Somatosensory & Motor Research

ISSN: 0899-0220 (Print) 1369-1651 (Online) Journal homepage: https://www.tandfonline.com/loi/ismr20

Cerebral haemodynamics during motor imagery ofself-feeding with chopsticks: differences between
dominant and non-dominant hand
Moemi Matsuo, Naoki Iso, Kengo Fujiwara, Takefumi Moriuchi, Goro Tanaka,
Sumihisa Honda, Daiki Matsuda & Toshio Higashi
To cite this article: Moemi Matsuo, Naoki Iso, Kengo Fujiwara, Takefumi Moriuchi, Goro Tanaka,
Sumihisa Honda, Daiki Matsuda & Toshio Higashi (2019): Cerebral haemodynamics during motor
imagery of self-feeding with chopsticks: differences between dominant and non-dominant hand,
Somatosensory & Motor Research, DOI: 10.1080/08990220.2019.1699044
To link to this article: https://doi.org/10.1080/08990220.2019.1699044

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SOMATOSENSORY & MOTOR RESEARCH
https://doi.org/10.1080/08990220.2019.1699044

ARTICLE

Cerebral haemodynamics during motor imagery of self-feeding with chopsticks:
differences between dominant and non-dominant hand
Moemi Matsuoa,b, Naoki Isoc, Kengo Fujiwaraa,d, Takefumi Moriuchia, Goro Tanakaa, Sumihisa Hondaa,
Daiki Matsudaa,e and Toshio HigashiaaUnit of Medical Science, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan; bCenter for Child Mental HealthCare and Education, Nagasaki University, Nagasaki, Japan; cTokyo Kasei University, Saitama, Japan; dZeshinkai General Incorporated
Association, Nagasaki Rehabilitation Hospital, Ginyamachi, Nagasaki, Japan; eThe Japanese Red Cross, Nagasaki Genbaku Hospital,
Nagasaki, Japan

ABSTRACTARTICLE HISTORY

Purpose: Motor imagery is defined as a dynamic state during which a subject mentally simulates agiven action without overt movements. Our aim was to use near-infrared spectroscopy to investigate
differences in cerebral haemodynamics during motor imagery of self-feeding with chopsticks using thedominant or non-dominant hand.
Materials and methods: Twenty healthy right-handed people participated in this study. The motor
imagery task involved eating sliced cucumber pickles using chopsticks with the dominant (right) or
non-dominant (left) hand. Activation of regions of interest (pre-supplementary motor area, supplementary motor area, pre-motor area, pre-frontal cortex, and sensorimotor cortex was assessed.
Results: Motor imagery vividness of the dominant hand tended to be significantly higher than that of
the non-dominant hand. The time of peak oxygenated haemoglobin was significantly earlier in theright pre-frontal cortex than in the supplementary motor area and left pre-motor area. Haemodynamic
correlations were detected in more regions of interest during dominant-hand motor imagery than during non-dominant-hand motor imagery.
Conclusions: Haemodynamics might be affected by differences in motor imagery vividness caused byvariations in motor manipulation.

Received 28 February 2019
Accepted 26 November 2019

Introduction
Motor imagery (MI) can be defined as a dynamic state during which a subject mentally simulates a given action without any overt movements (Decety 1996; Blefari et al. 2015).
With advances in neuroimaging systems, cortical activation
during MI tasks can now be measured. For example, in anear-infrared spectroscopy (NIRS) study, both motor execution (ME) and MI of hand-finger opposition tasks increased
cerebral blood flow in almost all of the same areas, including the prefrontal cortex (PFC), primary motor cortex, andsensorimotor cortex (SMC) (Wriessnegger et al. 2008). Also,in a transcranial magnetic stimulation study, the motorevoked potential, which is indicative of cortico-spinal excitability, was significantly larger during MI than during rest
(Roosink and Zijdewind 2010; Feurra et al. 2011). Moreover,a previous functional magnetic resonance imaging study
suggested that nearly the same cerebellar areas are activated during ME and MI of activities that require the use oftools, despite the activation differences among tasks(Higuchi et al. 2007).
Many studies have investigated cerebral activation during MI of simple tasks, but only a few have focussed on

