Neuroscience 275 (2014) 444–454

ACQUISITION OF INDIVIDUATED FINGER MOVEMENTS THROUGH
MUSICAL PRACTICE
S. FURUYA, a * A. NAKAMURA b AND N. NAGATA b

muscles (Kilbreath and Gandevia, 1994; Keen andFuglevand, 2004; Winges et al., 2008), and the shared
representation of individual ﬁngers in the motor cortex(Schieber and Hibbard, 1993; Sanes et al., 1995). Consequently, the motion of a single ﬁnger yields a covariationof motion at the adjacent ﬁngers (Häger-Ross andSchieber, 2000). This movement covariation may simplify
the control of relatively simple hand movements such as
grasping (Santello et al., 1998, 2002;Mason et al., 2001;
Gentner and Classen, 2006) and haptic exploration(Thakur et al., 2008) by reducing the dimensionality of
the control of multiple joints/muscles in the hand(Overduin et al., 2012; Santello et al., 2013). However,dexterous hand use, which represents skilled motor
behavior, requires moving multiple ﬁngers in an opposite
direction, or even independently, against these neuromuscular constraints. This motor skill is sometimes
impaired through development of overtraining-induced
neurological disorders such as focal dystonia (Curra
et al., 2004; Sohn and Hallett, 2004; Rosenkranz et al.,2009; Furuya and Altenmüller, 2013), which implicates
its association with neuroplasticity. Of particular importance is an understanding of the neuroplastic mechanisms subserving the individuated ﬁnger movements,
which may not only shed light on acquisition and loss ofhand dexterity, but also aid in designing an optimal program for facilitating ﬁne motor control for unskilled andelderly individuals (Shim et al., 2004) and for patients withmovement disorders that exacerbate dexterous hand use(Lang and Schieber, 2004a; Raghavan et al., 2006;
Brandauer et al., 2012; Park et al., 2012).
Previous studies that compared repetitive ﬁnger
movements between musicians and non-musicians usinga cross-sectional design demonstrated enhancement of
individuated ﬁnger movements in musicians (Parlitz
et al., 1998; Slobounov et al., 2002; Aoki et al., 2005). A
recent study also demonstrated the equal independenceof movements across ﬁngers in expert pianists (Furuya
et al., 2011a) over a wide range of movement rates(Furuya and Soechting, 2012), which diﬀered from the ﬁndings in musically untrained individuals (Häger-Ross andSchieber, 2000; Zatsiorsky et al., 2000; van Duinen and
Gandevia, 2011). These ﬁndings suggest the acquisitionof this motor skill through extensive training, presumably
through plastic neuromuscular adaptations (Jäncke,2009). However, several confounding factors remain, suchas the genetic predisposition of neuromuscular anatomy
and function and the explicit instruction provided throughmusic education. A longitudinal study would serve foraInstitute for Music Physiology and Musicians’ Medicine,
Hannover University of Music, Drama and Media, Germanyb
Graduate School of Science and Technology, Kwansei
Gakuin University, Japan

Abstract—Individuated ﬁnger movements represent a key
feature of hand dexterity. However, our understanding of
mechanisms underlying the acquisition of this motor skill
is limited. The present study aimed to identify the eﬀects
of daily motor training on acquisition of individuated ﬁnger
movements. Ten musically naı̈ve individuals performed
piano practice for 4 successive days, and hand kinematics
were evaluated using a motion capture system. The results
showed a decrease in movement covariation across ﬁngers
with practice, particularly at the ring and little ﬁngers. The
decrease was more pronounced in the pair of ﬁngers withlower independent control prior to the practice. Furthermore, a few ﬁnger pairs demonstrated facilitated movement
independence when the subject was provided with visualfeedback (VFB) regarding the rhythmic accuracy of motor
actions following each practice. The results provide evidence for the enhancement of individuated ﬁnger movements through dexterous hand use during piano practice,which suggests plastic adaptation of the neuromuscularsystem associated with independent control of ﬁnger movement. Ó 2014 IBRO. Published by Elsevier Ltd. All rights
reserved.

Key words: motor learning, neuroplasticity, ﬁne motor control, motor skill, hand dexterity.

INTRODUCTION
The neuromuscular architecture of the hand constrains
the independent control of individual ﬁnger movements.
The constraint includes the anatomical linkages
between the tendons and muscles of the hand (Leijnse
et al., 1993; Lang and Schieber, 2004a), the synchronousﬁring of motor neurons innervating into adjacent ﬁnger*Corresponding author. Address: Department of Information andCommunication Sciences, Sophia University, Japan, 4-4, Chiyoda-ku,
Tokyo, 1020081, Japan. Tel: +81-(0)3-3238-8575; fax: +81-(0)33238-3321.
E-mail address: auditory.motor@gmail.com (S. Furuya).
These authors contributed equally and are co-ﬁrst authors.
Abbreviations: DIP, distal-interphalangeal; IKI, inter-keystroke interval;
MCP, metacarpophalangeal; NFB, normal feedback; PIP, proximalinterphalangeal; ROM, range of motion; VFB, visual feedback.
http://dx.doi.org/10.1016/j.neuroscience.2014.06.031
0306-4522/Ó 2014 IBRO. Published by Elsevier Ltd. All rights reserved.
444

