Neuroscience Letters 577 (2014) 38–44

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Neuroscience Lettersjournal homepage: www.elsevier.com/locate/neulet

Extraction of practice-dependent and practice-independent ﬁnger
movement patterns
Shinichi Furuya a,∗,1 , Ayumi Nakamura b,1 , Noriko Nagata bab

Department of Information and Communication Science, Sophia University, Tokyo, Japan
Graduate School of Science and Technology, Kwansei Gakuin University, Nishinomiya, Japanh i g h l i g h t s• Little is understood about reorganization of hand movements through practice.
• Non-musicians performed piano practice over four days.
• Hand kinematics was measured using a motion capture system.
• Principal component analysis determined practice-dependent movement reorganization.
• The reorganization was characterized by enhancement of individuated ﬁnger movements.

a r t i c l e

i n f oArticle history:
Received 11 April 2014
Received in revised form 15 May 2014
Accepted 6 June 2014
Available online 13 June 2014
Keywords:
Multivariate analysis
Motor learning
Motor redundancy
Motor skill acquisitiona b s t r a c t
Extensive motor practice can reorganize movements of a redundant number of degrees of freedom (DOFs).
Using principal component (PC) analysis, the present study characterized the movement reorganizationof the hand that possesses a large number of DOFs during a course of practice. Five musically naïve individuals practiced to play a short sequence of melody with the left hand for four successive days, andtheir hand kinematics was measured using a motion capture system. The PC analysis of the hand joint
kinematics identiﬁed two distinct patterns of movement, which accounted for more than 80% of thetotal variance of movements. The second PC but not the ﬁrst PC changed through practice. A correlation
analysis demonstrated that the PC sensitive to the practice was characterized by coupled movements
across ﬁngers in the same direction. A regression analysis identiﬁed a decrease in the contribution ofthis PC to the hand movement organization through practice, which indicates a reduction of the movement covariation across ﬁngers and thus an enhancement of the individuated ﬁnger movements. The
results implicate potential of PC analysis to extract practice-invariant and practice-dependent movement
patterns distinctively in complex hand motor behaviors.
© 2014 Elsevier Ireland Ltd. All rights reserved.

1. Introduction
Extensive motor practice reorganizes movements at multiple
degrees of freedom (DOFs) so as to optimize motor performance. Previous studies have extensively investigated the effects ofpractice on multi-joint movements of the upper and lower extremities [11,14,16,22]. For example, learning to play the violin wascharacterized by a suppression of shoulder motion while maintaining elbow motion, which improved movement accuracy [14]. The
temporal coordination between the shoulder and elbow was also

∗ Corresponding author. Tel.: +81 3 3238 8575; fax: +81 3 3238 3321.
E-mail addresses: sfuruya@sophia.ac.jp, auditory.motor@googlemail.com(S. Furuya).
1
These authors contributed equally and are co-ﬁrst authors.
http://dx.doi.org/10.1016/j.neulet.2014.06.012
0304-3940/© 2014 Elsevier Ireland Ltd. All rights reserved.

changed through practicing the batting motion so as to rotate theshoulder and elbow in a proximal-to-distal order, which resulted in
an improved maximum speed of movement [22]. Compared withthe upper and lower extremities, the hand possesses a larger number of DOFs. However, little is known about the practice-related
movement reorganization.
Hand dexterity is a key motor skill that enables proﬁcient tooluse, such as grasping [18,19], typing [21], and musical performance [1,3,4,9]. Previous studies have characterized the movement
organization of multiple joints of the hand using various multivariate analyses [4,7,8,18,19,21,23]. For example, principal component(PC) analysis decomposed hand motion during grasping objectsof various sizes and shapes into two fundamental patterns ofﬁnger joint coordination [19]. Similarly, hand kinematics during
piano performance consisted of two or three movement patterns[4]. Therefore, it is likely that multivariate analyses can serve as

