self-regulation, cognitive and emotional flexibility
Measuring the Neural Correlates of Mindfulness with Functional Near-Infrared Spectroscopy
refers to ones’ ability to have flexibility when responding to one’s environment,
rather than following more rigid patterns of reactivity, where the environment has
more control over the individual than the individual has over his or her environment.
Values clarification refers to one’s ability to re-evaluate what one values, rather than
allowing one’s culture and society to define those values for us. Exposure refers to
one’s ability over time to experience even very strong (often negative) emotions with
greater objectivity and less reactivity (Shapiro et al., 2006). For example, repeated
exposure to an event that produces high levels of anxiety can result in less feelings
of anxiety and less relying on avoidance behaviors when encountering environmental
scenarios that traditionally cause significant emotional reactions.
We have also added two moderating factors onto Shapiro’s model of
mindfulness, which involve individual trait characteristics and environmental
characteristics, as both are likely to affect mindfulness (N. Medvedev et al., 2017).
A person’s ability to be mindful could be greatly affected by environmental
characteristics (i.e., environments that are loud and uncomfortable could inhibit
one’s ability to be mindful). Also, trait characteristics such as age, gender, and one’s
general ability to be mindful are individual characteristics that may play a role one’s
experience of state mindfulness. Figure 2 provides our adaptation of Shapiro’s
model of mindfulness with these additional trait and environmental considerations
added.
Figure 2: A model of state mindfulness (Shapiro et al., 2006), with
environmental and individual characteristics added.
THE UTILITY OF FNIRS FOR MEASURING
MINDFULNESS
Now that we have a theoretical model in which to ground meditation and mindfulness
research, we describe the aspects of the model that are likely to be measurable with
non-invasive fNIRS technology. In this section we first provide an overview of the
fNIRS device, and then we outline the neural and physiological correlates of
mindfulness that are measurable with fNIRS.
Recent advancements in biotechnology have resulted in development of the
lightweight, portable, non-invasive fNIRS brain measurement device, which is well
positioned to measure the neural correlates of mindfulness without interfering with
the process of meditation via clunky and uncomfortable sensors. We posit that fNIRS
Hirshfield, L., Ber gen-Cico, D., Costa, M., Jacob, R., Hincks, S., Russell, M.
can be used to empirically validate theoretical models of mindfulness and to enable
the real-time measurement of the construct. The fNIRS is a relatively new device that
can provide spatially accurate brain activity information like the fMRI (about 1cm
lower than that achieved by fMRI (A. Medvedev, 2013) (Gratton & Fabiani, 2009)),
but it can do so in ecologically valid experimental environments. The fNIRS holds
great potential for non-invasive brain measurement in naturalistic settings due to its
practical nature, ease of set-up, robustness to motion artifacts, and high spatial
resolution (Chance et al., 1993; L. Hirshfield et al., 2011; Izzetoglu et al., 2004). The
basis of fNIRS is the use of near-infrared light, which can penetrate through scalp
and skull to reach the cortex. Optical fibers are placed on the surface of the head for
illumination while detection fibers measure light which reflects back (Figure 3).
Figure 3: Light in the near infrared range is pulsed into the brain cortex and the
reflected light is determined by means of optical detectors.
Particularly, concentration changes in oxy- and deoxy- hemoglobin can be
distinguished (Chance et al., 1993). The fNIRS has higher spatial resolution than
EEG, making it possible to localize specific functional brain regions of activation, as
could be done with the constrictive fMRI device (Parasuraman & Rizzo, 2008).
However, a significant limitation of both fNIRS and EEG is that- unlike fMRI- they
are unable to measure deep brain structures like the amygdala, which is particularly
important in measurement of raw emotions. Fortunately, the outer region of the brain
is also filled with rich information relating to attention and emotion regulation. Since
the fNIRS was only recently introduced (in the mid-1990s) there has been a dramatic
increase in fNIRS research for brain measurement in recent years with the number
of fNIRS-related publications doubling every 3.5 years over the past twenty years
(Boas, Elwell, Ferrari, & Taga, 2014). For a review of the history of fNIRS, see
(Ferrari M & V., 2012 ) and (Boas et al., 2014). This large increase in research has
been accompanied by new advancements in newer fNIRS technology. For example,
research on the Fast Optical Signal (FOS) has found that it is possible for frequency-
domain fNIRS devices to measure the miniscule changes in membrane potentials
caused by neural activity, which could provide fNIRS with the fast temporal
resolution of EEG (Gratton & Fabiani, 2009), which measures the electrical
potentials caused by neurons firing in the brain with millisecond level precision.