KEYWORDS

Imagery (psychotherapy);
haemodynamics; motor
cortex; spectroscopy nearinfrared; rehabilitation;
mental practice

MI of complex tasks, such as activities of daily living(ADLs), which are relevant clinically. Some ADLs are
impacted by handedness. For example, self-feeding withchopsticks requires several types of movement, such as
arm and finger movements, and the use of tools, which
are difficult to execute with the non-dominant hand. Thus,
if the MI ability is also affected by handedness, thehaemodynamics during MI of self-feeding with chopsticks
might be different between the dominant and nondominant hand. Additionally, MI vividness might also differ
between hands; indeed, previous studies have suggested
that cerebral activity can differ according to MI vividness(Williams et al. 2012).
Hence, our hypotheses were as follows: there would be
differences in (1) cerebral haemodynamics and (2) MI vividness during MI of self-feeding with chopsticks between thedominant and non-dominant hand. Thus, our aim was touse NIRS to investigate differences in cerebral haemodynamics during MI of self-feeding with chopsticks using
the dominant or non-dominant hand. Our findings provide
basic knowledge for the use of MI in ADLs in clinical scenarios.

CONTACT Toshio Higashi
higashi-t@nagasaki-u.ac.jp
Unit of Medical Science, Nagasaki University Graduate School of Biomedical Sciences, Nagasakishi,
Nagasaki, Japan
Supplemental data for this article can be accessed here.
ß 2019 Informa UK Limited, trading as Taylor & Francis Group2

M. MATSUO ET AL.

Materials and methods
Subjects
Twenty healthy right-handed participants (14 women, 6 men;age: 26.9 ± 8.8 years) participated in this study. All participants were consistent right-handers according to the
Edinburgh handedness inventory (Oldfield 1971). After being
informed about the safety regulations for the study andacknowledging that they cannot be identified through thismanuscript, all participants provided written informed
consent for participation. No participants had a history ofneurological illness, head injury, major physical illness, or
psychiatric disorders. This study was conducted in the
Biomedical Science Department of Nagasaki University. It wasapproved by the Nagasaki University ethical review committee and complied with the Declaration of Helsinki (World
Medical Association 2001).

MI TaskThe MI task required that the participant imagine he or she
is eating sliced cucumber pickles using chopsticks, including
chopstick manipulation, bringing the pickle into the mouth,
chewing, and swallowing. MI was performed in two conditions: one was MI with the dominant hand (dominant MI),and the other was MI with the non-dominant hand (nondominant MI).
To decrease the variability in MI between participants, allparticipants completed a 5-min MI practice session with practice videos (Figure 1) before each task (S1A and S1B Video).
The practice video represented the first-person view, whichshowed that the sliced cucumber pickles were in a dish on atable; the person picked up a pickle with the chopsticks andbrought it to the mouth. The video was accompanied by thesound of pickles being eaten, which could be heard throughearphones. The practice video length (from chopsticks
manipulation to swallowing) was 10 s, which was the sameas the length of the actual MI task. To ensure consistency
between practice videos for the right and left hands, thevideo for the MI condition using the non-dominant hand
was created by inverting the video of the dominant hand.

Experimental protocol
The participants sat on a comfortable chair in a quiet room.
They were instructed to put their forearms in an intermediate position on the table, to relax, not to move their head orarms as much as possible, and not to speak during theexperiment. After showing the MI practice videos, NIRS measurements were collected as part of a block design, which
included three cycles of 20 s of the task and 30 s of rest
(Figure 2), as in a previous study (Iso et al. 2016). This block
design was consistent between the two conditions, comprising three cycles of the task (total 6 trials) in the dominant MI
condition and another three cycles of the task (total 6 trials)
in the non-dominant MI condition. All participants were
examined in both conditions.
The condition order (dominant MI and non-dominant MI)
was counter-balanced. The participants were asked toimagine eating pickles twice for 20 s and to imagine thatthey were actually doing it. Thus, they imagined kinaesthetic
imagery, which included sounds, and were instructed not tomove and to keep their eyes closed. Participants were
instructed to maintain the same position and to relax

Figure 1. Still image of the MI practice video. The video represents the first-person view. One practice cycle (from chopstick manipulation to swallowing) was 10-s
long, which was the same length as the actual MI task. The video for the non-dominant MI condition was created by inverting the dominant MI condition video.
MI: motor imagery.