S. Furuya et al. / Neuroscience Letters 577 (2014) 38–44

an effective means of extracting fundamental patterns of movement organization across ﬁngers and joints of the hand. However,when characterizing movements that evolve through practice, anincrease in dimensionality limits a conventional use of multivariate analysis that identiﬁes movement covariance across joints.
Furthermore, the strong constraints at both the biomechanicaland neurophysiological levels, being unique features of the hand[20], can prevent speciﬁc movement elements from changing withpractice, which can make it difﬁcult to identify movement features
sensitive to practice. Using a novel way of PC analysis, the present
study challenged for extracting individual movement patterns thatchange and remain through a course of practice separately. To thisend, musically naïve individuals were asked to practice a shortsequence of piano melody for four successive days, and their handkinematics were measured using a motion capture system. Basedon the previous ﬁndings of the superior independence of movements across ﬁngers for more skilled pianists [4], we postulated
that a practice-variant movement pattern plays a role in facilitating
the individuated ﬁnger movements.
2. Material and methods
2.1. Participants
Five musically naïve right-handed male individuals (age: 21–24yrs) participated in the experiment. None of the participants hadformal education in playing musical instruments prior to the experiment. The experimental protocol was approved by the local ethics
board of Kwansei Gakuin University, and the participants provided
informed consent prior to the experiment. The experiment was
performed according to the Declaration of Helsinki.
2.2. Experimental task
The current experiment includes 50 practice trials per day for 4
successive days (200 trials in total). During the practice, each participant played a certain tone sequence consisting of 12 strokes witha predetermined ﬁngering using the left hand (i.e., a sequence of“CDCECFDEDFEF” with the ﬁngering of “545352434232”; 2: Index,
3: Middle, 4: Ring, 5: Little). Each participant played a digital piano(YAMAHA, P-250) with an inter-keystroke interval (IKI) of 500 ms insynchronization with a metronome (2 strokes per second) at a predetermined loudness (mezzo-forte, 90 MIDI velocity). This task was
repeated 50 times per day, and the trials that included erroneous
stroke(s) and/or stronger or softer stroke(s) (±5 MIDI velocity) werediscarded and repeated. Data were measured at the ﬁrst 5 (“presession”) and last 5 (“post-session”) trials at each of the 4 successive
days (i.e., eight sessions in total).
Before starting the experiment, each participant was asked
to practice to familiarize themselves with both the given tone
sequence and the piano based on instructions from the experimenter. This familiarization session took approximately 5 min,during which the participants memorized the sequence and ﬁngering.

39

markers was recorded at 120 Hz using 13 high-speed cameras surrounding the piano. The camera locations were carefully arranged
so that the position data of all the markers would be recorded while
the target task was performed. The spatial resolution in the camera setting was 1 mm. The 3D time-position data of each marker
were obtained using a direct linear transformation method. All theprocedures were established in our previous study [5]. The position data were digitally smoothed at a low-pass cutoff frequency of
10 Hz using a second-order Butterworth digital ﬁlter.
2.3.1. Deﬁnition of joint angle
Using the position data of the individual markers in a threedimensional space, the angles at the metacarpophalangeal (MCP),
proximal-interphalangeal (PIP), and distal-interphalangeal (DIP)
joints for ﬂexion and extension were computed at the index, middle, ring, and little ﬁngers. The ﬂexion/extension angle of the PIP
and DIP joints, each of which has only one degree of freedom, canbe computed as an inner product. To compute the angle of the MCP
joint of a certain ﬁnger, we used position data that consisted of four
markers, including the proximal ends of this ﬁnger and its adjacentﬁnger(s) and the centers of the MCP and PIP joints of this ﬁnger.
Point X was deﬁned as a foot of a perpendicular from point P to
the plane ␣ that contains the points A, B, and C. The angle formed−→−→by vectors AX and AP was deﬁned as . Point X is on the plane
containing points A, B, and C, which yields the following equations.
−
→
−−→
−
→
−
→
PX = r PA + sPD + t PCX(1)
r+s+t =1

(2)

Then, the following equations hold true because PX is orthogonal to ␣.
→−→ −PX · AB = 0
(3)
→
−
→ −
PX · AC = 0(4)−→
Because points A, B, and C are observational data, PX can be eval−→
uated using Eqs. (1)–(4). Then, vector AX was evaluated as follows.
−→ −−→ −→AX = −PA + PX−→−→
Thus, the MCP joint angle formed by AX and AP (i.e., ) was
evaluated by the following equation.

