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ORIGINAL RESEARCH
published: 12 March 2021
doi: 10.3389/fncel.2021.653487
Frontiers in Cellular Neuroscience | www.frontiersin.org 1March 2021 | Volume 15 | Article 653487
Edited by:
Zhang Pengyue,
Yunnan University of Traditional
Chinese Medicine, China
Reviewed by:
Chunlei Shan,
Shanghai University of Traditional
Chinese Medicine, China
Xiquan Hu,
Third Affiliated Hospital of Sun Yat-sen
University, China
*Correspondence:
Guangqing Xu
guangchingx@163.com
Yue Lan
bluemooning@163.com
Specialty section:
This article was submitted to
Cellular Neurophysiology,
a section of the journal
Frontiers in Cellular Neuroscience
Received: 14 January 2021
Accepted: 22 February 2021
Published: 12 March 2021
Citation:
Ding Q, Lin T, Wu M, Yang W, Li W,
Jing Y, Ren X, Gong Y, Xu G and Lan Y
(2021) Influence of iTBS on the Acute
Neuroplastic Change After BCI
Training.
Front. Cell. Neurosci. 15:653487.
doi: 10.3389/fncel.2021.653487
Influence of iTBS on the Acute
Neuroplastic Change After BCI
Training
Qian Ding 1, Tuo Lin 1, Manfeng Wu 1, Wenqing Yang 1, Wanqi Li 1, Yinghua Jing 1,
Xiaoqing Ren 1, Yulai Gong2, Guangqing Xu 3
*and Yue Lan1
*
1Department of Rehabilitation Medicine, Guangzhou First People’s Hospital, School of Medicine, South China University of
Technology, Guangzhou, China, 2Sichuan Provincial Rehabilitation Hospital, Chengdu University of Traditional Chinese
Medicine, Chengdu, China, 3Department of Rehabilitation Medicine, Guangdong Provincial People’s Hospital, Guangdong
Academy of Medical Sciences, Guangzhou, China
Objective: Brain-computer interface (BCI) training is becoming increasingly popular in
neurorehabilitation. However, around one third subjects have difficulties in controlling
BCI devices effectively, which limits the application of BCI training. Furthermore, the
effectiveness of BCI training is not satisfactory in stroke rehabilitation. Intermittent theta
burst stimulation (iTBS) is a powerful neural modulatory approach with strong facilitatory
effects. Here, we investigated whether iTBS would improve BCI accuracy and boost the
neuroplastic changes induced by BCI training.
Methods: Eight right-handed healthy subjects (four males, age: 20–24) participated
in this two-session study (BCI-only session and iTBS+BCI session in random order).
Neuroplastic changes were measured by functional near-infrared spectroscopy (fNIRS)
and single-pulse transcranial magnetic stimulation (TMS). In BCI-only session, fNIRS
was measured at baseline and immediately after BCI training. In iTBS+BCI session,
BCI training was followed by iTBS delivered on the right primary motor cortex (M1).
Single-pulse TMS was measured at baseline and immediately after iTBS. fNIRS was
measured at baseline, immediately after iTBS, and immediately after BCI training.
Paired-sample t-tests were used to compare amplitudes of motor-evoked potentials,
cortical silent period duration, oxygenated hemoglobin (HbO2) concentration and
functional connectivity across time points, and BCI accuracy between sessions.
Results: No significant difference in BCI accuracy was detected between sessions
(p>0.05). In BCI-only session, functional connectivity matrices between motor cortex
and prefrontal cortex were significantly increased after BCI training (p’s <0.05). In
iTBS+BCI session, amplitudes of motor-evoked potentials were significantly increased
after iTBS (p’s <0.05), but no change in HbO2 concentration or functional connectivity
was observed throughout the whole session (p’s >0.05).
Conclusions: To our knowledge, this is the first study that investigated how iTBS
targeted on M1 influences BCI accuracy and the acute neuroplastic changes after BCI
training. Our results revealed that iTBS targeted on M1 did not influence BCI accuracy
Ding et al. Effects of iTBS on BCI Training
or facilitate the neuroplastic changes after BCI training. Therefore, M1 might not be an
effective stimulation target of iTBS for the purpose of improving BCI accuracy or facilitate
its effectiveness; other brain regions (i.e., prefrontal cortex) are needed to be further
investigated as potentially effective stimulation targets.
