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Enhancing Attention by Synchronizing Respiration and Fingertip Pressure: A Pilot Study Using Functional Near-Infrared Spectroscopy


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Sustained attention is a fundamental ability ensuring effective cognitive processing and can be enhanced by meditation practice. However, keeping a focused meditative state is challenging for novices because involuntary mind-wandering frequently occurs during their practice. Inspired by the potential of force-control tasks in invoking internal somatic attention, we proposed a haptics-assisted meditation (HAM) to help reduce mind-wandering and enhance attention. During HAM, participants were instructed to maintain awareness on the respiration and meanwhile adjust bimanual fingertip pressures to keep synchronized with the respiration. This paradigm required somatosensory attention as a physiological foundation, aiming to help novices meditate starting with the body and gradually gain essential meditation skills. A cross-sectional study on 12 novices indicated that the participants reported less mind-wandering during HAM compared with the classic breath-counting meditation (BCM). In a further longitudinal study, the experimental group with 10 novices showed significantly improved performance in several attentional tests after 5 days’ practice of HAM. They tended to show more significant improvements in a few tests than did the control group performing the 5-day BCM practice. To investigate the brain activities related to HAM, we applied functional near-infrared spectroscopy (fNIRS) to record cerebral hemodynamic responses from the prefrontal and sensorimotor cortices when performing HAM, and we assessed the changes in cerebral activation and functional connectivity (FC) after the 5-day HAM practice. The prefrontal and sensorimotor regions demonstrated a uniform activation when performing HAM, and there was a significant increase in the right prefrontal activation after the practice. We also observed significant changes in the FC between the brain regions related to the attention networks. These behavioral and neural findings together provided preliminary evidence for the effectiveness of HAM on attention enhancement in the early stage of meditation learning.
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fnins-13-01209 November 8, 2019 Time: 17:51 # 1
published: 12 November 2019
doi: 10.3389/fnins.2019.01209
Edited by:
Xianchun Li,
East China Normal University, China
Reviewed by:
Jack Adam Noah,
Yale University, United States
Dan Zhang,
Tsinghua University, China
Dang-Xiao Wang
Specialty section:
This article was submitted to
Brain Imaging Methods,
a section of the journal
Frontiers in Neuroscience
Received: 12 April 2019
Accepted: 25 October 2019
Published: 12 November 2019
Zheng Y-L, Wang D-X, Zhang Y-R
and Tang Y-Y (2019) Enhancing
Attention by Synchronizing
Respiration and Fingertip Pressure:
A Pilot Study Using Functional
Near-Infrared Spectroscopy.
Front. Neurosci. 13:1209.
doi: 10.3389/fnins.2019.01209
Enhancing Attention by
Synchronizing Respiration and
Fingertip Pressure: A Pilot Study
Using Functional Near-Infrared
Yi-Lei Zheng1,2, Dang-Xiao Wang1,2,3*, Yu-Ru Zhang1,3 and Yi-Yuan Tang4
1State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China, 2Peng Cheng
Laboratory, Shenzhen, China, 3Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing,
China, 4Department of Psychological Sciences, Texas Tech University, Lubbock, TX, United States
Sustained attention is a fundamental ability ensuring effective cognitive processing and
can be enhanced by meditation practice. However, keeping a focused meditative state
is challenging for novices because involuntary mind-wandering frequently occurs during
their practice. Inspired by the potential of force-control tasks in invoking internal somatic
attention, we proposed a haptics-assisted meditation (HAM) to help reduce mind-
wandering and enhance attention. During HAM, participants were instructed to maintain
awareness on the respiration and meanwhile adjust bimanual fingertip pressures to
keep synchronized with the respiration. This paradigm required somatosensory attention
as a physiological foundation, aiming to help novices meditate starting with the body
and gradually gain essential meditation skills. A cross-sectional study on 12 novices
indicated that the participants reported less mind-wandering during HAM compared
with the classic breath-counting meditation (BCM). In a further longitudinal study, the
experimental group with 10 novices showed significantly improved performance in
several attentional tests after 5 days’ practice of HAM. They tended to show more
significant improvements in a few tests than did the control group performing the 5-day
BCM practice. To investigate the brain activities related to HAM, we applied functional
near-infrared spectroscopy (fNIRS) to record cerebral hemodynamic responses from
the prefrontal and sensorimotor cortices when performing HAM, and we assessed the
changes in cerebral activation and functional connectivity (FC) after the 5-day HAM
practice. The prefrontal and sensorimotor regions demonstrated a uniform activation
when performing HAM, and there was a significant increase in the right prefrontal
activation after the practice. We also observed significant changes in the FC between
the brain regions related to the attention networks. These behavioral and neural findings
together provided preliminary evidence for the effectiveness of HAM on attention
enhancement in the early stage of meditation learning.
Keywords: sustained attention, cognitive improvement, meditation, functional near-infrared spectroscopy,
respiration, fingertip pressure
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Zheng et al. Effectiveness of HAM on Attention Enhancement
Maintaining attention on a current undertaking and resisting
the occurrence of unrelated thoughts are vital aspects of
human behaviors. Considerable literature has indicated the
broad benefits of intensive meditation practice on attentional
performance, mainly including better control over the
distribution of limited attentional resources (Van Leeuwen
et al., 2009;Slagter et al., 2011), improvement in executive
control (Tang et al., 2007;Van den Hurk et al., 2018), and
reduced mind-wandering (Mrazek et al., 2013). These findings
provide evidence for the claim that meditation benefits the
cognitive process, particularly the features of conflict monitoring
and attention control (Lutz et al., 2008;Malinowski, 2013;Posner
et al., 2015;Tang et al., 2015).
Meditation encompasses a family of practices, and currently,
focused attention meditation (FAM) is one of the most widely
used and investigated types (Tang and Posner, 2013;Lippelt
et al., 2014). FAM is usually the starting point for novice
meditators. During this meditation, practitioners are required
to maintain attention on specific content such as breathing or
a candle flame (Vago and Silbersweig, 2012). In the breath-
counting meditation (BCM), for example, practitioners focus
on the breathing constantly and count each respiratory cycle
silently from 1 to 10 and then start again from 1. Meanwhile,
they are instructed to let intervening thoughts pass by and keep
concentration on respiration (Levinson et al., 2014;Milz et al.,
2014). However, BCM is not as simple as it sounds, because
involuntary mind-wandering frequently happens, especially for
novices. They often report that they stopped counting, counted
over 10, or had to reflect intensively to figure out what
was the next count during the BCM practice. Given that
mind-wandering is a naturally occurring phenomenon, novices
are generally unaware of mind-wandering at the moment it
occurs. Consequently, keeping concentration continuously on
the breathing is so hard that the sufficient meditative time is
short in the early stage of meditation. Besides, practitioners’
attentional states are evaluated solely by themselves, and this
limits the ability of meditation coaches to provide a timely
reminder when the practitioners’ attention shifts away from
their breathing.
Human’s haptic channel is a unique sensory modality in
terms of combining sensory perception and motor output.
Compared with typical attentional tasks using visual stimuli,
haptic-related tasks could invoke internal somatic attention
and help participants control the focused spotlight of attention
spontaneously. There existed but a few attempts that investigated
the effects of somatosensory signals on attention and the
possibility of enhancing attention through haptic tasks (Bauer
et al., 2006;Dvorkin et al., 2013;Sanchez-Lopez et al., 2014).
Our previous work indicated that a 5-day training of force-
control tasks with pure haptic feedback could promote short-
term attention (Wang et al., 2014). Driven by the beneficial
effects of meditation on various attention functions, in this
paper, we proposed a haptics-assisted meditation (HAM) by
incorporating force control and meditation practice. We further
hypothesized that this novel paradigm (i) could help novices
reduce mind-wandering during the meditation practice and (ii)
has the potential in attention enhancement.
To validate the first hypothesis, 12 participants without
meditation experience conducted HAM and the traditional BCM
for the same duration. Self-reports for the mind-wandering
during the two meditation practices were recorded. Furthermore,
another 20 novices engaged in a longitudinal experiment
aiming to address the second hypothesis. In the experiment,
an experimental group and a control group performed a 5-day
training of HAM and BCM, respectively. A battery of tests was
conducted to assess attentional performance of the two groups
before and after the training. In addition, brain hemodynamic
responses of the experimental group were measured by functional
near-infrared spectroscopy (fNIRS) to explore the possible effects
of the short-term HAM training on brain attentional functions.
Functional near-infrared spectroscopy allows for non-
invasive monitoring of cerebral concentration changes in
oxygenated (oxy-Hb) and deoxygenated (deoxy-Hb) hemoglobin
without severe restrictions on body movements and physical
environment (Boas et al., 2014). These features make fNIRS
well suited for monitoring the neural activity during HAM
where natural movements of hands and metallic sensors are
necessary. fNIRS has been used to investigate neural activities
related to meditation in recent years and indicated a consistent
enhancement in the activation of the prefrontal area after
meditation practice. The increased activation in the prefrontal
cortex (PFC) tended to correlate with better performance in
the cognitive tests (Endo et al., 2013;Ji et al., 2019). Meditation
practitioners and non-practitioners showed a significant
difference in the prefrontal activation during meditation. An
increase in oxy-Hb and a decrease in deoxy-Hb were observed
in practitioners as compared with non-practitioners (Cheng
et al., 2010). Similar hemodynamic differences were also found in
the auditory cortex of meditation experts and controls (Gundel
et al., 2018). Their findings showed that experts had an increased
activation in auditory areas during meditation and also had a
more widespread activation pattern beyond the meditative task
itself, such as resting state, indicating possible long-term effects
on the brain. A longitudinal study further demonstrated that a
6-week practice significantly improved the oxy-Hb at all parts of
the PFC in meditation novices as compared with controls who
conducted relaxation training for the same duration (Gagrani
et al., 2018). For participants with some meditation experience, a
20-min meditation practice increased cerebral oxygenation and
enhanced their performance during the Stroop color–word task
(Deepeshwar et al., 2015). These findings uniformly suggested an
enhanced activation related to meditation practice and provided
evidences for the feasibility of fNIRS in meditation research.
With this background, the present study employed fNIRS to
assess the effects of the proposed HAM on cerebral hemodynamic
responses. Given that the PFC is a crucial structure for cognitive
functions, and the sensorimotor cortex (SMC) mainly involves
in body sensation and force control, we recorded fNIRS signals
from these regions and investigated how HAM affected the
cerebral activation in this study. Besides, functional connectivity
(FC) is defined as a robust temporal dependency among
neural activation patterns, which could indicate coordinated
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Zheng et al. Effectiveness of HAM on Attention Enhancement
activities between distinct cerebral regions and provide valuable
information about the brain communication network (Friston,
2011). For this, we also investigated the FC between the bilateral
PFC and SMC to better understand how these regions interacted
with each other during HAM.
