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Study of cerebral cortico-cortical coherence during motor practice!
TÉRCIO APOLINÁRIO-SOUZA
1
| GUILHERME M. LAGE
2
| LIDIANE A. FERNANDES
3
!
1
Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil!
2
Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil!
3
Universidade Federal de Juiz de Fora, Governador Valadares, MG, Brazil!
Correspondence to:!Tércio Apolinário-Souza. Address: R. Felizardo, 750 - Jardim Botânico, Porto Alegre - RS, Brasil; CEP: 90690-200.!
email: edf.tercio@gmail.com!
https://doi.org/10.20338/bjmb.v17i5.359
HIGHLIGHTS!
• Coh increases areas related to motor execution and
decreases in those that are less related.!
• The motor practice related to reduced cortico-cortical
communication in cognitive brain regions.!
• Upsurge in neural plasticity in motor-related area during
practice.!
ABBREVIATIONS!
AE Absolute timing error!
Coh Coherence!
d Cohen’s d!
EEG Electroencephalogram!
FIR Finite impulse response!
Fz Frontal region!
IQR Interquartile range!
LTP Long-term potentiation!
M1 Primary motor cortex!
PLV Phase Locked Value!
q1 First quartile!
q3 Third quartile!
RE Relative timing error!
PUBLICATION DATA!
Received 13 04 2023!
Accepted 21 06 2023!
Published 30 09 2023!
BACKGROUND: Coherence is one of the neural mechanisms related to communication and
plasticity. The literature presents two divergent results regarding coherence and motor practice.
One result suggests a decrease in coherence during practice, while the other indicates an
increase in coherence throughout practice.!
AIM: Considering these two divergent results in the literature, this study aimed to examine the
role of coherence in motor practice. We hypothesize that electrode pairs related to C3 (C3-P3
and C3-F3) show an increase of coherence during practice, while electrodes less related to
motor action (F4, C4, and P4) may exhibit decreased. !
METHOD: Twenty-four right-handed participants practice 120 trials of a sequential key-pressing
task. !
RESULTS: The results indicated, in the alpha upper and theta bands, from initiation to end of
practice, the coherence increased in the F3-C3 electrode pair and decreased in the C3-C4, C3-
P3, P3-P4, F3-P3, and C4-P4 electrodes pairs. !
CONCLUSION: The results partially confirmed the hypothesis. The coherence increases in the
electrode pairings related to the motor execution and decreases between the lesser related.
During the motor learning process, communication reduction occurred in groups of neurons not
associated with the stimulus, and the potentiation of synaptic plasticity within groups of neurons
associated with the stimulus occurred.!
KEYWORDS: Motor learning | Motor control | Implicit learning | Neurophysiology!
INTRODUCTION!
The practice itself is the most critical variable in motor skill acquisition ¹, whether in music, rehabilitation, or sports. With practice,
the learner improves performance, becomes more speedy and accurate, and decreases errors ². Within a single practice session, it is
already possible to observe performance improvement, with much of this improvement depending on error detection and correction
mechanisms ³.!
A series of psychophysiological studies using electroencephalogram (EEG) have shown mechanisms underlying the modification
of brain activity due to practice (Reuter, Booms and Leow
4
for review). The EEG, via scalp, records the electrical signals produced by the
neurons, neurons are embedded in assemblies in which they influence mutually through excitatory and inhibitory synaptic connections
5
.
Neuronal assemblies refer to a group or ensemble of neurons that exhibit synchronized activity to perform a specific function or represent
certain information within the brain
6
. Individual neurons communicate with each other through synaptic connections, forming complex
networks
7
. Within these networks, the neuronal assemblies are functional units that require coordinated activity of multiple neurons to
accomplish specific tasks, such as motor response
8
. These assemblies are dynamic and flexible, reconfiguring themselves according to
demands
9
. The execution of motor actions per se generates perceptual-motor demands that reconfigure neuronal assemblies
10
.!