Keywords: transcranial magnetic stimulation, brain computer interface, functional near-infrared spectroscopy,
intermittent theta burst stimulation, motor imagery
INTRODUCTION
Brain computer interface (BCI) can directly translate brain
activities reflecting the subject’s intention into motor commands
for controlling an external device (Abiri et al., 2019). The
external device can in turn provide BCI users with state-
dependent sensory feedback, which is known as closed-loop BCI
system (Johnson et al., 2018). Following stroke, the connection
between the peripheral muscles and sensorimotor cortex is often
disrupted due to cortical or subcortical lesions, which results
in hemiparesis. With BCI, stroke survivors are able to control
external devices bypassing the damaged physiological motor
output system (Daly and Wolpaw, 2008), including those with
severe hemiparesis who cannot actively participate in traditional
motor training (Ramos-Murguialday et al., 2013). The closed-
loop BCI system provides stroke survivors with a chance to
actively participate in motor training and activate their motor-
related cortices (Pichiorri et al., 2015). The effectiveness of BCI
training on motor recovery following stroke has been reported in
several clinical studies (Teo and Chew, 2014; Pichiorri et al., 2015;
Sun et al., 2017; Wu et al., 2019).
However, a large portion of BCI users (∼30% of healthy
adults and ∼40% stroke survivors) are unable to control BCI
systems effectively (Blankertz et al., 2010). This phenomenon is
called “BCI-illiteracy” problem (Vidaurre and Blankertz, 2010),
which largely limits the effectiveness of BCI training. “BCI-
illiteracy” has been suggested to result from insufficient event-
related desynchronization (ERD) of the mu rhythm (Buch et al.,
2008). Mu rhythm is typically observed over the sensorimotor
area with a frequency of 8–13 Hz and is attenuated during motor
imagery (i.e., mu ERD). As the amplitude of mu ERD plays a
crucial role in BCI decoding accuracy (Buch et al., 2008), it is
sometimes difficult for BCI systems to detect the subject’s motion
intention without a strong mu ERD (Kasashima et al., 2012). As
the amplitude of mu ERD is related to motor-related cortical
activation, it can be modulated by non-invasive brain stimulation
(NIBS) techniques (Hummel and Cohen, 2006). Therefore, NIBS
Abbreviations: BCI, brain computer interface; ERD, event-related
desynchronization; NIBS, non-invasive brain stimulation; tDCS, transcranial
direct current stimulation; rTMS, repetitive transcranial magnetic stimulation;
iTBS, intermittent theta-burst stimulation; MVC, maximal voluntary contraction;
EMG, electromyography; FDI, first dorsal interosseus; M1, primary motor cortex;
RMT, resting motor threshold; MEP, motor evoked potential; AMT, active motor
threshold; fNIRS, functional near-infrared spectroscopy; CSP, cortical silent
period; ROI, regions of interest; DLPFC, dorsal lateral prefrontal cortex; FP,
frontal polar; HbO2, oxygenated hemoglobin; HRF, hemodynamic response
function; PLV, phase locking value.
is a feasible approach for enhancing BCI performance and solving
“BCI-illiteracy” problem. The influence of transcranial direct
current stimulation (tDCS), a common form of NIBS, on BCI
training has been extensively investigated (Ang et al., 2015;
Kasashima-Shindo et al., 2015; Hong et al., 2017). It has been
reported that anodal tDCS applied on motor cortex can increase
mu ERD and improve BCI accuracy in both healthy adults (Wei
et al., 2013) and stroke survivors (Ang et al., 2015; Kasashima-
Shindo et al., 2015). Unfortunately, the increased BCI accuracy
cannot be transferred to improved effectiveness of BCI training
in motor recovery following stroke (Ang et al., 2015; Kasashima-
Shindo et al., 2015; Hong et al., 2017); this is possibly because
anodal tDCS could not facilitate neuroplastic changes induced by
BCI training. Therefore, how other forms of NIBS influence the
effects of BCI training needs to be investigated.
Repetitive transcranial magnetic stimulation (rTMS) is
another popular NIBS technique, which is frequently applied
to induce modulation of cortical activation. Among patterned
rTMS protocols, a modified form of rTMS known as theta-
burst stimulation (TBS) can induce longer-lasting neural effects
with shorter application time and lower stimulation intensities
compared with conventional rTMS paradigms. Intermittent TBS
(iTBS) has been suggested to have facilitatory neural effects
(Chung et al., 2016). When applied on the primary motor cortex
(M1), robust increase in cortical excitability usually lasts for 20–
30 min after iTBS (Huang et al., 2005, 2011; Chung et al., 2016).
Based on its convenience of use and strong neural modulatory
effect, iTBS has been widely applied as a powerful technique
to upregulate cortical excitability in clinical studies (Chung
et al., 2016; Chen et al., 2019). Therefore, iTBS might be an
effective approach for increasing BCI accuracy and facilitating
neuroplastic changes of BCI training.
