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Research
Cite this article: Hug F, Goupille C, Baum D,
Raiteri BJ, Hodges PW, Tucker K. 2015 Nature
of the coupling between neural drive and
force-generating capacity in the human
quadriceps muscle. Proc. R. Soc. B 282:
20151908.
http://dx.doi.org/10.1098/rspb.2015.1908
Received: 7 August 2015
Accepted: 26 October 2015
Subject Areas:
biomechanics, neuroscience
Keywords:
electromyography, physiological cross-sectional
area, quadriceps
Author for correspondence:
François Hug
e-mail: [email protected]
†
These authors contributed equally to this
work.
Nature of the coupling between neural
drive and force-generating capacity in the
human quadriceps muscle
François Hug1,2, Clément Goupille2, Daniel Baum3, Brent J. Raiteri4,
Paul W. Hodges1,† and Kylie Tucker1,3,†
1
School of Health and Rehabilitation Sciences, The University of Queensland, NHMRC Centre of Clinical Research
Excellence in Spinal Pain, Injury and Health, Brisbane, Australia
2
Laboratory EA 4334 ‘Movement, Interactions, Performance’, University of Nantes, Nantes, France
3
School of Biomedical Sciences, The University of Queensland, Brisbane, Australia, and 4School of Human Movement
and Nutrition Sciences, Centre for Sensorimotor Performance, The University of Queensland, Brisbane, Australia
FH, 0000-0002-6432-558X; KT, 0000-0003-4976-7483
The force produced by a muscle depends on both the neural drive it receives
and several biomechanical factors. When multiple muscles act on a single
joint, the nature of the relationship between the neural drive and forcegenerating capacity of the synergistic muscles is largely unknown. This
study aimed to determine the relationship between the ratio of neural drive
and the ratio of muscle force-generating capacity between two synergist
muscles (vastus lateralis (VL) and vastus medialis (VM)) in humans.
Twenty-one participants performed isometric knee extensions at 20 and
50% of maximal voluntary contractions (MVC). Myoelectric activity (surface
electromyography (EMG)) provided an index of neural drive. Physiological
cross-sectional area (PCSA) was estimated from measurements of muscle
volume (magnetic resonance imaging) and muscle fascicle length (threedimensional ultrasound imaging) to represent the muscles’ force-generating
capacities. Neither PCSA nor neural drive was balanced between VL and
VM. There was a large (r ¼ 0.68) and moderate (r ¼ 0.43) correlation between
the ratio of VL/VM EMG amplitude and the ratio of VL/VM PCSA at 20 and
50% of MVC, respectively. This study provides evidence that neural drive is
biased by muscle force-generating capacity, the greater the force-generating
capacity of VL compared with VM, the stronger bias of drive to the VL.
1. Introduction
The torque produced by a muscle depends on both the neural drive it receives and
several biomechanical factors such as its physiological cross-sectional area (PCSA),
force–length and force–velocity relationships, specific tension, and moment arm.
Human experiments have provided some evidence that the mechanical advantage
of a muscle (related to its moment arm) and the neural drive it receives during a
voluntary motor task is coupled [1–3]. For example, during a task where the
mechanical advantage of the first dorsal interosseous muscle is increased by increasing its moment arm through altered thumb posture, there is an adaptive increase
in the neural drive to that muscle [3]. This adaptive change in drive to match
mechanical efficacy is not universal; when the force-generating capacity of a
muscle is acutely decreased by muscle damage, activation of all synergist muscles
(including the injured muscle) increases rather than recruitment of only the noninjured muscles [4]. This strategy appears suboptimal and may be explained by
the limited potential for a short-term redistribution of neural drive between
some synergist muscles [5,6]. However, it is possible that the coupling between
drive and muscle force-generating capacity adapts over time, with practice and
repetition. Understanding the nature of the coupling between neural drive and
muscle force-generating capacity is a critical step toward a deeper understanding
of movement control in humans.
& 2015 The Author(s) Published by the Royal Society. All rights reserved.
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(a) Participants
Twenty-two healthy volunteers (mean + s.d., age: 27 + 7 years,
weight: 69 + 12 kg, height: 175 + 7 cm; 11 females) participated
in this study. Participants had no history of knee pain that had
limited function or required them to seek intervention from a
healthcare professional.