With practice, neurons are expected to change synchronization levels, increasing or decreasing specific neural response patterns
within neuronal assemblies
11
. This synchronized activity among neurons constitutes one of the principal mechanisms of memory formation
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for two principal reasons: cortico-cortical communication and mechanisms of plasticity
12
. In cortico-cortical communication terms,
synchronization may support neural communication by establishing transient associations between different brain areas
13
. For example,
when performing a throw, information regarding the ball's weight and color, the opponent's position vision, and the arm's speed are
processed in distinct brain regions. These pieces of information must be connected through a mechanism has ensured that the brain
associates them with the same action. The synchronization enables communication among the different brain regions. The mechanisms of
plasticity function of synchronization are associated with a classical concept of Hebbian learning
6
. Hebbian learning suggests that when
two neurons are repeatedly activated together, the strength of the synaptic connection between them is increased
6
. In this logic, the
increased synchronization results from several synaptic inputs arriving at postsynaptic neurons simultaneously (spatial summation),
enabling rapid depolarization
14
. This rapid depolarization increases the postsynaptic membrane potential above the firing threshold,
triggering the neural plasticity processes
15
.!
Two main results about synchronization and motor practice are found in the literature. The first result shows that synchronization
decreases with practice, reflecting the refinement of cortical resources
16
. Refinement of cortical resources refers to perfecting the available
resources within the brain's cortical areas to maximize their efficiency and capacity to perform specific functions. During motor practice,
reduced activity in frontal regions characterizes the refinement of cortical resources
17
. A series of processes occur through practice, leading
to decreased cognitive demand
18,19
. The frontal regions' activity is traditionally associated with cognitive processes
20
. Gentilli et al.
10
showed a reduction of synchronization in the early to late practice. This reduction occurred among electrode pairs considering the frontal
(Fz) region as the reference [Fz–F3, Fz–C3, Fz–C4, Fz–T4 (...)]. As mentioned above, the frontal regions’ activity would be more related
to the cognitive processes
20
, and the decreasing synchronization during practice would indicate movements less dependent on cognitive
processes
17
. In this rationale, synchronization is seen as cortico-cortical communication among brain areas
21
. As movements are less
dependent on cognitive processes with the advancement of practice
18,19
, the cortico-cortical communication between the frontal and other
areas could be decreased. Thus, the authors explain the reduction of synchronization as a decrease in cortico-cortical communication due
to a decrease in cognitive demand, typical of the motor learning process
10,22
.!
On the other hand, the previous results claim that with the practice, there is an increase in synchronization and not a decrease
23,24
. In this case, the increased synchronization during practice is an effect of the mechanisms of plasticity
11
. Kranczioch et al.
3
showed
better performance is related to increased synchronization between contralateral fronto-central and ipsilateral parieto-occipital brain
regions. The tenet here is that synchronous activity in the brain, precisely presynaptic and postsynaptic neurons, can lead to long-lasting
changes
25
.!
At first glance, one would consider these two kinds of results divergent. In the study of Gentilli et al.
10
, the synchronization
decreased considering reference to the frontal areas (Fz). While in the Kranczioch et al.
23
study, the synchronization was increased in the
local motor more related to movement. Perhaps, there is no divergence between results. The motor learning process may be associated
with a decrease in cortico-cortical communication among brain areas associated with cognitive processes
10
and a simultaneous increase
in neural plasticity processes in the brain areas related to movement
23
.!
Most often, researchers infer synchronization by observing coupling between different points. In animal experiments, researchers
record extracellular action potentials and analyze the local field potentials in the same or different regions. Human scalp EEG studies do
not measure action potentials. Here, synchronization refers either to the phase relation of EEG oscillations between two regions or
coherence. This way of inferring synchronization is similar to the one used by other studies in motor practice
10,22,23,26,27
.!