Neuroplastic changes after BCI training can be investigated
by many neuroimaging approaches, such as functional magnetic
resonance imaging (fMRI), electroencephalography (EEG), and
functional near-infrared spectroscopy (fNIRS), etc. (Yang et al.,
2019). Although being able to provide accurate information
about brain structure and neural activities, high cost and subject’s
head fixation have limited applications of fMRI in tasks that
require constant movement or real-time monitor (Strangman
et al., 2006; Mihara and Miyai, 2016). fNIRS is a non-invasive
neuroimaging tool that monitors cerebral and myocardial
oxygenation during tasks (Yang et al., 2019). Compared with
fMRI, fNIRS has some advantages including real-time monitor,
relatively low price, simplicity, and relatively high temporal
resolution. Compared with EEG, fNIRS has higher spatial
resolution and is less likely to be influenced by subject’s head
Frontiers in Cellular Neuroscience | www.frontiersin.org 2March 2021 | Volume 15 | Article 653487
Ding et al. Effects of iTBS on BCI Training
move during motor tasks (Yang et al., 2019). Therefore, fNIRS
is a suitable approach for monitoring immediate neuroplastic
changes after BCI training.
Here, we investigated how iTBS targeted on M1 influences the
BCI accuracy and acute neuroplastic changes of BCI training.
We used fNIRS to measure acute neuroplastic changes of
BCI training. We anticipated that BCI training would increase
brain activation and functional connectivity in the motor and
prefrontal cortices. iTBS would improve BCI accuracy and boost
the acute neuroplastic changes induced by BCI.
METHODS
Participants
Eight healthy adults volunteered for this two-session study
[four males; mean age: 21.6 (SD =1.2) years]. Participants
were included only if they were right-handed assessed with
the Edinburgh Handedness Inventory, and had no history of
neurological disorders, including no head or hand injuries.
Participants were excluded if: using medications that reduce
seizure threshold; pregnant; or any implanted device or metal
that might be affected by the magnetic field generated by TMS
was present.
Participants gave their written informed consent for the
experimental procedures that were approved by the Guangzhou
First People’s Hospital Human Research Ethics Committee.
The study was performed in accordance with the Declaration
of Helsinki.
Force Measurements
We tested maximal voluntary isometric pinch grip (MVC) in
both hands of each participant. Grip force assessment system
(BioFlex-H, Zhanghe Intelligent Co., Guangzhou, China) was
used to measure isometric pinch grip force in the “standard”
position (Ding and Patten, 2018) with real-time force feedback
displayed on a 24-inch television screen. Three MVC trials
were interspersed with rest intervals (2 min); the peak value was
carried forward as MVC for each hand.
Physiological Measures
Transcranial Magnetic Stimulation
Surface electromyography (EMG) was recorded from the first
dorsal interosseus (FDI) of both hands. Participants were seated
in a comfortable chair with back supported. The EMG raw signal
was amplified and band-pass filtered (3 Hz−3 kHz), digitized at a
sampling rate of 2,048 Hz with a 50 Hz notch filter enabled. EMG
data were written to disc for offline analysis.
TMS was performed using a NS5000 Magnetic Stimulator
(YIRUIDE Medical Co., Wuhan, China). TMS was applied
over M1 using a figure-of-eight-shaped coil (70 mm diameter)
positioned tangentially 45◦from midline to induce a posterior-
anterior current in the hemisphere. Participants were asked to
remain static while determining the optimal scalp position for
eliciting maximal responses in the contralateral FDI. Resting
motor threshold (RMT) was determined experimentally as
the lowest stimulation intensity that produced motor evoked
potentials (MEP) ≥50 uV in 50% of consecutive stimulations
at rest (Chen et al., 1998). Active motor threshold (AMT) was
determined experimentally as the lowest stimulation intensity
that produced MEPs ≥200 uV in 50% of consecutive
stimulations during grip at 10% MVC (Matsunaga et al., 2005;
Takechi et al., 2014). A neuronavigation system (Visor2, ANT
Neuro, Hengelo, Netherlands) was used to ensure reliable and
consistent coil positioning over the hotspot throughout the
experiment. Coil position error was controlled at <5 mm
displacement and <3◦relative to the target (Ding et al., 2018).
Stimulations were delivered at every 5–8 s. Both hemispheres
were tested in a random order.
Totally 40 single-pulse TMS pulses were delivered at 120%
RMT, with 20 stimuli at rest and 20 during grip at 10% MVC.
At rest condition, participants were instructed to completely
relax. EMG signals displayed on a computer screen were used
to provide feedback and assist participants in keeping the arm
and hand muscles quiet. During grip, participants produced
constant submaximal (10% MVC) isometric pinch grip with force
feedback displayed visually as a target zone (10 ±2% MVC)
within which the participant was instructed to maintain force.
Prior to the testing, participants practiced using visual feedback
to maintain the force trace within the target zone. TMS was
applied when the force trace was stable and maintained in the
target zone.
MEPs were analyzed offline using custom written Matlab
scripts (Matlab2019b, Mathworks, Inc., Natick, USA). EMG
data were demeaned, filtered using a fourth order Butterworth
filter (10–500 Hz), and signal averaged over 20 trials for each
condition. Averaged peak-to-peak MEP amplitudes of both rest
and grip condition were calculated (i.e., MEPrest and MEPactive,
respectively). Cortical silent period (CSP) was also calculated.