(b) Assessment of neural drive
(i) Experimental set-up
To test the between-day reliability of measures of VL and VM activation, participants attended two identical testing sessions
interspaced by 1– 2 days. Participants sat on a plinth with their
back and upper legs supported. The torso was reclined by 108
from upright and the knee (dominant leg, i.e. left leg for four
(ii) Surface electromyography
Myoelectrical activity was recorded from the test (dominant) leg
with surface EMG electrodes placed over the VM and VL. For
each muscle, a pair of self-adhesive Ag/AgCl electrodes (Blue
sensor N-00-S, Ambu, Denmark) was attached to the skin with an
inter-electrode distance of 20 mm (centre-to-centre) at the site recommended by SENIAM [21]. For both VL and VM, B-mode
ultrasound (Aixplorer, Supersonic Imagine, France) was used to
facilitate the placement of the electrodes longitudinally with respect
to the muscle fascicle alignment. The electrode locations were
marked on the skin using a waterproof marker to guide repeated
placement between days. Prior to electrode application, the skin
was cleaned with abrasive gel (Nuprep, D.O. Weaver & Co, USA)
and alcohol. The ground electrode (half of a Universal Electrosurgical Pad, 3M Health Care, USA) was placed on the skin over the tibia
of the test leg. EMG data were amplified 1000 times, band-pass filtered between 10 Hz and 500 Hz (Neurolog, Digitimer, UK) and
sampled at 2 000 samples s21 using a Power1401 Data Acquisition
System with Spike2 software (Cambridge Electronic Design, UK).
(iii) Mechanical data
Force was measured with a six-axis force sensor (Sensix, Poitiers,
France). Signals were amplified, sampled at 2 000 samples s21,
and low-pass filtered at 20 Hz (Power1401 Data Acquisition
System, Cambridge Electronic Design, UK).
(iv) Voluntary activation level
In order to estimate the maximal voluntary activation level, a doublet electrical stimulus (inter stimulus interval—10 ms) of 200 ms
duration and 400 V amplitude was delivered by a Digitimer
DS7AH constant current stimulator (Digitimer, UK) through large
surface electrodes placed over the femoral triangle (one-fifth of a
Universal Electrosurgical Pad) and on the gluteal fold (half of a Universal Electrosurgical Pad). The resting twitch of maximal amplitude
was determined by applying stimuli of increasing intensity in steps
of 10 mA, until knee extension force plateaued despite an increase in
current intensity. To ensure a maximal response throughout testing,
supramaximal stimulus intensity was used, corresponding to 120%
of the intensity that evoked a maximal resting twitch response
(mean: 271 + 84 mA, range: 132–444 mA).
(v) Experimental tasks
After a standardized warm-up and prior to commencement of
the experimental trial, six maximal isometric voluntary knee extensions were performed against a rigid resistance. Each contraction
was maintained for 3 s and separated by 120 s rest. Maximum
knee extension torque was considered the highest performance
(maximum voluntary contraction (MVC) torque). In order to
verify that participants produced true maximal activation and
thus confirm that both VL and VM were maximally activated, we
used the twitch interpolation technique during the last three
maximal knee extensions to quantify the voluntary activation
level. A supramaximal doublet stimulus was delivered during the
plateau of MVC when at maximal amplitude, and within 5 s in
the subsequent rest period to elicit superimposed and rest twitches,
respectively. Then, the experimental task involved matching submaximal target force set at 20 and 50% of MVC during short
(10–15 s) isometric knee extensions with 30 s rest between each
repetition. Contractions were repeated three times at each intensity.
The order of the contractions was randomized.
2
Proc. R. Soc. B 282: 20151908
2. Material and methods
participants) flexed to 808 from the horizontal. This angle was
chosen as it represents the optimal vastii muscle length, i.e. the
knee angle at which the maximal knee extension torque can be generated [19]. In addition, maximal voluntary activation is the
greatest at this angle [20]. A standard 5 cm wide support strap
was placed around the pelvis to minimize changes in body position
throughout the experimental task and participants crossed their
arms over their chest.
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Although many biomechanical models consider that the
neural drive is equally shared between synergist muscles
during submaximal tasks [7,8], relatively large differences in
motor drive (determined from electromyography (EMG)
amplitude) between synergist muscles and between participants has been demonstrated. For example, during walking
greater EMG amplitude is observed in the medial than the lateral gastrocnemius in 50% of participants, whereas the other
half has similar EMG amplitude of both muscles [9]. Similarly,
during a wide range of tasks, some [10,11], but not all [12],
studies report greater EMG amplitude of the lateral (vastus
lateralis, VL) than the medial (vastus medialis, VM) head of
the quadriceps with a large difference between individuals
(e.g. ratio of VL/VM activation ranges from 0.8 to 3 [10]).
Similarly, muscle PCSA, which is a key determinant of
force-generating capacity [13,14], is variable between individuals. For example, a cadaveric study reported variation in the
ratio of VL/VM PCSA from 0.9 to 2.2 [15]. The nature of the
coupling between neural drive and muscle force-generating
capacity is unknown. We have three hypotheses. First, neural
activation is adjusted to balance forces between synergist
muscles of differing force-generating capacities, in which case
the muscle with the lower force-generating capacity would
receive greater neural drive. Second, neural activation is targeted to reduce the overall neural cost, in which case the
muscle with the higher force-generating capacity would receive
greater neural drive. Third, there is no relationship between
neural drive and force-generating capacity. In the latter two
cases, the force produced by the two synergist muscles would
be imbalanced. Imbalance such as this has been proposed to
underpin the development of some musculoskeletal conditions.