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S. Furuya et al. / Neuroscience 275 (2014) 444–454

The experiment consisted of 50 practice trials for 4
successive days (200 trials in total). During the practice,each participant played a certain tone sequence
consisting of 12 strokes with a predetermined ﬁngeringthat used all possible ﬁnger pairs of the left hand(Fig. 1A). In each trial, the four ﬁngers were used threetimes. The index, middle, ring, and little ﬁnger always
struck a key of F, E, D, and C, respectively. The thumb
was not included because our previous study
demonstrated diﬀerent movement patterns between the
thumb and the ﬁngers (Furuya et al., 2011a). We chosethe non-dominant left hand because this hand is less frequently used in daily and sports activities compared withthe dominant hand. The participant played a digital piano(YAMAHA, P-250) with an inter-keystroke interval (IKI) of
500 ms in synchronization with a metronome (two strokes
per second) at a predetermined speed with which eachkey was depressed (90-MIDI velocity; note that MIDI
velocities provided by the interface range from 1 to127). This task was repeated 50 times per day, among
which the ﬁrst ﬁve and last ﬁve trials were used to recordand assess the hand kinematics (Fig. 1B). During theA

50 trials

post
45-50

1-5

45-50

1-5

45-50

1-5

45-50

2

C
visual feedback

al

al

tri

tri

us

Ten musically naı̈ve adult individuals were randomly
assigned to two groups. One group (one female, fourmales; age: 21–24 yrs) was provided explicit VFB
regarding the rhythmic accuracy of movements (i.e.,
VFB) following each practice trial (VFB group). The agematched control group (ﬁve males; age: 21–24 yrs) did
not receive any explicit VFB regarding performance(normal feedback (NFB), group). All participants wereright-handed, with a laterality index of 89.1 ± 8.1(all > 80) (Oldﬁeld, 1971). All participants had neither formal education in playing musical instruments prior to theexperiment nor other expertise requiring dexterous use

nt

io

rre

1-5

3

cu

Day 1

pre

3
5 2
4 3
4
2
2: index, 3: middle, 4: ring, 5: little

Day 2

B

5

rhythmic error (ms)

5 4

ev
pr

Participants

Experimental design

Day 3

EXPERIMENTAL PROCEDURESof the hand (e.g. sewing, painting). The experimental protocol was approved by the local ethics board of Kwansei
Gakuin University, and all participants provided informed
consent prior to the experiment. The experiment was performed according to the Declaration of Helsinki.

Day 4

better understanding whether the superior independent
control of ﬁnger movements in musicians is a product ofnature or nurture (Wan and Schlaug, 2010).
Piano performance provides a rich and naturalenvironment that requires independent ﬁnger control
such as a precisely timed strike and release of keys insuccession with multiple ﬁngers (Furuya et al., 2011a).
The spontaneous covariation of movements across ﬁngers may elicit unwanted tones and/or distorted rhythm
and articulation. Thus, a mastery of piano playing should
accompany skill acquisition for moving ﬁngers independently. In addition to extensive piano practice, speciﬁc
instruction in piano can play a role in the acquisition of
independent control of ﬁnger movements. For example,
during motor training of the independent control of staticforce production in ﬁngers, visual feedback (VFB) regarding the motor performance facilitated independent ﬁnger
control (Chiang et al., 2004). This result suggests theimportance of providing extrinsic information on movement accuracy for acquiring this skill. Since perceptual
abilities that are also not well ﬁne-tuned in untrained
individuals as compared to musicians (Kraus andChandrasekaran, 2010) may make it diﬃcult to gain precise information on movement error, extrinsic VFB may
aid in facilitating feedback error learning (Kawato, 1999).
Indeed, motor skill acquisition with explicit VFB activates
distinct neural networks (Debaere et al., 2003; Ronsse
et al., 2011).
The primary goal of the study was to identify theeﬀects of daily musical training on individuated ﬁnger
movements. Based on the previous ﬁndings of lower
independent movement control in the middle and ringﬁngers compared with the index and little ﬁngers for theuntrained non-musicians (Häger-Ross and Schieber,
2000; Zatsiorsky et al., 2000) but not for the expert pianists (Furuya et al., 2011a), we hypothesized a larger
learning gain at the ﬁngers with potentially low independent control. It was also postulated that joints with a larger
range of motion (ROM) would display a greater learningeﬀect. We also assessed the eﬀect of practicing with provision of VFB regarding the rhythmic accuracy of motor
actions on individuated ﬁnger movements. We hypothesized improvements in independent ﬁnger control following musical training and its facilitation through providing
explicit VFB.

kinematic analysis

Fig. 1. (A) The practice task on musical score. The number below
each note speciﬁes the ﬁngering (2, 3, 4, and 5 correspond to theindex, middle, ring, and little ﬁngers, respectively). (B) The experimental ﬂow. Each day of training consisted of 50 practice trials. The
initial and ﬁnal ﬁve trials were used for analysis, deﬁned as the pretraining and post-training sessions, respectively. (C) Visual feedbackon rhythmic accuracy of keystrokes during one previous (left bar) andcurrent (right bar) trials. The plot was visually presented on a PC
monitor located in front of a participant after each trial only for the
VFB group.