Figure 2. Experimental protocol. NIRS measurements were collected as part of a block design, which included three cycles of 20 s for the task and 30 s of rest. The
5-min MI practice session was completed before NIRS measurements were obtained, and MI vividness was measured by the VAS following the NIRS measurements.
MI: motor imagery; NIRS: near-infrared spectroscopy; VAS: visual analogue scale.

SOMATOSENSORY & MOTOR RESEARCH

3

without thinking during the rest period. They were notified
when the task or rest session began by the words “start” or“stop,” announced by the researcher.
The visual analogue scale (VAS), which is used to assess
subjective MI vividness (Lotze et al. 2003; Lotze andHalsband 2006; Malouin et al. 2008; Ikeda et al. 2012), wasemployed to investigate MI vividness after each MI task. The
participants were asked how vividly they could imagine
the MI task and were instructed to mark their responses on
the VAS, which ranged from 0 mm (0%; none) to 100 mm(100%; very vivid).

NIRS measurements
NIRS is non-invasive and easy to use, and its reliability andreplicability have been demonstrated previously (Kono et al.
2007; Maslehaty et al. 2012; Iso et al. 2016). It can detectchanges of cerebral oxygenation while subjects are performing tasks, even in clinical scenarios. In this study,changes in oxygenated haemoglobin (oxy-Hb), deoxygenated haemoglobin (deoxy-Hb), and total haemoglobin levels
were recorded using the ETG-4000 system (Hitachi Medical
Co., Tokyo, Japan) with 24 channels, which consisted of
4  4 optode probe sets (8 emitters and 8 detectors). Eachprobe was placed 3.0 cm away from the other probes. TheNIRS channels were positioned according to the international 10–20 electroencephalography placement system(Okamoto et al. 2004), and Cz was positioned as a replicable marker of the probes, as determined by previous studies (Figure 3) (Okamoto et al. 2004; Saimpont et al. 2016).
The present system used two wavelengths of near-infrared
light, which were approximately 625 and 830 nm, and thereflected light was sampled at 10 Hz. This system estimateschanges in oxy-Hb levels at a depth of 3.0 cm below
the scalp.
Activation of the pre-supplementary motor area (PreSMA),
supplementary motor area (SMA), bilateral PFC, bilateral premotor area (PMA), and bilateral SMC were assessed as theregions of interest in this study (Figure 3). The activity levelof the PreSMA was detected by channels 2, 5, and 6; that of
the SMA by channels 9, 12, 13, and 16; of the left PFC(L-PFC) by channels 1 and 4; and of the right PFC (R-PFC) by
channels 3 and 7. Moreover, the activity level of the left PMA(L-PMA) was detected by channels 8, 11, and 15; that of theright PMA (R-PMA) by channels 10, 14, and 17; of the left
SMC (L-SMC) by channels 18 and 22; and of the right SMC(R-SMC) by channels 21 and 24 (Hatakenaka et al. 2007;
Sagari et al. 2015; Iso et al. 2016).
The detected haemoglobin levels were converted tochanges in oxy-Hb and deoxy-Hb levels based on the modified Beer–Lambert approach (Cope and Delpy 1988; Obrig
and Villringer 2003). In this study, we used the mean oxy-Hb
concentration changes and peak oxy-Hb timing to signify
brain activation caused by the MI task, as oxy-Hb levels represent a sensitive parameter to detect brain activation (Hoshi
et al. 2001). The moving average method (window: 5.0 s) wasused to remove short-term motion artefacts from the analysed data. The obtained data were analysed using the