CSP onset was defined as the point at which the average rectified
EMG amplitude remained below threshold for 5 ms. CSP offset
was defined as the point at which the amplitude returned to
and remained above threshold for 5 ms. The CSP duration was
quantified by the time interval between CSP onset and offset
(Triggs et al., 1992; Classen et al., 1997; Urbin et al., 2015).
Functional Near-Infrared Spectroscopy
fNIRS data was acquired using a 46 multichannel fNIRS
instrument (BS-3000, Wuhan Znion Technology Co., Wuhan,
China). Channels between each transmitter and receiver were
placed with reference to the 10–20 system. The two probe
sets were inserted into a nylon cap and then placed on the
participant’s head. One of the probe sets was placed on the
prefrontal area (24 optodes, 37 channels), and the other one was
placed on the right motor cortex (8 optodes, 9 channels). The 46-
channel montage placement is shown in Figure 1. A chin strap
was used to secure the cap in place to reduce cap movement. Prior
to recording, a NIR gain quality check was performed to ensure
data acquisition was neither under-gained nor over-gained. Data
were recorded at wavelengths of 695 and 830 nm.
For each fNIRS testing, participants were asked to rest for 60 s,
followed by the force tracking task (60 s grip and 30 s rest for 3
times) (Figure 2A). At rest condition, participants were asked to
sit in an armchair with eyes closed. During the force tracking task,
subjects were asked to use a pinch grip to produce submaximal
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Ding et al. Effects of iTBS on BCI Training
FIGURE 1 | fNIRS 46-channel montage placement. There were 37 channels placed on the prefrontal cortex, and eight channels placed on the right motor cortex.
isometric contraction to track the criterion trajectory (Figure 2B)
as accurately as possible. The force tracking task included force
production, force maintenance and force release phase.
Regions of interest (ROIs) were selected via Polhemus
PATRIOT digitizer channel registration analyses. After tasks
were completed, subjects were instructed to keep the fNIRS cap
on while the experimenters carefully removed the optodes. A
measuring tape was used to find the center point (i.e., Cz) on the
head. Measurements were taken from the left auricular lobule to
the right auricular lobule, and from the nasion to the inion. Once
the Cz point was determined, a magnet was positioned on it and
the subject was moved so that the inion was 10 cm away from
the transmitter. Five head base reference points were measured
using the stylus, which are nasion, left tragus, right tragus, inion,
and Cz. All other optical fiber points were measured in numerical
order afterwards. Selected ROIs were M1, left and right dorsal
lateral prefrontal cortex (DLPFC), left and right frontal polar
(FP). All channels with >50% overlap within a region were
averaged together based on MRIcro registration (Rorden and
Brett, 2000; Wan et al., 2018).
Fluctuations in concentration of delta oxygenated hemoglobin
(HbO2) were calculated from changes in detected light intensity
according to the modified Beer-Lambert Law, assuming constant
scattering (Sakatani et al., 2006). Data preprocessing was
performed after delta HbO2 signals were obtained. We used the
moving average filter was 3 s (Huo et al., 2019). A processing
method based on moving standard deviation and cubic spline
interpolation was then applied to remove motion artifacts
(Scholkmann et al., 2010). Artifacts were distinguished by
identifying the sliding window standard deviation above a certain
threshold and were removed by cubic spline interpolation. The
physiologic signals are removed from the data using the low
pass filer with a cut-off of 0.2 Hz. The low-frequency drift was
removed by a high pass filter of 0.01 Hz cut-off frequency (Arun
et al., 2020). 60s task period and 10s rest period before the
trial were extracted from the data. Baseline correction was then
performed on each trial to ensure that the beginning of each task
period was approximately zero. The average time series for each
ROI (i.e., hemodynamic response function, HRF) was calculated
for the force tracking task. The averaged amplitude of each HRF
was calculated for statistical analysis.
Functional connectivity was calculated in both time and
frequency domain. In the time domain, correlation approach
was used to estimate the strength of the pairwise Pearson’s
correlation between ROIs (Pannunzi et al., 2017). In frequency
domain, coherence and phase locking value (PLV) were used
to analyze the level of synchronization of the fNIRS signals.
The Welch’s averaged, modified periodogram method (Welch,
1967), was performed to calculate the squared coherence between
ROIs. PLV was calculated to indicate the stability of the phase
difference between two time series [for calculation details see
Briels et al. (2020)]. All connectivity matrices were Fisher’s
z-transformed (Arun et al., 2020) to the set of Gaussian
distributed values and the zscores were used for further
statistical analysis.
Interventions
BCI Training
Motor imagery-based BCI training system was developed as
shown in Figure 3A. EEG signals were recorded using 16 active
electrodes (g.LADYbird, g.Tec Medical Engineering GmbH,
Schiedlberg, Austria). The real-time EEG signals were amplified
(g.USBamp, g.Tec Medical Engineering GmbH, Austria) and
then computer processed. Video clips were played in a 24-inch
computer screen to guide the participants to complete motor
imagery tasks. An exoskeleton hand was used to provide sensory
feedback in hand grasping/opening tasks. A mu ERD score (0–
100) was displayed on the screen to provide real-time feedback.