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S. Furuya et al. / Neuroscience 275 (2014) 444–454

task, the trunk was in an upright position with no movement while the right arm and hand were kept relaxed
and placed on the side of the trunk. The left upper-arm
and forearm was perpendicular and horizontal to theground, respectively.
Prior to data recording on the ﬁrst day, eachparticipant in both the VFB and NFB groups wasallowed to practice to familiarize themselves with both
the given tone sequence and the piano by accepting
instructions from the experimenter, which tookapproximately 5 min. All participants memorized thesequence and ﬁngering during the familiarization
session so that they could play without errors.
Data measurement and analysis
Provision of VFB regarding rhythmic accuracy of
movements. In order to explicitly provide only VFB
group with VFB regarding a speciﬁc variable regarding
how precisely the player kept the timing of successive
keystrokes, MIDI data were collected during theexperiment from the piano using a custom-made script
written in JAVA with a time resolution of 1 ms. This script
allowed us to record the time at which each key wasdepressed and released and to compute the IKI as theinterval from key depression to key depression. To
provide VFB regarding movement accuracy
explicitly,
P11
the rhythmic error, deﬁned as
i¼1 jIKIi  500j=11(i indicates the intervals between successive key
strokes), was computed as an index of rhythmic
inaccuracy during each trial only in the VFB group. Here,the subtracted number from IKI (i.e. 500 ms) represents
ideal performance given a target speed of 2 Hz. Notethat this error index only correctly evaluates rhythmicity
at this particular playing speed. If a participant playedslightly slower or faster even with perfectly regular IKIs
(i.e. perfectly regular rhythm), this index would still be
evaluated as an error by this formula. The computed
error value was visually provided as two adjacent bar
plots (Fig. 1C; left bar: one previous trial, right bar: acurrent trial) on a computer screen located in front ofeach participant following each trial.
Measurement of motion capture data. Twenty-six
spherical reﬂective markers were mounted on the handand forearm to identify anatomical landmarks for
digitalization. These markers were placed on the skinover the ﬁngertips and on the three joint centers of all
ﬁve digits, the proximal ends of the metacarpal bones,
and the distal ends of the radius and ulna (Fig. 2A). The
motion of the reﬂective markers was recorded at 120 Hz
using 13 high-speed cameras surrounding the piano(Fig. 2B). The camera locations were carefully arranged
so that position data of all markers would be recorded
while performing the target task. 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 procedures were establishedin our previous study (Furuya et al., 2011b). The position
data were digitally smoothed at a low-pass cutoﬀ frequency of 20 Hz using a second-order Butterworth digitalﬁlter, which aids in eliminating high-frequency noise that
becomes particularly large when computing derivativevalues of the position data and joint angle values using
a dot product.
Computation of joint angle. Using the threedimensional position data of individual markers, theﬂexion/extension angles at the metacarpophalangeal(MCP), proximal-interphalangeal (PIP), and distalinterphalangeal (DIP) joints were computed at the index,
middle, ring, and little ﬁngers. To compute the angle ofthe MCP joint of a certain ﬁnger, we used position datathat consisted of four markers (Fig. 2C), including theproximal ends of this ﬁnger (marker C) and its adjacentﬁnger (marker B), and the centers of the MCP (marker
A) and PIP (marker P) joints of this ﬁnger. Here, when
computing the MCP angle of the middle and ring ﬁnger,
the point B corresponded to one on the ring and middle
ﬁnger, respectively. A foot of a perpendicular from thepoint P to the plane a that contains the points A, B, and!
!
C was deﬁned as X. The angle formed by AX and AP
was deﬁned as h. Here, X is on the plane containing
points A, B, and C, which yields the following equations.
!
!
!
!
PX ¼ rPA þ sPD þ tPC

ð1Þrþsþt¼1

ð2Þ

The following equations hold true because PX is orthogonal to a.

! !
PX  AB ¼ 0
! !
PX  AC ¼ 0

ð3Þ
ð4Þ

Because points A, B, and C are observational data,!
PX can be evaluated using Eqs. (1–4). Then, the vector!
AX was evaluated as follows.
! !
! 
AX ¼ PA þ PX

ð5Þ

!
!
The MCP joint angle formed by AX and AP (i.e., h) was therefore
evaluated as follows.

0

1
! !
B AP  AX C 180
h ¼ arccos@ !  !A p AP    AX 

ð6Þ

Here, it is anatomically possible that the MCP joint
hyper-extends and, thus, that the value of h exceeds p.
Therefore, depending on the positional relationshipbetween points P and X, the MCP joint angle was
evaluated as follows:8>< p þ h; Py > Xy
hMCP ¼ p  h; Py < Xy>:p; Py ¼ Xy

ð7Þwhere Py and Xy indicate the y-coordinate of points P and X,
respectively.