 −→ −→  = arccos

AP · AX−→ −→|AP| · |AX|

×

180


It is anatomically possible that the MCP joint hyper-extends andthus that the value of  exceeds . Therefore, depending on thepositional relationship between points P and X, the MCP joint angle
was evaluated as follows:MCP =⎧⎨  +  Py > Xy⎩

−Py < Xy+

Py = Xy

where Py and Xy indicate the y-coordinate of points P and X, respectively.

2.3. Data recording and analysis
During the experiment, MIDI data that include the time at which
each key was depressed and released were collected from the pianowith a time resolution of 1 ms. The kinematic data were timenormalized so that each inter-keystroke interval became 100.
Twenty-six spherical reﬂective markers were placed on the participants’ hand to determine anatomical landmarks. These markers
were put on the skin over the ﬁngertips and on the 3 joint centers of all 5 digits, the proximal ends of the metacarpal bones andthe distal ends of the radius and ulna. The motion of the reﬂective

2.3.2. Principal component analysis
To characterize changes in the patterns of hand movement kinematics among various DOFs at the hand over the 4 days of practice,
we performed PC analysis. The PC analysis identiﬁed patterns ofcovariation of the time-varying joint kinematics across the practicesessions. The input to the PC analysis was the averaged joint angular velocity for each session. Each of the 8 practice session vectors
consisted of a series of 12 joint velocity waveforms (3 joints × 4
ﬁngers). As opposed to the previous method that extracts movement covariance across joints [19], the current study uses an input

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S. Furuya et al. / Neuroscience Letters 577 (2014) 38–44A
0.5

Pre-session

C 90

Index
MiddleRing
LittlePost-session

Waveforms of pvPC
0.4

100

MCP joint (deg/s)

80

WC1

0.3

0.1

0

Day1 Day2 Day3 Day4

B 0.8
0.6

WC2

0.4

60

50
0
-50
-100
-150

50

L

R

L

M

L

I

R

M

R

I

M

I

L

R

L

M

L

I

R

M

R

I

M

I

L

R

L

M

L

I

R

M

R

I

M

I

100
40

PIP joint (deg/s)

0.2

variance accounted for by individual PCs (%)

70

30

0.2

50

0

-50

20

0

-100

-0.2
10

-0.4

150
100
Day1 Day2 Day3 Day4

Practice session

0
PC1

PC2

Fig. 1. Results of PC analysis. (A and B) Group means of the weighting coefﬁcientsover four days of practice (=eight practice sessions) for PC1 and PC2. (C) A groupmean of the variance accounted for by the ﬁrst two PCs. An error bar indicates astandard deviation across the participants.

DIP joint (deg/s)

-0.6

50
0
-50
-100
-150

matrix that consists of the practice session and a series of angularvelocity waveforms of all the joints, and this allowed the covariance of movements across sessions to be computed. We conducteda separate analysis for each of the ﬁve participants.
The PC analysis in the present study results in n basic PC waveforms, which are computed from the n × n covariance matrix ofthe n practice-session vectors (n = 8). The covariance calculation
removes the mean from each of the n columns of the input matrix.
Thus, the angular velocity waveforms at each joint for each practicesession could be perfectly reconstructed as the average angularjvelocity at the jth joint for the ith practice session (mean ˙ i ) plus
ja weighted sum of the n PC waveforms (PC 1. . .n ) at the jth joint:jj˙ i = mean ˙ i + PC1j × W 1i + · · · + PCnj × Wni