Figure 3. NIRS channel probe sets. NIRS channels were positioned according to
the international 10–20 electroencephalography placement system, and the Cz
electrode was positioned as a replicable marker of the probes. The SMC, PMA,
PFC, PreSMA, and SMA were assessed as the regions of interest. NIRS: nearinfrared spectroscopy; PFC: prefrontal cortex; PMA: pre-motor area; PreSMA:
pre-supplementary motor area; SMA: supplementary motor area; SMC: supplementary motor cortex.

integral mode, which calculates the average waveform fromthree cycles of data acquisition. We determined the pre-taskbaseline as the mean over the 5.0 s prior to the task period,
and the post-task baseline as the mean over the last 5.0 s ofthe post-task period. We applied linear fitting to the data
between these two baselines. The first 5.0 s of each task
were excluded from the analyses to ensure that the data
would not affected by the nerve–blood vessel coupling system, a phenomenon in which brain blood vessel ectasia andincreased brain blood flow require time to occur. Thus, thelast 5.0–20.0 s (total: 15.0 s) of the task period were used foranalyses (Figure 4), and all data for the three cycles were calculated using the signal-averaging technique. Channels withhigh noise levels were marked using a high-pass filter at
3.0 Hz, which was 0.1 standard deviation above the wave
analysis, so that it would not be affected by noise or hyperactivation due to skin blood flow (Takahashi et al. 2011). The
mean, peak time, and standard deviation were examined forall participants.

4

M. MATSUO ET AL.

Figure 4. Method for calculating oxy-Hb changes. Linear fitting was applied to the data between pre-task (blue arrow) and post-task (green arrow) periods. The
thick black arrow above the red curve indicates the MI period, and the thin arrow indicates the rest period. The vertical axis represents oxy-Hb concentration(mMmm), and the horizontal axis represents the time course of one cycle. The first 5 s of the MI tasks were excluded from the analyses. The final 5–20 s (total 15 s)of the task period were used for the analyses. MI: motor imagery; oxy-Hb: oxygenated haemoglobin.

Electromyography measurements
Surface electromyography (Neuropack Sigma MEB-5504,
Nihon Kohden, Japan) was used to monitor the participants’muscles and to ensure that they did not move. The electromyography electrodes were positioned over three spots asfollows: the left masseter muscle and the right and left firstdorsal interosseous muscles, which are related to self-feeding
activities. The electromyography frequency waves fluctuatedbetween 5 Hz–3 kHz and were converted to digital data witha sampling rate of 2 kHz using an A/D conversion device(PowerLab16/30, AD Instruments, Australia). The electromyography measurements showed that the finger and faceposture were unaltered in each participant, with no evidenceof obvious muscle activation. Thus, all the participants’ data
were included in the analyses.

Statistical analysis
VAS values, mean oxy-Hb values, and peak oxy-Hb time during the MI tasks were calculated for both conditions (dominant MI and non-dominant MI). The Wilcoxon’s signed-rank
test was used to examine differences in VAS values between
the dominant MI and non-dominant MI conditions. Two-way
repeated measures analysis of variance (ANOVA) was performed with the “regions of interest” (PreSMA, SMA, L-PFC,
R-PFC, L-PMA, R-PMA, L-SMC, R-SMC) and “condition” (dominant MI, non-dominant MI) as within-subject factors, to examine oxy-Hb differences, both regarding the mean oxy-Hb
values and peak oxy Hb times. The peak times in all participants were compared using Bonferroni test, while effect sizes
were calculated for the mean values and peak times. In addition, in order to reveal the haemodynamic correlations
between regions of interest, raw oxy-Hb data were convertedto oxy-Hb z-score data, and the relationships among regionsof interest were determined. The z-score represents the value

with a mean value of 0 and a standard deviation of 1
(Tsunashima and Yanagisawa 2009). The z-score was automatically calculated for each participant using the ETG-4000
system. Next, Spearman’s rank correlation was used to examine the relationship between cerebral haemodynamics andregions of interest during MI. The statistical analysis software
Statistical Package for the Social Sciences (Version 22.0, IBM,
Japan) was used for data analysis. Differences with a p-value<0.05 were considered as statistically significant, while differences with a p-value <0.10 were considered to indicate atrend towards significance.