Twelve students without meditation experience from Beihang
University participated in experiment 1. They were randomly
assigned to group A and B. Group A carried out BCM on the
first day and HAM at the same time of the second day, whereas
group B carried out HAM on the first day and BCM on the
second day. Another 20 naive students from Beihang University
who were not involved in experiment 1 were recruited for
experiment 2. They were assigned to an experimental or control
group randomly with the restriction that gender composition was
matched between groups. Experimental participants attended
HAM for 5 days for 60 min/day. The controlled participants
were given the same number and length of training sessions but
attended BCM. There was no significant difference in gender,
age, and years of education across groups for both experiments
(independent-samples t-tests on age and years of education;
see Table 1).
All participants in the experiments were right-handed and
meditation naive without any previous meditation experience
and have normal or corrected-to-normal visual acuity. All
of them were confirmed having no history of mental health
problems and not receiving any psychopharmacological
treatments. Written informed consent was obtained from all
participants before the study, and this study was approved by
the State Key Laboratory of Virtual Reality Technology and
Systems of China.
Figure 1 illustrates the experiment procedures. Prior to
formal experiments, participants were provided with written
instructions and sufficient practice to make sure they were
thoroughly familiar with the requirements. In experiment 1,
participants performed the HAM and BCM practices separately
at the same time for two consecutive days. Each practice lasted
75 min, split into four sessions of 15 min with 5-min breaks
between two adjacent sessions. For each participant, the number
of reported mind-wandering episodes during the HAM and BCM
practices was recorded separately.
In experiment 2, the experimental and control groups
conducted a 5-day training of HAM and BCM, respectively.
Both groups performed a 60-min training each day, including
three sessions of 20 min with a 5-min break following each
session. The two groups were given a battery of tests before
training and immediately after the final training session.
These tests were arranged in two consecutive days to avoid
fatigue. Specifically, the Blink, Stroop, Reading, and Gabor
tasks were performed on the first day, and the attention
network task (ANT), sustained attention to response task
(SART), and Math task were on the second day. There was
a 10-min break after each test. Besides, fNIRS measurements
were conducted when the experimental participants performed
ANT and SART in pretests and posttests. fNIRS signals
during the last training session were also recorded from all
experimental participants.
Haptics-Assisted Meditation
Figure 2 shows the procedure of HAM during which participants
sat in a chair in front of a fixed force-sensor plate and tied a
respiration sensor to their abdomen. They also wore an eyeshade
for eliminating visual disturbance and a pair of head-mounted
earmuffs for eliminating noise. Because deep breathing practice
was proved to be beneficial for a variety of cognitive functions
and widely used in meditation, participants were instructed
to keep a constant, slow, and deep diaphragmatic breathing
during the task (Brown and Gerbarg, 2009). More specific
requirements for breathing were described in our previous study
(Zheng et al., 2019).
Each 20-min session of HAM consisted of four 5-min blocks.
During block 1, participants conducted the breath-counting task
that is the same as what the control group did. During block 2,
“positive mapping,” participants pressed two force sensors using
their left and right index fingertips synchronously, increasing the
fingertip forces when inhaling and decreasing the forces when
exhaling. They were asked to try their best to control the change
of fingertip forces to match with their respiratory rhythm. Block
3, “negative mapping,” was similar to block 2 with the difference
that participants were required to decrease the forces when
inhaling and increase the forces when exhaling. During block 4,
“hybrid mapping,” participants performed positive mapping or
negative mapping every 10 respiratory cycles and then switched
to another mapping mode. The participants, therefore, needed to
TABLE 1 | Demographics of participants.
Experiment 1 Experiment 2
Group A Group B p-values HAM group BCM group p-values
Gender 2 F, 4 M 2 F, 4 M \ 4 F, 6 M 4 F, 6 M \
Mean age (SD) 24.2 (1.7) 23.7 (1.6) 0.62 23.1 (1.7) 23.1 (2.3) 1.00
Mean years of education (SD) 17.7 (1.8) 17.2 (0.8) 0.54 16.5 (1.3) 17.2 (1.9) 0.35
F denotes females, and M denotes males. Years of education indicate the number of academic years a person completed in a formal program provided by elementary
and secondary schools, universities, colleges, or other formal postsecondary institutions.
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FIGURE 1 | Experimental procedures. (A) Experiment 1. Twelve participants carried out HAM and BCM on two consecutive days. (B) Experiment 2. Twenty
participants were allocated randomly to the experimental or control group who conducted 5-day training of HAM or BCM, respectively. Both groups conducted a
battery of tests before and after the training. For the experimental group, fNIRS signals during ANT and SART before and after the training, and during the last
training session were recorded. HAM, haptics-assisted meditation; BCM, breath-counting meditation; fNIRS, functional near-infrared spectroscopy; ANT, attention
network task; SART, sustained attention to response task.
FIGURE 2 | Haptics-assisted meditation. Participants conducted the breath-counting task in block 1. In block 2, “positive mapping” mode, participants were
required to increase the fingertip pressure when inhaling and decrease when exhaling. Block 3, “negative mapping” mode, is similar to block 2 with the difference
that participants decreased the fingertip pressure when inhaling and increased when exhaling. In block 4, “hybrid mapping” mode, participants performed the
positive mapping or negative mapping mode every 10 respiratory cycles.
perform the breath counting simultaneously to ensure the correct
switch between the two mapping modes. At the end of each block,
participants would hear a short music to remind them to enter
into the next block.
It should be noted that a simplified HAM was performed
in experiment 1. While pressing with bilateral index fingers in
experiment 2, participants were only required to press one force
sensor using their right index fingers in the simplified task and
thus were allowed to report mind-wandering using left hands
conveniently. During experiment 1, participants performed two
sessions of positive mapping and two sessions of negative
mapping in the sequence of positive, negative, positive, and
negative. They were instructed to immediately report whenever
they became aware of mind-wandering by clicking a digital
counter (GSQ100, Paulone Inc., China) in their left hands
and then refocused on the task. Mind-wandering here was
defined as “forgetting to press the sensor when breathing in or
out,” “performing the wrong mapping mode,” or “being aware
that thoughts had drifted away from breathing or pressing.”
These guidelines were instructed to all participants before the
experiment. Besides, participants were not required to report
mind-wandering in experiment 2.
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Zheng et al. Effectiveness of HAM on Attention Enhancement
Data of respiration and fingertip force were recorded at
a sample rate of 200 Hz. A respiration sensor belt (used
in combination with NeXus-10 Mark II, Mindmedia Inc.,
Netherlands) was tied to the participant’s abdomen to record
respiratory signals. Two force sensors (FSG15N1A, Honeywell
Inc., United States) were mounted on a fixed plate to measure
the fingertip pressures exerted by two index fingers.
Breath-Counting Meditation
During BCM, participants sat in a spacious and silent room and
held a digital counter in their left hands. They were instructed
to sit on a chair comfortably, close eyes, and silently count
each respiratory cycle from 1 to 10 and then repeat from
1. The requirements for the breathing here were consistent
with the requirements in HAM. The participants were also
asked to concentrate entirely on the task and press the counter
immediately to report the mind-wandering whenever they
became aware of losing track of the task. Specifically, mind-
wandering here refers to “stopping counting,” “counting over 10,
or “had to reflect intensively to figure out what was the next
count.” Following the report, participants should bring the focus
back to the breathing and count again from 1.
When performing BCM, participants followed a compact disc
with body posture adjustment and relaxed practice accompanied
by a music background. Instructions on the compact disc were
from standard guided audio recordings. A coach observed facial
and body cues to identify those who were struggling with the
practice and gave proper instructions immediately after the
training session. At the end of each session, the counters’ number
of all participants was recorded and reset to zero. The number
of subjective mind-wandering reports was recorded using digital
counters (GSQ100, Paulone Inc., China) from all participants.
Test Tasks
Five computerized attentional tasks (ANT, SART, Blink, Gabor,
and Stroop) and two real-world tasks (Reading and Math) were
used to assess the changes of attentional performance from
pretests to posttests. We increased the difficulty of these tasks
because all participants in this study were healthy graduate
students. Thus, some parameters of these tasks such as the
number of trials, duration of stimulus, and time limit for response
were modified based on the parameters used in previous studies.
Attention network task has been used to assess the effects of
meditation on three basic aspects of attention including alerting,
orienting, and executive control (Fan et al., 2002;Tang et al.,
2007). In each trial of the task, first, there was a fixation cross at
the center of the screen for 500 ms. Then, a cue was presented for
100 ms to inform the target would occur right now and to provide
information on where the target would be. Following the cue,
there was a fixation period for 300 ms, and then the target and
flankers appeared simultaneously. Participants needed to respond
to the direction (left or right) of the arrow target by clicking the
corresponding buttons on the keyboard as fast and accurately
as possible. The arrow target was surrounded by flankers that
pointed in either the same or opposite direction. The target and
flankers were presented until the participant responded, but for
no longer than 1,000 ms. After participants made a response, the
target and flankers disappeared immediately, and there would
be a short resting period for 1,500 ms. Following this period,
participants received the next trial until completing 108 trials.
The whole task lasted for about 6 min. The average accuracy
and response time (RT) were also calculated to assess the overall
performance of the task. Subtraction of RTs was used to obtain
scores for the performance in alerting, orienting, and executive
control, as described by Fan et al. (2002).
Sustained attention to response task is a go/no-go continuous
performance task that requires participants to respond to the go
stimuli as quickly as possible, and no response to no-go stimulus
appears infrequently (Helton et al., 2009;Levinson et al., 2014).
In this task, participants received 240 trials corresponding to 240
single digits (24 each from 0 to 9). In each trial, a random digit
was presented for 500 ms at the center of the computer screen
and followed by a fixation cross for 500 ms. Participants were
instructed to respond to each digit via a key press as fast as
possible when the digit appeared but to withhold their response
when that digit was a 3. The digit would disappear, and the next
digit would appear no matter the participant responded or not.
The duration from digit to digit lasted for 1,000 ms, and thus,
the whole task took 4 min. The RT and accuracy were calculated
for all trials. The accuracy indicated the whole proportion of
trials successfully responding to go stimuli and not responding
to no-go stimuli.