The ﬂexion/extension angle of the PIP and DIP joints,
each of which has only one degree of freedom, can becomputed as an inner product as follows:
0

1
! !
B PA  PD C 180
hPIP ¼ arccos@ !  !A p PA    PD 

ð8Þ

S. Furuya et al. / Neuroscience 275 (2014) 444–454

447

Fig. 2. (A) Reﬂective markers on the left hand. (B) The motion capture system consisted of 13 high-speed cameras. (C) The markers used to
calculate the joint angle.

hDIP ¼ arccos

! ! !
DP  DE180!
pjDP j  jDEj

ð9Þ

where D and E are markers on the DIP joint center and the tip of
the ﬁnger, respectively (Fig. 2C). Consequently, Eqs. (7–9)
evaluate the ﬂexion/extension angles of the MCP, PIP, and DIP
joints, respectively, at each moment.

A time-course of angular kinematics at all joints of all
ﬁngers was time-normalized so that each IKI becomes
100 timepoints (Furuya et al., 2011a; Furuya andSoechting, 2012).
Correlation analysis. To assess the independence of
movements across ﬁngers, a correlation coeﬃcient of
the time-varying joint motion was computed between theﬁnger used for the keystroke (‘‘striking ﬁnger’’) and oneof the other three ﬁngers (‘‘non-striking ﬁnger’’). For
each of the four ﬁngers, the coeﬃcient was computed
during the period from one previous strike to one
following strike with the target ﬁnger (i.e., threesuccessive strikes). Because each trial included threestrikes with each of the ﬁngers, the coeﬃcient wascomputed for each of the three strikes and thenaveraged. This value was averaged across the ﬁrst ﬁve
trials (‘‘pre-training session’’) and across the ﬁnal ﬁve
trials (‘‘post-training session’’) for each of the 4 days
(Fig. 1B). The correlation coeﬃcient was computed at
the MCP, PIP, and DIP joints for each participant. To
compare the coeﬃcient value across participants, thevalue (r) was z-transformed (Fisher transformation) inorder to statistically compare the correlation coeﬃcients
between groups and sessions.
Rhythmic error of keystrokes. To assess movement
accuracy of the individual ﬁngers, the rhythmic error
was computed for each of the four ﬁngers based on
MIDI information during the period from one previous
strike to one following strike with the target ﬁnger (i.e.,
three successive strikes). Because each trial included
three strikes with each of the ﬁngers, the rhythmic error
was computed for each of the three strikes and thenaveraged. This value was averaged across the ﬁrst ﬁve
trials (‘‘pre-training session’’) and across the ﬁnal ﬁve
trials (‘‘post-training session’’) for each of the 4 days.
Statistics
Due to the small number of participants, we performed a
non-parametric permutation test using an ‘‘ezPerm’’
function included in a package ‘‘ez’’ in R (ver. 3.0.2), in
order to statistically evaluate the eﬀects of daily piano
practice on the movement covariation across ﬁngers.
Independent variables included were ‘‘practice session’’,‘‘ﬁnger pair’’, ‘‘joint’’ (within variable), and ‘‘group’’(between variable). Here, the independent variable of‘‘ﬁnger pair’’ included 12 levels (three ﬁnger pairs
between the striking and non-striking ﬁngers for each of
the four striking ﬁnger). For post hoc tests for multiple

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S. Furuya et al. / Neuroscience 275 (2014) 444–454
VFB

NFB
220
210

Ring MCP
Little MCP

day 1 pre (degree)

Middle MCP
Ring MCP

190

200

180

190
170
180
160
170
150

160
150

140

140
L R L M L I R M R I M I

130

220

200

210

190

200
day 4 post (degree)

200

L R L M L I R M R I M I

180

190
170
180
160
170
150

160
150

140

140
L R L M L I R M R I M I

130Normalized time

L R L M L I R M R I M I
Normalized time

Fig. 3. A time-varying angular position of the MCP joint at the ring and little ﬁngers in a representative participant in the NFB group (left panel), andthe MCP joint at the middle and ring ﬁngers in another representative participant in the VFB group (right panel). These ﬁnger pairs were chosenbecause of signiﬁcant eﬀects of session. The top and bottom panels indicate the pre-training session on the ﬁrst day and the post-training session
on the ﬁnal day, respectively. The x-axis indicates the normalized time so that each strike corresponds to each tick. I, M, R, and L indicate the index,
middle, ring, and little ﬁngers, respectively.

comparisons, Wilcoxon Test was performed using a‘‘wilcox.test’’ function in R. To test the hypothesis thatﬁnger pairs with potentially lower independent control
yield a larger practice eﬀect, a linear regression analysis
was performed using datasets of correlation coeﬃcientsof movements across all possible ﬁnger pairs in allparticipants. Using the rhythmic error of keystrokes as anindependent variable, we also run a non-parametricpermutation test by treating ‘‘practice session’’, ‘‘ﬁnger’’(within variables), and ‘‘group’’ (between variable) as
dependent variables. This test was performed in order toassess whether the practice-dependent change in therhythmic accuracy of keystrokes varies in relation to the
VFB provision.

