ORIGINAL RESEARCH
published: 15 April 2021
doi: 10.3389/fnhum.2021.603069

Hemodynamic Signal Changes
During Motor Imagery Task
Performance Are Associated With
the Degree of Motor Task Learning
Naoki Iso 1* , Takefumi Moriuchi 2 , Kengo Fujiwara 3 , Moemi Matsuo 3 , Wataru Mitsunaga 3 ,Takashi Hasegawa 3 , Fumiko Iso 3 , Kilchoon Cho 1 , Makoto Suzuki 1 and Toshio Higashi 3
1

Faculty of Health Sciences, Tokyo Kasei University, Saitama, Japan, 2 Department of Occupational Therapy, Nagasaki
University Graduate School of Biomedical Sciences and Health Sciences, Nagasaki, Japan, 3 Department of Health
Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan

Purpose: This study aimed to investigate whether oxygenated hemoglobin (oxy-Hb)
generated during a motor imagery (MI) task is associated with the motor learning levelof the task.

Edited by:
HIdeki Miyaguchi,
Hiroshima University, Japan
Reviewed by:
Takayuki Nakahachi,
The University of Tokyo, Japan
Senichiro Kikuchi,
Gunma University, Japan*Correspondence:
Naoki Iso
iso-n@tokyo-kasei.ac.jp;
isokinakochan@yahoo.co.jp
Specialty section:
This article was submitted toBrain Imaging and Stimulation,a section of the journal
Frontiers in Human Neuroscience
Received: 05 September 2020
Accepted: 12 March 2021
Published: 15 April 2021
Citation:
Iso N, Moriuchi T, Fujiwara K,
Matsuo M, Mitsunaga W,
Hasegawa T, Iso F, Cho K, Suzuki M
and Higashi T (2021) Hemodynamic
Signal Changes During Motor Imagery
Task Performance Are AssociatedWith the Degree of Motor Task
Learning.
Front. Hum. Neurosci. 15:603069.
doi: 10.3389/fnhum.2021.603069

Methods: We included 16 right-handed healthy participants who were trained toperform a ball rotation (BR) task. Hemodynamic brain activity was measured usingnear-infrared spectroscopy to monitor changes in oxy-Hb concentration during the
BR MI task. The experimental protocol used a block design, and measurements were
performed three times before and after the initial training of the BR task as well as
after the final training. The BR count during training was also measured. Furthermore,
subjective vividness of MI was evaluated three times after NIRS measurement using the
Visual Analog Scale (VAS).
Results: The results showed that the number of BRs increased significantly with training(P < 0.001). VAS scores also improved with training (P < 0.001). Furthermore, oxy-Hb
concentration and the region of interest (ROI) showed a main effect (P = 0.001). An
interaction was confirmed (P < 0.001), and it was ascertained that the change in oxy-Hb
concentrations due to training was different for each ROI. The most significant predictorof subjective MI vividness was supplementary motor area (SMA) oxy-Hb concentration(coefficient = 0.365).
Discussion: Hemodynamic brain activity during MI tasks may be correlated with task
motor learning levels, since significant changes in oxy-Hb concentrations were observed
following initial and final training in the SMA. In particular, hemodynamic brain activity in
the SMA was suggested to reflect the MI vividness of participants.
Keywords: motor imagery, motor learning, oxygenated hemoglobin, ball rotation task, motor area

Abbreviations: ANOVA, analysis of variance; BCI, brain computer interface; BR, ball rotation; deoxy-Hb, deoxygenatedhemoglobin; fMRI, functional magnetic resonance imaging; KVIQ, Kinesthetic and Visual Imagery Questionnaire; MI, motor
imagery; NIRS, near-infrared spectroscopy; oxy-Hb, oxygenated hemoglobin; PET, positron emission tomography; Left PFC,
left prefrontal cortex; Right PFC, right prefrontal cortex; pre-SMA, pre-supplementary motor area; SMA, supplementary
motor area; Left PMA, left pre-motor area; Right PMA, right pre-motor area; Left SMC, left somatosensory motor
cortex; Right SMC, right somatosensory motor cortex; ROI, region of interest; SMA, supplementary motor area; SMC,
somatosensory motor cortex; TMS, transcranial magnetic stimulation; VAS, visual analog scale.