Attentional blink task (Blink) is a classic test to assess the level
of the attentional-blink deficit (Van Leeuwen et al., 2009). The
deficit indicates that when two targets (T1 and T2) embedded in a
rapid stream of events are presented in close temporal proximity,
the second target is often not seen and is believed to result
from competition between the two targets for limited attentional
resources. A recent study has found a smaller attentional blink
and reduced brain-resource allocation to the first target after
3 months of meditation training (Slagter et al., 2007). In this
study, we also used this task to assess the possible effects of
HAM on the distribution of attentional resources. Each trial
started with a 1,780-ms fixation cross, followed by a rapid serial
stream of 15 or 19 letters. The letters were from the alphabet
except B, I, O, Q, and S, because these letters are likely to be
confused with numbers. Each letter was presented randomly
for 25 ms, followed by a 34-ms blank. For each trial, one or
two letters were replaced by a random number between 2 and
9. When only one letter was replaced by a number, a second
letter was replaced with a blank screen (T2-absent trial). There
could be short (336 ms) or long (672 ms) interval between the
first (T1, a number) and second target (T2, a number or the
blank screen). Participants were asked to report T1 and T2 by
typing the numbers in order on a keyboard after the stream
ended. If participants were sure no T2 was presented, they entered
zero for this number, whereas they had to guess T2 if they
thought T2 had been presented but could not ensure which
number it was. They were informed to report as accurately as
possible, and there was no time limit for response. The next
trial began after the second response. Participants performed
102 trials of the task, consisting of 48 short-interval/T2-present
trials, 18 long-interval/T2-present trials, 18 short-interval/T2-
absent trials, and 18 long-interval/T2-absent trials with a random
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order. The proportion of the correct trials (T1 and T2 were both
reported correctly) was calculated as task accuracy.
Stroop words task has been used to investigate the effects
of meditation practice on executive functions and has revealed
significant differences between meditators and non-meditators
(Moore and Malinowski, 2009;Moore et al., 2012). Stimuli in the
task were the four words RED, BLUE, GREEN, and YELLOW,
presented in the same color as the written word in congruent
trials (e.g., RED presented in red) or in different colors in
incongruent trials (e.g., RED presented in blue). Participants were
instructed to indicate the color of each word while ignoring
the semantic meaning of the word. These words in the task
were presented in Chinese characters, given that all participants
engaging in this study are Chinese. In each trial, a fixation
cross was first presented at the center of the screen for 500 ms,
followed by the color word that was presented for 1,000 ms.
Participants were required to enter their responses by pressing
the corresponding keys as fast and accurately as possible when
the word appeared. Four keys on the keyboard were color coded
and used to provide comfort for the participant when responding,
including the keys “a” (red, left middle finger), “.” (yellow, left
index finger), “x” (green, right index finger), and “ ’ ” (blue,
right middle finger). The whole test consisted of 96 trials that
included 64 incongruent trials and 32 congruent trials. These
trials were presented in a random order. Each incongruent
stimulus appeared in each of the three other colors with equal
frequency. After the test, the accuracy for all trials and the RT for
incongruent trials were calculated for each participant.
The Gabor patch discrimination task (Gabor) in the present
study was modified from a task in the study of Wang et al. (2014).
The task consisted of 100 trials. In each trial, four pictures with
different angles of stripes were presented on the center of the
computer screen for 300 ms, and each picture was followed by a
300-ms blank (see Figure 3). The fifth picture was then displayed
for 300 ms after a 3-s blank. Participants were provided 3 s to
report whether the final picture had appeared in the previous
four pictures via key pressing. The next trial began after the
response. There could be 18 pictures with a minimum angle
difference of 10in this task, as shown in Figure 3B. The five
pictures presented in each trial were assigned randomly from the
18 pictures with a restriction that the minimum angle difference
across the five pictures was not less than 30. For example, the
pictures of 0, 20, and 30would not appear in the following
four pictures if the picture of 10had been presented first. This
restriction aimed to ensure clear discrimination for the presented
pictures for all participants in each trial. Accuracy of the task
was calculated to assess the performance of sustained attention
and working memory.
The five tests mentioned above were all presented on
a 21-in. cathode-ray tube (CRT) monitor (100-Hz refresh
rate, 1,024 ×768 resolution) and were developed in the C
programing environment. The fixation cross and stimulus in
these tasks always appeared centrally on the screen. Before each
test, participants performed 20 practice trials at least to get
familiar with the test.
We also conducted two non-computerized but more real-
world tasks (Math computation and Reading task as follows)
to assess the possible transfer effect of HAM on attention
enhancement to activities in real life. A similar math computation
task has been used in our previous study and revealed significant
enhancement of performance after a 5-day training of force-
control tasks (Wang et al., 2014). In this test, 32 lines of Arabic
numbers (the font of Times New Roman, size 16 pt) were printed
on a piece of A4 paper, and each line included 30 numbers with
a range of 0–9. Participants were required to mark the pairs
of two adjacent numbers whose sum equaled to 10 as many as
possible within the time limit of 8 min. They were informed to
mark the numbers line by line with a pen and were not allowed
to go back to previous lines to change their answers. The score
of the test was computed manually by subtracting the number
of wrongly marked pairs from the number of correctly marked
pairs. In addition, the sequences of digits were modified for the
posttest, but the total number of pairs summing to 10 was the
same as the pretest.
In the Reading task, four passages were selected from the
texts with the same difficulty level in the Nelson-Denny Reading
Comprehension Tests (Feng et al., 2013). These passages were all
related to history with similar numbers of words (approximately
450 words) and were modified to develop an easier version
considering all participants are not native speakers of English.
The modification was done by translating and marking difficult
or low-frequency English words with Chinese. The four passages
were then assigned to two sets. The two sets were tested as the
pretest and posttest with a balanced order across participants. Ten
single-choice comprehension questions and 20 misspelled words
were designed for each set. The questions were closely related to
the content of the passages. The misspelled word was done by
exchanging the order of two adjacent letters, but the meaning
of the word was not easy to be misunderstood; for example,
“young” was written as “yuong.” Participants were required to
answer the questions, mark the misspelled words they found with
circles, and mark “/” in the position they were reading whenever
they became aware of mind-wandering. Participants would be
stopped when the test duration exceeded 40 min. The task score
was computed as S1 +S2 S3 S4 with the presence of
four components: number of right answers of the single-choice
comprehension questions (S1), number of the misspelled words
that were found accurately (S2), number of right-spelled words
but marked as misspelled (S3), and number of mind-wandering
self-reports (S4).
Temporal Synchronization
Figure 4 shows the example data of respiration and fingertip
forces during the HAM practice. Descent of the black curve
in the figure represents inhalation, and the curve’s ascent
represents exhalation. The rise of the red and blue curves
represents the increase of the left and right index fingertip forces,
respectively. Similarly, the going down of the curves represents
the decrease of the forces.
The temporal synchronization (1TS) was defined as the time
difference between the moment that respiration signals and
fingertip forces begin to change. 1TS for the left and right hands
was computed separately, considering that there could exist a
difference between the dominant and non-dominant hands. Four
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FIGURE 3 | Gabor patch discrimination task. (A) Procedure of one trial. (B) Eighteen pictures that could be presented in the task.
1TS values can be computed in one respiratory cycle as listed
in Table 2. With 1TS in block 2 taken as an example, the four
1TS values include the time difference (i) between the moment of
beginning to inhale and beginning to increase the fingertip forces,
(ii) between the moment of inhalation end and the moment of
the forces reaching the maximum, (iii) between the moment of
beginning to exhale and beginning to decrease the forces, and
(iv) between the moment of exhalation end and the moment
of the forces reaching the minimum. The B1, B2, B3, and B4
denote the data points of beginning to inhale, inhalation ending,
beginning to exhale, and exhalation ending, respectively. The L1,
L2, L3, and L4 (R1, R2, R3, and R4) denote the data points of
left (right) index fingertip force beginning to increase, reaching
a maximum, beginning to decrease, and reaching a minimum,
respectively. Considering that the respiratory and fingertip force
signals reached a plateau near the peak and valley, calculating
those start and end data points of the plateaus was the key to the
algorithm for computing 1TS.
FIGURE 4 | Example data of respiration signals and forces from bilateral index
fingers in (A) positive mapping and (B) negative mapping.
We developed the algorithm in MATLAB (2018b,
MathWorks, Inc., United States) and stated the steps as
follows. First, a bandpass filter (0.5–100 Hz) and moving
average filtering were used to remove apparent noise. Second,
we segmented the filtered dataset into segments of each
respiratory cycle by setting the minimum interval among
two adjacent peaks and the minimum amplitude of peaks.
So the moments of the maximum points and minimum
points in each data segment can be obtained. Last, for each
data segment, the least square method was used to obtain a
straight line that passed the maximum or minimum point
to fit the plateaus of peak or valley. The intersections of this
straight line with the filtered signals were then calculated.
Generally, more than one intersection would be obtained. The
intersection with the minimum abscissa (i.e., the corresponding
moment) was the start point of the peak or valley plateau,
and the intersection with the maximum abscissa was the
end of the plateau.
Near-Infrared Spectroscopy
An fNIRS system (NirScan, DanYang HuiChuang Medical
Equipment Inc., China) with three wavelengths (740, 808, and
850 nm) of near-infrared light was used to record cerebral
hemodynamic changes with a sampling rate of 17 Hz. Twenty-
two emitters and 24 detector probes were plugged into a soft
cap with an inter-optode distance of 30 mm. The measurement
channels were located midway between each emitter–detector
pair. A total of 64 channels were symmetrically distributed in
bilateral hemispheres and were positioned over the PFC and SMC
in accordance with the international 10–20 electrode placement
system (Jasper, 1958). The emitter R marked in Figure 5
was placed in Cz.
We measured fNIRS signals from all participants when they
performed ANT and SART in the pretest and posttest and
when they conducted the last session of HAM practice. We also
recorded 5 min of fNIRS signals during resting state before each
fNIRS measurement. For the fNIRS data, variables of interest
were relative changes in the concentration of oxy-Hb and deoxy-
Hb compared with that at baseline. The baseline was defined
as 1 min at rest before the ANT and SART, as well as before
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TABLE 2 | Definition of temporal synchronization in positive and negative mapping.
Mode Definition of 1TS (time difference between Calculation of 1TS
the moment of event A and B)
Event A Event B Left hand Right hand
Positive mapping Starting to inhale Starting to increase force |T(B1) T(L1)| |T(B1) T(R1)|
Inhalation end Force reaching the maximum |T(B2) T(L2)| |T(B2) T(R2)|
Starting to exhale Starting to decrease force |T(B3) T(L3)| |T(B3) T(R3)|
Exhalation end Force reaching the minimum |T(B4) T(L4)| |T(B4) T(R4)|
Negative mapping Starting to exhale Starting to increase force |T(B1) T(L1)| |T(B1) T(R1)|
Exhalation end Force reaching the maximum |T(B2) T(L2)| |T(B2) T(R2)|
Starting to inhale Starting to decrease force |T(B3) T(L3)| |T(B3) T(R3)|
Inhalation end Force reaching the minimum |T(B4) T(L4)| |T(B4) T(R4)|
each block of HAM. We should note that there was no 1-
min rest before each block for those HAM sessions without
fNIRS measurements. During the resting state and baseline
periods, participants were instructed just to rest and relax without
a specific focus.
Data Analysis and Statistics
All analyses for behavioral and fNIRS data were completed by
MATLAB (2016b, MathWorks, Inc., United States). For the self-
reports in experiment 1, we conducted paired t-tests to compare
the number of mind-wandering reported in HAM and BCM.