For example, an imbalance of force generation between VL and
VM has been speculated to contribute to the development and/
or persistence of patellofemoral pain [16].
The primary aim of this study was to determine the relationship between the ratio of neural drive measured during isometric
tasks and the ratio of muscle torque-generating capacity between
two mono-articular heads of the quadriceps muscle group in
humans, i.e. VL and VM. Muscle torque-generating capacity
was determined from the muscle’s PCSA. As the moment arm
relative to the midpoint of the tibiofemoral flexion/extension
axis is not different for these muscles [17,18], it was not considered in this study. For the relationship between neural drive
and muscle PCSA to be meaningful, we first needed to demonstrate that the distribution of neural drive between the heads of
the quadriceps is repeatable between days.
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3
3
4
1
2
3
4
5 cm
Figure 1. Individual example of muscle segmentation and muscle volume reconstruction. First, vastus medialis (darker grey in printed version / red in online version) and vastus
lateralis (lighter grey in printed version/ orange in online version) muscles were manually segmented from each axial image (note that only four images from a total of 68 used to
generate this reconstruction are depicted on the right panels). Then, muscle volume was reconstructed using 3D image analysis software (left panel). (Online version in colour.)
(vi) Force and electromyography data analysis
All force and EMG data were processed using Matlab (The
Mathworks, Nathick, USA). MVC force was calculated from
the first three maximal knee extensions as the maximal force
measured over a 300 ms time window. The percentage of voluntary activation was measured during the last three maximal knee
extensions according to the equation given by Todd et al. [22]:
superimposed twitch
voluntary activation (%Þ ¼ 100 1 :
resting twitch
ð2:1Þ
The ‘superimposed twitch’ amplitude was defined as the
difference between the peak torque induced by the stimulation
and the maximal voluntary force averaged over 300 ms before
the stimulation.
To determine the maximal EMG amplitude achieved during
the first three maximal knee extension tasks, the root mean
square (RMS) of the EMG signal was calculated over a time
window of 300 ms and the maximal value was considered as
the maximal activation level. During the submaximal isometric
knee extensions, the RMS EMG amplitude was calculated over
5 s at the middle of the force plateau. These values were normalized to the maximal RMS EMG values determined as described
above. The ratio of neural drive between VL and VM was
calculated from these normalized values as:
ratio of neural drive ¼
VL RMS EMG
:
VL RMS EMG + VM RMS EMG
Siemens Healthcare, Germany) with the participants in supine
position, lying with their hip and knee fully extended. The
three-dimensional (3D) Vibe sequence was chosen to enhance
the separation between muscles, thus improving the accuracy
of the segmentation (repetition time: 20 ms, echo time: 1.57 ms,
field of view: 300 400 mm2, voxel size: 0.78 0.78 5 mm,
flip angle: 258). Slice thickness was 5 mm without an inter-slice
gap. Three volumes were acquired (acquisition time: 5:43 min
per volume) to image the anatomical structures from iliac crests
to the articular surface of the lower border of the patella. All
data were transferred in DICOM format.
MR images were analysed using 3D image analysis software
(Mimics, Materialise, Belgium). Both VL and VM were segmented manually and independently by two operators (C.G., D.B.).
Each slice was analysed from the distal slice, where the VM
could first be visualized to the most proximal slice at the level
of VL insertion (figure 1). As reported previously from cadaveric
studies [23], vastus intermedius and VL were fused in some slices
of the more proximal regions (10 + 8% of the slices). As proposed
by Barnouin et al. [24], we maintained a ‘bean’ shape for VL and
consistently used the visible landmarks on the preceding and subsequent images to assist the segmentation between muscles. The
volume of each muscle was calculated from 3D reconstruction
(option ¼ optimal; Mimics).
(ii) Fascicle length
ð2:2Þ
(c) Assessment of muscle physiological cross-sectional
area
(i) Magnetic resonance imaging
Volumetric acquisitions of the thighs were performed using a 3 T
magnetic resonance imaging (MRI) scanner (Magnetom Trio,
Because of its architecture, classical two-dimensional (2D)
ultrasound techniques cannot be used to reliably estimate VM
fascicle length [25,26]. Therefore, we used freehand 3D ultrasound scans to measure fascicle length of both this muscle and
VL. 3D ultrasound involves combining multiple 2D ultrasound
images of the muscle with 3D motion data of the transducer’s
orientation and position to reconstruct the muscle in 3D
[27,28]. 2D B-mode ultrasound images were acquired by manually moving the ultrasound transducer along the approximate
midline of each muscle belly in a transverse orientation and
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1
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(b) VM
1 cm
(iii) Physiological cross-sectional area
PCSA ¼
Figure 2. Individual example of reconstructed ultrasound images from freehand
3D ultrasound for the vastus lateralis (VL) (a) and the vastus medialis (VM) (b)
muscles. The best sagittal plane re-slice images were determined visually as the
images that displayed the clearest continuous muscle fascicles. Generally, one or
two fascicles per re-slice image were analysed. (Online version in colour.)