RESULTSTime-varying angular position data
Fig. 3 illustrates the time-varying joint angular position
before (upper-panel) and after (lower-panel) the 4 daysof practice with (A) and without (B) accuracy feedbackin two representative individuals. The displayed ﬁnger
pairs at both conditions were chosen because of thepresence of signiﬁcant eﬀect of practice (see below).
For striking and lifting a key, the MCP joint of thestriking ﬁnger underwent a succession of extension,
ﬂexion, and extension, which formed a tri-phasic
waveform. Overall, this tri-phasic pattern became more
pronounced after the practice. For instance, during theinitial keystroke with the ring ﬁnger for an individual in
the NFB group (Fig. 3A), the little MCP joint displayeda larger extension motion at the post-training sessionof the ﬁnal day compared to the pre-training sessionof the ﬁrst day. This change accentuated theindividuated movements of the ring and little ﬁngers.
Similarly, during the initial keystroke with the ringﬁnger in an individual with VFB (Fig. 3B), the middle
MCP joint extended more at the post-training sessionof the ﬁnal day than at the pre-training session of theﬁrst day.

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S. Furuya et al. / Neuroscience 275 (2014) 444–454

Correlation coeﬃcients of joint motion between
ﬁngersThe group mean of the correlation coeﬃcient of the joint
angular motion between the striking and non-striking
ﬁngers was computed for the VFB and NFB groups.
Table 1 summarizes the statistical results of the nonparametric permutation test using group, practicesession, ﬁnger pair, and joint as independent variables.
A signiﬁcant three-way interaction between group,
session, and ﬁnger pair was evident, which indicatesthat at some speciﬁc ﬁnger pairs, changes in thecorrelation coeﬃcient value over practice session
diﬀered between the two groups. Fig. 4 displays theresults of the ﬁnger pairs where a post hoc test
identiﬁed diﬀerences between sessions or between
groups (i.e. during the keystroke with either the ring or
little ﬁngers). For instance, a practice-dependent
decrease in the correlation coeﬃcient was observed
only for the VFB group but not for the NFB group at the
middle-ring ﬁnger pair at the MCP joint (Fig. 4A), andvice versa at the ring-little ﬁnger pair at the MCP joint(Fig. 4B) during the ring ﬁnger keystroke. In addition,
there were also signiﬁcant interaction eﬀects between
session and ﬁnger pair, and between group and ﬁnger
pair. The former interaction indicates a diﬀerent eﬀect ofpractice across ﬁnger pairs (e.g. no practice-dependentchange was evident only in Fig. 4D). The latterinteraction indicates a group diﬀerence only at somespeciﬁc ﬁnger pairs (e.g. a group eﬀect in Fig. 4C, D but
not in Fig. 4A, B).
A question then arises why only a limited number ofﬁnger pairs displayed the eﬀect of piano practice thatinvolves all ﬁngers equally. To test our hypothesis
whether the movement covariation prior to the piano
practice was associated with the practice eﬀect, we
initially classiﬁed the group mean of the correlationcoeﬃcient into two clusters according to whether there
was a signiﬁcant diﬀerence between sessions (i.e., apractice eﬀect; Fig. 5A). The right boxplot, which
includes three pairs of ﬁngers with a signiﬁcant practiceTable 1. Results of non-parametric permutation test for correlationcoeﬃcients
Eﬀect

p ValueGroup
Practice session
Finger pair
Joint
Group  Practice session
Group  Finger pair
Practice session  Finger pairGroup  Joint
Practice session  Joint
Finger pair  Joint
Group  Practice session  Finger pairGroup  Practice session  Joint
Group  Finger pair  Joint
Practice session  Finger pair  Joint
Group  Practice session  Finger pair  Joint

0.906
0.256
0.112
0.044
0.867
0.013
0.015
0.327
0.233
0.603
0.037
0.126
0.708
0.369
0.579

A number in bold indicates p < 0.05.