We also used paired t-tests to compare the performance in
the pretests and posttests within the HAM or BCM group in
experiment 2. In order to assess the difference in the performance
changes between the two groups, we first calculated the change
of performance by subtracting the performance in pretests from
the performance in posttests for each group, and then we
compared the performance changes between the two groups by
independent-samples t-tests.
For fNIRS data, the preprocessing was conducted using the
open-source software HOMER21implemented in MATLAB. All
recorded signals were first processed by the principal component
analysis approach to remove signal contamination, such as
systematic artifacts (Tak and Ye, 2014). The motion artifact
segments greater than five standard deviations from the mean
were detected and replaced with their spline interpolation on
the basis of neighboring signals. The corrected signals were then
bandpass filtered between 0.01 and 0.2 Hz to remove baseline
drift and physiological noises. The filtered optical data were
finally converted into hemoglobin signals in accordance with the
modified Beer–Lambert law (Cope and Delpy, 1988).
Data of oxy-Hb and deoxy-Hb were individually averaged
according to the task condition (ANT_pre, ANT_post,
SART_pre, SART_post, and Block1, 2, 3, and 4 in HAM) for
each channel. The first 60 s of all these conditions was discarded
from the analysis to give participants enough time to reach a
steady state. The analysis for the HAM period was performed by
the block, resulting in a 240-s average for each participant and
each block. The fNIRS analyses for the conditions of ANT_pre,
ANT_post, SART_pre, and SART_post were performed by task
excluding wrong trials (i.e., only including all correct trials).
Then, the values of oxy-Hb and deoxy-Hb were converted into
z-scores by a Fisher z-statistics before statistical tests.
We further analyzed FC using the Pearson correlation
coefficient and a seed-based approach (Tak and Ye, 2014). The
hemoglobin data during the task period (discarding the first
60 s as described above) were used to compute the Pearson
correlation coefficients between the seed channel and all the
other channels, generating a 64 ×64 r-value correlation matrix
for each participant and each condition. These individual-level
correlation coefficients were then converted to z-values via
Fisher’s r-to-ztransformation to improve normality, resulting in
eight 64 ×64 z-value FC matrices corresponding to the eight
conditions for each participant. To perform a group-averaged
FC comparison, we first averaged these individual z-value FC
matrices within each condition and then converted the group-
level z-value matrix into an r-value matrix via Fisher’s z-to-r
transformation for each condition. For reasons of conciseness,
only the FC analysis of oxy-Hb data was reported in this study,
although the deoxy-Hb data had gone through the same analysis.
Statistical analyses were applied to the values of oxy-Hb,
deoxy-Hb, and the FC matrices separately in a channel-wise
manner. Paired t-tests were conducted for each channel to test
whether there were significant differences before and after the
HAM training (p<0.05). We also performed paired t-tests for
each channel to identify the channels showing significant changes
in each block of HAM as compared with those during the resting
state. Here, the method of false discovery rate (FDR) was applied
to each channel to control the family-wise error rate (Singh and
Dan, 2006). Significance values reported in this study were those
that survived the FDR.
Behavioral Results
Performance Changes After Haptics-Assisted
Meditation and Breath-Counting Meditation Training
Figure 6A presents the number of mind-wandering self-reports
in experiment 1. The paired t-test indicated significantly less
mind-wandering (p= 0.003) in HAM (1–7, mean 3.27 ±2.195)
than in BCM (3–28, mean 13.09 ±7.981). One participant
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FIGURE 5 | fNIRS measurement. (A) Photograph of fNIRS measurement on a participant. (B) Schematic of imaging pad. Twenty-two emitters (red dots) and 24
detectors (blue dots) were symmetrically placed on the bilateral hemispheres and constituted 64 measurement channels (gray lines), allowing for the prefrontal and
sensorimotor cortices to be measured. The emitter R was placed in Cz of the international 10–20 system. (C) Anatomical position of each measurement channel.
was excluded from the analysis because he reported drowsiness
during the experiment.
For experiment 2, we compared the attentional performance
in the pretests and posttests using paired t-tests. After the HAM
training, significant improvements were observed in the accuracy
of the Gabor test (p= 0.034), and in the scores of Reading
(p= 0.031) and Math tests (p= 0.002), as shown in Table 3.
The performance of the Stroop task also improved significantly
(p= 0.003), indicated by a decrease in the mean RT. Besides,
the accuracy in Blink, ANT, SART, and Stroop also increased,
although these improvements were statistically insignificant. The
mean RT of SART and the orienting time, executive time,
and mean RT of ANT were slightly shorter than those in the
pretests. For the BCM group, we found significantly improved
performance in Math (higher scores, p= 0.006) and Stroop
(shorter RT, p= 0.028). However, the mean RT of SART became
longer (p= 0.006) after the training.
Moreover, we computed the changes of performance
for HAM and BCM group separately and then compared
the difference in the changes between the two groups by
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FIGURE 6 | (A) Number of mind-wandering self-reports during HAM (red
bars) and BCM (black bars). (B) Comparison of performance changes
between the HAM (red bars) and BCM groups (black bars). The vertical axis
indicates the task performance. The horizontal axis indicates the percentage
of performance change after training [i.e., (Post – Pre)/Pre ×100%]. The
positive values marked in gray represent that increased performance is
expected for the task; the negative values marked in gray represent that
decreased performance is expected. and ∗∗ denote significant changes of
performance within the HAM or BCM group; ,p<0.05; ∗∗,p<0.01.
denotes a significant difference in the performance changes between the
HAM and BCM groups (p<0.05). HAM, haptics-assisted meditation; BCM,
breath-counting meditation.
independent-samples t-tests (see Figure 6B). A significant
difference was observed only in the mean RT of SART
(2.62% decrease in HAM group and 6.60% increase in
BCM group, p= 0.006). Nevertheless, for HAM group, we
found (i) greater improvements in the accuracy of SART
(p= 0.334), Stroop (p= 0.722), and Gabor (p= 0.332); (ii)
greater improvements in the scores of Math (p= 0.180) and
Reading (p= 0.553); and (iii) greater decrease in the mean
RT of Stroop (p= 0.414) and ANT (p= 0.104) than those for
BCM group. These findings suggested that the HAM group
showed greater improvements in several tests than did the
BCM group, although none of these improvements were
statistically significant.
Behavioral Performance During Haptics-Assisted
Meditation Training
We used the temporal synchronization (1TS) to assess the
attentional performance during HAM. The decrease in 1TS
indicates improved performance. Figure 7A shows the mean
1TS values in blocks 2, 3, and 4 from all sessions. Because
all participants engaged in this study were right-handed, and
there could exist a difference in 1TS between the dominant and
non-dominant hands, we computed the 1TS for the left and
right hands separately. The mean 1TS derived from the left
and right hands both remained around 400 ms, and there was
no significant difference between the bilateral hands, although
the 1TS performed by the left hand seemed to be more stable.
Besides, we did not find a significant difference of 1TS across the
three blocks, but the variability of 1TS across subjects in block
2 (positive mapping) tended to be smaller than that in the other
two blocks (negative and hybrid mapping).
Furthermore, participants evaluated their attentional level
using a score of 1–10, with 10 being the highest level of attention
at the end of each HAM session. Figures 7B,C present the
average attentional scores for the whole session and each block,
respectively. The subjective attentional scores for the session
and the block both showed a trend of increase as the practice
TABLE 3 | Performance of HAM group in pretoup separately and then compasests and posttests.
Tasks Index Pre (mean ±SD) Post (mean ±SD) Change % t-value p-value
Blink Accuracy 0.86 ±0.67 0.87 ±0.08 1.16 0.515 0.619
ANT Alerting time (ms) 36.89 ±17.32 41.16 ±15.87 11.58 0.895 0.394
Orienting time (ms) 21.87 ±12.91 19.66 ±9.37 10.11 0.35 0.734
Executive time (ms) 64.06 ±19.43 62.05 ±14.10 3.13 0.308 0.765
Response time (ms) 510.58 ±32.94 495.14 ±28.00 3.02 1.814 0.103
Accuracy 0.98 ±0.02 0.99 ±0.01 0.71 1.397 0.196
SART Response time (ms) 308.55 ±39.30 300.45 ±29.24 2.62 1.126 0.289
Accuracy 0.95 ±0.05 0.96 ±0.03 1.48 1.155 0.278
Stroop Response time (ms) 820.12 ±98.24 751.25 ±101.44 8.40 3.927 0.003∗∗
Accuracy 0.97 ±0.02 0.98 ±0.02 0.83 1.000 0.343
Gabor Accuracy 0.65 ±0.06 0.69 ±0.07 5.87 2.493 0.034
Reading Scores 19.30 ±6.20 23.80 ±4.21 23.32 2.558 0.031
Math Scores 162.40 ±27.76 203.90 ±34.13 25.55 4.432 0.002∗∗
Change = (Post Pre)/Pre ×100%. p<0.05. ∗∗ p<0.01. ANT, attention network task; SART, sustained attention to response task.
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FIGURE 7 | Behavioral performance during HAM training. (A) Mean 1TS values of all sessions for block 2 (positive mapping), block 3 (negative mapping), and block
4 (hybrid mapping). The green patterns represent 1TS computed from the right, and the yellow patterns represent 1TS from the left. (B) Mean attentional scores for
the whole session reported by all participants. (C) Mean attentional scores for each block. HAM, haptics-assisted meditation.
sessions increased. The variability of the scores for the whole
session between participants also tended to be smaller over time.
These findings suggested that HAM enhanced sustained attention
ability from subjective judgment.
Neural Results
Hemodynamic Changes After Haptics-Assisted
Meditation Training
Mean concentrations of oxy-Hb and deoxy-Hb when performing
ANT and SART in the posttests compared with the pretests
by paired t-tests with FDR thresholding. Figure 8 presents a
visual depiction of changes in oxy-Hb and deoxy-Hb concerning
various cortical regions. Data from oxy-Hb showed a significant
increase in the post-ANT in several channels over the PFC
(channels 1, 20, and 25; p= 0.021, 0.003, and 0.008, respectively),
as well as in the left SMC (channels 27, 30, and 40; p= 0.009,
0.032, and 0.017, respectively) and the right SMC (channels
52 and 58; p= 0.026 and 0.015, respectively) when compared
with the pre-ANT. For the deoxy-Hb, we observed a significant
decrease in channels 1, 2, and 20 (p= 0.032, 0.039, and
0.040, respectively) over the PFC in the post-ANT. Besides,
there was a significant increase in oxy-Hb over the right PFC
(channel 25, p= 0.031) in the post-SART. A significant decrease
in deoxy-Hb was also observed in this channel (p= 0.022)
when compared with the pre-SART. Additionally, after the
HAM training, we also found an increase in oxy-Hb and a
decrease in deoxy-Hb over the right PFC during the resting
state. However, no significant changes were observed following
thresholding by FDR.