where W1i –Wni are the weighting coefﬁcients (WC) for the ith
practice session. The PCs are ranked such that PC1 accounts forthe largest portion of the variance. We then quantitatively determined the correspondence of PCs across the pianists based on thetechnique that we used previously [4,7].
2.3.3. Correlation analysis
To assess the independence of movements across ﬁngers at
the individual PC waveforms, a correlation coefﬁcient of the timevarying joint motion was computed between the ﬁnger used forthe keystroke (“striking ﬁnger”) and one of the other 3 ﬁngers(“non-striking ﬁnger”). For each of the 4 ﬁngers, the coefﬁcient wascomputed during the period from one previous strike to one following strike with the target ﬁnger (=3 successive strikes). Because
each trial included 3 strikes with each of the ﬁngers, the coefﬁcient was computed for each of the 3 strikes and then averaged.
This value was averaged across the ﬁrst 5 trials (“pre-session”) andacross the ﬁnal 5 trials (“post-session”) for each of the 4 days. The
correlation coefﬁcient between the striking and non-striking ﬁngers was computed at each of the MCP, PIP, and DIP joints for eachparticipant.

One inter-keystroke interval

Fig. 2. Time-varying waveforms of the joint angular velocity of the pvPC (i.e., PC2)
for the index (blue), middle (green), ring (red), and little (black) ﬁngers at the
MCP (top panel), PIP (middle panel), and DIP (low panel) joints in one representative participant. Flexion and extension correspond to negative and positive values,
respectively. X axis: time normalized by each inter-keystroke interval. I, M, R, and
L indicate a moment of keystroke with the index, middle, ring, and little ﬁngers,
respectively. (For interpretation of the references to color in this ﬁgure legend, thereader is referred to the web version of this article.)

2.4. Statistics
To statistically evaluate the change in the WC over the practicesessions, we used a one-way repeated-measures analysis of variance (ANOVA) with the “practice session” (within variable, 8 levels)as the independent variable and Tukey post-hoc tests for multiple comparisons. A statistical analysis was performed using R (ver.
3.0.2).
3. Results
Fig. 1 illustrates the group means of the weighting coefﬁcient at the 8 practice sessions (pre/post × 4 days) for the ﬁrst
and second PCs. The weighting coefﬁcient of PC1 was constantacross the practice sessions (Fig. 1A). The one-way ANOVA withrepeated measure showed no signiﬁcant main effect (F(7,28) = 1.42,
p = 0.235, eta-squared = 0.26). By contrast, the weighting coefﬁcient of PC2 decreased across the sessions (Fig. 1B). The ANOVA
yielded a signiﬁcant main effect of session (F(7,28) = 3.67, p = 0.006,
eta-squared = 0.48). A post-hoc test revealed smaller weighting
coefﬁcients of both the pre- and post-sessions on day 4 comparedto the pre- and post-sessions on day 1. The results indicate that
PC2 but not PC1 represents a ﬁnger movement pattern that varied
in relation to practice. In the following section, we refer to PC1 as
the practice-invariant PC (piPC) and PC2 as the practice-variant PC(pvPC).