were subsequently transformed into the global coordinate system
using Stradwin software (v.5.0; Mechanical Engineering, Cambridge University, Cambridge, UK) (figure 2). B-mode
ultrasound images were recorded at 10– 15 Hz using a 60 mm
linear transducer (L-14– 5 W/60 Linear, Ultrasonix) with a central frequency of 10 MHz and maximal depth of 70 mm.
Position and orientation of the transducer were recorded by
tracking a cluster of four markers rigidly attached to the ultrasound transducer using a four-camera optical motion analysis
system (Optitrack, NaturalPoint, USA). Prior to scanning, the
relationship between the image coordinate system and the cluster
coordinate system was determined using a single-wall phantom
calibration protocol available in the software package and used
in previous studies [27,28].
Participants were seated on a plinth. Their torso was reclined
by 108 from upright and their knee flexed 85– 908 from the horizontal. This position closely matched the position used for neural
drive measurements. A 2 cm thick acoustic stand-off pad (Aquaflex, Parker Laboratories, Fairfield, NJ, USA) was positioned
between the transducer and the leg to enhance visualization of
the muscle belly. The transducer was translated in the distal –
proximal direction along the leg, covering the midline/thickest
portion of each muscle from their distal insertion to approximately 75% of femur length. The total scan time was 15 s and
the average distance between frames was 1 mm. This was
repeated two to four times per muscle, depending on the quality
of the reconstructed sagittal plane image re-slices.
For each acquisition, the best plane re-slice images were determined visually as the images that displayed the clearest
continuous muscle fascicles, which was presumed to correspond
to the plane of muscle fascicles (figure 2). Because most of the fascicles exhibited a small curvature, we used a segmented line (with
a spline fit) to model the fascicle and calculate its length (ImageJ
V1.48, National Institute for Health, USA). The fascicle length
measurements were performed by two operators (F.H. and
C.G.). For VL, three to four fascicles were measured at distal
and proximal locations (total of six to eight fascicles per participant). For VM, four fascicles were measured superficially (top
one-third of the muscle) and two fascicles were measured deep
within the muscle (about two-thirds of maximum muscle thickness). Then, values were averaged across measurements within a
muscle to get a representative fascicle length measurement per
muscle. The greater number of measurements taken from the
superficial compared with the deep part of VM intentionally
biased the average fascicle length to the more superficial values.
muscle volume
:
fascicle length
ð2:3Þ
Note that the knee angle used for 3D ultrasound data collection was close to the optimal knee angle (see above) and,
therefore, the measured fascicle length was considered as the
optimal fascicle length. Then, we calculated the ratio of PCSA
between VL and VM as:
ratio of PCSA ¼
VL PCSA
:
VL PCSA + VM PCSA
ð2:4Þ
(d) Statistics
Note that after the first session, one participant developed knee
pain and was not able to perform the second session. This participant was excluded from the experiment and data are, therefore,
reported for 21 participants. Further, significant co-contraction of
the hamstring muscles was induced by the superimposed twitch
in one participant. The maximal voluntary activation data are
reported for 20 participants.
Statistical analyses were performed in Statistica (Statsoft,
USA). Distributions consistently passed the Shapiro– Wilk normality test and all data are reported as mean + s.d. The level
of significance was set at p , 0.05. The MVC torque determined
from the first three maximal knee extensions (without twitch
interpolation) was compared with the MVC torque determined
from the next three maximal knee extensions (with twitch interpolation) using a paired t-test. The potential for difference in
normalized RMS EMG and PCSA between muscles was also considered using a paired t-test. To determine the robustness of the
EMG measures between the two sessions and the inter-operator
reliability for MRI and 3D US data, the intra-class correlation
coefficient (ICC), the standard error of measurement (SEM),
and the coefficient of variation (CV) were calculated. Finally,
the relationship between the VL/VM EMG ratio and VL/VM
PCSA ratio was tested using Pearson’s correlation coefficient.
As proposed by Cohen [30], we report a correlation of 0.5 as
large, 0.3 as moderate, and 0.1 as small.