Furthermore, the changes in FC matrices across all channels
after the HAM training are shown in Figure 9A. Each pixel
value in the 64 ×64 correlation matrix corresponds to the
value of the change in the group-average Pearson correlation
coefficients for oxy-Hb. Paired t-tests with the FDR adjustments
indicated that there was a significant increase in FC between
several channels of the medial PFC and the SMC during resting
state. In the posttest, a significantly decreased FC between
the left PFC and the right SMC was observed during ANT
as compared with the pre-ANT. There was also a uniformly
decreased FC between the PFC and SMC in the post-SART. These
channel–pairs that increased or decreased significantly (p<0.05)
after the training are presented in Figure 9B with red or blue
lines, respectively.
Hemodynamic Responses During Haptics-Assisted
Meditation Sessions
We performed paired t-tests with FDR correction for each
channel to identify the channels that changed significantly in each
block of HAM when compared with those at its baseline (resting
state before the block). In block 1, data from oxy-Hb suggested a
trend of increase in most parts of the PFC and SMC. The results
from paired t-tests showed a significant increase in channels 12
and 26 (p= 0.046 and 0.043); however, the significant values did
not survive after the FDR thresholding. The deoxy-Hb data also
demonstrated a trend of decrease in part of the PFC and the
medial SMC, although there was no statistical significance.
Although a slight increase in oxy-Hb and a decrease in deoxy-
Hb were observed in block 1, we found a uniform and widespread
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FIGURE 8 | Changes in the concentration of oxy-Hb and deoxy-Hb after training (post minus pre). t-value maps obtained by paired t-tests on (A) oxy-Hb and (B)
deoxy-Hb. Each number in these maps denotes the numbering of optical channels. The numbers marked in bold denote that the changes in these channels were
significant statistically. The positive t-values in the color bar represent higher concentration in posttests than in pretests, and the negative values represent lower
concentration than in the pretests.
activation pattern when participants performed blocks 2, 3, and
4, indicated by increased oxy-Hb and decreased deoxy-Hb in
most regions of the bilateral PFC and SMC. During block 2,
participants showed a significant increase in oxy-Hb in channel
26 located in the right PFC and a decrease in deoxy-Hb in this
channel. We also observed the decreased deoxy-Hb in the left
PFC (channels 1 and 9). Over the SMC, there was significantly
increased oxy-Hb in channels 30, 40, 41, 53, and 55 and decreased
deoxy-Hb in channels 30, 40, 51, and 53. For block 3, we observed
a similar but more widespread activation in the PFC relative to
block 2, including significantly increased oxy-Hb in channels 9,
12, 25, and 26 and decreased deoxy-Hb in channels 1, 7, 9, and
26. There was also a significant increase in oxy-Hb in the medial
SMC (channels 40, 41, 46, 47, 53, and 54) and the right SMC
(channels 63 and 64), as well as a significant decrease in deoxy-Hb
in channels 27, 51, and 53.
When performing block 4, participants also showed
significantly increased oxy-Hb in the PFC including channels 1,
9, 15, 25, and 26. Although there was also a widespread increase
in oxy-Hb over most regions of SMC, only the channels 53 and
54 showed a statistically significant increase following the FDR
thresholding. The deoxy-Hb data demonstrated a significant
decrease in channels 1 and 9 located in the left PFC, as well as
in channels 30, 51, 53, and 64 located in the SMC. Figure 10
presents the t-value maps of oxy-Hb and deoxy-Hb for each
block, and Table 4 summarizes these statistical results.
We further compared the differences in FC between each
block and its baseline. Figure 11A shows the group-level FC
across all channels for each block, and Figure 11B marks those
channel–pairs that significantly increased or decreased compared
with those at baseline (paired t-tests with FDR thresholding,
p<0.05) by red or blue lines, respectively. When performing
block 1, participants showed a significant decrease in the FC
between the PFC (left PFC in particular) and the right SMC. Block
2 and block 3 demonstrated a similar finding that FC within the
PFC and the SMC became stronger than during resting state,
and only several channels showed weaker coupling between the
PFC and SMC. For block 4, although a few channels within the
PFC and SMC demonstrated enhanced connectivity, FC between
most channels in the PFC and SMC tended to decrease when
performing the task.
The present study proposed and implemented a HAM paradigm
for attention enhancement. A series of behavioral performance
and fNIRS findings suggested an improvement of the proposed
paradigm as compared with a classical BCM paradigm. In
terms of the behavioral performance, participants reported less
mind-wandering in HAM than in BCM. Furthermore, the
experimental group performing the 5-day HAM training showed
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FIGURE 9 | Changes in FC after training (post minus pre). (A) FC matrices across all channels. Each number (1–64) in the x-ory-axis denotes the numbering of
optical channels. The color bar indicates the value of the Pearson correlation coefficient. (B) FC that increased or decreased significantly (p<0.05) after the training,
marked with red or blue lines, respectively. FC, functional connectivity.
FIGURE 10 | t-value maps obtained by paired t-tests on (A) oxy-Hb and (B) deoxy-Hb for each block. The numbers marked in bold denote that these channels
showed significant changes than during resting state.
significant improvements in the performance of Stroop, Gabor,
Reading, and Math tests after the training. Although there
was uniform enhancement in the performance of these tests
observed among the HAM group, the control group just had
significant improvements in the Math and Stroop tests and even
a reduction in the SART performance after the 5-day BCM
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TABLE 4 | Summary of the t-test results of oxy-Hb and deoxy-Hb.
Comparison Variable Brain region Channel number p-values
Block 2 – Resting Oxy-Hb PFC 26 0.021
SMC 30, 40, 41, 53, 55 0.023, 0.015, 0.002, 0.006, 0.013
Deoxy-Hb PFC 1, 9, 26 0.010, 0.008, 0.027
SMC 30, 40, 51, 53 0.013, 0.007, 0.020, 0.033
Block 3 – Resting Oxy-Hb PFC 9, 12, 25, 26 0.028, 0.015, 0.008, 0.012
SMC 40, 41, 46, 47, 53, 54, 63, 64 0.002, 0.017, 0.014, 0.004, 0.003, 0.001, 0.023
Deoxy-Hb PFC 1, 7, 9, 26 0.027, 0.005, 0.007, 0.003
SMC 27, 51, 53 0.003, 0.03, 0.02
Block 4 – Resting Oxy-Hb PFC 1, 9, 15, 25, 26 0.011, 0.015, 0.019, 0.021, 0.017
SMC 53, 54 0.023, 0.013
Deoxy-Hb PFC 1, 9 0.007, 0.007
SMC 30, 51, 53, 64 0.013, 0.023, 0.030, 0.023
PFC, prefrontal cortex; SMC, sensorimotor cortex.
FIGURE 11 | FC for four HAM blocks. (A) FC matrices across all channels. (B) FC that increased or decreased significantly (p<0.05) marked by red or blue lines,
respectively. FC, functional connectivity; HAM, haptics-assisted meditation.
training. The experimental group showed significantly greater
improvement in the SART after 5 days of HAM than did
the controlled BCM group. The HAM group tended to show
greater improvements in the Stroop, Gabor, Math, and Reading
tests than did the BCM group, although these improvements
were not significant statistically. These outcomes after only
5 days of training suggested the potential of HAM for more
considerable improvement in attention performance as the
training sessions increased.
The desired meditative state stresses a balanced state of
relaxation while focusing on attention, and this state is considered
an essential factor in improving the efficacy of exercise on
cognitive function (Walsh and Shapiro, 2006;Macdonald et al.,
2013). The HAM paradigm was designed to help novices
maintain this balanced state in terms of the following aspects.
First, the paradigm integrated several vital components of
meditation, including deep breathing, mental imagery, body
sensations, and mindfulness training. These components have
shown broad positive effects on attention control and emotion
regulation in previous studies (Tang et al., 2015). When
performing HAM, practitioners breathed deeply, slowly, and
stably and needed to focus attention on their fingers to keep
synchronized with respiration. Such a process was expected
to induce a state of restful alertness. Besides, the design
of the two-hand response was also introduced to allow a
comfortable and relaxed posture because most participants had
reported unnatural posture for the one-hand response in the
preliminary experiment.
Second, single focus is considered a critical factor that
facilitates the efficacy of attention training (Lippelt et al., 2014).
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However, this factor might be at risk in the proposed paradigm, as
HAM was a dual task of breath awareness and force control, and
participants might need to switch attention between the two sub-
tasks. Nevertheless, participants in this study scarcely reported
that their attention on the breathing was distracted by the force
control. Instead, they tended to report the breath focusing and the
force control as one integrated task. For example, they reported
in their post-questionnaires that “These two tasks seem to be
one, and it is very natural,” “I imaged an energy flow circulating
between my abdomen and fingertips,” and “I felt my body as
a whole when performing the task.” Therefore, the dual-task
design of HAM did not prevent practitioners from maintaining
the desired state of relaxation and focusing. The improvement in
the performance of attentional tests after the HAM training also
supported that the design did not risk the effectiveness of HAM
on attention enhancement.
Finally, we designed a relatively complicated paradigm
for HAM. Precisely, participants needed to perform four
progressive blocks (i.e., breath counting, positive mapping,
negative mapping, and hybrid mapping) in each session. The
breath-counting block first provided participants a brief relaxed
period to help them get into the desired state gradually. The
following three blocks were similar but had differences in the
mapping mode, which was designed to improve the participant’s
engagement in the practice. Besides, the paradigm of block
4 required to count and retain the number of respiratory
cycles and thus combined meditation practice with working
memory training. Given that working memory training also
benefits attentional functions (Klingberg, 2010;Spencer Smith
and Klingberg, 2015), this combination was expected to amplify
the training effect over the use of only one of these components
for novice meditators.
Many styles of meditation practice rely on thought control,
such as focusing on the breath and paying attention to the
present moment. However, the approach of controlling thoughts
might be not optimal for novices because they have to struggle
to control their thoughts from mind-wandering in the early
stage of meditation. Compared with the traditional meditation
practice stressing thought control, HAM required concentration
on a specific response from the body, while reducing reliance
on the control of thoughts, which may be more accessible for
novices. Besides, compared with BCM, HAM required a higher
cognitive load and thus inhibited participants from spontaneous
and frequent mind-wandering. Considering that a high cognitive
demand might be a potential factor that influences the efficacy of
exercise on cognitive function (Ji et al., 2017, 2019), the higher
cognitive load in HAM suggested a stronger training dose than
pure breath awareness meditation and thus may accelerate the
practitioner’s access to meditative states.