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monitor brain activation in the region of interest by devisingprobe placement. Therefore, we have been studying whether
NIRS can be used as a neurophysiological assessment method.
NIRS is highly portable and widely applicable, which allows its
use during bedside care and treatment since it is less restrictive
and non-invasive. Thus, it could be easily used in the field of
rehabilitation. Several studies have examined brain activation
during MI using NIRS and cerebral hemodynamics during MI ofa tapping task (Iso et al., 2016), swallowing (Kober and Wood,2014), and eating have been shown (Matsuo et al., 2020). In
addition, several studies have aimed to increase the effect of MI
by feeding the cerebral hemodynamics measured by NIRS backto patients to enhance brain activation (Mihara et al., 2012; Kober
et al., 2014, 2018; Ota et al., 2020).
However, although the experience in tasks and the degreeof learning have been shown to affect MI (Mulder et al., 2004;
Mulder, 2007; Schuster et al., 2011), no studies have examined
how much they affect the changes in cerebral hemodynamics
during MI. We previously examined the performance of motions
and cerebral hemodynamics during MI (Iso et al., 2016), andthe effects of the dominant/non-dominant hand (Matsuo et al.,2020). The results showed an increase in cerebral hemodynamicchange in the PMA and SMA, comparable to that observed
during exercise. While TMS or fMRI studies have alreadyreported the effects of experience and competence in MI tasks
on the excitability of the primary motor cortex (Szameitat et al.,2007; Tsukazaki et al., 2012; Wriessnegger et al., 2014), no NIRS
studies have determined such effects. Moreover, there are no
NIRS studies that have examined the association between taskcompetence and activity in the SMA involved in motor learning.
To develop future neurophysiological assessment methods using
NIRS, it is necessary to determine the cerebral hemodynamics ofeach region associated with MI by considering the effects of theexperience in tasks and the degree of learning.
The objective of the present study was to examine therelationship between changes in cerebral hemodynamics during
MI and the degree of task learning. The degree of task learning
was examined using a ball rotation (BR) task, which has been usedin many studies (Nojima et al., 2012; Suzuki et al., 2013; Horiba
et al., 2019). We examined motor-related areas that exhibit theequivalent level of activation to that during the performance of
motions using NIRS.

INTRODUCTION
To effectively perform motor imagery (MI), it is important toensure the vividness of participants’ MI in objective terms. MI
is defined as mental rehearsal in which an individual simulates
an objective action within the brain without performing actual
motions, and similar brain activation caused by performing
motions is observed (Jeannerod, 2001; Kimberley et al., 2006). MI
allows rehearsal without performing the actual motions and has
been used as a tool for practicing in the sports field (Guillot andCollet, 2008; MacIntyre et al., 2018). Performing MI repeatedly,
which is called mental practice, has been used not only in the
sports field, but also in the field of rehabilitation in recent
years. MI can also be executed in patients who have difficulty
performing motions, particularly in those with cerebrovasculardisease, and it has been reported as an effective method for
improving motor function (Page et al., 2001, 2011; Liu et al.,
2004; Sharma et al., 2006; Riccio et al., 2010). As a treatment
strategy, MI is regarded as a method for complementing motion
performances due to the exhibition of similar brain activation tothat caused by performing motions, which has been reported tochange brain plasticity (Ruffino et al., 2017; Li et al., 2018; Yoxon
and Welsh, 2019).
It is important to evaluate the effectiveness of MI to induce
similar brain activation to that caused by performing motions.
Participants’ MI ability, clarity, means, and experience in MI tasks
are factors that have been shown to affect effectiveness (Mulder
et al., 2004; Mulder, 2007; Schuster et al., 2011). To assess the MI
ability, questionnaires such as the Kinesthetic and Visual Imagery
Questionnaire (KVIQ) (Malouin et al., 2007) and the Revised
Movement Imagery Questionnaire (Gregg et al., 2010) have beendeveloped and used in the clinical setting. The Visual Analog
Scale (VAS) has been used for the assessment of the subjective
clarity of MI, and it has an advantage in that the clarity can beassessed for each task of MI used in mental practice (Lotze andHalsband, 2006; Ikeda et al., 2012). However, these assessments of
MI ability and MI clarity are subjective and the clarity with which
the participants can perform MI cannot be objectively assessed.
Therefore, we consider that neurophysiological assessment isnecessary for directing brain activation during MI similar tothat during motion. There have been reports of studies on brain
activity during MI using brain imaging devices such as functional
magnetic resonance imaging (fMRI) and positron emission
tomography (PET) (Stephan et al., 1995; Ruby and Decety,2003; Solodkin et al., 2004; Hanakawa et al., 2008). Studies
using fMRI and PET showed deactivation of the primary motor
cortex, and activation of the premotor cortex (PMA) and the
supplementary motor area (SMA) (Hanakawa et al., 2003; Lotze
et al., 2003; Cunnington et al., 2005). Previous neurophysiologicalstudies using transcranial magnetic stimulation (TMS) have also
reported excitatory changes in the primary motor cortex during
MI (Kasai et al., 1997; Stinear and Byblow, 2003; Pelgrims et al.,2011). The results regarding the activation sites in the brain varydepending on the device. In addition, some devices are highly
restrictive; thus, it is not easy to use such devices for assessmentin actual rehabilitation settings. Although spatial resolution isinferior to PET and fMRI, near-infrared spectroscopy (NIRS) can

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MATERIALS AND METHODS
Participants
The target sample size of this study was based on 80% statisticalpower to detect changes in task learning with a 0.40 effect size anda two-sided α-level of 0.05. A sample size of 10 was calculatedby G∗ Power (Faul et al., 2007, 2009). The participants were
16 neurologically healthy right-handed adults (age: 31.6 ± 4.0,
male: 13, female: 3). Hand dominance was determined using the
Edinburgh Handedness Inventory (Oldfield, 1971). None of theparticipants had switched handedness. The MI abilities of theparticipants were evaluated using the KVIQ (77.8 ± 2.24 points).
The present study was approved by the ethics committee of the2