Results
Comparison of subjective MI vividness between
MI conditions
There was no significant difference in subjective MI vividness
between the dominant and non-dominant MI conditions.
However, the mean VAS value of the dominant MI condition
was higher than that of the non-dominant MI condition,
showing a trend towards significance (p ¼ 0.073, Z ¼ 1.793,
Figure 5; S2 Dataset).

Comparison of haemodynamics between MI conditions
The time course of oxy-Hb changes during MI (all participants’ grand average waves) is shown in Figure 6 (S3Dataset). Two-way ANOVA for the mean oxy-Hb values
revealed a significant “regions of interest  condition” interaction [F(7,13) ¼ 2.115, p < 0.05, g2 ¼ 0.111], whereas there wasno main effect of “region of interest” [F(7,13) ¼ 0.753, p > 0.05,
g2 ¼ 0.041] or “condition” [F(1,19) ¼ 0.250, p > 0.05, g2 ¼ 0.014].
The peak oxy-Hb time in each region of interest for the nondominant versus dominant MI were as follows: PreSMA:
10.8 s versus 11.4 s; SMA: 11.8 s versus 12.0 s; L-PFC: 10.9 s

SOMATOSENSORY & MOTOR RESEARCH

5

Figure 5. Comparison of VAS scores between motor imagery conditions. The
VAS data were analysed using Wilcoxon’s signed-rank test. The red bar indicates the mean oxy-Hb value during the non-dominant MI task, whereas theblue bar indicates the mean oxy-Hb value during the dominant MI task. The
mean dominant MI value was higher than the mean non-dominant MI value,
and the results showed a trend towards a statistically significant difference. MI:
motor imagery; oxy-Hb: oxygenated haemoglobin; VAS: visual analogue scale.

versus 10.6 s; R-PFC: 8.6 s versus 9.9 s; L-PMA: 12.9 s versus
12.1 s; R-PMA: 11.7 s versus 10.9 s; L-SMC: 12.0 s versus 11.1 s;
and R-SMC: 9.7 s versus 11.6 s. Two-way ANOVA for the peakoxy-Hb times revealed a significant main effect of “regions ofinterest” [F(7,13) ¼ 2.626, p < 0.05, g2 ¼ 0.169], whereas there
was no main effect of “condition” [F(1,19) ¼ 0.030, p > 0.05,
g2 ¼ 0.001] or “regions of interest  condition” interaction[F(7,13) ¼ 1.194, p > 0.05, g2 ¼ 0.049]. Bonferroni test revealed
significant differences in the peak oxy-Hb times wereobserved between the SMA and R-PFC (p ¼ 0.050) andbetween the R-PFC and L-PMA (p ¼ 0.046).

Relationship between haemodynamics and regions ofinterest for the MI conditions
Based on the significant “region of interest  condition” interaction, we examined the relationship between cerebral
haemodynamics and regions of interest during MI. The correlation results are shown in Tables 1, 2, and Figure 7. The
data were converted from raw oxy-Hb levels to oxy-Hb
z-scores. A total of 25 statistically significant correlations
were found for the regions of interest in dominant MI data,
and these were spread across both hemispheres. In contrast,
only 17 statistically significant correlations were found forthe regions of interest in non-dominant MI data, and thesewere only found within the left hemisphere.

Discussion
In this study, we used NIRS to investigate cerebral haemodynamics during MI of self-feeding with chopsticks. Our aim
was to verify if there were cerebral activation differences
between dominant and non-dominant MI conditions.

Figure 6. Time course of oxy-Hb changes. The time course of oxy-Hb changes
during MI (all participants’ grand average waves) is shown. The waveform wascalculated by averaging the data measured over three cycles in a block design.
The red line indicates oxy-Hb changes during the non-dominant MI task, andthe blue line indicates oxy-Hb changes during the dominant MI task. The pinkbackground indicates the MI task time (0–20 s), while the skin-coloured background indicates the rest time (20–50 s). MI: motor imagery; oxy-Hb: oxygenated haemoglobin.