Because the HAM paradigm is suitable for novices and
raises the possibility of amplifying the training effect, we
hypothesized that a short period of training might benefit
the self-regulation of attention networks. Although 5 days of
training was indeed short for novices, we have reasons to expect
that the 5-day HAM practice could lead to improvements in
attention functions. Previous findings indicated that the 5-day
meditation practice with the integrative body–mind training
method induced significantly better attention and control of
stress than in a control group given relaxation training (Tang
et al., 2007). Our previous work also demonstrated that 5 days
of training with a force-control task improved the efficiency
of the executive attention networks (Wang et al., 2014). Raz
et al. (2005) found changed brain processes even after a single
meditation session. Besides, there existed viewpoints that the
amount of time participants spent meditating each day, rather
than the total number of hours of meditative practice over their
lifetime, affects performance on attentional tasks (Chan and
Woollacott, 2007). Taken together, a short (5 days) but intensive
(1 h/day) training was conducted in this study. Nevertheless, we
should note that for a short period of training, the high-quality
practice was necessary for every session, and the experimental
process needed to guarantee that each practice session achieved
a good result. A longer period of practice is still needed in
future research to investigate the long-term effect of HAM on
attention functions.
Our previous study has validated that the synchronization
between the force and respiration signals can be used as an
objective marker of attentional state (Zheng et al., 2019). Thus,
another advantage of the HAM paradigm might lie in the
possibility of real-time monitoring of the attentional state.
The monitoring allows novices to obtain real-time feedback
of their brain state and, therefore, could promote the training
by proper intervention or guidance. In the present study, the
algorithm for computing 1TS had an average error of about
50 ms and indicated an overall accuracy of 87.5% because
participants performed an approximately 400-ms 1TS during
the training. The accuracy met the requirements for assessing
the changes in attentional performance over time in this study.
It should be noted that the current algorithm was executed
offline, and how to improve the accuracy and speed of the
algorithm for online calculation is needed to be investigated in
further research.
Functional near-infrared spectroscopy findings in this study
suggested that an increased brain activation could occur after
the practice, which was consistent with most previous literature.
We found significantly increased oxy-Hb and decreased deoxy-
Hb over the right PFC when performing ANT and SART in the
posttests. There was also an increasing trend of oxy-Hb in the
right PFC during the resting state. These findings support the
assumption that meditation beginners who need to overcome
habitual ways of internally reacting to one’s unrelated thoughts
might show a greater prefrontal activation (Allen et al., 2012).
Achieving the focused meditative state involves the attentional
control effort in the early stage of meditation, thus activating
the prefrontal regions. It should be noted that this study used
the task-level (block-level) design for the fNIRS data analysis.
Future research is advised to investigate event-related responses
within trials of the testing task, as well as during the meditation
practice, which might provide more information regarding brain
attention functions. Neural recordings for the control group are
also necessary to understand the differences between HAM and
traditional meditation types.
Moreover, although the FC analyses were limited in terms of
the small sample size, this study suggested consistent findings
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Zheng et al. Effectiveness of HAM on Attention Enhancement
with previous studies showing that meditation increased certain
synchronous activities within default mode network (DMN)
(Jang et al., 2011). Following the training, we observed a
stronger FC between the medial PFC and SMC at resting state.
Given that the medial PFC and inferior parietal lobule are
considered key regions of the DMN involved in self-referential
processing (Northoff et al., 2006;Sajonz et al., 2010), the stronger
connectivity may be interpreted as increased self-monitoring
and cognitive control over the DMN function (Brewer et al.,
2011). Besides, we observed a reduced FC between most channels
over the PFC and SMC in the post-SART, as well as between
some channels of the PFC and temporal cortex in the post-
ANT. Similar findings were shown in previous fMRI studies.
For example, one cross-sectional study on pain processing
demonstrated decreased connectivity of executive and pain-
related brain regions in meditators (Grant et al., 2011). We
accordingly speculated that the reduced coupling at the tasking
state might be associated with increased attentional control
because participants did not have to pay so much effort for the
stimulus monitoring and response inhibition after the training.
Nevertheless, whether reduced coupling between these brain
regions indicates improved attentional regulation needs further
investigation with a larger number of subjects. Besides, the
current fNIRS system did not provide the accurate neuro-
navigation information corresponding to the brain anatomical
structure; thus, findings regarding more precision regions of
interest were not discussed in this study. Further FC analysis for
more precision brain regions such as the dorsolateral PFC might
provide additional information.
On the basis of these behavioral and fNIRS results,
we surmised that HAM affected brain attentional functions
positively by activating the attention network, SMC, and the
regions related to working memory. The effective activations
were obtained by the specific design of the HAM paradigm that
involved body sensations, muscle control, attention switching
within the internal body, and working memory practice. HAM
incorporated respiratory perception with force control, requiring
somatosensory attention as a physiological foundation and thus
provided a useful way for novices to obtain a series of key
meditation skills, including (i) how to feel directly when mind
has wandered from its sensory focus, (ii) how to control somatic
attention to compete with internally focused rumination, (iii)
how to obtain familiarity with the fluctuations of sensations of
breathing and forces, and (iv) how to control the attentional
spotlight to shift from one to another flexibly. Together, these
enhanced skills might become a crucial early stage of attention
enhancement (Kerr et al., 2013).
This study adds to the literature demonstrating the potential
value of human haptic channels in cognitive training. Preliminary
results demonstrate the improvements in behavioral performance
and brain activation following 5 days of practice, which provides
evidence for the effectiveness of HAM on attention enhancement
in the early stage of meditation learning. On the basis of the
present work, future longitudinal studies with larger subject
populations and controlled neural recordings will be necessary to
validate the conclusions further. Another possible place for future
research is assessing the moment-to-moment attentional state
during the practice by online computing of the synchronization
between breathing and force signals. The online method can be
used to link fNIRS measurements to attentional performance and
possibly develop a real-time neurofeedback paradigm.
The raw data supporting the conclusions of this manuscript will
be made available by the authors, without undue reservation, to
any qualified researcher.
Experiments were approved by the State Key Laboratory of
Virtual Reality Technology and Systems of China. Written
informed consents were obtained from all participants.
Y-LZ and D-XW designed the research plan, organized
the study, and wrote the manuscript. Y-RZ and D-XW
administered the experiments. All authors coordinated the
data analysis, interpreted the data, discussed the results, and
edited the manuscript.
This work was supported by the National Key Research and
Development Program under grant no. 2017YFB1002803 and
the National Natural Science Foundation of China under
grant no. 61572055.
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Conflict of Interest: 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.
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Frontiers in Neuroscience | 18 November 2019 | Volume 13 | Article 1209
... In the second fNIRS study, hemispheric lateralization was reported with an increase in O2Hb in the right PFC when intentional attention toward breathing was induced during a cognitive synchronization task, namely a linguistic task [12]. According to previous studies, the right PFC appears to support the execution of IA tasks [6,23,24] and sustained and goal-directed attention [18]. ...
... Thirdly, as indicated in the literature reported above, we aim to observe a potential lateralization effect even in terms of inter-brain coherence, with a potential right hemisphere lateralization effect connected to interoceptive focus [12,23,24] and a left hemispheric activation predominance for positive emotions derived from the synchronization. ...
... The higher inter-brain coherence in the left compared with the right PFC observed for both synchronization tasks in the interoceptive condition can be explained by taking into consideration the role of the left PFC in synchronization. In fact, former studies reported a right hemisphere lateralization effect connected to interoceptive focus [12,23,24]. However, they did not apply IA to social synchronization tasks. ...
Full-text available
Grossberg’s classification of adaptive resonance mechanisms includes the cognitive-emotional resonances that support conscious feelings and recognition of them. In this regard, a relevant question concerns the processing of signals deriving from the internal body and their contribution to interpersonal synchronization. This study aims to assess hemodynamic inter-subject coherence in the prefrontal cortex (PFC) through functional near-infrared spectroscopy (fNIRS) hyperscan recording during dyadic synchronization tasks proposed with or without a social frame and performed in two distinct interoceptive conditions: focus and no focus on the breathing condition. Individuals’ hemodynamic data (oxygenated and de-oxygenated hemoglobin (O2Hb and HHb, respectively)) were recorded through fNIRS hyperscanning, and coherence analysis was performed. The findings showed a significantly higher O2Hb coherence in the left PFC when the dyads performed the synchronization tasks with a social frame compared with no social frame in the focus condition. Overall, the evidence suggests that the interoceptive focus and the presence of a social frame favor the manifestation of a left PFC interpersonal tuning during synchronization tasks.
... Second, although this study employed a paper-based manual to instruct the participants, previous studies used audio-guided instruction, and meditation was practiced while listening to those instructions. Since naïve meditators find it difficult to be aware of their spontaneous thought during meditation (Zheng et al., 2019), audio-guided instruction may be highly effective in suppressing it and returning their attention to an object than to prior provided instructions. The difference in the methods of providing instructions may be partially responsible for these unexpected results. ...
Full-text available
Objectives A single session of brief focused attention meditation (FAM) has a state effect, which temporarily enhances response inhibition processes. However, previous research has two unanswered questions: (i) How long does the state effect last? (ii) How does effort toward FAM relate to the resulting state effect? Method Thirty-nine healthy participants participated in two sessions: FAM and sham meditation (SHAM). The participants conducted each meditation for 10 min. The state effect on response inhibition processes was observed as Stroop task performance immediately before and after each meditation, and 20, 40, and 60 min after each meditation. In addition, the subjective effort toward meditation was evaluated using a questionnaire immediately after each meditation. Results An analysis of variance revealed a significant interaction between session and time. In the post-hoc analysis, FAM showed significantly better Stroop task performance than the SHAM 60 min after meditation. Furthermore, using correlational analysis, we found that at 60 min, the higher the subjective effort, the better Stroop task performance. Conclusions In contrast to previous findings, the state effect was not found immediately after FAM but instead 60 min after. The results can be partially explained by cognitive fatigue; that is, the FAM may have the state effect of preventing cognitive fatigue. This state effect is greater when the subjective effort is greater.
... Remove both baseline shifts and spike artifacts by using parameter-free motion correction method . The band-pass filtering of the optical density signals was set between 0.01 and 0.2 Hz to remove baseline drift and physiological noise (Zheng et al., 2019;Si et al., 2021). The optical density transformed to HBO and HBR concentrations (Tak and Ye, 2014). ...