3. Results
(a) Neural drive
The MVC torque measured during the first three maximal isometric knee extensions performed without twitch interpolation
(236 + 83 Nm (range: 118–418 Nm)) was not significantly
different to that measured just before the twitch interpolation
during the subsequent three maximal contractions (233 +
87 Nm (range: 111–420 Nm); p ¼ 0.084). For both sessions,
all participants (except one participant for session 2) reached
a voluntary activation greater than 90% (mean ¼ 98.8 + 2.8%
(range: 90.7–100%) and 97.6 + 3.6% (range: 87.3–100%) for
sessions 1 and 2, respectively). The maximal EMG amplitude
Proc. R. Soc. B 282: 20151908
PCSA was calculated from muscle volume (cm3) and fascicle
length (cm) as follows [13]:
4
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This is because we consistently observed that the deeper fascicles
are less representative of the whole muscle. It is important to note
that the analysis was also performed giving the same weight to
deep and superficial compartments (data not presented), and
the main outcomes of this study remained unchanged. Note that
3D ultrasound data for VL of one participant exhibited some artefacts and, therefore, we used 2D ultrasound to measure the fascicle
length, using methods previously used on VL [29].
(a) VL
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Table 1. Neural drive to the vastus lateralis (VL) and vastus medialis (VM) muscles during the isometric knee extension tasks. ICC, intra-class coefficient
correlation; SEM, standard error of measurement; MVC, maximum voluntary contraction.
isometric knee extension (50% MVC)
VM EMG (% max)
VL/VM ratio
VL EMG (% max)
VM EMG (% max)
VL/VM ratio
day 1
15.9 + 6.0
13.8 + 5.9
53.8 + 11.4
40.0 + 9.7
37.5 + 7.9
51.5 + 5.8
day 2
ICC
14.2 + 5.1
0.83
12.9 + 6.3
0.67
53.1 + 11.0
0.86
38.0 + 9.3
0.94
36.6 + 9.1
0.68
50.9 + 6.4
0.74
SEM
2.6
3.5
4.2
2.2
4.9
3.1
measured over the first three maximal contractions was, therefore, considered to represent the maximal neural drive to both
VL and VM.
For the isometric knee extensions performed at both 20
and 50% of MVC, the inter-day reliability of the normalized
VL and VM RMS EMG amplitude was good to excellent
(ICC . 0.67 and SEM , 4.2% of MVC; table 1). Notably,
reliability was less for VM than for VL, but remained good
(table 1). The ratio of VL/VM EMG amplitude demonstrated
good to excellent reliability between days (ICC . 0.74), indicating that the participants adopted a robust coordination
strategy between these synergist muscles. Considering this
overall good to excellent reliability, normalized EMG data
were averaged between the two days for further analysis.
The mean ratio of VL/VM EMG amplitude was 53.5 +
11.1% (range: 33.6–74.7%) and 51.2 + 6.1% (range: 42.2–
64.7%; figure 3) for the isometric knee extension performed at
20 and 50% of MVC, respectively. Notably, there was large
variability between individuals (figure 3b,c) with an almost
equal number of participants demonstrating greater VL RMS
EMG and those demonstrating greater VM RMS EMG. Consistent with this, the normalized RMS EMG amplitude was not
different between VL and VM at either 20% ( p ¼ 0.24) or
50% of MVC ( p ¼ 0.33).
(b) Physiological cross-sectional area
The inter-operator reliability of MRI manual segmentation
was excellent for both VL (ICC ¼ 0.99, SEM ¼ 12.3 cm3, and
CV ¼ 2.0%) and VM (ICC ¼ 0.99, SEM ¼ 6.2 cm3, and CV ¼
1.7%). The inter-operator reliability of fascicle length measurements was also excellent for both VL (ICC ¼ 0.95, SEM ¼
0.29 cm, CV ¼ 2.5%) and VM (ICC ¼ 0.98, SEM ¼ 0.25 cm,
CV ¼ 2.0%). Consequently, volume and fascicle length data
were averaged between the two operators for further analysis.
Muscle volume was systematically larger for VL than VM
(mean difference: 35.2 + 18.6%, p , 0.0001; table 2). As VM
muscle fascicles were longer than those of VL (7.8 + 9.8%,
p ¼ 0.007, table 2), VL PCSA was systematically larger than
VM PCSA (45.2 + 20.3%, p , 0.0001; table 2). The ratio of
PCSA between VL and VM was consistently greater than
50%, i.e. 59.0 + 3.4%. There was a large variability between
participants with values ranging from 52.9 to 64.3% (figure 3).
(c) Relationship between neural drive and muscle
physiological cross-sectional area
There was a large significant correlation (r ¼ 0.68, p ¼ 0.0008;
figure 3) between the ratio of VL/VM EMG amplitude and
the ratio of VL/VM PCSA during the isometric knee
extensions performed at 20% of MVC. There was a moderate
correlation (r ¼ 0.43, p ¼ 0.051; figure 3) between these variables for the isometric knee extensions performed at 50%
of MVC, although this relationship narrowly missed the
threshold for statistical significance.