We found that vividness during dominant MI tended to besignificantly higher than that during non-dominant MI. There
was no main effect of “region of interest” or “condition,” but
we found a significant “region of interest  condition” interaction for the mean oxy-Hb values. Moreover, we observed
significant differences in the peak oxy-Hb times between the
SMA and R-PFC, as well as between the R-PFC and L-PMA. In
addition, more correlations among regions of interest were
found during dominant than non-dominant MI.
In the present study, we found that vividness during thedominant MI task, which involved skilful manipulation ofchopsticks, had a mean value that tended to be higher thanthat during the non-dominant MI task, which involved nonskilful manipulation of chopsticks. A previous study using
functional magnetic resonance imaging combined with transcranial magnetic stimulation reported a significant activation
only in the right precentral gyrus during a complex MI task
performed with the left hand, as compared to performed
with the right hand, although there were many overlapping
activation areas between the two tasks (Kuhtz-Buschbeck
et al. 2003). Another functional magnetic resonance imaging
study suggested that the neural correlates for the imager’s

6

M. MATSUO ET AL.

Figure 7. Haemodynamic correlations during motor imagery. The oxy-Hb z-scores were analysed using Spearman’s rank correlation. The regions of interest connected by black lines represent significant correlations in both conditions. The regions of interest connected by red dotted lines represent significant correlations in
one condition. Regions-of-interest correlations were found in both hemispheres for the dominant MI condition but only in the left hemisphere for the non-dominant MI condition. MI: motor imagery; oxy-Hb: oxygenated haemoglobin.

Table 1. Haemodynamic correlations for motor imagery of the dominant hand.
(q)

PreSMA

SMA

L-PFC

R-PFC

L-PMA R-PMA L-SMC R-SMC

PreSMA
SMA
L-PFC
R-PFC
L-PMA
R-PMA
L-SMC
R-SMC

1.000
0.75
0.883
0.792
0.617
0.794
0.74
0.719

1.000
0.574
0.451
0.792
0.771
0.534
0.515

1.000
0.74
0.402
0.735
0.656
0.626

1.000
0.326
0.492
0.642
0.604

1.000
0.659 1.000
0.533 0.561 1.000
0.451 0.56 0.862

1.000

PFC: prefrontal cortex; PMA: pre-motor area; PreSMA: pre-supplementary
motor area; SMA: supplementary motor area; SMC: sensorimotor cortex.
p < 0.05 by Spearman’s rank correlation.
The oxygen-haemoglobin (oxy-Hb) z-scores were analysed using Spearman’srank correlation. The shaded cells with asterisks indicate significant correlations. Twenty-five significant correlations were found.

Table 2. Haemodynamic correlations for motor imagery of the non-dominant hand.
(q)

PreSMA

SMA

L-PFC

R-PFC

L-PMA R-PMA L-SMC R-SMC

PreSMA
SMA
L-PFC
R-PFC
L-PMA
R-PMA
L-SMC
R-SMC

1.000
0.746
0.725
0.707
0.721
0.444
0.771
0.272

1.000
0.561
0.66
0.883
0.532
0.621
0.104

1.000
0.537
0.421
0.296
0.782
0.463

1.000
0.568
0.245
0.776
0.412

1.000
0.597
0.65
0.088

1.000
0.297
0.201

1.000
0.661

1.000

PFC: prefrontal cortex; PMA: pre-motor area; PreSMA: pre-supplementary
motor area; SMA: supplementary motor area; SMC: sensorimotor cortex
p < 0.05 by Spearman’s rank correlation.
The oxygen-haemoglobin (oxy-Hb) z-scores were analysed using Spearman’srank correlation. The shaded cells with asterisks indicate significant correlations. Seventeen significant correlations were found.

perceived MI vividness are very specific (Lorey et al. 2011).
Based on the above, MI vividness might have affected
haemodynamics and may be dependent on the given
MI task.