Additionally, several companies have recently introduced wireless fNIRS devices,
making the device even better suited for use in ecologically valid settings (Ferrari M
& V., 2012 ). As the fNIRS research community continues to improve the device and
its accompanying analysis techniques, we expect the highly practical tool to become
increasingly useful in research labs and beyond for measuring neural correlates of
mindfulness.
State Mindfulness: Measuring Mindfulness with fNIRS
Measuring the Neural Correlates of Mindfulness with Functional Near-Infrared Spectroscopy
The goal of this chapter is to demonstrate the utility of fNIRS for objectively
measuring the state of mindfulness in real-time. As depicted in Figure 2, state
mindfulness is moderated by individual differences (i.e., level of expertise in
mindfulness, gender, attitudinal disposition) and environmental characteristics (i.e.,
quiet versus noisy environment, surrounded by others or alone). Once an individual
is experiencing mindfulness, the three axioms of attention, attitude, and intention are
all intertwined, contributing to one’s changing psychological state during
mindfulness (For example, states such as curiosity, empathy, joy, love, and self-
perception are among the complex states found throughout mindfulness research).
These psychological states are likely to be very complex, but we can gain a better
understanding by breaking them down into their neural correlates. The neuroscience
literature has linked many cognitive processes to specific brain areas, which are
called neural correlates (Camerer, Lowenstein, & Prelec, 2004; Crockett, Clark,
Tabibnia, Lieberman, & Robbins, 2008; Dimoka, 2012). While it is often assumed
that there is a simple one-to-one mapping between processes and brain areas, in
reality it is more complex with a many-to-many mapping between brain activations
and human processes (Poldrack, 2006). Thus, a complex psychological construct
would typically map onto and activate multiple brain regions.
In this section we look at these intertwined elements of attention, attitude,
and intention and we describe research that has been conducted using fNIRS to
measure neural correlates of these three elements.
Attitude, Intent and fNIRS:
The attitude that one brings to mindfulness and the intention that one has for a given
mindfulness session are interwoven and overlapping constructs. As Shapiro noted,
intention involves having a personal vision, which is often dynamic and evolving,
that includes a self-understanding of why one is practicing mindfulness. Obviously,
one’s attitude during mindfulness can not be fully disentangled from his or her
intentions during that session. In this section we describe fNIRS studies that have
measured various elements of intent and attitudes as it relates to the practice of
mindfulness.
The term ‘attitude’ is used by Shapiro to refer to the personal qualities that
one brings to the process of attention during mindfulness, as one can approach
attention with a cold, critical quality, or with an affectionate, compassionate and
curious quality. This usage is in line with the large amount of prior research on the
construct of attitudes, which has played a large role in social psychology for decades
(Crano & Prislin, 2008). There are various psychological factors involved in attitude
formation, and there are overlapping theoretical models that been proposed to define
‘attitude’. Most models agree that both cognition and affect are two central
components of attitudes (Crano & Prislin, 2008). The cognitive basis for an attitude
has been defined as beliefs, judgments and thoughts one has while the affective basis
for an attitude involves emotions (McGuire, 1969) and these two components interact
to directly affect one’s information processing in different ways. fNIRS has been
successfully used to measure people’s affect using a range of experimental
paradigms (Bandara, Velipisalar, Bratt, & Hirshfield, 2017; Doi H, Nishitani S, &
K., 2013). In particular, recent work in our lab demonstrated the capability of
classifying and distinguishing between affective states on the valence and arousal
dimensions using a 52-channel fNIRS device. Our results showed that the
dorsolateral prefrontal cortex (DLPFC) was recruited during changes in emotional
valence and arousal. This is in line with prior research on the brain, which has found
the DLPFC to be activated during emotion regulation (Bandara et al., 2017). This
can be particularly useful in mindfulness training for individuals who aim to self-
Hirshfield, L., Ber gen-Cico, D., Costa, M., Jacob, R., Hincks, S., Russell, M.
regulate negative emotions such as stress or fear. In another fNIRS experiment we
found DLPFC activation accompanied by activation in Broca’s region while
participants experienced highly stressful stimuli (L. M. Hirshfield et al., 2014).