S. Furuya et al. / Neuroscience Letters 577 (2014) 38–44

Index-Middle
0.2

41

Index-Ring
0.15

Index-Little
0.15

MCP
PIP
DIP

0.15

0.1
0.1
0.1

correlation coefficient

0.05
0.05
0.05
0
0
0
-0.05
-0.05
-0.05
-0.1
-0.1

-0.15

-0.15

4
st
Po
e4
Pr
3
st
Po
e3
Pr
2
st
Po
e2
Pr
1
st
Po
e1
Pr

4
st
Po
e4
Pr
3
st
Po
e3
Pr
2
st
Po
e2
Pr
1
st
Po
e1
Pr

4
st
Po
e4
Pr
3
st
Po
e3
Pr
2
st
Po
e2
Pr
1
st
Po
e1

Pr

Middle-Ring
0.25

Middle-Little
0.15

Ring-Little
0.2

0.2

0.15
0.1

0.15

correlation coefficient

0.1

0.1
0.05
0.05
0.05
0
0
0

-0.05
-0.05
-0.05
-0.1

-0.1

-0.15

-0.1

-0.15

4
st
Po
e4
Pr
3
st
Po
e3
Pr
2
st
Po
e2
Pr
1
st
Po
e1

Pr

4
st
Po
e4
Pr
3
st
Po
e3
Pr
2
st
Po
e2
Pr
1
st
Po
e1

Pr

4
st
Po
e4
Pr
3
st
Po
e3
Pr
2
st
Po
e2
Pr
1
st
Po
e1
Pr

practice session

Fig. 3. The group mean of the correlation coefﬁcient values reconstructed by multiplying the correlation coefﬁcients of the pvPC and its corresponding weighting coefﬁcientsover four days of practice (=eight practice sessions) for each pair of index, middle, ring and little ﬁngers at the MCP (red), PIP (green), and DIP (blue) joints. An error bar
represents one standard error across participants. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)

Fig. 1C shows the group mean of variance accounted for by theﬁrst 2 PCs. Overall, the piPC accounted for more than 70% of thetotal variance, and the pvPC accounted for approximately 10% of
the total variance.
Fig. 2 shows the time-varying pvPC waveforms that represent
the joint angular velocity at the MCP, PIP, and DIP joints of the ﬁngers in one representative participant. Overall, the joint angularvelocity of pvPC covaried across ﬁngers. For example, during theﬁrst keystroke with the ring ﬁnger (i.e., the ﬁrst “R” in Fig. 2), thering ﬁnger rotated in the same direction as the remaining three ﬁngers, particularly at the MCP joint. The correlation coefﬁcient of thejoint angular velocity between the striking and non-striking ﬁnger
at the pairs of index–middle, index–ring, index–little, middle–ring,

middle–little, and ring–little for the pvPC was 0.49, 0.20, 0.23, 0.46,
0.34, and 0.33 at the MCP joint, 0.18, 0.19, 0.06, 0.02, 0.10, and0.42 at the PIP joint, and 0.29, 0.21, 0.13, 0.29, 0.06, and 0.51 at
the DIP joint, respectively. The positive coefﬁcient values conﬁrm
covariation of joint motion across ﬁngers for the pvPC.
The observation of the movement pattern that represents
the covariation of joint rotation across ﬁngers for the pvPC andthe progressive decrease of the corresponding WC value withpractice indicate that the pvPC functions to decrease the amountof movement covariation across ﬁngers with practice. To assessa role of the pvPC in changes of ﬁnger movement organizationin relation to practice, we computed a scalar product of the correlation coefﬁcients of pvPC waveforms and the corresponding