Subjects could adjust their motor imagery strategy according to
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Ding et al. Effects of iTBS on BCI Training
FIGURE 2 | Illustration of fNIRS testing. (A) fNIRS testing procedure. There are two states of fNIRS testing, which are the 60s resting state and 270s force tracking
task. Force tracking task consists of three trials, with each trial followed by 30s rest. (B) Force tracking task paradigm. Each block of force tracking task includes force
production phase, force maintenance phase and force release phase, with 4s rest break between two adjacent blocks.
this index to achieve higher scores and control the BCI robot
more effectively (Sun et al., 2017).
EEG signals were referenced to a unilateral earlobe. The signal
from 16 active electrodes was sampled at 256 Hz. The real-time
EEG signals were processed by the amplifier using a band-pass
filter (2–60 Hz) to remove artifacts and a notch filter (48–52 Hz)
to remove power line interference. All electrodes were filled with
salt water to ensure the transmission impedance remained below
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Ding et al. Effects of iTBS on BCI Training
FIGURE 3 | BCI training system. (A) Illustration of the closed-loop system. This BCI system consists of an EEG amplifier collecting real-time EEG, a PC analyzing EEG
signal and providing visual and auditory feedback, and a robot hand providing sensory feedback. (B) The location of EEG electrodes. There were 16 active electrodes
placed on the motor and sensory area and a reference electrode placed on the right ear.
1 kOhm. The EEG electrodes were placed over the central area
according to the 10–20 system (Figure 3B). EEG signals from the
C3 and C4 electrodes were used for BCI control.
During the action observation, a dark screen was first
displayed for 2 s, followed by a white cross for 2 s. Then, a text
cue of “hand grasp” or “hand open” was displayed for 2 s. A
video clip with a duration of 6 s was then displayed. Subjects were
asked to observe the actions and imagine they were performing
those actions without actually moving their hands. The mu ERD
score was calculated based on the EEG signal during the video.
If the mu ERD score was above 60, the robot hand would assist
the subject in completing the hand grasp/open task during the
following 3 s, which was considered as a successful trial. If the
mu ERD score was below 60, the robot hand would not move,
which was considered as an unsuccessful trial. The mu ERD
score was then shown for 2 s. Each trial ended with the display
of a dark screen for 3 s. During each B CI training, the trial
repeated for 100 times and video clips of the grasping/opening
hand was shown alternately at a random order. There was
30 s rest break after every 10 trials. Each BCI training took
about 40–50 min in total. The B CI accuracy was then calculated
by the number of successful trials divided by the number of
total trials.
Intermittent Theta Burst Stimulation
iTBS was applied over the right M1 and was delivered using a
NS5000 Magnetic Stimulator (YIRUIDE Medical Co., Wuhan,
China). The TBS pattern consist of bursts containing three pulses
at 50 Hz repeated at 5 Hz and an intensity of 80% AMT. A 2 s train
of TBS was repeated every 10 s for a total of 190 s (600 pulses)
(Huang et al., 2005). Participants were asked to stay relaxed
during the application of iTBS.
Experimental Procedures
This study included a BCI-only session and iTBS+BCI session
in a random order with approximately 7 days apart. The
order of two sessions was determined by the random integers
generated in Matlab2019b (Mathworks, Inc., Natick, USA). For
the odd numbers, BCI-only session would be the first session
and iTBS+BCI session would be the second session. For the
even numbers, iTBS+BCI session would be the first session
and BCI-only session would be the second session. In BCI-only
session, fNIRS was tested at baseline and immediately after BCI
tasks. In iTBS+BCI session, BCI training were followed by iTBS.
Single-pulse TMS was tested at baseline and immediately after
iTBS. fNIRS was tested at baseline, immediately after iTBS, and
immediately after BCI training (Figure 4).
Statistical Analysis
All data analyses and statistics were performed in Matlab2019b
(Mathworks, Inc., Natick, USA). Data were tested using the
Kolmogorov-Smirnov test and found to be normally distributed.
In the BCI-only session, paired-sample t-tests were used to
compare averaged HbO2 amplitude and functional connectivity
matrices (including correlation, coherence and PLV) in each
ROI before and after BCI. In the iTBS+BCI session, repeated-
measures ANOVA were used to compare averaged HbO2
amplitude and functional connectivity matrices at baseline, after
TBS and after BCI. In the iTBS+BCI session, paired t-test was
used to compare TMS measures (including MEPrest, MEPactive
and CSP) before and after iTBS. Paired-sample t-test was also
used to compare the averaged mu ERD score during BCI training
between two sessions. False discovery rate corrections were used
for multiple comparisons. Statistical significance was established
at p<0.05.