Activation in Broca’s area has been tied to highly stressful and fearful scenarios such
as those experienced by individuals with post-traumatic stress disorder (Hull, 2002).
We posit that measuring the DLPFC and Broca’s area during mindfulness sessions
can shed light on the emotional component of one’s experience of mindfulness.
When we consider both the attitude and intention that one brings to a
mindfulness session, we open the door to measuring a range of psychological states
that one may experience during mindfulness. These states could involve elements of
curiosity, self-reflection, or feelings of love and empathy, to name a few. For
example, Leung and colleagues found that expert meditators that engaged in the
loving-kindness Theravada tradition had more gray matter volume in their right
angular gyrus than novice meditators. Since the right angular gyrus is related to
cognitive empathy, the authors suggest that the loving-kindness meditation caused
this increase in gray matter (Leung et al., 2013). See Appendix B in Dimoka
(Dimoka, 2012) for a review of fMRI studies looking at the neural correlates for
several of these complex psychological states. Many of these complex psychological
states will engage brain regions in participants’ Theory of Mind (ToM) brain regions.
ToM is a research paradigm concerned with understanding how individuals attribute
beliefs, desires, and intentions to themselves and to others. The brain regions most
typically implicated in ToM reasoning include regions in the medial prefrontal cortex
(MPFC), anterior cingulate cortex (ACC), and the bilateral temporoparietal junction
(TPJ) (Mahya, Mosesa, & Pfeifera, 2014). fNIRS has been used to measure ToM
brain regions (Bowman, 2015; L. Hirshfield, Bobko, & Barelka, 2017).
Attention and fNIRS: Attention is a complex construct, involved in how we attend
to and actively process specific information in our environment in the presence of
external stimuli. Goal-driven attention is referred to as top-down or endogenous
attention, whereas stimulus-driven attention is referred to as bottom-up or exogenous
attention, being driven by external events in the environment (Jonides, 1981; M. I.
Posner, Snyder, & Davidson, 1980). Allocating attention over short time periods can
be referred to as phasic orienting, while maintaining attention over longer time
periods is referred to as sustained attention, or vigilance (MacLean et al., 2009). One
initial goal of mindfulness training is to increase one’s ability to control his or her
attention on the present (increased endogenous attention), and studies have shown
that mindfulness does indeed increase sustained focused attention on the present
moment (Jha, Krompinger, & Baime, 2007; Y. Tang, Holzel, & Posner, 2015). With
increased expertise, mindfulness has been shown to increase one’s ability to
maximize both endogenous and exogenous attention. For example, a novice
meditator may focus sustained endogenous attention on his or her breath, whereas an
expert meditator is able to focus attention more broadly, with his or her attention
receptive to the whole field of awareness, remaining in an “open state so that it can
be directed to currently experienced sensations, thoughts, emotions, and
memories”(Jha et al., 2007). The Task-Positive Network (TPN) is a network of brain
regions found to be active during endogenous attention-demanding tasks, and the
default mode network (DMN) is a network of interconnected regions shown to be
active when a person is not focused on the outside world and the brain is at wakeful
rest. The DMN is shown to be active when one’s mind is wandering, which very
often involves thinking about oneself or others, which is associated with a large body
of literature involved in social cognition, often referred to as Theory of Mind (ToM)
(Schilbach et al., 2006). Activation of the TPN inhibits the DMN and vice versa.
Measuring the Neural Correlates of Mindfulness with Functional Near-Infrared Spectroscopy
During TPN states we have increased conscious attention towards the
external environment through our five senses, towards our internal bodily states, and
to the willful execution of physical and mental action. However, the type of stimulus
will impact the relationship between TPN and DMN activation and in disease states
like post-traumatic stress (PTSD) there is often dysregulation in the coupling
between TPN and DMN activation. Mindfulness-based practices will also change the
relationship between TPN and DMN activation; and should regulate the coupling
between the two for people with post-traumatic stress (PTSD) following sustained
and regular practice. It is also noteworthy that during mindfulness-based practices
there are changes in the activation of TPN and DMN, For example during
mindfulness-based meditation the TPN should be activated while the DMN would
be deactivated; however, during the body scan there may be fluctuation between the
TPN and DMN (R. Lee, 2015; Lin et al., 2017).