Considering this divergence in literature, there is a need to further examine the role of coherence in motor practice. We
hypothesize that, from initiation to end of practice, the coherence will increase in the electrode pairings more related to the motor execution
and decrease among less related electrode pairings to the motor execution. The primary motor cortex of the contralateral hemisphere
predominantly originates the descending projections that control unilateral movements
28
. In the present study, we used the right hand to
control movement. The C3 is equivalent to the M1 contralateral hemisphere for our motor task. Thus, we expect electrode pairs related to
C3 (C3-P3 and C3-F3) to exhibit increased coherence. On the other hand, electrodes less related to motor action, such as F4, C4, and P4,
may exhibit a decrease in coherence. Researchers have highlighted that brain activity in the theta and alpha frequency bands highly
responds to cognitive processing
29
. As is well known, the process of motor practice involves cognitive processing
18,19
; thus, using these
two frequency bands could indicate cognitive processing. Our study aims to shed light on this matter by exploring the potential coexistence
of two explanations for coherence results. Specifically, we investigate the possibility that the system operates in both ways, with increased
coherence observed in areas more closely associated with motor action. In contrast, areas less relevant to the task exhibit decreased
coherence.!
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METHODS!
Participants!
Twenty-four right-handed participants (12 men and 12 women), ranging from 18 to 35 years old (mean = 26.07; standard deviation
= 5.58), participated in this experiment. All participants had normal or corrected-to-normal visual acuity in both eyes and no prior motor task
experience. The sample size was defined using the Power Analysis package from r (the official release of the package: http://cran.r-
project.org/web/packages/pwr/). The analysis indicated a sample size of 23.51 subjects, considering an effect size of 0.7, the power of the
test as 0.95, and alpha as 5%. A local ethics committee approved the study (CAAE 24116513.2.0000.5149). All participants provided
written informed consent after receiving a full explanation of the study.!
Apparatus!
A computer, color monitor, and numeric keypad were positioned on a standard table in the laboratory room. A custom-made
software program (https://github.com/edftercio/pressing_sequential_keys) was used to control the experimental task in the LabVIEW
software (National Instruments, Texas, EUA). Participants were asked to sit on a chair in front of the computer monitor and to adjust the
numeric keypad position to use it comfortably. They used it with their right hand.!
The B-Alert×10 sensor headset (Advanced Brain Monitoring Inc., Carlsbad, CA, USA) was used to acquire the
electroencephalography (EEG). Nine Ag/AgCl EEG electrodes were located at F3, Fz, F4, C3, Cz, C4, P3, POz, and P4, according to the
international 10–20 system. The pairs of electrodes were chosen based on the same positioning as previous studies
3,22, 27,26,30
. Two
electrodes on the mastoid bones (left and right) were used as the reference and ground. The sampling rate was 1,024 samples/s for all
channels and transferred in real-time via Bluetooth link to a host computer where the B-Alert software (Advanced Brain Monitoring Inc.,
Carlsbad, CA) is stored. All electrode impedances were maintained below 5 kΩ.!
Task!
Participants were instructed to use their right hand's index finger to sequentially press four keys (2, 8, 6, and 4) on the numeric
keypad
31,32
. In this task, the learner needs to press a sequence of keys with two goals: (a) learn the partial (or relative) timing between
key-pressed and (b) learn the total (or absolute) time between the first and the last key of the sequence. The partial time between each key
was (1) 22.2% (key 2 to 8), (2) 44.4% (key 8 to 6), and (3) 33.3% (key 6 to 4) and total time was 900 ms
33,34,35,36
. Partial time is relative to
the total time executed. Thus, the two task goals are dissociated, meaning it is possible to achieve the partial goal without achieving the
total goal. For instance, the learner executed a total time of 1000 ms with partial times of 22.22% (key 2 to 8), 44.4% (key 8 to 6), and
33.3% (key 6 to 4). In this example, there was a total time error of 100 ms (absolute timing error) and no error in the partial time (relative
timing error). After each trial, the knowledge of the results was displayed on the screen immediately. The knowledge of results included the
partial time between each key, the relative timing error (highlighted in Figure 1), and the total time performed.!