4. Discussion
We aimed to determine the relationship between the neural
drive measured during isometric tasks and the force-generating
capacity of VL and VM. This study has two major novel findings. First, participants used individualized strategies of
coordination between VL and VM to perform the isometric
knee extension tasks, and these strategies were robust between
days. Second, there was a large (20% of MVC) or moderate (50%
of MVC) correlation between the ratio of VL/(VL þ VM) EMG
amplitude and the ratio of VL/(VL þ VM) PCSA, indicating
that drive was biased by force-generating capacity; the greater
the force-generating capacity of VL compared with VM, the
stronger bias of drive to the VL. This leads to an imbalance of
individual muscle force between these synergist muscles, the
magnitude of which varies greatly between participants.
These results are crucial to improve our current understanding
of the complex interplay of individual muscle forces in the
development of successful coordination strategies.
(a) Between-day reliability of neural drive
The inter-day reliability of the normalized VL and VM RMS
EMG amplitude was good to excellent (table 1) indicating that
the participants adopted a robust coordination strategy between
these synergist muscles. Notably, reliability was less for VM
(albeit remaining good) than for VL. Although we cannot rule
out the possible influence of methodological issues such as
crosstalk and amplitude cancellation, we do not believe that it
would be significantly different between VM and VL. To support our assertion, a study that used elastography, which is
insensitive to both crosstalk and amplitude cancellation,
suggested a more variable force production in VM than VL
across repeated isometric knee extensions [31]. It is, therefore,
likely that this variability has a neurophysiological basis that
might be related to the fact that in addition to knee extension,
VM contributes to the control of the patellofemoral joint.
(b) Neural drive is not balanced between vastus
lateralis and vastus medialis
Whatever muscles are under consideration, most studies that
focus on coordination strategies during motor tasks report
Proc. R. Soc. B 282: 20151908
VL EMG (% max)
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isometric knee extension (20% MVC)
5
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10
8
6
4
2
0
(b)
6
average
VL = VM
0
(c)
6
4
2
0
4
2
0
10 20 30 40 50 60 70 80 90 100
VL/(VL + VM) EMG amplitude (%)
(e)
VL/(VL + VM) PCSA (%)
r = 0.68
p = 0.0008
65
60
55
50
20
6
40
60
VL/(VL + VM) EMG amplitude (%)
0
70
10 20 30 40 50 60 70 80 90 100
VL/(VL + VM) EMG amplitude (%)
r = 0.43
p = 0.051
65
60
55
50
80
20
40
60
VL/(VL + VM) EMG amplitude (%)
80
Figure 3. Relationship between the ratio of neural drive and the ratio of muscle force-generating capacity between the vastus lateralis (VL) and vastus medialis
(VM) muscles. (a) Group distribution of the ratio of physiological cross-sectional area (PCSA) between VL and VM. (b,c) Group distribution of the ratio of neural drive
between VL and VM measured during submaximal isometric knee extensions performed at 20% (b) and 50% (c) of maximum voluntary contraction (MVC). (d,e) The
correlation between the ratio of neural drive and the ratio of PCSA. The strong (d ) and moderate (e) correlations indicate that the greater the force-generating
capacity of VL compared with VM, the stronger bias of drive to the VL. (Online version in colour.)
Table 2. Morphological and architectural data for the vastus lateralis (VL) and vastus medialis (VM) muscles. For comparison with previously published studies,
data are presented separated by gender. fl, optimal fascicle length; PCSA, physiological cross-sectional area.
vastus lateralis
volume (cm3)
fl (cm)
PCSA (cm2)
vastus medialis
males
females
mean
males
females
mean
702.0 + 127.3
436.7 + 58.8
563.0 + 165.7
536.6 + 70.0
319.7 + 60.8
422.9 + 128.0
12.7 + 1.2
55.4 + 8.6
10.9 + 0.6
40.1 + 4.6
11.7 + 1.3
47.4 + 10.2
14.2 + 1.2
37.8 + 4.2
11.2 + 0.7
28.4 + 5.1
12.7 + 1.8
32.9 + 6.6
values averaged over a group of participants. However, this commonly used methodology conceals possible variability between
participants [32,33]. In this way, the ratio between synergist
muscles, such as the VL and VM, is often reported as close to
50%. This implies an equal contribution of synergist muscles to
a given submaximal task and was the case for the current investigation; mean VL/(VL þ VM) EMG ratio was 53.5 and 51.2% for
knee extension performed at 20 and 50% of MVC, respectively.