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session. The final training was designed for participants to reachthe level of competence sufficient to carry out BR tasks. They
were asked to self-practice at their own pace, avoiding effects
from muscle or mental fatigue. About an hour was given for selfpractice. Furthermore, subjective vividness of MI was evaluatedthree times after NIRS measurement using the VAS (Lotze
and Halsband, 2006; Ikeda et al., 2012). The subjects marked alocation on a 100-mm horizontal line, the two ends of whichwere labeled “0 = None at all” and “100 = Very vivid image,”according to the vividness of the imagery they experienced.

medical corporation Toujinkai and conducted after we obtained
consent for participation in writing from all the participants.

Experimental Procedure
The participants sat on a comfortable chair and placed theirhands on the table. The experimental task was a BR in the palm, inwhich the learning of the motion can be expected to be achievedby short-term practice of the actual motion (Kawashima et al.,1998). The BR is a task of rotating two iron balls in the palm
counterclockwise, which has been used in many studies (Nojima
et al., 2012; Suzuki et al., 2013; Horiba et al., 2019). We explained
the BR task to the participants, showed them how it worked, andmade them actually perform the task in order to understand it.
However, we avoided participants learning the BR task and only
aimed to get them to understand the content of the task in a shortperiod of time. The protocol of the entire experiment is shownin Figure 1. First, cerebral hemodynamics during the BR-MI task
was measured. A block design was used for the measurements, inline with previous research (Wriessnegger et al., 2008; Amemiya
et al., 2010; Iso et al., 2016; Matsuo et al., 2020). The participants
were instructed to perform the BR-MI task for 30 s and thenmaintain a resting condition for 40 s. NIRS measurement takes
about 5 min depending on the block design. We instructed theparticipants to perform MI while feeling the muscles as if they
were actually performing the motions. We also instructed them tomaintain the same posture as that taken during the MI task while
they rested and relaxed without thinking of anything. They were
instructed to execute the MI task with their eyes closed duringMI, as well as during rest, and not to move. In addition, after
we fully explained the experimental protocol to the participants,
the flow of the task was guided with beeping sounds during theexperiment. The measurement of cerebral hemodynamics was
performed a total of three times: before and after the initialtraining of the BR task and after the final training.
Thus, to determine the relationship between cerebral
hemodynamics and task competence, participants completed
five 1-min sessions of BR tasks involving real movements as
the initial training, and the number of BR task completions wasmeasured at each session. The rest period between sessions was
about 3 min. Further, participants were asked to self-practiceas the final training for improving task competence before
completing the sixth 1-min session of BR tasks. The physicaltraining (final) was performed until the subject was satisfied, andthe number of BR task completions was measured at the sixth

NIRS Measurement and Analysis
For NIRS measurements, we used a 24-channel system (ETG4000; Hitachi Medical Co., Tokyo, Japan) equipped with 4 × 4
optode probe sets (eight incident lights and eight detector fibers),
resulting in a total of 24 channels at an inter-optode distanceof 3.0 cm. The NIRS channels were placed according to theinternational 10–20 system, and the Cz position was used as amarker to ensure replicable placement of the optodes (Okamoto
et al., 2004; Tsuzuki et al., 2007). A total of eight regions of interest(ROIs) were selected based on previous studies (Hatakenaka et al.,2007; Amemiya et al., 2010; Sagari et al., 2015). The optodes werepositioned using a custom-made cap that covered the right andleft dorsolateral prefrontal cortex (PFC), pre-SMA, SMA, dorsal
PMA, and somatosensory motor cortex (SMC). The areas andoptodes covering them were as follows: left SMC, channels 18 and
22; right SMC, channels 21 and 24; SMA, channels 9, 12, 13, and16; pre-SMA, channels 2, 5, and 6; left PMA, channels 8, 11, and15; right PMA, channels, 10, 14, and 17; left PFC, channels 1 and
4; and right PFC, channels 3 and 7 (Figure 2). Channels 19, 20,
and 23 were not further analyzed.
The continuous-wave NIRS system uses two different
wavelengths (625 and 830 nm), which were both used in thisstudy. Relative changes in the absorption of near-infrared light
were sampled at 10 Hz, and these values were converted tochanges in the concentration of oxy-Hb and deoxygenatedhemoglobin (deoxy-Hb) based on the modified Beer-Lambertapproach (Cope and Delpy, 1988; Obrig and Villringer, 2003).
We used changes in the oxy-Hb concentration as an indicator offluctuations in the regional cerebral blood volume, as an earlier
NIRS signal study using a perfused rat brain model proposed that
oxy-Hb and not deoxygenated hemoglobin, is the most sensitive
parameter for an activation study (Hoshi et al., 2001). Oxy-Hb
is an indicator of local neural activity, rather than of fluctuations

FIGURE 1 | The experimental protocol. The physical practice (initial and final training) was performed in three phases classified as pre, post, and post 2. The training
was performed in six sessions. The black arrow shows the timeline of this protocol. The horizontal row of task represents the MI of ball rotation or physical practice ineach item. The initial training consisted of five sessions of 1 min each. The final training consisted of 60 min of self-training and 1 min of one session. MI, motor
imagery; BR, ball rotation task; NIRS, near-infrared spectroscopy; VAS, visual analog scale.