Figure 1. Motor Task. On the left side of the figure, it is possible to observe the monitor screen before the execution, indicating goals (partial times and the total time). On the
right side, the completion of the task is shown, indicating the feedback at the bottom of the partial times (total relative error and error for each segment) and the total time. In
this example, the learner produced a relative error of 37% (|22-17|+|44-31|+|33-52|) and a total time of 1211. In the figure's center is a highlight of the relative error and an
example of the execution of the sequence 2, 8, 6, and 4.!
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Procedures and Experimental Design!
The exact instructions were given to each participant about the information displayed on the computer screen. All subjects were
required to be as accurate as possible regarding partial and total time goals. Before each trial, partial and total time goals were displayed
on the computer screen. After the task execution, the results were presented on the screen for at least six seconds. Participants were
instructed to spend the time they needed to compare their results with the goals. After six seconds, a sign requiring the initiation of the next
trial was presented. Participants were instructed to start whenever they wanted the next trial after receiving the sign to start. When a
participant pressed the keys incorrectly, a warning was displayed, and the trial was repeated. No participant pressed the keys incorrectly.
The experiment consisted of all participants performed 120 trials at the time goals, 22.2% (key 2 to 8), 44.4% (key 8 to 6), and (3) 33.3%
(key 6 to 4) and total time goal of 900 ms.!
Measurements!
We used relative timing error (RE) and absolute timing error (AE) as task performance measures. We used RE to measure
proficiency in learning the relative timing dimension and AE to measure proficiency in learning the absolute timing dimension
37
. The RE
was determined as the sum of differences between the partial time performed and the partial time goal for each segment. It was computed
as the following:!
RE
n
= (|S1
n
− 22.22|) + (|S2
n
− 44.44|) + (|S3
n
− 33.33|)!
S1, S2, and S3 are the values in each segment (S1 key 2 to 8, S2 key 8 to 6, and S3 key 6 to 4) performed in each trial (n). The values of
each segment were relativized by total time, as the following:!
S1
n
= ts/0.01 ∗ T ; S2
n
= ts/0.01 ∗ T ; S2
n
= ts/0.01 ∗ T!
ts is the time (in ms) in each segment and T is the total time (in ms) in each trial n. We computed AE as the difference between the total
time performed (time spent between pressing keys 2 and 4) and the total time goal.!
We used the Phase Locked Value (PLV) according to the method previously described in Lachaux
38
to measure Coherence
(Coh) between two pairs of EEG electrodes. This method quantification of frequency-specific synchronization (i.e., transient phase-locking)
between two electrodes signals
38
. We computed the following:!
where θ(t, n) is the phase difference [i.e., φ1(t, n)- φ(t, n)]. Although a complex Gabor wavelet can be used, we computed the phase value
using the Hilbert transform. We used the following electrodes combination: F3-F4, C3-C4, P3-P4, F3-C3, F3-P3, F4-C4, F4-P4, C3-P3, and
C4-P4.!
EEG signal processing!
We applied the 60 Hz notch filter to the signal. Then, the zero-phase pass-band filter (lower cutoff of 0.1 Hz and an upper cutoff
of 100 Hz) and the zero-phase high-passed filter (lower cutoff of 1 Hz) were applied to the signal to remove the DC offset. Finally, the
multidimensional median filter was used with a window size of 10 ms.!
Subsequently, all electrodes were filtered in the interest bands using a finite impulse response (FIR) filter using the window
method. The bands were theta (4–7 Hz), alpha lower (8–10 Hz), and alpha upper (10–12 Hz).!
Posteriorly, we computed the phase value through the Hilbert transform. After, the signals were separated (epoch) in three
moments: (1) planning - the interval between the sign for initiation of trial and key press 2; (2) execution - the interval between pressing
keys 2 and 4; (3) processing - the interval between key press 4 and the sign for initiation of the subsequent trial. Data from the motor task
and EEG were synchronized offline by an algorithm developed in Python (https://osf.io/q8yjp/). The details of offline synchronization are
provided in Nogueira et al.