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Ding et al. Effects of iTBS on BCI Training
FIGURE 4 | Experimental procedure. In BCI-only session, fNIRS was tested at baseline and after BCI training (top panel). In iTBS +BCI session, fNIRS was tested at
three times points: baseline, after TBS and after BCI. Single-pulse (sp) TMS was tested at baseline and after iTBS. The order of those two sessions was randomized.
RESULTS
All eight participants completed two sessions of experiment, with
five participants completed BCI-only session first. No adverse
effect was reported.
BCI Accuracy
The mean BCI accuracy was 82.63% (SD =3.6) and 81.50%
(SD =3.0) in the BCI-only session and iTBS+BCI session,
respectively. There was no difference in BCI accuracy between
two sessions (p>0.05).
TMS Measures
There was significant increase in amplitudes of MEPrest and
MEPactive in the contralateral hand (i.e., left hand) after iTBS (p’s
=0.02 and 0.04, respectively) (Figure 5). No significant change in
CSP duration was revealed in the contralateral hand after iTBS (p
>0.05). There was also no significant change in MEP amplitude
or CSP duration in the ipsilateral hand (i.e., right hand) after iTBS
(p’s >0.05).
HRF
There was no significant difference in averaged amplitude of
HbO2 concentration change throughout the whole experiment
in either session (Figure 6).
Functional Connectivity Analysis
For resting-state functional connectivity, increased correlation
between motor cortex and right DLPFC was observed after BCI
training in the BCI-only session (p=0.005) (Figure 7). No
significant difference in resting-state functional connectivity was
observed throughout the iTBS+BCI session (p’s >0.05).
For functional connectivity measured during force tracking
task, increased coherence between motor cortex and left DLPFC
was observed after BCI training in the BCI-only session (p
=0.032) (Figure 8). In addition, increased PLV was observed
between motor cortex and left FP after BCI training in the BCI-
only session (p=0.037). There was no significant difference
in functional connectivity measured during force tracking task
throughout the iTBS +BCI session.
DISCUSSION
This study for the first time investigated the acute neuroplastic
changes after BCI training using fNIRS. We also investigated
how iTBS targeted on M1 influenced BCI accuracy and
neuroplastic changes induced by BCI training. Results revealed
that functional connectivity between motor cortex and prefrontal
cortex was acutely increased after BCI training. After iTBS,
increased cortical excitability was observed, but brain activation
or functional connectivity remained unchanged. iTBS targeted on
M1 did not influence BCI accuracy or facilitate the neural effects
induced by BCI training.
Acute Neuroplastic Change After BCI
Training
Results revealed acute neuroplastic change in functional
connectivity between motor cortex and prefrontal cortex after
BCI training. To our knowledge, ours is the first study that
investigated the acute neural plasticity induced by BCI training
using fNIRS. The acute neural adaptation of BCI training has
been investigated using TMS and MRI (Xu et al., 2014; Mrachacz-
Kersting et al., 2016; Nierhaus et al., 2019). TMS studies reported
that MEP amplitude was increased after BCI training for up to
30 min in both stroke survivors (Mrachacz-Kersting et al., 2016)
and healthy adults (Xu et al., 2014), suggesting BCI training
could induce a prolonged increase in cortical excitability in the
trained hemisphere (Bai et al., 2020). Nierhaus et al. (Nierhaus
et al., 2019) used functional and structural MRI after only 1 h
of BCI training to investigate immediate brain plasticity. Results
revealed increased BOLD activity in the left sensorimotor area
of trained hemisphere during motor imagery after BCI training,
suggesting BCI training facilitates recruitment of cortical motor
neurons during motor imagery. Our findings extend the acute
neural adaptations after BCI training from cortical excitability
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Ding et al. Effects of iTBS on BCI Training
FIGURE 5 | Changes in TMS parameters after iTBS in two hemispheres. (A) In the left hand (i.e., the contralateral hand to iTBS), MEPrest and MEPactive were
significantly increased compared with baseline, and there was no significant change in CSP duration after iTBS. (B) In the right hand, there was no significant change
in any TMS parameter after iTBS. *Indicates significant changes after iTBS.
FIGURE 6 | Average time series of HRF across participants. There was no significant change in average HbO2 amplitude at any time point in either session. In
BCI-only session (top panel), blue indicates HRF at baseline, and pink indicates HRF after BCI training. In iTBS+BCI session (bottom panel), gray indicates HRF at
baseline; blue indicates HRF after TBS; and pink indicates HRF after BCI training. Error bars are standard error.
and brain activation to functional connectivity of the cortical
networks which has not been previously investigated.
As reduced functional connectivity between brain regions
has been observed in stroke survivors (Arun et al., 2020), our
findings suggest a potential neural mechanism underlying the
effectiveness of BCI training in neurorehabilitation. Based on
results from our current study and previous studies (Xu et al.,
2014; Mrachacz-Kersting et al., 2016; Nierhaus et al., 2019),
the neuroplastic state of motor imagery-related cortical network
(including sensorimotor cortex, prefrontal cortex, etc.) is elevated
after BCI training. This provides a possibility for improving the
effectiveness of traditional motor training in stroke survivors
by priming with BCI training, which needs to be tested in
future studies.