eﬀect (Fig. 4A–C), displayed higher values comparedwith the left boxplot, which includes the remaining
coeﬃcient values without a signiﬁcant practice eﬀect.
This result indicates that the practice eﬀect was evident
for the ﬁnger pairs with low independence. To probe thisidea more directly, a linear regression analysis was
performed by using datasets of the correlationcoeﬃcients that pooled all ﬁnger pairs and participantsin both groups at the MCP joint (Fig. 5B). There was asigniﬁcant relation between the correlation coeﬃcient at
the pre-training session of the ﬁrst day and the change
in the correlation value following the whole practice (i.e.,
day 4 post-training – day 1 pre-training) at all ﬁnger
pairs for all participants (Fig. 5B; p < 0.001, R2 = 0.28
by a linear regression analysis). The negative
correlation indicates a larger decrease in the movement
covariation following the practice at the ﬁnger pair with
low independence.
ROM at individual joints
To assess the amount of motion at each joint during a
trial, the ROM was computed at each joint as thediﬀerence between the maximum and minimum joint
angles. A non-parametric permutation test using groupand session as independent variables found that neither
the interaction nor the main eﬀect of these variables
was signiﬁcant at the index, middle, ring, or little ﬁngers(p > 0.05). Thus, we averaged the ROM acrosssessions and groups and compared it across the MCP,
PIP, and PIP joints (Fig. 6). A non-parametricpermutation test conﬁrmed a signiﬁcant main eﬀect of
joint at each of the four ﬁngers (index, p < 0.001;
middle, p < 0.001; ring, p < 0.001; little, p < 0.001).
Post-hoc tests revealed a larger ROM at the MCP jointcompared with the PIP and DIP joints (p < 0.01 at all
ﬁngers). On average, the ROM at the MCP joint waslarger than the PIP and DIP joint by 64.7% and 89.7%,
respectively.
Rhythmic accuracy of keystrokes by the individual
ﬁngersThe group mean of the rhythmic error was computed for
the VFB and NFB groups over the four practice days
(Fig. 7). A non-parametric permutation test using group,
practice session, and ﬁnger as independent variables
yielded a signiﬁcant two-way interaction between groupand practice session (p = 0.001). The interaction eﬀect
conﬁrmed a greater decrease in the error with practicefor the VFB group than the NFB group, which indicateseﬀectiveness of the present VFB for aiding inrhythmically accurate keystrokes.

DISCUSSION
In the present article, we investigated the eﬀects of piano
practice on individuated ﬁnger movements. A practiceeﬀect was evident, particularly at the MCP joint during
the keystroke with the ring and little ﬁngers, and it
yielded more pronounced movement individuation. Thisﬁnding provides evidence for the facilitation of

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S. Furuya et al. / Neuroscience 275 (2014) 444–454

A

B

Ring strike: M-R (MCP)

correlation coefficientof movements between fingers

0.8

NFB
VFB

0.6

0.6

0.4

0.4

0.2

0.2

0

0

-0.2

-0.2

-0.4

-0.4

-0.6

-0.6
pre post pre post pre post pre post
day1
day2
day3
day4

pre post pre post pre post pre post
day1
day2
day3
day4

correlation coefficientof movements between fingers

C

D

Little strike: R-L (MCP)
0.8

0.8

0.6

0.6

0.4

0.4

0.2

*
* * *
*

Ring strike: R-L (MCP)

0.8

*

0.2

0

0

-0.2

-0.2

-0.4

-0.4

-0.6

-0.6
pre post pre post pre post pre post
day1
day2
day3
day4

Little strike: R-L (PIP)

*

* *
* *
*

pre post pre post pre post pre post
day1
day2
day3
day4

Fig. 4. The group mean and standard error of the correlation coeﬃcients of the MCP joint motion between the middle and ring ﬁngers during the ringﬁnger strike (A), between the ring and little ﬁngers during the ring ﬁnger strike (B), between the ring and little ﬁngers during the little ﬁnger strike (C),and the PIP joint motion between the ring and little ﬁngers during the little ﬁnger strike (D) for the individuals in the NFB (red circle) and VFB (bluetriangle) groups. The x-axis indicates the pre- and post-training sessions over 4 successive training days. An error bar indicates one standard errorof the mean (SEM) across participants. A horizontal line in red and blue indicates a signiﬁcant diﬀerence between sessions (p < 0.05) at each of
NFB and VFB group, respectively. An asterisk in black indicates a signiﬁcant group diﬀerence (p < 0.05). (For interpretation of the references tocolor in this ﬁgure legend, the reader is referred to the web version of this article.)

independent control of ﬁnger movements through piano
practice. Although a number of studies havedemonstrated reorganization of movements of the upperand lowerextremities through learning (Southard, 1989;
Mason et al., 2001; Hodges et al., 2005; Yang and
Scholz, 2005; Konczak et al., 2009), this study, to our best
knowledge, is the ﬁrst that characterized practice-related
kinematic reorganization of the hand movements in termsof movement independence across ﬁngers. A diﬃculty inmoving ﬁngers independently originates from the innate

constraints of the hand, including the anatomical connection of tendons across ﬁngers (Leijnse et al., 1993; Langand Schieber, 2004a), the synchronized activity of motor
units in diﬀerent compartments of the multi-tendoned ﬂexors and extensor of the digits (Kilbreath and Gandevia,
1994; Keen and Fuglevand, 2004; Winges et al., 2008),
and the shared representation of individual ﬁngers in the
motor cortex (Schieber and Hibbard, 1993; Sanes et al.,1995). However, playing the piano requires moving ﬁngers against these biomechanical and neurophysiological