Although the neural drive was not significantly different between
these muscles at the group level, individual data revealed a wide
range of VL/(VL þ VM) EMG ratios; an almost equal number of
participants demonstrated greater VL EMG and greater VM
EMG (e.g. range of VL/(VL þ VM) EMG ratio: 33.6–74.7% and
42.2–64.7% at 20 and 50% of MVC, respectively). Pal et al. [10]
reported similar between-subject variability during walking
(ratio calculated as VL/VM ranged between 0.8 and 3). Although
the VL and VM muscles might share most of their drive, they also
receive muscle-specific neural drive [34]. Interestingly, the
amount of muscle-specific neural drive varies between participants [34], which might explain the between-subject variability
in VL/(VL þ VM) EMG ratios.
Of note, the range of ratios observed in this study was smaller, and the group mean was closer to 50%, when the knee
extensions were performed at 50% of MVC compared with
those performed at 20% of MVC. This is consistent with previous
observations made by Pincivero and Cohen [35], where VL and
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no. participants
knee extension 50% of MVC
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(d) 70
VL/(VL + VM) PCSA (%)
10 20 30 40 50 60 70 80 90 100
VL/(VL + VM) PCSA (%)
knee extension 20% of MVC
0
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knee extension 20% MVC
(b)
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no. participants
no. participants
6
3
2
1
0
0
4
2
0
0
10 20 30 40 50 60 70 80 90 100
VL/(VL + VM) index of force (%)
Figure 4. (a,b) Balance of force between the vastus lateralis (VL) and vastus medialis (VM) muscles. The index of force is calculated as the product of normalized
electromyography (EMG) amplitude with physiological cross-sectional area (PCSA). These data highlight a high inter-individual variability with the majority of participants exhibiting more force produced by VL than VM. (Online version in colour.)
VM activation converged at near-maximal force levels, despite
differences at submaximal levels. A convergence of the activation
levels of VL and VM at higher levels of contraction is logical, as
intensities close to MVC require the complete activation of all
synergist muscles. Within the current experiment, the convergence of activation levels is highlighted by the near full
voluntary activation of the quadriceps (and thus of both VL
and VM) during MVCs as determined by the twitch interpolation
technique. Overall, our results demonstrate an imbalance of
neural drive between synergist muscles that differs between participants, and that this imbalance is more likely to occur at lower
contraction intensities. This is important as lower contraction
intensities are common during daily life activities.
(c) Muscle force-generating capacity is not balanced
between vastus lateralis and vastus medialis
We considered the ratio of PCSA to represent the balance of
muscle force-generating capacity between VL and VM.
PSCA considers both fascicle length and muscle volume. Previous data on the PSCA of VM is lacking because of technical
limitations (reviewed in [25]). In particular, muscle fascicles
within the distal part of VM are arranged relatively parallel
to the skin surface, and are longer than the width of conventional 2D ultrasound transducers. Therefore, the whole VM
muscle fascicle cannot be visualized or estimated as is possible for muscles with a pennate arrangement, such as VL.
Taking advantage of freehand 3D ultrasound, our study is
one of the first to estimate the PCSA of VM in vivo. We
have shown that VM PCSA is systematically smaller than
VL PCSA. Similar to the VL/(VL þ VM) neural drive ratios,
a wide range of VL/(VL þ VM) PCSA ratios were observed
between participants (range 52.9– 64.3%). This inter-subject
variability in PCSA concurs with data reported for four adolescent girls using diffusor tension MRI (ratio calculated as
VL/VM ranged between 1.1 and 3.1; average ¼ 2.1) [36].
Similar variability was also reported in a study that measured
cadaveric muscle volumes from 12 individuals (range: 47.3 –
68.3%, average: 61.5%) [15]. Until now it was unknown if or
how this imbalance of force-generating capacity between
synergist muscles relates to the distribution of neural drive.
(d) The relationship between neural drive and muscle
force-generating capacity
Many possible combinations of VL and VM activation may produce a given submaximal knee extension torque. For example, it
is possible that the nervous system might balance the force
between synergist muscles of differing force-generating
capacity by driving the muscle with the lower force-generating
capacity at a higher level. However, this was not observed in the
present study. Rather, we found a large (20% of MVC) or moderate (50% of MVC) positive linear correlation between the ratio
of VL/(VL þ VM) EMG amplitude and the ratio of VL/(VL þ
VM) PCSA, which indicates that neural drive is biased by
force-generating capacity; the greater the force-generating
capacity of VL compared with VM, the stronger bias of drive
to the VL. This strategy seems logical considering the optimal
control theory [37], which proposes that motor patterns are
selected from many possibilities ensuring that movement
costs (e.g. control, energetic, mechanical) are constantly minimized. However, our results may also support an alternative
theory, the good enough theory, which proposes that a hierarchy
of sensorimotor networks gradually adapt through trial-anderror learning to produce effective movements which are good
enough to achieve the task goal [38]. Despite providing strong
evidence of a positive relationship between neural drive and
muscle PCSA, our results cannot explain the origin of this
relationship. For example, it is possible that an individual’s
muscle morphology and architecture underlies the relationship,
such that the nervous system develops/adapts optimally to bias
drive to the muscle with larger PCSA. Alternatively, good enough
neural coordination strategies may underpin muscle morphology and architecture, such that the muscle with greater
drive leads to biased development of PCSA. However, as
some participants distributed less drive to VL than VM
(especially at 20% of MVC) and all participants had larger VL
PSCA, muscle morphology/architecture is unlikely to depend
on neural drive alone, unless there is a ‘ceiling’ to the development of VM. Finally, it is important to consider that the positive
correlation we report might not indicate a causal relationship.