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Background Electrical stimulation of the cerebellar fastigial nucleus (FNS) has been shown to protect animals against cerebral ischemic injury. However, the changes in cortical activation as a response to FNS have not been illustrated in humans. Objective This study aims to detect functional connectivity changes in the brain of stroke patients, and investigate the cortical activation caused by FNS through measuring the oxygenated hemoglobin concentration (HBO) in the cerebral cortex of stroke patients and healthy controls (HCs). Methods This study recruited 20 patients with stroke and 20 HCs with all the following factors matched: age, gender and BMI. The experiment session was made up of the pre-task baseline, FNS task period, and post-task baseline. FNS task period contains 5 blocks, each block encompassing the resting state (30 s) and the FNS state (30 s). HBO signals were acquired by functional near-infrared spectroscopy (fNIRS) from the Prefrontal Cortex (PFC), the Motor Cortex (MC) and the Occipital Cortex (OC) throughout the experiment. The Pearson correlation coefficient was used to calculate the resting-state functional connectivity strength between the two groups, and the general linear model (GLM) was used to calculate the activation of 39 fNIRS channels during FNS in stroke patients and HCs, respectively. Results The coupling strength of stroke patients were significantly decreased in the following regions: right MC and left MC ( t = 4.65, p = 0.0007), right MC and left OC ( t = 2.93, p = 0.04), left MC and left OC ( t = 2.81, p = 0.04). In stroke patients, the changes in cerebral oxygenated hemoglobin (ΔHBO) among 12 channels (CH) in the bilateral PFC and bilateral MC regions were significantly increased during the FNS state (FDR corrected p < 0.05) compared with the resting state. In HCs, only 1 channel was increased (FDR corrected p < 0.05) in the left PFC during FNS. Conclusion By using the FNS and fNIRS techniques, the characteristics of functional connectivity were found to decrease in stroke patients. It was also noticed that FNS activates the PFC and MC regions. These findings may help to guide functional rehabilitation in stroke patients.
... It should be noted that no significant effect was found for the lateralization factor in this study, perhaps suggesting that the effect of the combination of IA and a social purpose in this context does not recruit a significantly unbalanced hemispherical process. This result is interesting because it is partially in contrast with previous neuroscientific research has demonstrated activation of different portions of the right hemisphere during the execution of interoceptive attention/awareness (IAA) tasks (Farb et al., 2007;Zheng et al., 2019;Balconi and Angioletti, 2021a). ...
Full-text available
This research explored the effect of explicit Interoceptive Attentiveness (IA) manipulation on hemodynamic brain correlates during a task involving interpersonal motor coordination framed with a social goal. Participants performed a task requiring interpersonal movement synchrony with and without a social framing in both explicit IA and control conditions. Functional Near-Infrared Spectroscopy (fNIRS) was used to record oxygenated (O2Hb) and deoxygenated hemoglobin (HHb) changes during the tasks. According to the results, the prefrontal cortex (PFC), which is involved in high-order social cognition and interpersonal relations processing, was more responsive when inducing the explicit focus (IA) on the breath during the socially framed motor task requiring synchronization, as indicated by increased O2Hb. In the absence of a broader social frame, this effect was not significant for the motor task. Overall, the present study suggests that when a joint task is performed and the individual focuses on his/her physiological body reactions, the brain hemodynamic correlates are “boosted” in neuroanatomical regions that support sustained attention, reorientation of attention, social responsiveness, and synchronization. Furthermore, the PFC responds significantly more as the person consciously focuses on physiological interoceptive correlates and performs a motor task requiring synchronization, particularly when the task is socially framed.
... Finally, a lateralization effect, for which the right PFC was more responsive than the left one in the explicit IA condition during the linguistic synchronization task, was observed. According to previous studies, the right frontal portions of the cerebral cortex seem to support the execution of IA tasks [15][16][17]35], and the right DLPFC, in particular, appears to support sustained and goal-directed attention [18]. For the first time, this pilot study suggested the involvement of the right DLPFC in tasks involving interpersonal coordination and a simultaneous explicit focus on its interoceptive correlates. ...
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Currently, there is little understanding of how interoceptive attentiveness (IA) affects brain responses during synchronized cognitive or motor tasks. This pilot study explored the effect of explicit IA manipulation on hemodynamic correlates of simple cognitive tasks implying linguistic or motor synchronization. Eighteen healthy participants completed two linguistic and motor synchronization tasks during explicit IA and control conditions while oxygenated (O2Hb) and deoxygenated (HHb) hemoglobin variations were recorded by functional Near-Infrared Spectroscopy (fNIRS). The findings suggested that the brain regions associated with sustained attention, such as the right prefrontal cortex (PFC), were more involved when an explicit focus on the breath was induced during the cognitive linguistic task requiring synchronization with a partner, as indicated by increased O2Hb. Interestingly, this effect was not significant for the motor task. In conclusion, for the first time, this pilot research found increased activity in neuroanatomical regions that promote sustained attention, attention reorientation, and synchronization when a joint task is carried out and the person is focusing on their physiological body reactions. Moreover, the results suggested that the benefits of conscious concentration on physiological interoceptive correlates while executing a task demanding synchronization, particularly verbal alignment, may be related to the right PFC.
... For example, the effects of the neuropsychological educational approach to cognitive remediation were assessed by pre-and post-comparison of the oxyHb changes during 2-back tasks (Pu et al., 2014). Similarly, the effects of haptic-assisted meditation were evaluated using the attention network and sustained attention to response tasks (Zheng et al., 2019); further, the effects of acceptance and commitment therapy were assessed using behavioral tasks including editing, mirror image tracking, and circle tracking (Ong et al., 2020). ...
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This perspective article discusses the importance of evidence-based psychotherapy and highlights the usefulness of near-infrared spectroscopy (NIRS) in assessing the effects of psychotherapeutic interventions as a future direction of clinical psychology. NIRS is a safe and non-invasive neuroimaging technique that can be implemented in a clinical setting to measure brain activity via a simple procedure. This article discusses the possible benefits and challenges of applying NIRS for this purpose, and the available methodology based on previous studies that used NIRS to evaluate psychotherapeutic effects. Furthermore, this perspective article suggests alternative methodologies that may be useful, namely, the single- and multi-session evaluations using immediate pre- and post-intervention measurements. These methods can be used to evaluate state changes in brain activity, which can be derived from a single session of psychotherapeutic interventions. This article provides a conceptual schema important in actualizing NIRS application for evidence-base psychotherapy.
... Overall, fNIRS is good for when you want a small, wearable device that is movement robust, especially for neuromonitoring and neurorehabilitation. In the context of self-transcendence, fNIRS has been used to measure positive emotions such as awe, gratitude, and love (Hu et al., 2019), brain states during mindfulness meditation (Gundel et al., 2018), sustained attention meditation (Zheng et al., 2019), and drug-induced altered states of consciousness with psilocybin (Scholkmann et al., 2019). ...
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Self-transcendence has been characterized as a decrease in self-saliency (ego disillusionment) and increased connection, and has been growing in research interest in the past decade. Several measures have been developed and published with some degree of psychometric validity and reliability. However, to date, there has been no review systematically describing, contrasting, and evaluating the different methodological approaches toward measuring self-transcendence including questionnaires, neurological and physiological measures, and qualitative methods. To address this gap, we conducted a review to describe existing methods of measuring self-transcendence, evaluate the strengths and weaknesses of these methods, and discuss research avenues to advance assessment of self-transcendence, including recommendations for suitability of methods given research contexts.
Mind wandering, also known as task-unrelated thought, refers to the drift of attention from a focal task or train of thought. Because self-caught measures of mind wandering require participants to spontaneously indicate when they notice their attention drift, self-caught methodologies provide a way to measure mind wandering with meta-awareness. Given the proposed role of meta-awareness in mental health and psychological interventions, an overview of existing self-caught methodologies would help clinicians and researchers make informed decisions when choosing or adapting a mind wandering or meta-awareness measure. This systematic review included 39 studies after 790 studies were assessed for eligibility. All studies operationalised mind wandering as instances of attention drift from a primary task. Three types of primary task were identified: (1) tasks adapted from computerised continuous performance tests (CPT) of sustained attention, (2) tasks involving focusing on the breath or a stream of aural beats, akin to in-vivo mindfulness meditation, (3) tasks involving an everyday life activity such as reading. Although data on mind wandering without meta-awareness (e.g., measured with probe-caught measures) was also obtained in many studies, such data was not always used in conjunction with self-caught mind wandering data to determine level of mind wandering meta-awareness. Few studies reported both reliability and validity of the measures used. This review shows that considerable methodological heterogeneity exists in the literature. Methodological variants of self-caught mind wandering methodologies are documented and examined, and suggestions for future research and clinical work are suggested.
Functional near-infrared spectroscopy (fNIRS) classification of mental states is of important significance in many neuroscience and clinical applications. Existing classification algorithms use all signal-collected brain regions as a whole, and brain sub-region contributions have not been well investigated. This paper proposes a functional region decomposition (FRD) method to incorporate brain sub-region contributions and enhance fNIRS classification of mental states. Specifically, the method iteratively decomposes the brain region into multiple sub-regions to maximize their contributions with respect to the validation accuracy and coverage of brain sub-regions. Then for the fNIRS data in brain sub-regions, features are extracted and classified to output the predictions. The final predictions are determined by fusing predictions from multiple brain sub-regions with stacking. Experiments on a publicly available fNIRS dataset showed that the proposed functional region decomposition method led to 9.01% and 10.58% increase of classification accuracy for the methods related to slope-based features and mean concentration change features, respectively. Therefore, the proposed method can decompose the brain region into sub-regions with respect to their functional contributions and fundamentally enhance the performance of mental state classification.
Objective: Parkinson's disease (PD) is a common neurodegenerative brain disorder, and early diagnosis is of vital importance for treatment. Existing methods are mainly focused on behavior examination, while the functional neurodegeneration after PD has not been well explored. This paper aims to investigate the brain functional variation of PD patients in comparison with healthy controls. Approach: In this work, we propose brain hemodynamic states and state transition features to signify functional degeneration after PD. Firstly, a functional near-infrared spectroscopy (fNIRS)-based experimental paradigm was designed to capture brain activation during dual-task walking from PD patients and healthy controls. Then, three brain states, named expansion, contraction, and intermediate states, were defined with respect to the oxyhemoglobin and deoxyhemoglobin responses. After that, two features were designed from a constructed transition factor and concurrent variations of oxy- and deoxy-hemoglobin over time, to quantify the transitions of brain states. Further, a support vector machine classifier was trained with the proposed features to distinguish PD patients and healthy controls. Results: Experimental results showed that our method with the proposed brain state transition features achieved classification accuracy of 0:8200 and F score of 0:9091, and outperformed existing fNIRS-based methods. Compared with healthy controls, PD patients had significantly smaller transition acceleration and transition angle. Significance: The proposed brain state transition features well signify functional degeneration of PD patients and may serve as promising functional biomarkers for PD diagnosis.