42

S. Furuya et al. / Neuroscience Letters 577 (2014) 38–44

Index-Middle

0.2

Index-Ring

0.2

0.15

0.15

0.1

0.1

Index-Little

0.15

0.1

0.05

correlation coefficient

0.05

0.05
0
0

0
-0.05

-0.05

-0.05

-0.1

-0.1

-0.1

MCP
PIP
DIP

-0.15

-0.2

-0.15

-0.15

-0.2

-0.2

correlation coefficient

4
st
Po
e4
Pr
3
st
Po
e3
Pr
2
st
Po
e2
Pr
1
st
Po
e1
Pr

4
st
Po
e4
Pr
3
st
Po
e3
Pr
2
st
Po
e2
Pr
1
st
Po
e1

Pr

4
st
Po
e4
Pr
3
st
Po
e3
Pr
2
st
Po
e2
Pr
1
st
Po
e1
Pr

Middle-Ring

0.15

Middle-Little

0.15

Ring-Little

0.15

0.15

0.1

0.1

0.1

0.05

0.05

0.05

0

0

0

-0.05

-0.05

-0.05

-0.1

-0.1

-0.1

-0.15

-0.15

-0.15

-0.2

-0.2
4
st
Po
e4
Pr
3
st
Po
e3
Pr
2
st
Po
e2
Pr
1
st
Po
e1
Pr

4
st
Po
e4
Pr
3
st
Po
e3
Pr
2
st
Po
e2
Pr
1
st
Po
e1
Pr

4
st
Po
e4
Pr
3
st
Po
e3
Pr
2
st
Po
e2
Pr
1
st
Po
e1
Pr

practice session

Fig. 4. The group mean of the reconstructed correlation coefﬁcient values using the piPC and its corresponding weighting coefﬁcients over practice sessions for each pair of
the four ﬁngers at the MCP (red), PIP (green), and DIP (blue) joints. An error bar represents one standard error across participants. (For interpretation of the references tocolor in this ﬁgure legend, the reader is referred to the web version of this article.)weighting coefﬁcient values at each session for each participant.
The reconstructed correlation coefﬁcients over the 8 practice sessions represent the amount of contribution of the pvPC to changes
in the amount of movement covariation with practice. Fig. 3 depicts
the group mean values of the reconstructed correlation coefﬁcient values at all ﬁnger pairs over the 8 practice sessions forthe pvPC. Overall, all pairs of ﬁngers at each joint demonstrated
a decrease of the coefﬁcient value with practice. This conﬁrms
that the pvPC played a role in the progressive decrease of the
movement covariation across ﬁngers with practice. A linear regression analysis was performed for the group mean values at eachpair of ﬁngers. Table 1 summarizes the result, which shows asigniﬁcant decrease of the reconstructed correlation coefﬁcientwith practice at most ﬁnger pairs, particularly of the MCP and
DIP joints.
Similarly, correlation coefﬁcient values were also reconstructedusing piPC, and Fig. 4 depicts the group mean values at all ﬁnger
pairs over the 8 practice sessions. Overall, the correlation coefﬁcient values were constant across the sessions at all ﬁnger pairs andjoints. Although a linear regression identiﬁed signiﬁcant changes
with practice at some ﬁnger pairs and joints (Table 1), their regression coefﬁcients were all fairly small and close to zero (<0.01). The
result conﬁrms a lack of apparent changes in the amount of the
movement covariation across ﬁngers with practice for PiPC.

S. Furuya et al. / Neuroscience Letters 577 (2014) 38–44

43

Table 1Results of linear regression analysis for the reconstructed correlation coefﬁcients from piPC and pvPC.
piPC
MCP

PIP

DIP

Regression coefﬁcientWC1 × rWC1 × rIR
WC1 × rIL
WC1 × rMR
WC1 × rML
WC1 × rRL
WC1 × rIM
WC1 × rIR
WC1 × rIL
WC1 × rMR
WC1 × rML
WC1 × rRL
WC1 × rIM
WC1 × rIR
WC1 × rIL
WC1 × rMR
WC1 × rML
WC1 × rRL
IM

−0.002
0.007
0.004
0.001
0.004
−0.004−0.0030.001
0.001−0.0030.002
0.004−0.0020.002
0.000−0.002
0.002
0.003

R2
0.35
0.45
0.50
0.17
0.36
0.65
0.42
0.08
0.78
0.31
0.43
0.59
0.66
0.68
0.00
0.26
0.64
0.42

p

pvPC

0.121
0.068
0.050
0.305
0.116
0.016
0.084
0.491
0.004
0.151
0.078
0.025
0.014
0.012
0.881
0.199
0.018
0.081

WC2 × rWC2 × rIR
WC2 × rIL
WC2 × rMR
WC2 × rML
WC2 × rRL
WC2 × rIM
WC2 × rIR
WC2 × rIL
WC2 × rMR
WC2 × rML
WC2 × rRL
WC2 × rIM
WC2 × rIR
WC2 × rIL
WC2 × rMR
WC2 × rML
WC2 × rRL
IM

Regression coefﬁcientR2

p−0.021−0.019−0.015−0.027−0.009−0.014−0.009−0.012−0.003−0.018−0.006−0.004−0.018−0.008−0.008−0.015−0.016−0.022

0.81
0.64
0.91
0.61
0.42
0.27
0.21
0.46
0.63
0.43
0.38
0.11
0.78
0.72
0.87
0.64
0.84
0.87

0.002
0.017
<0.001
0.023
0.082
0.183
0.260
0.065
0.019
0.079
0.103
0.429
0.004
0.008
0.001
0.017
0.001
0.001

A bold number indicates p < 0.05. WC1: weighting coefﬁcient of piPC, WC2: weighting coefﬁcient of pvPC. r: correlation coefﬁcients between two ﬁngers. I, M, R, and L
indicates the index, middle, ring, and little ﬁnger, respectively.