27
.!
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$
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Statistical analysis!
Outlier analyses (intra-group, trial-by-trial) were performed for all measurements (RE, AE, and Coh). It was computed as the
following:!
ref = IQR*1.5!
threshold_up = q1 + ref!
threshold_down = q3 + ref!
where IQR represents the interquartile range, q1 denotes the first quartile, and q3 represents the third quartile. In the presence of outlier
data, we initially removed the outliers. We then calculated a new average value and replaced the outlier value with the updated average
value. In addition, the data were filtered through the median filter and moving average with a windows size of 12 trials to ER and EA and
Coh with a windows size of 12 ms.!
We did a t-test for dependent samples for EA, ER, and Coh (for each electrodes combination separately). We organized all
measurements into two blocks of 12 trials for analysis: the first block of the first 12 trials and the last block of the last 12 trials.!
A significant difference at the level of α = 0.05 was adopted for all statistical analyses. Effect sizes were calculated using Cohen’s
(d).!
RESULTS!
Performance!
Descriptive analyses of absolute timing error and relative timing error are shown in Figure 2. Inferential analysis detects the
difference between the initial and final parts of practice in the absolute timing error [t(23) = 2.59, p = 0.01, d = 0.52] and relative timing error
[t(23) = 8.76, p < 0.01, d = 1.79]. In both measures, the first block of practice showed more errors than the last.!
Figure 2. Performance analyses between the initiate and end of practice. (A) absolute timing error and (B) relative timing error of two blocks of 12 trials - 12 first trials (first)
and 12 last trials (last). circle = mean of blocks. dots = individual means of blocks. * = p < 0.05.!
EEG coherence!
Alpha lower!
Descriptive analyses of the first 12 trials and last 12 trials of practice are shown in Figure 3. As principal results, the analysis
revealed differences between the initial and final part of practice during execution at the C3-C4 and during the planning and processing at
the P3-P4 (Table 1). In all situations above, the Coh decreased from initiate to end of practice.!
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Figure 3. Alpha lower. circle = mean of blocks. dots = individual means of blocks. * = p < 0.05.!
Table 1. EEG coherence in the alpha lower.!
moment!
t!
df!
p!
d!
moment!
t!
df!
p!
d!
F3-F4!
planning!
0.24!
23!
0.80!
0.04!
F4-C4!
planning!
-0.8!
23!
0.43!
0.16!
execution!
0.15!
23!
0.87!
0.03!
execution!
-0.15!
23!
0.87!
0.02!
processing!
1.27!
23!
0.21!
0.13!
processing!
0.85!
23!
0.4!
0.19!
C3-C4!
planning!
1.84!
23!
0.07!
0.33!
F4-P4!
planning!
-0.17!
23!
0.86!
0.02!
execution!
2.81!
23!
<0.01!
0.41!
execution!
1.61!
23!
0.12!
0.32!
processing!
1.86!
23!
0.07!
0.25!
processing!
0.52!
23!
0.6!
0.04!
P3-P4!
planning!
3.68!
23!
<0.01!
0.56!
C3-P3!
planning!
1..77!
23!
0.08!
0.26!
execution!
1.86!
23!
0.07!
0.30!
execution!
0.55!
23!
0.58!
0.11!
processing!
2.91!
23!
<0.01!
0.32!
processing!
1.30!
23!
0.20!
0.14!
F3-C3!
planning!
-1.00!
23!
0.32!
0.20!
C4-P4!
planning!
1.55!
23!
0.13!
0.25!
execution!
-0.09!
23!
0.92!
0.01!
execution!
1.95!
23!
0.06!
0.38!
processing!
-1.12!
23!
0.27!
0.20!
processing!
2.03!
23!
0.05!
0.27!
F3-P3!
planning!
0.56!
23!
0.57!
0.09!
execution!
0.32!
23!
0.74!
0.54!
processing!
1.43!
23!
0.16!
0.14!
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Alpha upper!