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Hemodynamic Changes Reflect Motor Learningnoise levels were marked using a 3.0 Hz high-pass filter, and noise
components were separated and analyzed using wave analysis.
Channels whose standard deviation exceeded 0.08 were assumed
to be influenced by excessive noise and were thus excluded(Iso et al., 2016; Matsuo et al., 2020). In addition, during NIRS
measurement, the contraction of participants’ thenar muscles
was monitored using electromyography (MYOTRACE400 EM501, Sakai Medico, Japan). Electromyography is a techniquethat provides biofeedback of muscle activity. During NIRS
measurements, motion was monitored by electromyography,
which was set to sound when an EMG of 50 µV or higher
appeared. Subjects with clear muscle activity during NIRS
measurements were excluded, but there were no subjects
excluded in this study. For statistical analyses, one-way repeatedmeasures analysis of variance (ANOVA) was performed for thenumber of BR task completions and the VAS score, followed bya Bonferroni post hoc test. To measure oxy-Hb changes, twoway ANOVA was performed, with training and ROI as factors,
followed by the Bonferroni post hoc test. Five hundred pieces ofbootstrap oxy-Hb data were generated for each ROI and VAS
in before, initial, and final trainings by randomly drawing aseries of actual sample data from the oxy-Hb and VAS score
to elicit the difference of variable data between before, initial,
and final trainings due to the limited actual sample size. Thisbootstrap resampling method is widely used in demographic
studies (Suzuki et al., 2020). Then, differences in the bootstrap
oxy-Hb data between eight ROIs and three training phases (i.e.,
before, initial, and final trainings) were compared by two-way
ANOVA. In addition, a generalized linear model with a gaussian
distribution was used to estimate the relationship between thebootstrapping oxy-Hb and VAS scores (Kawanabe et al., 2018).
The significance level was set at 5%. The statistical software
used was IBM SPSS statistics 26 for one-way ANOVA and twoway ANOVA, Python language for bootstrapping, and R 3.5.2
software (R Foundation for Statistical Computing, Vienna, Wien,
Austria) for generalized linear model analysis.

FIGURE 2 | Channel configuration of the 4 × 4 optode probe set. The
24-channel NIRS probe set was positioned over the motor areas. Blackcircles indicate the positions of NIRS emitters and detectors. The black
numbers represent the channels, and the colored boxes show the regions ofinterest. According to the international 10–20 placement system, Cz was used
as a marker position to ensure replicable placement of the optodes. Left PFC,
left prefrontal cortex; Right PFC, right prefrontal cortex; Pre-SMA,
pre-supplementary motor area; SMA, supplementary motor area; Left PMA,
left pre-motor area; Right PMA, right pre-motor area; Left SMC, left
somatosensory motor cortex; Right SMC, right somatosensory motor cortex;
NIRS, near-infrared spectroscopy.

RESULTSThe number of BRs in each session, VAS scores before and after
the initial training, and after the final training (pre, post, and post2), and the oxy-Hb in each brain area are shown in Figures 4–13.
Figure 4 shows the time course of oxy-Hb and deoxy-Hb ineach ROI (Z-score). The results of the generalized linear model
analysis for predictors of VAS scores are shown in Table 1.
There was a significant effect of training on the number ofBRs [F (5,90) = 8.245, P < 0.001; Figure 5]. The number of BRs
increased with training, and there was a significant difference
observed between the sessions six (P < 0.05) compared to thosein the initial training session. After the final training session,
the average number of BRs was 86.1, which was larger than thatobserved in the five initial training sessions.
A significant effect on the VAS score was also seen, with the
VAS score improved by training [F (2,45) = 17.287, P < 0.001;
Figure 6]. There was a significant improvement from 40.1 mm(before the initial training) to 65.0 mm (P < 0.001) (afterin the regional cerebral blood volume (Hoshi et al., 2001). We
determined the pre-task baseline as the mean over the 5 s prior
to the task period, and the post-task baseline as the mean overthe last 5 s of the post-task period (Figure 3). We applied linear
fitting to the data between these two baselines (Marumo et al.,2009; Pu et al., 2012). The data were converted into Z-scores
using values at 0–5 s from the onset of resting as baselines. We
used the average measured 5–30 s after the task had started,
considering the time required for changes in oxy-Hb. The average
was calculated for each of the eight ROIs. Channels with highFrontiers in Human Neuroscience | www.frontiersin.org

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FIGURE 3 | Methods of calculating oxy-Hb change and Z score. The red curve represents the NIRS waveform in the sham case. This waveform was created byaveraging the data measured over three cycles in a block design. Linear fitting was applied to the data between the pre-task (5 s) and post-task (5 s) periods (green
arrows). The shaded yellow frame indicates the task period, and the non-shaded frame shows the rest period. The vertical axis represents oxy-Hb concentration(mMmm), and the horizontal axis represents the time course. Z-scores of oxy-Hb measured between 0 and 5 s during the pre-task were calculated. The mean value
score measured between 5 and 30 s during the task was calculated. oxy-Hb, oxygenated hemoglobin; NIRS, near-infrared spectroscopy.