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The purpose of this study is to investigate the effects of different types of acute exercise on cognitive function and cerebral oxygenation. A within-subject design was adopted. In total, 20 healthy older adults were enrolled in the study. They came to the laboratory individually on four separate days and completed four conditions of activity. Four conditions were sedentary reading control (RC), cognitive exercise (CE), physical exercise (PE) and cognitive + physical exercise (CE + PE). During these visits, participants completed the Stroop task before and immediately after the experimental condition, which consisted of 15 min of aerobic exercise, verbal fluency task (VFT), and dual task. The Stroop task included the following two conditions: a naming condition and an executive condition. The fNIRS is an optical method using near-infrared light to measure relative changes of oxygenated (O 2 Hb) and deoxygenated (HHb) hemoglobin in the cortex. The results indicate that acute exercise facilitates performance for executive tasks, not only combined cognition, but also the different results between combined exercise and single exercise. The fNIRS findings showed that acute single exercise influences oxygenation for executive tasks but not for naming tasks. Greater improvement was observed in the post-exercise session of combined exercise during the modified Stroop. These findings demonstrate that acute single exercise, single cognition exercise, and combined exercise enhanced the performance of the inhibition control task. Only acute combined exercise has a general facilitative effect on inhibition control. Combined exercise was shown to be superior to single exercise for task-efficient cerebral oxygenation and improved oxygen utilization during cortical activation in older individuals. Also, to maximize the performance of cognition it may be important for older adults to take part in more cognitive demand exercise or take more kinds of exercise.
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Mind wandering happens when one train of thought, related to a current undertaking, is interrupted by unrelated thoughts. The detection and evaluation of mind wandering can greatly help in understanding the attention control mechanism during certain focal tasks. Subjective assessments such as random thought-probe and spontaneous self-report are the ways previous research has assessed mind wandering. Here we propose a task in which participants are asked to simultaneously control respiration and fingertip pressure. They are instructed to click a force sensor at the exact moment of inhalation and exhalation of their respiration. The temporal synchronization between the respiratory signals and the fingertip force pulses offers an objective index to detect mind wandering. Twelve participants engaged in the proposed task in which self-reports of mind wandering are compared with the proposed objective index. The results show that the participants reported significantly more mind-wandering episodes during the trials with a larger temporal synchronization than they did during those trials with a smaller temporal synchronization. The findings suggest that the temporal synchronization might be used as an objective marker of mind wandering in attention training and exploration of the attention control mechanism.
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The purpose of this study was to investigate the benefits of exercises with different cognitive demands for cognitive functions (Executive and non-Executive) in healthy older adults. A cross-sectional design was adopted. In total, 84 healthy older adults were enrolled in the study. They were categorized into the Tai Chi group (TG), the brisk walking group (BG) or the control group (CG). Each participant performed the Stroop task and a digit comparison task. The Stroop task included the following three conditions: a naming condition, an inhibition condition and an executive condition. There were two experimental conditions in the digit comparison task: the non-delay condition and the delay condition. The results indicated that participants of the TG and BG revealed significant better performance than the CG in the executive condition of cognitive tasks and fitness. There was no significant difference of reaction time (RT) and accuracy rate in the inhibition and delay conditions of cognitive tasks and fitness between the TG and BG. The TG showed shorter reaction time in the naming and the executive conditions, and more accurate in the inhibition conditions than the BG. These findings demonstrated that regular participation in brisk walking and Tai Chi have significant beneficial effects on executive function and fitness. However, due to the high cognitive demands of the exercise, Tai Chi benefit cognitive functions (Executive and non-Executive) in older adults more than brisk walking does. Further studies should research the underlying mechanisms at the behavioural and neuroelectric levels, providing more evidence to explain the effect of high-cognitive demands exercise on different processing levels of cognition.
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Background: Many common disorders across the lifespan feature impaired working memory (WM). Reported benefits of a WM training program include improving inattention in daily life, but this has not been evaluated in a meta-analysis. This study aimed to evaluate whether one WM training method has benefits for inattention in daily life by conducting a systematic review and meta-analysis. Methods: We searched Medline and PsycINFO, relevant journals and contacted authors for studies with an intervention and control group reporting post-training estimates of inattention in daily life. To reduce the influence of different WM training methods on the findings, the review was restricted to trials evaluating the Cogmed method. A meta-analysis calculated the pooled standardised difference in means (SMD) between intervention and control groups. Results: A total of 622 studies were identified and 12 studies with 13 group comparisons met inclusion criteria. The meta-analysis showed a significant training effect on inattention in daily life, SMD=-0.47, 95% CI -0.65, -0.29, p<.00001. Subgroup analyses showed this significant effect was observed in groups of children and adults as well as users with and without ADHD, and in studies using control groups that were active and non-adaptive, wait-list and passive as well as studies using specific or general measures. Seven of the studies reported follow-up assessment and a meta-analysis showed persisting training benefits for inattention in daily life, SMD=-0.33, 95% CI -0.57 -0.09, p=.006. Additional meta-analyses confirmed improvements after training on visuospatial WM, SMD=0.66, 95% CI 0.43, 0.89, p<.00001, and verbal WM tasks, SMD=0.40, 95% CI 0.18, 0.62, p=.0004. Conclusions: Benefits of a WM training program generalise to improvements in everyday functioning. Initial evidence shows that the Cogmed method has significant benefits for inattention in daily life with a clinically relevant effect size.
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Meditation can be defined as a form of mental training that aims to improve an individual's core psychological capacities, such as attentional and emotional self-regulation. Meditation encompasses a family of complex practices that include mindfulness meditation, mantra meditation, yoga, tai chi and chi gong 1. Of these practices , mindfulness meditation — often described as non-judgemental attention to present-moment experiences (BOX 1) — has received most attention in neuroscience research over the past two decades 2–8. Although meditation research is in its infancy, a number of studies have investigated changes in brain activation (at rest and during specific tasks) that are associated with the practice of, or that follow, training in mindfulness meditation. These studies have reported changes in multiple aspects of mental function in beginner and advanced meditators, healthy individuals and patient populations 9–14. In this Review, we consider the current state of research on mindfulness meditation. We discuss the methodological challenges that the field faces and point to several shortcomings in existing studies. Taking into account some important theoretical considerations, we then discuss behavioural and neuroscientific findings in light of what we think are the core components of meditation practice: attention control, emotion regulation and self-awareness (BOX 1). Within this framework, we describe research that has revealed changes in behaviour, brain activity and brain structure following mindfulness meditation training. We discuss what has been learned so far from this research and suggest new research strategies for the field. We focus here on mindfulness meditation practices and have excluded studies on other types of meditation. However, it is important to note that other styles of meditation may operate via distinct neural mechanisms
Background: Glaucoma (POAG) is a kind of neurodegenerative disease known to be closely associated with stress and adverse quality of life (QOL). Stress has also been shown to be involved in etiopathogenesis of primary open angle glaucoma (POAG). Complementary treatment in form of mindfulness based stress reduction (MBSR) has been reported to improve QOL, brain oxygenation and decrease markers of stress. With this premise, a randomized controlled trial was carried out to assess the effect of MBSR on intraocular pressure, subjective QOL and objective markers of stress and brain oxygenation in patients with POAG. Methods: Sixty patients were randomized into intervention and control groups. Intervention group underwent 45 minutes of MBSR daily for 6 weeks in addition to standard medical treatment while controls received only standard medical treatment. Inclusion criteria were patients with POAG, age >45 years, best corrected visual acuity >6/60. Patients with other ocular co-morbid conditions contributing to vision loss, systemic diseases, patients already practicing meditation in any form were excluded. An assessment of IOP, brain oxygenation using functional near infrared spectroscopy (fNIRS), QOL (WHO-BREF QOL) and stress markers in serum (cortisol, β-endorphins, interleukin-6, brain derived neurotrophic factor (BDNF), reactive oxygen species) was made at baseline and at 6 weeks. Results: 21 female and 39 male patients were enrolled with a mean age of 57.28±9.37 years. All parameters were comparable between two groups at baseline. At 6 weeks mean level of IOP decreased significantly in intervention group (15.9±1.8 mmHg to 14.4±1.21 mm Hg, p-value 0.0001) as compared to control group (15.7±1.4 mmHg to 15.65±1.41, p-value 0.41). fNIRS showed significant improvement in oxygenated hemoglobin change (ΔHbO) in intervention group in the prefrontal cortex (p-value < 0.0001) as compared to control group (p-value 0.52). WHO-BREF QOL score increased significantly in intervention group (86.6±6.16 to 93.3±5.66, p-value 0.0001) as compared to control (89±7.25 to 89.07±3.24, p-value 0.74).Mean serum cortisol decreased significantly in intervention group (497±46.37 ng/ml to 447±53.78 ng/ml, p-value 0.01) as compared to control group (519.75±24.5 to 522.58±26.63 ng/ml, p-value 0.64). Mean β-endorphin levels increased significantly (33±5.52pg/ml to 43.27pg/ml, p-value < 0.0001) as compared to control group (34.78±4.1pg/ml to 36.33pg±4.07pg/ml p-value 0.27). Interleukin-6 decreased significantly in intervention group (2.2±0.5 ng/ml to 1.35±0.32 ng/ml, p-value < 0.0001) as compared to control group (2.03±0.37 to 2.17±0.34 ng/ml p-value 0.25). BDNF increased significantly in intervention group (52.24±6.71 to 63.25±13.48 ng/ml p-value 0.004) as compared to control group (53.23±5.82 to 54.42±5.66 ng/ml p-value 0.54). ROS decreased significantly in intervention group (1596.19±179.14 to 1261±244.31 RLU/min/104 neutrophils p-value 0.0001) as compared to control group (1577.5±172.02 to 1662.5±84.75 RLU/min/104 neutrophils p-value 0.16). Conclusions: A short term course of MBSR was associated with significant improvement in brain oxygenation and QOL along with a reduction in IOP and stress markers. MBSR may be a useful as an adjunct to standard treatment in patients with POAG and potentially decrease the risk of glaucoma progression.
Mindfulness meditation as a therapeutic intervention has been shown to have positive effects on psychological problems such as depression, pain or anxiety disorders. In this study, we used functional near-infrared spectroscopy (fNIRS) to detect differences in hemodynamic responses of meditation experts (14 participants) and a control group (16 participants) in a resting and a mindfulness condition. In both conditions, the sound of a meditation bowl was used to find group differences in the auditory system and adjacent cortical areas. Different lateralization patterns of the brain were found in expert meditators while being in a resting state (amplified left hemisphere) or being in mindfulness state (amplified right hemisphere). Compared to the control group, meditation experts had a more widespread pattern of activation in the auditory cortex, while resting. In the mindfulness condition, the control group showed a decrease of activation in higher auditory areas (BA 1, 6 and 40), whereas the meditation experts had a significant increase in those areas. In addition, meditation expert had highly activated brain areas (BA 39, 40, 44 and 45) beyond the meditative task itself, indicating possible long-term changes in the brain and their positive effects on empathy, meta cognitive skills and health.
This chapter provides an overview of some of the empirical research that has been done on meditation with primary attention directed toward investigations identifying its physical and neurological manifestations and correlates, as well as to studies examining its impact on psychological and social functioning. The chapter explains general trends and salient findings to illustrate the potency of meditation across most recognized domains of human functioning. Meditation has been increasingly integrated into clinical interventions and at present there is a fairly extensive body of literature looking at the effects of meditation and/or meditative techniques incorporated into conventional therapies (e.g., mindfulness-based cognitive behavioral therapies) on both physical and psychological health and pathology with clinical samples.