Descriptive analyses of the first 12 trials and last 12 trials of practice are shown in Figure 4. As principal results, the analysis
revealed differences between the initial and final part of practice during planning at the P3-P4, during planning and execution at the F3-C3,
during planning and execution at the C3-P3, and during planning at the C4-P4 (Table 2). In the P3-P4, C3-P3, and C4- P4, the Coh
decreased from initiate to end of practice. On the other hand, in the F3-C3, the Coh increase from initiate to end of practice.
!
Figure 4. Alpha upper. circle = mean of blocks. dots = individual means of blocks. * = p < 0.05.!
Table 2. EEG coherence in the alpha upper.!
moment!
t!
df!
p!
d!
moment!
t!
df!
p!
d!
F3-F4!
planning!
0.37!
23!
0.70!
0.05!
F4-C4!
planning!
-1.24!
23!
0.22!
0.23!
execution!
0.12!
23!
0.23!
0.18!
execution!
0.15!
23!
0.87!
0.02!
processing!
0.63!
23!
0.52!
0.05!
processing!
-1.63!
23!
0.11!
0.26!
C3-C4!
planning!
1.71!
23!
0.10!
0.38!
F4-P4!
planning!
0.11!
23!
0.91!
0.01!
execution!
1.74!
23!
0.09!
0.42!
execution!
-0.03!
23!
0.97!
<0.05!
processing!
0.88!
23!
0.38!
0.16!
processing!
<0.05!
23!
0.99!
<0.05!
P3-P4!
planning!
2.48!
23!
0.02!
0.40!
C3-P3!
planning!
2.33!
23!
0.02!
0.35!
execution!
1.50!
23!
0.14!
0.26!
execution!
2.20!
23!
0.03!
0.36!
processing!
1.29!
23!
0.20!
0.15!
processing!
0.71!
23!
0.48!
0.09!
F3-C3!
planning!
-2.29!
23!
0.03!
0.26!
C4-P4!
planning!
2.16!
23!
0.04!
0.36!
execution!
-2.15!
23!
0.04!
0.35!
execution!
0.87!
23!
0.38!
0.15!
processing!
-1.49!
23!
0.14!
0.21!
processing!
1.78!
23!
0.08!
0.19!
F3-P3!
planning!
-0.39!
23!
0.70!
0.05!
execution!
1.98!
23!
0.05!
0.28!
processing!
0.62!
23!
0.54!
0.06!
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Theta!
Descriptive analyses of the first 12 trials and last 12 trials of practice are shown in Figure 5. As principal results, the analysis
revealed differences between the initial and final part of practice during execution at the C3-C4, during execution and processing at the P3-
P4, during execution at the F3-C3, during processing at the F3-P3, and during planning at the C4-P4 (Table 3). In the C3- C4, P3-P4, F3-
P3, and C4-P4, the Coh decrease from initiate to end of practice. On the other hand, in the F3-C3, the Coh increase from initiate to end of
practice.!
Figure 5. Theta. circle = mean of blocks. dots = individual means of blocks. * = p < 0.05.!
Table 3. EEG coherence in the theta.!
moment!
t!
df!
p!
d!
moment!
t!
df!
p!
d!
F3-F4!
planning!
-0.10!
23!
0.91!
0.01!
F4-C4!
planning!
-0.18!
23!
0.85!
0.04!
execution!
1.63!
23!
0.11!
0.25!
execution!
-0.09!
23!
0.92!
0.01!
processing!
<0.01!
23!
0.99!
<0.01!
processing!
-1.33!
23!
0.19!
0.27!
C3-C4!
planning!
1.69!
23!
0.10!
0.32!
F4-P4!
planning!
1.37!
23!
0.18!
0.28!
execution!
2.09!
23!
0.04!
0.40!
execution!
1.78!
23!
0.08!
0.29!
processing!
1.74!
23!
0.09!
0.39!
processing!
1.37!
23!
0.18!
0.19!
P3-P4!
planning!
1.99!
23!
0.05!
0.36!