4. Discussion
The present study investigated a methodological advantage ofPC analysis for assessing the reorganization of hand movements
during a course of piano practice. The results demonstrated that
the PC analysis successfully extracted two distinct movement patterns; one of these remains during practice, and the other decreases
with practice. The latter pattern was characterized by covariationof movements across ﬁngers. The ﬁnding that this movement element diminished during a course of practice indicated a decreaseof movement covariation and thus a facilitation of the individuatedﬁnger movements. To the best of our knowledge, this is the ﬁrst
study that uses PC analysis to identify a speciﬁc movement patternthat varies with practice. Therefore, it is possible that this analyticaltechnique serves as an effective means that describes practicerelated reorganization of DOFs of the motor system in a variety ofcomplex motor behaviors that involve motions at a large number ofjoints, such as object manipulation, ﬁnger-spelling, and surgery. It
is also likely that the current method can identify maladaptive reorganization of movements by a development of movement disorders
such as focal hand dystonia [2,12,13].
Our observation of the facilitation of movement independence
across ﬁngers through piano practice was in agreement with theﬁndings of previous studies that compared pianists of different
skill levels. For example, during repetitive piano keystrokes, skilled
pianists showed a smaller amount of spillover of ﬁnger force exertion into the adjacent ﬁngers than unskilled piano players [17]. Our
recent study also found equal independence of movements acrossﬁngers for pianists [4], which is different from non-musicians, whoshowed lower movement independence at the middle and ring ﬁngers than at the other ﬁngers [10]. The present ﬁnding extends
these ﬁndings by showing a causal relationship between piano
practice and independent ﬁnger control. Presumably, the facilitation of independent movement control is associated with the
improved maximum speed of piano playing after daily practice [6].
By contrast, the movement pattern independent of the practiceaccounted for more than 70% of the total variance of movements.
This highlights robustness of the hand movement pattern against
training. The movement pattern was characterized by negative coupling between the index and ring ﬁngers, between the index andlittle ﬁngers, and between the middle and little ﬁngers. This multiﬁnger coordination pattern is similar to the pattern observed during
dexterous hand grasping [8,19]. Robustness of this coordination

pattern against the short-term practice may explain why musicians
undergo more than 10,000 h of practice before age 20 [15].
One limitation of the present study was a lack of understanding of the roles of higher PCs. The PC analysis found that the ﬁrst
two PCs accounted for 81.2% of the variance of movements. This
indicates that higher PCs accounted for 18.8% of the total varianceof movements. Although our PC analysis demonstrated that higher
PCs did not display any common trends across the participants, it
is possible that these PCs functioned to either increase or decrease
the movement independence at speciﬁc ﬁnger pairs. Previous studies have shown that the higher PCs of the hand movements are notnoise but are rather responsible for the dexterous control of ﬁngers[19]. A further study is needed to uncover the roles of higher PCs in
the movement reorganization of the hand through piano practice.

Conﬂict of interest statement
The authors declare that there are no conﬂicts of interest.