FIGURE 4 | Time course of hemodynamic signal changes for pre- and post- training (initial and final) in each ROI. The vertical axis represents Z score of oxy-Hb anddeoxy-Hb, and the horizontal axis represents the time course. The blue line represents deoxy-Hb through all training. The figure shows changes in oxy-Hb anddeoxy-Hb during the task in the Left PFC, Right PFC, Pre-SMA, Left PMA, Right PMA, SMA, and Left SMC. Shaded yellow frame indicates the task period. Left
PFC, left prefrontal cortex; Right PFC, right prefrontal cortex; pre-SMA, pre-supplementary motor area; SMA, supplementary motor area; Left PMA, left pre-motorarea; Right PMA, right pre-motor area; Left SMC, left somatosensory motor cortex; Right SMC, right somatosensory motor cortex; oxy-Hb, oxygenated hemoglobin;
deoxy-Hb, deoxygenated hemoglobin; ROI, region of interest.

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TABLE 1 | The results of the generalized linear model analysis forpredictors of VAS score.
ROI

CoefficientStandard error

t value

P value

Left PFC

0.0374

0.037

1.021

0.307

Right PFC

0.1454

0.05

2.908

0.001

**

Pre-SMA

0.1866

0.046

4.069

0

***

Left PMA

0.2129

0.038

5.553

0

***

Right PMA

0.1649

0.06

2.77

0.001

**

SMA

0.3654

0.05

7.321

0

***

Left SMC

0.3398

0.059

5.763

0

***

Right SMC

0.2203

0.073

3.025

0.001

**

The coefficients across seven ROIs ranged between 0.15 and 0.36. *P < 0.05,**P < 0.01, and ***P < 0.001.
Left PFC, left prefrontal cortex; Right PFC, right prefrontal cortex; pre-SMA, presupplementary motor area; SMA, supplementary motor area; Left PMA, left premotor area; Right PMA, right pre-motor area; Left SMC, left somatosensory motor
cortex; Right SMC, right somatosensory motor cortex; ROI, region of interest; VAS,
visual analog scale.
FIGURE 6 | VAS scores for pre- and post- training (initial and final). The line
connecting the dots indicates that there was a significant difference on
performing identified in the post hoc analysis. Vertical bars represent thestandard error. ∗ P < 0.05, ∗∗ P < 0.01, and ∗∗∗ P < 0.001. VAS, visual analog
scale.

did vary between different regions (Figure 8). Post hoc tests
also showed a significant difference between ROIs, indicatinglarge oxy-Hb changes in the left PMA. There was an interaction
between training and ROI [F (14,11976) = 39.597, P < 0.001], andoxy-Hb levels in the eight ROIs changed in differing directions
pre- and post-training (Figure 9). Post hoc tests showed asignificant increase in oxy-Hb changes in the post initial training,and maintenance or a decrease oxy-Hb changes in post final
training (Figure 10). In the SMA, oxy-Hb changes increased
with each training. There was no significant difference in oxy-Hbchanges in many ROIs in the pre initial training, and the overall
oxy-Hb changes were not observed (Figure 11). In the pre-initialtraining, the pre-SMA showed significant oxy-Hb changes whencompared to the left SMC and Left PMA (P < 0.05), the right
SMA showed significant oxy-Hb changes when compared to theleft PMA (P < 0.05), and the left PFC showed significant oxy-Hbchanges when compared to the left SMC (P < 0.05) (Figure 11).
In the post initial training, increase of oxy-Hb changes wereobserved in the overall ROIs. The left PFC showed oxy-Hbchanges, and significant differences were observed comparedto the right PFC (P < 0.01), right PMA, SMA, and bilateral
SMC (P < 0.001) (Figure 12). Additionally, left PMA and preSMA showed significant oxy-Hb changes compared to right PMA(P < 0.01), SMA, and bilateral SMC (P < 0.001) (Figure 12);compared with other regions, the SMA had significantly lower
oxy-Hb changes, which was significantly different from all,except for left SMC (P < 0.01, P < 0.001) (Figure 12). In thepost final training, the SMA showed highest oxy-Hb changes
which was significantly different from all other ROIs (P < 0.01,
P < 0.001), and followed by the left PMA (Figure 13). The left
PFC showed significantly lower oxy-Hb changes than all otherregions (P < 0.01, P < 0.001) (Figure 13).