C3-P3!
planning!
1.20!
23!
0.24!
0.27!
execution!
2.36!
23!
0.02!
0.38!
execution!
1.62!
23!
0.11!
0.38!
processing!
3.67!
23!
<0.01!
0.47!
processing!
1.98!
23!
0.05!
0.24!
F3-C3!
planning!
0.01!
23!
0.98!
<0.01!
C4-P4!
planning!
2.36!
23!
0.02!
0.40!
execution!
-2.86!
23!
<0.01!
0.72!
execution!
0.48!
23!
0.63!
0.08!
processing!
1.11!
23!
0.27!
0.17!
processing!
1.02!
23!
0.31!
0.12!
F3-P3!
planning!
-0.31!
23!
0.75!
0.07!
execution!
0.79!
23!
0.43!
0.20!
processing!
2.40!
23!
0.02!
0.28!
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DISCUSSION!
We analyzed the role of coherence in motor practice. From the initial to the end of practice, we hypothesize that the coherence
will increase in the electrode pairings more related to the motor execution and decrease among lesser related electrode pairings to the
motor execution. Specifically, we expected electrode pairs related to C3 (C3-P3 and C3-F3) to exhibit increased coherence. On the other
hand, electrodes less related to motor action, such as F4, C4, and P4, would decrease coherence. Overall, the results confirm the study’s
hypotheses. In the alpha upper and theta bands, from initiation to end of practice, the coherence increased in the F3-C3 electrode pair and
decreased in the C3-C4, P3-P4, F3-P3, and C4-P4 electrodes pairs.!
As expected, from initiation to end of practice, the coherence increased in the electrode pairings more related to the motor
execution. Our results show, at the alpha upper and theta bands, the F3-C3 electrode pars increase the coherence from initiation to end of
practice. These results corroborate the literature showing the increased coherence in the brain areas associated with the stimulus
39,40
. It
is well-established that motor practice induces plastic changes in the brain
41
. Most specifically, studies have been showing that when a
new motor skill is learned, there is a shift of activity from the prefrontal regions to the premotor, such as the primary motor cortex (M1)
42,43
.
Unilateral movements are controlled by descending projections originating, predominantly, in the primary motor cortex of the contralateral
hemisphere
20
. In the present study, the right hand was used to control movement. Thus, the C3 is equivalent to the M1 contralateral
hemisphere for our motor task. It is possible to speculate that the motor learning process strengthened the connection between M1 and
prefrontal areas via plasticity mechanisms
44
. The literature has indicated that phase synchronization may be related to long-term
potentiation (LTP)
11
. LTP is one of the mechanisms of synaptic potentiation that produces long-lasting synaptic strengthening
45
,
characterized by postsynaptic neurons firing after presynaptic neurons (10-20 ms), generating the strengthening of synapses
25
. It is
possible that during the motor learning process, the practice produces stimuli to induce LTP in brain areas related to movement execution
44
.!
Also, we expected, throughout practice, the coherence decrease in the electrode pairings less related to the motor execution.
Our results show that at the alpha upper and theta bands, the C3-C4, P3-P4, F3-P3, and C4-P4 electrodes pairs decreased the coherence
from initiation to end of practice. Refining neural areas is possible to explain this result
46
. One of the characteristics of skilled performance
is movement efficiency; this efficiency can be described in different terms
47
. In the muscular energy cost terms, it would be characterized
by the prime mover muscles (or agonist) tensioning and the absence of antagonist muscles tension
48
. In neural terms, neural efficiency
can be characterized by deactivating regions associated with irrelevant information processing
46
. This notion is supported by previous
studies examining power-spectral EEG
16,26,49
. It is possible to speculate that the coherence between frontal, posterior, and ipsilateral-motor
areas is associated with irrelevant information processing in the motor task of study
16,46
. Thus, the practice may be led to a refinement of
neural areas, decreasing the participation of brain areas less related to movement.!