In this study, there were no significant differences in oxyHb mean values, indicating that oxy-Hb levels equally
increase during dominant and non-dominant MI of self-feeding using chopsticks. However, peak oxy-Hb times were significantly different between the SMA and R-PFC andbetween the R-PFC and L-PMA, indicating that peak oxy-Hb
during self-feeding MI tasks occurs significantly earlier in the
R-PFC than in the SMA or L-PMA. In addition, we converted
the raw haemodynamic data to z-score data, in order toexamine the dynamic oxy-Hb relationships among regions ofinterest. Haemodynamic response correlations were foundboth for the right and left hemispheres during dominant MI;
however, such correlations were found only in the left hemisphere during non-dominant MI.
A previous study using NIRS, which investigated cerebral
haemodynamics during swallowing ME and MI by comparing
patients with cerebral lesions and healthy participants,
showed that the peak oxy-Hb time during MI is longer inpatients with cerebral lesions than in healthy adults (Kober
et al. 2015). This result implies that if participants find it difficult to execute the MI, the peak oxy-Hb time is prolonged.
Thus, we hypothesised that the peak oxy-Hb time during thenon-dominant MI task, which is more difficult to execute,would be longer than that during the dominant MI task,
which is easier to execute. However, our results indicated nostatistical differences in the peak oxy-Hb time between thetwo conditions. In addition, the peak oxy-Hb times were significantly different between the R-PFC and SMA and between
the R-PFC and L-PMA during self-feeding MI. Nevertheless,these results are not yet sufficient to discuss the underlying
mechanisms. Thus, we could not clearly conclude whether
difficulty in performing MI is associated with the peak oxyHb time. Hence, further research is required on this topic.
Previous MI studies have reported that MI is executed
dominantly by the left hemisphere (Stinear et al. 2006), andSOMATOSENSORY & MOTOR RESEARCH

that it is more difficult to activate the right than the left
hemisphere using MI tasks (Wang et al. 2014). Moreover, in astudy that compared brain connectivity, stronger connectivity was observed in the left hemisphere, including the SMC,
PMA, and SMA during a dominant MI task in healthy adults,
while stronger connectivity was observed in the right hemisphere during the same task in patients with right hemiplegia (Wang et al. 2016). Therefore, cortical activation occurs
primarily in the left hemisphere during MI tasks; however,the left and right hemispheres are connected to each other,and it is possible that the connectivity strength is reflectedby MI vividness. Hence, it is possible that haemodynamic correlations are affected by MI vividness, as more correlations
among regions of interest were found during dominant MI.
This means that, in the case of the self-feeding activity withchopsticks, it was more difficult for participants to perform
MI vividly during the non-dominant MI condition (i.e.,
manipulate the chopsticks with the left hand), which is representative of movement non-proficiency, than during thedominant MI condition (i.e., manipulate the chopsticks withthe right hand), which is representative of movement proficiency. That may explain why the haemodynamic response
correlations among regions of interest were limited in the
non-dominant MI condition.

Research limitations
In this study, the participants were all right-handed healthy
adults who were skilful at chopsticks manipulation; thus, it isunclear whether our results are generalisable to left-handed
individuals and those with disease, disability, or who are notskilful with chopsticks. Second, no participants reported avery low rate of MI vividness; therefore, it remains unclear
whether haemodynamic differences would be observed
between participants with high and low MI vividness. Third,
it is unclear whether the MI of self-feeding results in similar
haemodynamics responses as the ME of the same self-feeding activity or other ADLs. Finally, the sample size of ourstudy was small. Based on these limitations, future studies
should include patients with various conditions who might
benefit from mental practice of various tasks, including lefthanded subjects, and should compare MI with ME of selffeeding activity. In addition, cerebral activation during MI ofother ADLs should be assessed.

Conclusion
The haemodynamic response correlations were spread acrossboth hemispheres during MI with the dominant hand but
were restricted to the left hemisphere during MI with thenon-dominant hand. These differences suggest that MI vividness differences are caused by movement proficiency differences, which may affect haemodynamic responses. The
present study provides further insight regarding MI, as well
as new ideas about the use of MI for rehabilitation of ADLs.
Thus, our results may contribute to the development of astandardised methodology for mental practice using MI in
rehabilitation.

7

Disclosure statement
No potential conflict of interest was reported by the authors.

Data availability statement
Data sets are available with clicking the links of cited supplemental
online materials in the text.

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