References[1] K.C. Engel, M. Flanders, J.F. Soechting, Anticipatory and sequential motor control in piano playing, Exp Brain Res. 113 (1997) 189–199.
[2] S. Furuya, E. Altenmüller, Finger-speciﬁc loss of independent control of movements in musicians with focal dystonia, Neuroscience 247 (2013) 152–163.
[3] S. Furuya, E. Altenmüller, Flexibility of movement organization in piano performance, Front. Hum. Neurosci. 7 (2013) 173.
[4] S. Furuya, M. Flanders, J.F. Soechting, Hand kinematics of piano playing, J. Neurophysiol. 106 (2011) 2849–2864.
[5] S. Furuya, T. Goda, H. Katayose, H. Miwa, N. Nagata, Distinct inter-joint coordination during fast alternate keystrokes in pianists with superior skill, Front.
Hum. Neurosci. 5 (2011) 50.
[6] S. Furuya, A. Nakamura, N. Nagata, Transfer of piano practice in fast performance of skilled ﬁnger movements, BMC Neurosci. 14 (2013) 133.
[7] S. Furuya, J.F. Soechting, Speed invariance of independent control of ﬁnger
movements in pianists, J. Neurophysiol. 108 (2012) 2060–2068.
[8] R. Gentner, J. Classen, Modular organization of ﬁnger movements by the humancentral nervous system, Neuron 52 (2006) 731–742.
[9] W. Goebl, C. Palmer, Temporal control and hand movement efﬁciency in skilledmusic performance, PLoS One 8 (2013) e50901.
[10] C. Häger-Ross, M.H. Schieber, Quantifying the independence of human ﬁnger movements: comparisons of digits, hands, and movement frequencies, J.
Neurosci. 20 (2000) 8542–8550.
[11] N.J. Hodges, S. Hayes, R.R. Horn, A.M. Williams, Changes in coordination, control
and outcome as a result of extended practice on a novel motor skill, Ergonomics
48 (2005) 1672–1685.
[12] H.C. Jabusch, H. Vauth, E. Altenmüller, Quantiﬁcation of focal dystonia in
pianists using scale analysis, Mov. Disord. 19 (2004) 171–180.

44

S. Furuya et al. / Neuroscience Letters 577 (2014) 38–44[13] H.C. Jabusch, D. Zschucke, A. Schmidt, S. Schuele, E. Altenmüller, Focal dystonia in musicians: treatment strategies and long-term outcome in 144 patients,Mov. Disord. 20 (2005) 1623–1626.
[14] J. Konczak, H. Vander Velden, L. Jaeger, Learning to play the violin: motor
control by freezing, not freeing degrees of freedom, J. Mot. Behav. 41 (2009)
243–252.
[15] R.T. Krampe, K.A. Ericsson, Maintaining excellence: deliberate practice andelite performance in young and older pianists, J. Exp. Psychol. Gen. 125 (1996)
331–359.
[16] P.V. McDonald, R.E. van Emmerik, K.M. Newell, The effects of practice on limb
kinematics in a throwing task, J. Mot. Behav. 21 (1989) 245–264.
[17] D. Parlitz, T. Peschel, E. Altenmüller, Assessment of dynamic ﬁnger forcesin pianists: effects of training and expertise, J. Biomech. 31 (1998)
1063–1067.

[18] M. Santello, M. Flanders, J.F. Soechting, Patterns of hand motion during grasping
and the inﬂuence of sensory guidance, J. Neurosci. 22 (2002) 1426–1435.
[19] M. Santello, M. Flanders, J.F. Soechting, Postural hand synergies for tool use, J.
Neurosci. 18 (1998) 10105–10115.
[20] M.H. Schieber, M. Santello, Hand function: peripheral and central constraints
on performance, J. Appl. Physiol. 96 (2004) 2293–2300.
[21] J.F. Soechting, M. Flanders, Flexibility and repeatability of ﬁnger movements
during typing: analysis of multiple degrees of freedom, J. Comput. Neurosci. 4(1997) 29–46.
[22] D. Southard, Changes in limb striking pattern: effects of speed and accuracy,
Res. Q. Exerc. Sport 60 (1989) 348–356.
[23] P.H. Thakur, A.J. Bastian, S.S. Hsiao, Multidigit movement synergies of thehuman hand in an unconstrained haptic exploration task, J. Neurosci. 28 (2008)
1271–1281.