Finally, neural communication and plasticity may support each other. Two brain areas communicating through phase
synchronization will probably induce synaptic plasticity between these areas
11
. Likewise, if connections between two brain areas have
been strengthened, these regions will be more likely to communicate with each other
5
. Our results suggest that communication and
plasticity mutually support each other. Namely, the increase or decrease of coherence reflects the dynamic of strengthening brain areas
more related to movement and weakening less related. It is possible to speculate that during the process of motor learning, the reduction
of synaptic transmission in groups of neurons not associated with the stimulus and the potentiation of synaptic transmission within groups
of neurons associated with the stimulus
50
.!
Some results challenge the logic of the study. For example, the C3-P3 electrode pair in the upper alpha band showed increased
coherence from initiation to the end of practice. Since these electrode pairs are related to motor execution, we expected an increase rather
than a decrease. It is possible to speculate that the time of the feedback presentation may have impacted this result. A minimum interval
of 6 seconds was provided between each trial. However, participants could have extended this interval as there was no time constraint for
initiating the subsequent trial. In other words, participants could take as much time as they deem necessary to process the feedback.
Parietal regions, where the P3 electrode is located, are more associated with information integration via feedback
51, 52
. It is speculated that
this time interval may have been crucial in increasing the demand for cognitive feedback processing, resulting in a non-essential type of
processing. Thus, similar to frontal regions, reducing communication between these two regions could be an important mechanism for
refining cortical resources. However, this issue awaits further studies with specific design.!
Limitations and Directions for Further Research!
A possible limitation of this study was the motor task duration. In the study, the planning moment duration was around 1.5
seconds, the execution moment duration was around 0.930, and the processing moment was 6 seconds. Except for the processing moment,
all moments we have had short circles of EEG signals in each trial because we used high-frequency bands (4-12 Hz) for analysis. One
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problem in short circles is the edge effect of specific epochs. However, we avoid edge effects purely, first, bandpass filtering and Hilbert
transforming the continuous signal, then epoch afterward (as described in the "2.6 EEG signal processing section"). Thus, we suggest that
future studies use a task with more duration for each epoch. Another limitation of the study was the number of task subjects. As can be
observed, there was a wide dispersion of data in the EEG measure. As indicated in the methods section, sample size calculation was
performed using common parameters for behavioral data. However, it is suggested that future studies modify the parameters, taking into
account this variability in the EEG measure.!
CONCLUSION!
The results of our study suggest that the motor learning process may be a decrease in cortico-cortical communication among
brain areas associated with cognitive processes
30
, also an increase in neural plasticity processes in the brain areas related to movement
3
. It is possible that neural communication and plasticity may support each other. Two brain areas communicating through phase
synchronization will probably induce synaptic plasticity between these areas
11
. Likewise, if connections between two brain areas have
been strengthened, these regions will be more likely to communicate with each other
5
. Thus, our results suggest that communication and
plasticity mutually support each other.!
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ACKNOWLEDGMENTS
We thank Vinicius Rezende Carvalho for assistance with EEG analysis.
Citation: Apolinário-Souza T, Lage GM, Fernandes LA. (2023). Study of cerebral cortico-cortical coherence during motor practice. Brazilian Journal of Motor Behavior,
17(5):254265.
Editor-in-chief: Dr Fabio Augusto Barbieri - São Paulo State University (UNESP), Bauru, SP, Brazil. !
Associate editors: Dr José Angelo Barela - São Paulo State University (UNESP), Rio Claro, SP, Brazil; Dr Natalia Madalena Rinaldi - Federal University of Espírito Santo
(UFES), Vitória, ES, Brazil; Dr Renato de Moraes – University of São Paulo (USP), Ribeirão Preto, SP, Brazil.!
Copyright:© 2023 Apolinário-Souza, Lage and Fernandes and BJMB. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non
Commercial-No Derivatives 4.0 International License which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source
are credited.
Funding: Nothing to report.
Competing interests: The authors have declared that no competing interests exist.
DOI: https://doi.org/10.20338/bjmb.v17i5.359