Acute Neuroplastic Change After iTBS
In line with previous literature (Huang et al., 2005, 2011; Cirillo
et al., 2017), our results revealed increased MEP amplitudes after
iTBS on M1. However, we did not observe any change in HbO2
concentration or functional connectivity in the motor cortex
or prefrontal cortex after iTBS. To our knowledge, there was
only one previous study using fNIRS to investigate acute neural
adaptation after iTBS on M1 (Mochizuki et al., 2007). Similar
to what we found in this current study, no change in HbO2
Frontiers in Cellular Neuroscience | www.frontiersin.org 8March 2021 | Volume 15 | Article 653487
Ding et al. Effects of iTBS on BCI Training
FIGURE 7 | Functional connectivity (correlation) change measured at rest in BCI-only session. (A) Correlations between each ROI at baseline. (B) Correlations
between each ROI after BCI training. (C) The correlation between right motor cortex and right DLPFC was significantly increased after BCI training. L refers to left. R
refers to right. DLPFC refers to dorsal lateral prefrontal cortex. FP refers to frontal polar area.
concentration in the ipsilateral motor cortex or prefrontal cortex
was reported after iTBS in Mochizuki et al.’s study (Mochizuki
et al., 2007).
Similar to iTBS, high-frequency rTMS is another type of
non-invasive brain stimulation that has been used to upregulate
cortical excitability. Some studies used fNIRS to investigate acute
neural adaptation during and after high frequency rTMS on
M1 (Li et al., 2017, 2019). Reduced HbO2 (Li et al., 2017) and
functional connectivity (Li et al., 2019) in motor cortex and
prefrontal cortex were observed in both hemispheres during
rTMS, which returned to baseline after rTMS.
Collectively, there is lack of a direct relationship between
hemodynamic response or functional connectivity and cortical
excitability after iTBS or high-frequency rTMS. The neural
mechanisms remain unclear. It has been suggested that
the activation of sympathetic nervous system during rTMS
application might be a possible mechanism (Li et al., 2019). The
activation of sympathetic nervous system maintains a constant
cerebral blood flow through vasoconstriction. This may prevent
the potential dilatation of cerebral vessels induced by iTBS or
high-frequency rTMS, thus no change in HbO2 concentration
can be detected after stimulation (Li et al., 2019). Further
studies are needed to investigate the mechanisms underlying
the disassociated relationship between cortical excitability and
hemodynamic response after NIBS.
The Influence of iTBS on BCI Training
Inconsistent with our hypothesis, iTBS targeted on M1 did not
influence BCI accuracy or facilitate the neuroplastic changes
induced by BCI training. The mechanism is still unclear. To
our knowledge, no published study has investigated the acute
effect of excitatory rTMS (including iTBS) on BCI training. There
were some studies investigating the influence of excitatory tDCS
on BCI training (Wei et al., 2013; Ang et al., 2015; Kasashima-
Shindo et al., 2015; Hong et al., 2017). It has been reported that
anodal tDCS effectively increased mu ERD during BCI training
and improved BCI accuracy in both healthy adults (Wei et al.,
2013) and stroke survivors (Ang et al., 2015; Kasashima-Shindo
et al., 2015). The different results might be due to the different
stimulation paradigms. Although similar neural mechanisms are
shared by iTBS and tDCS, iTBS has much higher temporal
resolution compared with regular tDCS. In those aforementioned
studies (Wei et al., 2013; Ang et al., 2015; Kasashima-Shindo
et al., 2015; Hong et al., 2017), tDCS electrodes were relatively
large and placed on motor cortex that covers not only M1 but
also premotor cortex and supplementary motor cortex, while
in our current study iTBS was precisely targeted on M1 with
the guidance of neuronavigation system. It has been suggested
that motor imagery requires not only M1 but also a distributed
brain network including premotor cortex, supplementary motor
cortex and prefrontal cortex, etc. (Sharma et al., 2009; Bauer
et al., 2015). Because of the low spatial resolution, tDCS applied
on the motor cortex might activate a broader motor imagery-
related cortical network compared with iTBS targeted on M1,
and thus effectively influence BCI performance. Based on this
speculation, dual or multiple sites of iTBS might be more likely
to improve BCI performance, which needs to be tested in
the future. Difference between stroke and healthy adults may
also contribute to different results between ours and previous
studies (Ang et al., 2015; Kasashima-Shindo et al., 2015). As
stroke survivors often have reduced mu ERD and the poorer
BCI performance, there is larger room for stroke survivors to
increase mu ERD or improve BCI accuracy compared with
healthy adults.
Apart from BCI accuracy, iTBS on M1 did not facilitate
the acute neuroplastic change induced by BCI training either.