451

S. Furuya et al. / Neuroscience 275 (2014) 444–454

A 0.6

B 1

60
Δr = - 0.46 r - 0.03
p = 5.8 x 10
R 2 = 0.28

**
**

MCP
PIP

-10

DIP

0.5

50

**
**

**
**

**
**

0.4
0.5

Range of motion (deg)40Δr (day4 post - day1 pre)r (day1 pre)

0.3

0.2

0.1

0

30

20

0

-0.5
-0.1

10

-0.2

0
Index
-0.3

n.s

significant

-1
-1

-0.5

0
0.5
r (day1 pre)MiddleRing

Little1Fig. 5. (A) Correlation coeﬃcients of the MCP joint motion between
all possible pairs of ﬁngers during the strike with each of the four
ﬁngers at the pre-training session of the ﬁrst day. The values wereclassiﬁed into two clusters according to whether there was asigniﬁcant diﬀerence between sessions (right) or not (left). A threshold that distinguishes these two clusters is located at approximately
3.5 (a horizontal dotted line). The red and blue areas of the box plot
indicate the 95% conﬁdence interval and one standard deviation ofeach cluster, respectively. n.s.: non-signiﬁcant (B) The relationshipbetween the correlation coeﬃcient at the pre-training session of theﬁrst day and the change in the coeﬃcient value after the whole
practice (i.e., day 4 post-training – day 1 pre-training) for the MCP
joint motion between all possible ﬁnger pairs during the strike witheach of the four ﬁngers in all participants. Each dot corresponds toone ﬁnger pair of one participant (3 ﬁnger pairs  4 ﬁngers  10
participants = 120 points). A dotted line was drawn by the linear
regression analysis. A negative regression indicates a larger
decrease in the coeﬃcient value at the ﬁnger pair with a higher
coeﬃcient value prior to the practice. (For interpretation of thereferences to color in this ﬁgure legend, the reader is referred to the
web version of this article.)constraints to perform a precisely timed sequence of
movements with multiple ﬁngers (Repp, 1995; Furuya
et al., 2011a). Therefore, a lack of independent control
of ﬁnger movements can result in an unwanted interaction
between the ﬁngers that are responsible for striking andlifting the keys, which may distort the rhythm, duration,
and articulation of tone.
Repetition of the coupled movements between theﬁnger ﬂexion for striking and the extension for lifting
under the strong temporal constraints during piano
practice can elicit neuroplastic changes in the motor
system (Kang et al., 2013). For example, compared withmusically naı̈ve individuals, musicians demonstrated
reduced surround inhibition between muscles connected

Fig. 6. The group mean of the range of motion (ROM) at the MCP(black), PIP (gray), and DIP (white) joints of the index, middle, ring,and little ﬁngers during the practice. The value was averaged acrosssessions and groups, due to a lack of their signiﬁcant eﬀects (see themain text). An error bar indicates one standard error of the mean
(SEM) across participants. ⁄⁄p < 0.01; ⁄p < 0.05.

to diﬀerent ﬁngers (Kang et al., 2012). Some studies havealso reported a weaker synchronization of motor unit
activity within a single hand muscle (Semmler andNordstrom, 1998; Semmler et al., 2004) and a less pronounced diﬀerence in the cortical activity associated with
the index and ring ﬁngers for musicians compared with
non-musicians (Slobounov et al., 2002). These neuroplastic adaptations may underlie the facilitation of the individuated ﬁnger movements through piano practice. The
practice eﬀect observed at the MCP joint, but not the
PIP and DIP joints, may be attributed to a distinct diﬀerence in the amount of motion across the joints during
the practice (Fig. 6).
Interestingly, only a few pairs of ﬁngers speciﬁcallyexhibited the practice eﬀect, although all ﬁngers wereequally used in the current practice. Prior to thepractice, the amount of movement independence wasparticularly low at these ﬁnger pairs (Fig. 5A), whichindicates that the practice eﬀect was limited to theﬁngers with intrinsically low independent control. Thisidea was further supported by the results of theregression analysis, which demonstrated a larger
improvement in independence at the ﬁnger with lower
independence (Fig. 5B). These ﬁndings imply adiﬀerence in neuromuscular plasticity across ﬁngers.
Previous
studieshavedemonstrated
equal
independence of movements across ﬁngers for expert
pianists (Furuya et al., 2011a), but not for musically452

S. Furuya et al. / Neuroscience 275 (2014) 444–454

rhythmic error of keystrokes (ms)A 200

Index finger keystroke
NFB
VFB

B 200

150

150

100

100

*
*

50

* *
*

C

0

D

150
rhythmic error of keystrokes (ms)-50

pre post pre post pre post pre post
day1
day2
day3
day4

Ring finger keystroke

pre post pre post pre post pre post
day1
day2
day3
day4

Little finger keystroke
200

150

100

100

*

*

*

* *
* *

0

-50

* *

50

200

50

* * *

* *

0

-50

Middle finger keystroke

50

* * *
* *

0

pre post pre post pre post pre post
day1
day2
day3
day4

-50 pre post pre post pre post pre post
day1

day2

day3

day4

Fig. 7. The group mean and standard error of the rhythmic error of keystrokes with each of the index (A), middle (B), ring (C), and little (D) ﬁngers
for the individuals in the NFB (red circle) and VFB (blue triangle) groups. The x-axis indicates the pre- and post-training sessions over 4 successive
training days. An error bar indicates one standard error of the mean (SEM) across participants. A horizontal line in red and blue indicates asigniﬁcant diﬀerence between sessions (p < 0.05) at each of NFB and VFB group, respectively. An asterisk in black indicates a signiﬁcant groupdiﬀerence (p < 0.05). (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)