FIGURE 5 | Ball rotation score for each session. The line connecting the dots
indicates that there was a significant difference in the post hoc analysis.
Vertical bars represent the standard error. ∗ P < 0.05, ∗∗ P < 0.01, and∗∗∗ P < 0.001.

the initial training). However, although the score improved to73.3 mm after the final training (P < 0.001), there was no
significant difference between the VAS score after the initialtraining and that after the final training (P = 1.000).
With regard to oxy-Hb levels, a main effect of training[F (2,11976) = 305.422, P < 0.001] was noted, with significant
oxy-Hb changes observed in the pre- and post-initial and post
final training (Figure 7). The oxy-Hb changes were highest
after the post initial training and slightly decreased after thepost final training. Further, a main effect of ROI was noted[F (7,11976) = 16.798, P < 0.001], indicating that oxy-Hb changes

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FIGURE 7 | Changes in oxy-Hb pre- and post-training (initial and final) for all ROI. The line connecting the dots indicates that there was a significant difference in thepost hoc analysis. Vertical bars represent the standard error. ∗ P < 0.05, ∗∗ P < 0.01, and ∗∗∗ P < 0.001. oxy-Hb, oxygenated hemoglobin; ROI, region of interest.

FIGURE 8 | Changes in all stages of oxy-Hb pre- and post-training (initial and final) for each ROI. The line connecting the dots indicates that there was a significant
difference in the post hoc analysis. Vertical bars represent the standard error. Left PFC, left prefrontal cortex; Right PFC, right prefrontal cortex; pre-SMA,
pre-supplementary motor area; SMA, supplementary motor area; Left PMA, left pre-motor area; Right PMA, right pre-motor area; Left SMC, left somatosensory
motor cortex; Right SMC, right somatosensory motor cortex; oxy-Hb, oxygenated hemoglobin; ROI, region of interest.

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FIGURE 9 | Changes in oxy-Hb for each ROI pre- and post-training (initial and final). The Z score of oxy-Hb changes showed a significant interaction. Black dottedline represents the Left PFC. Dark gray dotted line represents the Right PFC. Light gray dotted line represents the pre-SMA. Yellow line represents the Left PMA.
Orange line represents the Right PMA. Red line represents the SMA. Blue line represents the Left SMC. Light blue line represents the Right SMC. Vertical bars
represent the standard error. Left PFC, left prefrontal cortex; Right PFC, right prefrontal cortex; pre-SMA, pre-supplementary motor area; SMA, supplementary motorarea; Left PMA, left pre-motor area; Right PMA, right pre-motor area; Left SMC, left somatosensory motor cortex; Right SMC, right somatosensory motor cortex;
oxy-Hb, oxygenated hemoglobin; ROI, region of interest.

The generalized linear model indicated that the oxy-Hbchanges for seven ROIs were significant predictors of VAS score.
The coefficients across seven ROIs ranged between 0.15 and0.36 (Table 1).

Changes in Subjective MI Vividness
Before and After Training
There was a significant improvement in the VAS score after
training, and the MI clarity improved. A previous study has
shown that MI clarity improves with learning of tasks (Robinet al., 2007; Lawrence et al., 2013). The present study also showedlearning of the task by training, which is considered to haveaffected the improvement in subjective MI clarity. However,since there was no significant difference between after the initialtraining and after the final training, compared to the degree oflearning, the changes in the MI clarity are not considered tobe completely related proportionally. At a stage where a certain
degree of learning is obtained, the stage of the MI clarity is
considered high; thus, in the case of a high degree of learning,further analysis may be necessary. As MI vividness improved
noticeably through the initial training, no significant difference
was observed in the final training. Thus, although there was apositive relationship between MI vividness and task competence,the subjective vividness of MI was relatively high from the earlyround of training due to exposure to tasks.

DISCUSSION
Changes in the Number of BRs After
Training
The number of BRs increased in every successive session,i.e., compared to the first session, it significantly increased in
the second session and so on. Despite short-term training,
the number increased significantly; thus, we consider that
there was task learning to a certain extent. Compared to theprevious study by Kawashima et al. (1998), the participants
were not considered to have reached the level of complete
learning based on the number of sessions and performanceand reached the equivalent degree of learning after the final
training. In the final training, instead of repeating the session,
BR tasks were performed over time until the subjects weresatisfied. Since a significant difference was found between
Session 6 and the other sessions after the final training, it isconsidered that Session 6 was the stage where the tasks werelearned considerably.

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Changes in the Oxy-Hb Levels Before
and After Training
The main effect of training was noted, and significant oxyHb changes were observed after the initial and final training,

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FIGURE 10 | Changes in oxy-Hb for each ROI pre- and post-training (initial and final). The line connecting the dots indicates that there was a significant difference
identified in the post hoc analysis. Vertical bars represent the standard error. ∗ P < 0.05, ∗∗ P < 0.01, and ∗∗∗ P < 0.001. Left PFC, left prefrontal cortex; Right PFC,
right prefrontal cortex; pre-SMA, pre-supplementary motor area; SMA, supplementary motor area; Left PMA, left pre-motor area; Right PMA, right pre-motor area;
Left SMC, left somatosensory motor cortex; Right SMC, right somatosensory motor cortex; oxy-Hb, oxygenated hemoglobin; ROI, region of interest.