Similarly, previous studies reported that anodal tDCS on motor
cortex did not facilitate neuroplastic change or improve the
effectiveness of BCI training in stroke survivors (Ang et al.,
2015; Kasashima-Shindo et al., 2015; Hong et al., 2017). Possibly
Frontiers in Cellular Neuroscience | www.frontiersin.org 9March 2021 | Volume 15 | Article 653487
Ding et al. Effects of iTBS on BCI Training
FIGURE 8 | Functional connectivity change measured during force tracking task in BCI-only session. (A) Coherence between each ROI at baseline. (B) Coherence
between each ROI after BCI training. (C) Coherence between right motor cortex and left DLPFC was significantly increased after BCI training. (D) PLV between each
ROI at baseline. (E) PLV between each ROI after BCI training. (F) The PLV between right motor cortex and left FP was significantly increased after BCI training. L refers
to left. R refers to right. DLPFC refers to dorsal lateral prefrontal cortex. FP refers to frontal polar area.
because the use of BCI-only requires modulation of neural
activities in M1 (Wander et al., 2013), M1 has been the most
common target of NIBS in BCI literature (Wei et al., 2013; Ang
et al., 2015; Kasashima-Shindo et al., 2015; Hong et al., 2017; Shu
et al., 2017). However, acquisition of BCI proficiency requires a
distributed brain network, including prefrontal cortex, premotor
cortex, and posterior parietal cortex, etc. (Wander et al., 2013).
In addition, the role that M1 plays in the process of motor
imagery might not be as important as brain regions with higher
cortical functions (e.g., prefrontal cortex) (Moghadas Tabrizi
et al., 2019); this may explain why iTBS on M1 did not positively
influence the effect of BCI training. Taken together, M1 may not
be the best stimulation target for improving BCI accuracy or
effectiveness. Further studies are needed to explore other brain
regions as potentially effective stimulation targets for improving
the effectiveness of BCI training.
Limitations
As a pilot study, the sample size of current study is small (N
=8). In addition, our sample only includes young adults, so
cautions are needed when generalizing our findings to other
populations, such as aging population or stroke survivors. Future
studies are needed to test our results in other populations with
larger sample sizes.
Another limitation is that current study did not include sham
iTBS condition in our study design. We acknowledge that the
lack of sham iTBS condition may weaken the convincingness of
the conclusions. In addition, due to the limited number of fNIRS
channels, we did not monitor the neuroplastic changes in the
left M1 (the non-stimulated hemisphere) and may miss some
neuroplastic changes. Further studies are still needed to include
sham iTBS condition and measure neuroplastic changes in both
motor cortices.
Frontiers in Cellular Neuroscience | www.frontiersin.org 10 March 2021 | Volume 15 | Article 653487
Ding et al. Effects of iTBS on BCI Training
CONCLUSIONS
Our pilot study systematically investigated how iTBS targeted
on M1 influences BCI accuracy and the acute neuroplastic
changes induced by BCI training. Our results revealed that iTBS
targeted on M1 did not influence BCI accuracy or facilitate the
neuroplastic changes induced by BCI training, suggesting that
M1 might not be an effective stimulation target of iTBS for the
purpose of improving BCI accuracy or facilitate the effectiveness
of BCI training. Other brain regions (i.e., prefrontal cortex)
are needed to be further investigated as potentially effective
stimulation targets.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by Guangzhou First People’s Hospital Human
Research Ethics Committee. The patients/participants
provided their written informed consent to participate in
this study.
AUTHOR CONTRIBUTIONS
QD, YL, and GX designed the experiment. QD, TL, MW, WY,
WL, YJ, XR, and YG conducted the experiments. QD reduced and
analyzed the data. QD, YL, and GX interpreted the data. QD and
YL wrote the manuscript. All authors contributed to the article
and approved the submitted version.
FUNDING
This work was supported by the National Science Foundation
of China [Grant Numbers: 81772438 (YL), 81974357 (YL),
82072548 (GX), and 81802227 (TL)]; the Guangzhou
Municipal Science and Technology Program [Grant Number:
201803010083 (YL)]; the Fundamental Research Funds for the
Central University [Grant Number: 2018PY03 (YL)]; National
Key R&D Program of China [Grant Number: 2017YFB1303200
(YL)]; Guangdong Basic and Applied Basic Research Foundation
[Grant Number: 2020A1515110761 (QD)]; and Guangzhou
Postdoctoral Science Foundation (QD).
ACKNOWLEDGMENTS
We would like to thank all our participants for their interest and
time investment.
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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.
Copyright © 2021 Ding, Lin, Wu, Yang, Li, Jing, Ren, Gong, Xu and Lan. This is an
open-access article distributed under the terms of the Creative Commons Attribution
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provided the original author(s) and the copyright owner(s) are credited and that the
original publication in this journal is cited, in accordance with accepted academic
practice. No use, distribution or reproduction is permitted which does not comply
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