untrained individuals (Häger-Ross and Schieber, 2000;
Zatsiorsky et al., 2000), which also suggests a diﬀerent
capacity of training across ﬁngers.
Overall, the correlation coeﬃcient values wererelatively closer to zero for the VFB group compared to
the NFB group (Fig. 4). In addition, a clear groupdiﬀerence was present for the movement independence
between the ring and little ﬁngers during the little ﬁnger
keystroke at both MCP and PIP joints. This ﬁnding
suggests that VFB has beneﬁcial eﬀects on enhancing
the independent control of ﬁnger movements, which
corroborates with previous ﬁndings of facilitation of
motor skill acquisition by augmented feedback (Debaere
et al., 2003; Ronsse et al., 2011; Sigrist et al., 2013).
Precise information on movement accuracy may serveas an error signal to correct motor commands and update

internal representation of the hand neuromuscular system
via supervised learning (Kawato, 1999). The explicit
provision of error-related information can be of particularhelp for untrained individuals whose perceptual abilities
are not well-developed (Herholz and Zatorre, 2012). The
enhancing eﬀect can also be associated with anincreased neuroplasticity of the motor system by visual
attention (Stefan et al., 2004).
A question arises to which extent the current practiceeﬀects transfer to untrained motor tasks. Our recent study
demonstrated that 4-days of piano practice at a
submaximal tempo enhanced the maximum speed ofﬁnger movements not only at the trained sequence but
also the untrained sequence (Furuya et al., 2013). However, the maximum speed of repetitive ﬁnger tapping
while immobilizing the remaining digits did not increase.

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S. Furuya et al. / Neuroscience 275 (2014) 444–454

In combination with the present ﬁnding, eﬀects of practiceon independent control of ﬁnger movements may generalize to tasks similar to the practice motor task, and to irrelevant tasks only through practicing for longer period and/or in wider contexts of movements.
The current study may have potential implications forthe use of piano practice to enhance the hand dexterityof elderly individuals and patients with movement
disorders. The independent control of ﬁnger movements
plays a crucial role in the dexterous use of tools andhands. A loss of this motor function through aging (Shimet al., 2004) and movement disorders such as stroke(Lang and Schieber, 2004b; Raghavan et al., 2006), cerebellar damage (Brandauer et al., 2012), and Parkinson’s
disease (Park et al., 2012) severely degrades the qualityof life. Playing the piano has several advantages overconventional physical therapies such as a constraintinduced movement therapy; these advantages include
the auditory feedback of the motor actions (Furuya andSoechting, 2010) and the emotion and reward evokedby a musical performance (de Manzano et al., 2010;
Nakahara et al., 2011). These enriched sensory and cognitive experiences may serve as ingredients driving neuroplastic adaptations in the motor system and thereby
facilitate ﬁne motor control (Rodriguez-Fornells et al.,2012; Grau-Sanchez et al., 2013). However, the current
study cannot rule out alternative explanations such as ashift in attentional focus across the practice sessions to
the goal of the action rather than the movements involvedin producing the goal.
Several limitations of the present study should be
improved in future studies, such as a limited number ofparticipants, lack of neurophysiological evaluation of thepractice eﬀect, and evaluation of diﬀerences in transfer
and retention eﬀects between groups. In particular, it
should be evaluated whether the results would changewith a larger sample size. Another interesting issue is acomparison of practice eﬀects across diﬀerent
perceptual modalities used for feedback presentation(Ronsse et al., 2011).
In sum, the present study demonstrated enhancementof the individuated ﬁnger movements after daily piano
practice. The learning gain was larger at the ﬁnger pairs
with potentially low independent movement control.
Provision of extrinsic VFB regarding rhythmic error of
keystrokes facilitated the practice eﬀect selectively for asubset of ﬁnger pairs. The results highlight the potentialof piano practice for improving the dexterous use of thehand, which is of particular importance for elderly
individuals and patients with movement disorders
causing loss of hand dexterity.

CONFLICT OF INTEREST
The authors declare no conﬂict of interest.

CONTRIBUTIONS
Conceived and designed the experiments: SF, Performed
the experiments: AN, Analyzed the data: SF AN, Wrotethe paper: SF AN NN.

Acknowledgements—We thank Dr. Floris van Vugt and Dr.
Michael Grossberg for their assistance to non-parametric statistics using a permutation test.

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(Accepted 16 June 2014)(Available online 25 June 2014)