FIGURE 11 | Changes in oxy-Hb for each ROI pre-training (initial round). The line connecting the dots indicates that there was a significant difference in the post hoc
analysis. Vertical bars represent the standard error. ∗ P < 0.05, ∗∗ P < 0.01, and ∗∗∗ P < 0.001. Left PFC, left prefrontal cortex; Right PFC, right prefrontal cortex;
pre-SMA, pre-supplementary motor area; SMA, supplementary motor area; Left PMA, left pre-motor area; Right PMA, right pre-motor area; Left SMC, left
somatosensory motor cortex; Right SMC, right somatosensory motor cortex; oxy-Hb, oxygenated hemoglobin; VAS, visual analog scale; ROI, region of interest.

compared to before the initial training. The results of this study
matched those of previous studies in which oxy-Hb changes in
the motor-related areas increased as the MI vividness increased(Mihara et al., 2012; Kober et al., 2014, 2018). The training

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facilitated learning of the MI task and enabled participants toform MI more vividly, increasing the oxy-Hb levels.
However, in this study, the oxy-Hb changes decreased in the
motor-related area after the final training in which the learning of

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FIGURE 12 | Changes in oxy-Hb for each ROI post-training (initial round). The line connecting the dots indicates that there was a significant difference in thepost hoc analysis. Vertical bars represent the standard error. ∗ P < 0.05, ∗∗ P < 0.01, and ∗∗∗ P < 0.001. Left PFC, left prefrontal cortex; Right PFC, right prefrontal
cortex; pre-SMA, pre-supplementary motor area; SMA, supplementary motor area; Left PMA, left pre-motor area; Right PMA, right pre-motor area; Left SMC, left
somatosensory motor cortex; Right SMC, right somatosensory motor cortex; oxy-Hb, oxygenated hemoglobin; ROI, region of interest.

during MI (Amemiya et al., 2010; Iso et al., 2016). Similarly,
in this study, significantly higher activity was observed in thebilateral PFC, bilateral PMA, pre-SMA, and SMA. In addition,
the bilateral SMC showed low activation in this study. Previous
imaging studies have shown that SMC activity directly relates
to movement output (Obrig et al., 1996; Christensen et al.,2000), while other studies showed increased activation of the
primary motor cortex during MI with TMS (Lotze et al., 1999).
Furthermore, some studies using NIRS during MI have shown
activation of the primary motor cortex. Although the SMC is
activated during MI, its role is in movement output, and it ispossible that for this reason the hemodynamic signal changes
were lower than for other ROIs.
However, in addition to the main effects of training and
ROI, interactions were observed, and it was clarified that the
hemodynamic signal changes differ depending on the ROI. In
particular, the SMA showed an increase in the hemodynamic
signal changes as training progressed compared to other ROIs.
The SMA is activated in the late stage, suggesting its activation
when habituation of movements, including motor planning andpreparation, has already been established (Tyszka et al., 1994;
Hikosaka et al., 1996; Nakamura et al., 1998; Sakai et al., 1998;
Kasess et al., 2008; Guillot et al., 2012). It is an area responsible
for performing MI-related activity, including motor preparation(Mihara and Miyai, 2016). With progression of learning the MI
task, participants were able to evoke MI more vividly, leading to a

MI tasks was promoted. The difference between this and previous
research is that this research was conducted by adopting the tasks
used for motor learning (Mihara et al., 2012; Kober et al., 2014,2018). Previous studies have stated that brain activation is highwhen the task is difficult, and not activated enough when the task
is low in difficulty (Hatakenaka et al., 2007; Amemiya et al., 2010;
Sagari et al., 2015). Considering that the tasks were being learned
after the final training, it is considered that the MI performed
after the final training was relatively easy to carry out. Therefore,
it is considered that the difficulty level of the task is at the stage
where subjects performed MI easily, and the hemodynamic signal
changes during MI are also considered to have decreased as an
overall change in the motor-related area.
However, it has also been reported that hemodynamic signal
changes during MI differ depending on the ROI (Wriessnegger
et al., 2014; Iso et al., 2016; Matsuo et al., 2020), and this study
also confirmed the main effect of ROI. The hemodynamic signal
changes in the left PMA showed significantly high activation.
The PMA has been reported to be deeply involved in motor
learning, and activation of the PMA was consistent with previous
studies (Tyszka et al., 1994; Hikosaka et al., 1996; Nakamura
et al., 1998; Sakai et al., 1998; Kasess et al., 2008; Guillot et al.,2012). In addition, other ROIs showed higher activity comparedto the bilateral SMC. The PFC and pre-SMA are also areas
involved in early learning of exercise (Nakamura et al., 1998;
Sagari et al., 2015), and SMA has been reported to activate

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Hemodynamic Changes Reflect Motor Learning