The fNIRS device holds great potential for the measurement of the attention
states described above. For example, the use of fNIRS to measure activation of the
TPN, particularly the LPFC which is responsible for attentional- direction, decision-
making, working memory and cognitive control, should provide objective measure
of neural changes form mindfulness-based meditation and contemplative practices.
Mindfulness-based practice improves working memory, attentional direction,
emotional regulation, and executive function (EF). The TPN is engaged when we are
in the present moment. Therefore, LPFC activity should increase in response to
mindfulness practices. fNIRS can evaluate the association between the PFC
activation and EF (Moriguchi Y & K., 2013) as well as emotion processing (Doi H
et al., 2013). These are viable measures of cognitive processes for working memory
and attention (Snyder, 2013) which are often impacted by stress, trauma, and
depression. Cognitive and emotional responses measured using functional near
infrared spectroscopy (fNIRS) and galvanic skin response (GSR) sensors found
fNIRS and GSR can be used to measure changes in cognitive and emotional
responses associated with EF and working memory and conflict monitoring. In one
prior experiment, we used the 8-channel ISS Oxyplex (Figure 2) to measure the level
of workload on a myriad of executive functions while participants worked with
simplistic tasks such as the stroop task and nback tasks from the cognitive
psychology literature along with more complex tasks that involved searching the
internet for specific trivia content and working with a driving simulator (L. Hirshfield
et al., 2011). Research has found that dysphoric attention was associated to
hypoactivation of the DLPFC, and that dysphoric elaboration was associated to
hyperactivation of the medial PFC, and the measurement of activity in these regions
using fNIRS could be used to explore cognitive processes that might represent
changes in depression and depressive relapse. Furthermore, although much of the
DMN is located deep within the brain where the fNIRS cannot measure, cortical
regions of the ToM network have been measured with fNIRS (Durantin, Dehais, &
Delorme, 2015), and in scenarios when the mind-wandering is in the context of social
cognition (thinking of oneself and others) the fNIRS is well positioned to measure
ToM brain regions such as the temporoparietal junction and the medial prefrontal
cortex (Bowman, 2015).
PROPOSING A 3-DIMENSIONAL APPROACH TO
MEASURE MINDFULNESS WITH FNIRS
In (Hincks et al., 2017), we argued for the importance of describing psychological
states at a systems neuroscience level in the context of building brain-computer
interface (BCI) applications that depend on live measurements of brain activation.
Hirshfield, L., Ber gen-Cico, D., Costa, M., Jacob, R., Hincks, S., Russell, M.
Unless there is a strong coupling between the psychological state under investigation
and the underlying activity of the brain as portrayed by the physiological sensor, then
system adaptations will behave randomly, and our brain measurement is unlikely to
be effective. The purpose of this section is to understand aspects of mindfulness in
terms of three dimensions, and give a theory for how to detect mindfulness on the
basis of physiological sensors (with a focus on fNIRS in particular). The three
dimensions are:
1. Entropy (Random vs Stable)
2. Attentional Direction(Top-down vs Bottom-up)
3. Predominant network (TPN vs DMN)
We believe these three dimensions fulfill the following criteria for effective user
dimensionalization in the context of designing brain-computer interfaces. For a state
to be detectable on the basis of physiological sensors, the two poles of the dimension
(e.g. random vs. stable) should differ as much as possible in terms of global energy
consumption. Alternatively, the differential energy consumption between the two
states must be as spatially distributed as possible so that spatially well resolved brain
monitors can interrogate the state on the basis of probe location. Finally, for the state
to be useful in BCI, the dimension should suggest information about the user which
a User Interface designer can understand and map onto system adaptations.
The proposed dimensionalization depends on a Bayesian conceptualization
of mind and brain (Friston, 2010). The Bayesian Brain Hypothesis postulates that a
fundamental goal of the brain is to efficiently predict and suppress external sensory
signals using existing internal representations, and to continuously update these
models to minimize future error (Friston, 2010). The brain can be regarded as a
hierarchical prediction and error correction machine (Clark, 2013), in which data
flows bidirectionally, with prediction flowing from the top-down and the residual
difference between the prediction and data (error), modifying the internal
bookkeeping used to make those prediction from the bottom-up. Computation in the
brain (and energy consumption) should therefore be maximized when input from the
environment has enough predictable data to warrant interest from some top-down
network, but offers enough new information so that those networks reorganize
themselves into machinery that better understands reality in the future. When the
input is novel relative to the brain and interesting, more prediction error flows up the
Bayesian hierarchy, thus stimulating a cascade of data to flow up the information
processing hierarchy (Carhart-Harris et al., 2014).
This condition of engaged surprise is known as entropy in cognitive
neuroscience, and we believe it is the first and most obvious attribute to describe any
cognitive state, especially in the context of physiological sensors. Extreme entropy
states include the continuous experience of being an infant (since infants lack the
explanatory tools to suppress external stimuli), schizophrenic psychosis (which may
be seen as impoverished top-down reality testing), psychedelic states (which interact
with neuroreceptors in hierarchically central filters in the default mode network) and
creativity (where existing models undergo reorganization by original material)
(Carhart-Harris et al., 2014). As scientists whose empirical tools are generally ill-
equipped to discuss the basic phenomenon of introspectable experience, we let the
reader evaluate on their own terms the possible relationship between entropy, its
realization on neural substrate as increased and random computation, and the
phenomenological richness of conscious life.
The criteria listed above for effective dimensionalization suggests that
Entropy (Random vs. Stable) is plausibly detectable because it is a barometer for the
amount of computation and energy consumption in the brain. With the same criteria,
Measuring the Neural Correlates of Mindfulness with Functional Near-Infrared Spectroscopy
the Predominant Network (TPN vs. DMN) dimension could be detected in practice
in the event that these networks are spatially distributed; indeed, most regions in the
brain appear to have a bias in one direction or the other (Glasser, Coalson TS, &
Robinson EC, 2016). It is worth noting how these two dimensions relate to each
other. The brain’s capacity to model and react to some exogenous signal likely
determines whether the brain will enter a TPN state. The entropic framework thus
predicts that when the informational bandwidth established between the brain and
environment is low, the user will retreat into a more endogenous (DMN) mode of
being.
In (Hincks et al., 2017), we found support for the hypothesis that both fNIRS
and EEG measure user entropy and the predominant network of the brain. In that
study, the classification accuracy of a machine learning algorithm calibrated to detect
differences between task-positive and resting states on the basis of fNIRS and EEG
data decayed significantly in the second session of the experiment as compared to
the first session, whereas the same measure remained consistent when the machine
learning algorithm classified differences between two conditions of the task. This
finding supports the hypothesis that fNIRS and EEG are highly sensitive to the
amount of entropy in the user’s brain since the task (which amounts to new inputs to
the user and novel demands for output) produces a mental state that is differentiable
from the resting state in the first session, but when the user’s expectations and
capabilities are better calibrated in the second session of the experiment, the user’s
state ceases to be as differentiable from the resting state, which, as described above,
is the default state visited by the brain when the informational bandwidth between
task and user is too low.
In this neural architecture, a third dimension (attentional direction) may
describe the relative distribution of the networks modulating the signal from the top-
down and the networks pushing a signal from the bottom-up. A mixture of top-down
and bottom-up processing is likely required for the signal to absorb human attention.
But it may nonetheless be possible to meaningfully separate states that are more
bottom-up from those that are top-down. This third dimension - whether the state of
the user’s brain is set from the top-down using neural resources that (depending on
how free will is conceived) generate or are generated by user agency versus the state
being determined by a bottom-up source from the environment or a spontaneous
endogenous process outside of user control - is closely related to the concept of
mindfulness. In fact, one might recursively define mindfulness as top-down
processing that only occurs in brains that have properly encoded some concept of
mindfulness.
Cognitive neuroscience in general is difficult because it relies on the
capacity to experimentally induce and track the states that a given subject is visiting.
Dimensions like entropy and predominant network can be probabilistically induced
by manipulating the information burden of the environment. But it seems harder to
manipulate the extent to which a subject is willfully exerting top-down mindfulness
processes without simultaneously manipulating their degree of entropy and
predominant network. For this reason, we recommend considering experimental
methodologies which may seem pseudoscientific to conventionally trained scientists
in order to parse subject mindfulness. Specifically, the subject under investigation
could contribute information to the experiment regarding their moment-to-moment
experience which informs how concurrent brain imaging data should be labeled.