FIGURE 13 | Changes in oxy-Hb for each ROI post-training (final). The line connecting the dots indicates that there was a significant difference in the post hoc
analysis. Vertical bars represent the standard error. ∗ P < 0.05, ∗∗ P < 0.01, and ∗∗∗ P < 0.001. Left PFC, left prefrontal cortex; Right PFC, right prefrontal cortex;
pre-SMA, pre-supplementary motor area; SMA, supplementary motor area; Left PMA, left pre-motor area; Right PMA, right pre-motor area; Left SMC, left
somatosensory motor cortex; Right SMC, right somatosensory motor cortex; oxy-Hb, oxygenated hemoglobin; ROI, region of interest.

are also similar, the oxy-Hb change in the SMA best represents
the vividness of MI. The SMA was reported to be activated inprevious NIRS studies during MI (Mihara et al., 2012; Kober et al.,2014, 2018; Matsuo et al., 2020). Our present results showed thatthe selection of these areas was adequate for examining the MI
vividness in terms of changes in cerebral hemodynamics using
NIRS as an objective index for these changes. Our results werealso consistent with the findings of a previous study (Mihara
and Miyai, 2016), which reported that these areas are responsible
for MI. An important aspect in mental practice is how vividly
an individual can perform MI; if MI vividness can be evaluatedneurophysiologically, MI can be used effectively in rehabilitation.
MI is already used in neurofeedback systems such as Brain
Computer Interface (BCI) and Functional electrical stimulation
therapy (Khan et al., 2015; Koo et al., 2016; Likitlersuang
et al., 2018). Our results demonstrate that hemodynamic signal
changes in the SMA are associated with MI vividness, which
can be used not only for mental practice but also for othertechniques such as BCI.
Therefore, the hemodynamic signal changes in motor relatedareas centered on the SMA using NIRS might be used as aneurophysiological assessment for MI vividness.

gradual increase in changes in cerebral blood flow in the SMA,
which was one of the ROIs in this study. However, the SMA
did not show high activation until the learning stage was quiteadvanced. The results of this study were consistent with those ofprevious studies regarding motor learning (Tyszka et al., 1994;
Hikosaka et al., 1996; Nakamura et al., 1998; Sakai et al., 1998;
Kasess et al., 2008; Guillot et al., 2012).
Therefore, our results show that training increased thehemodynamic signal changes in all ROIs, with the greatest
increase observed in the PMA. However, only SMA theshowed an increase in the hemodynamic signal with training
compared to other ROIs.

Relationship Between Oxy-Hb Changes
and Subjective Vividness of MI
A generalized linear model showed that oxy-Hb changes in seven
ROIs, other than the left PFC, were significant predictors of
VAS score. In particular, the SMA coefficient was the highest,
which was a significant predictor for estimating VAS scores, andwas shown to be most associated with VAS. Since the change
in VAS and the change in oxy-Hb of the SMA due to training

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Hemodynamic Changes Reflect Motor Learning

Limitations

ETHICS STATEMENT

In the present study, we used BR as the learning task,
which primarily relies on fingers dexterity; therefore, therelationship between MI vividness and cerebral hemodynamicchanges obtained in this study might be limited to MI of theupper extremities.
In addition, this study was conducted on healthy subjects;
when using MI vividness as an evaluation tool in patients withstroke it is necessary to consider the injured area.

The studies involving human participants were reviewed andapproved by the Ethics Committee of the Medical Corporation
Toujinkai. The patients/participants provided their written
informed consent to participate in this study.

AUTHOR CONTRIBUTIONS
NI, TM, KF, and THi conceived and designed the experiments.
NI, TM, KF, WM, THa, FI, and THi performed the experiments.
NI, MM, KF, WM, and THi analyzed the data. TM, THa, KC,MS, and THi created the experimental program. NI, TM, FI,
and THi wrote the article. All authors discussed the results andcontributed to the final manuscript.

Conclusion
The BR task used in the present study showed a clear motion
learning from practicing the actual motions, thereby causing
significant improvement in subjective MI vividness. In termsof hemodynamic signal changes, a main effect of training and
ROIs were observed.
Moreover, the coefficients for estimating relationships arehighest in the SMA, a key predictor for estimating VAS scores,
which suggests that it is the best region for detecting MI
vividness. Thus, our findings suggest that future studies should
examine neurophysiological indices for MI vividness based on the
hemodynamic signal changes of SMA.

FUNDING
This study was supported partly by JSPS KAKENHI (Grant Nos.
17K13056 and 18H03133). The funders had no role in this study.

DATA AVAILABILITY STATEMENTACKNOWLEDGMENTS

The raw data supporting the conclusions of this article will bemade available by the authors, without undue reservation.

We would like to thank all the participants of Miharadai Hospital
who participated in this study.

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Conflict of Interest: The authors declare that the research was conducted in theabsence of any commercial or financial relationships that could be construed as apotential conflict of interest.
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Cho, Suzuki and Higashi. This is an open-access article distributed under the termsof the Creative Commons Attribution License (CC BY). The use, distribution or
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April 2021 | Volume 15 | Article 603069