In (Hincks, Afergan, & Jacob, 2016), we explored an alternative approach
to interpreting ongoing fNIRS data. In that study, the author performed a total of
fourteen 4-backs and 0-backs. In an n-back, participants repeated the number heard
n iterations ago. The author reports that his solution to solving the 4-back entailed
sub-vocally rehearsing a 4-item mental buffer, continuously shuffling the order of
Hirshfield, L., Ber gen-Cico, D., Costa, M., Jacob, R., Hincks, S., Russell, M.
elements as a new item is heard. The author had practiced n-backs before, and had
set n to be so difficult that he would not be able to solve it unless his attention was
exclusively executing the described strategy. In the three-dimensional approach
described in this section, his mental state was therefore low entropy (since he had
practiced the task), task-positive (as it required instantiating an input-output loop
with the environment), and top-down (since the strategy required willful control of
mental state). In the session reported in the paper, the author reports successfully
implementing the mental strategy in all but one case when mind wandering prevented
task focus. His introspected state therefore shifted to a high entropy, bottom-up DMN
mode. As is evident from the figure in the paper, the fNIRS data pertaining to 4-
backs and 0-backs are clearly different in all but the trial in which the author reported
distraction.
FUTURE WORK
In this chapter we built on the definition of mindfulness from Kabat-Zinn and
expanded upon by Bishop (Bishop et al., 2004), and then Shapiro et al (2006), and
we demonstrated the utility of the non-invasive, lightweight, and highly practical
functional near-infrared spectroscopy (fNIRS) device for measuring the neural
correlates of state mindfulness in the brain. Future research is needed to determine
whether engagement in mindfulness based practices experience is associated with
a greater shift towards activation in the task-positive neural network (TPN) and
thus away from the default mode network (DMN). People with higher levels of
trait mindfulness should have significantly more TPN activation than people who
with low levels of trait mindfulness. Future research should evaluate the
convergence of fNIRS measure of the TPN’s prefrontal cortex and executive
function engagement in conjunction with self report cognitive measures for
executive function and attention. Using fNIRS to measure elements of
mindfulness in real-time also opens the door to adaptive systems, whereby a
mindfulness-based training program may adapt its feedback in real-time based on
the individual’s current psychophysiological measures of attitude, intention, or
attention. Real time measurement places more technical constraints on the
problem. The constraint is typically not in computer performance, but rather in
needing to make a decision on a single trial or even during a single trial using
streaming data before all the data has been received. We have paired fNIRS data
with machine learning techniques for single trial classification of mental states
ranging from mental workload (L. Hirshfield et al., 2011), to emotional states
(Bandara et al., 2017), to types of multitasking (Solovey et al., 2012), and the
prospect of transitioning this machine learning work to the mindfulness domain
is promising.
Building on the premise of real-time systems, one area that we consider to
be particularly promising is using virtual reality (VR) to create highly controlled,
immersive environments to facilitate mindfulness practice. Virtual Reality is a
“presence inducing media” (Riva G, Botella C, & Baños R, 2015). There are
multiple related definitions of presence (K. Lee, 2004), but the simplest way to think
about the concept is a sense of being there, where “there” is the virtual environment.
That idealized virtual environment can be used to both isolate novice practitioners
from overly stimulating environments (Kosunen I, Salminen M, Järvelä S, Ruonala
A, & Ravaja N, 2016) as well as create an idealized environment that elicits its own
positive emotional experience. The idea of a special place for contemplative practices
is not new; often people have a room or vacation spot or type of environment they
Measuring the Neural Correlates of Mindfulness with Functional Near-Infrared Spectroscopy
seek out for a sense of peace; natural environments often induce a sense of peace
even before the meditation begins (Lymeus, Lundgren, & Hartig, 2017).
When we couple well-designed, replicable, and easily accessible
environments with a physiological measurement system like an EEG or fNIRS we
can create adaptive environments that guide users towards some idealized state.
Whether we measure the emotional or cognitive state, we can create environments
that change according to the condition of the user. These adaptations can be aural or
visual; either way, they are intended to help guide the focus of the practitioner back
to the practice of meditation. These environments are very helpful for novices,
basically serving as a set of training wheels. However, research has found that
experienced practitioners benefit as well (Kosunen I et al., 2016). In the future, we
expect to see more brain-computer interface systems designed to facilitate certain
aspects of meditation, helping more people initiate and sustain engagement in the
practice of mindfulness meditation.
Hirshfield, L., Ber gen-Cico, D., Costa, M., Jacob, R., Hincks, S., Russell, M.
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