Regardless of the basis of the correlation between neural drive
and muscle force-generating capacity, our results provide
strong evidence of an imbalance of force produced by VL and
VM that varies considerably between participants.
(e) Balance of force between vastus lateralis and vastus
medialis
If the product of normalized EMG amplitude with PCSA is considered as an index of force (arbitrary units), the ratio of this index
between VL and VM ranges from 38.4 to 84.0% (mean: 61.4 +
12.3%) and 48.8 to 71.0% (mean: 59.9 + 7.5%) for knee extensions
performed at 20 and 50% of MVC, respectively (figure 4). This
Proc. R. Soc. B 282: 20151908
10 20 30 40 50 60 70 80 90 100
VL/(VL + VM) index of force (%)
average
VL = VM
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knee extension 50% MVC
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This experiment requires consideration of several methodological aspects. First, consistent with most previous studies,
muscle fascicle length was measured at rest (for review, see
[25]). This is because we used 3D ultrasound that requires a
scan time of approximately 15 s during which motion of the
muscle/limb must be minimized. It is important to consider
that because muscle fascicle length decreases during muscle
contraction, the muscle’s PCSA increases [44]. We do not
believe that the increase in PCSA during contraction will
have influenced the conclusion of our study. This is because
during the submaximal contractions the relatively higher activation of one muscle (VL or VM, depending on the
participants) would be associated with relatively greater shortening of the fascicle length and consequently with relatively
greater increases in PCSA of that muscle. Consequently, the
correlation between the ratio of EMG and the ratio of PCSA
would be strengthened further.
Second, the estimation of neural drive provided by surface
EMG may be affected by physiological and non-physiological
factors (reviewed in [45]). The most important to consider in
our experiment are crosstalk, amplitude cancellation, and
spatial variability of motor unit recruitment. First, crosstalk
was limited by following the SENIAM recommendations
and by checking the appropriate electrode location using ultrasound (i.e. fascicle direction and muscle borders). Second,
EMG amplitude was normalized to that recorded during
MVC. This normalization procedure has been shown to
reduce the effect of amplitude cancellation on the EMG signals
[46]. Finally, as surface EMG represents the algebraic summation of the motor unit action potentials under the
recording electrodes, it is possible that preferential recruitment
of superficial motor units for VL at low contraction intensities
5. Conclusion
This study provides new insight into the strategy for force
sharing between synergist muscles and provides evidence
that drive is biased by force-generating capacity; the greater
the force-generating capacity of VL compared with VM, the
stronger the bias of drive to the VL. Although this is efficient
in some respects (e.g. minimizes the neural drive), this strategy
is very likely to result in force (and also torque) imbalance
between the synergist muscles. Further research is needed to
determine the potential role of this imbalance in the completion
of successful coordination strategies/movements and its longterm effects, particularly in the occurrence/persistence of
musculoskeletal conditions.
Ethics. Participants provided informed written consent. The ethics
committee of The University of Queensland approved the study
and all procedures adhered to the Declaration of Helsinki.
Data accessibility. Data deposited in Dryad Digital Repository: http://
dx.doi.org/10.5061/dryad.h3b27.
Authors’ contributions. Conception and design of the experiments: F.H.,
P.W.H., and K.T.; collection, analysis, and interpretation of data:
F.H., C.G., D.B., B.R.; drafting the article or revising it for important
intellectual content: F.H., C.G., B.R., P.W.H., and K.T. All authors
approved the final version of the manuscript.
Competing interests. We declare we have no conflict of interest.
Funding. NHMRC provide research fellowships for P.H.: ID401599 and
K.T.: ID1009410. Project support was provided by an NHMRC
program grant (P.H.: ID631717), Center of Advanced Imaging,
University of Queensland ( project support ID15003) and Région
Pays de la Loire (QUETE).
Acknowledgements. The experiments were conducted in the ‘The University of Queensland, NHMRC Centre of Clinical Research Excellence
in Spinal Pain, Injury and Health, School of Health and Rehabilitation
Sciences, Brisbane, Australia’. The authors thank Dr Olga Panagiotopoulou for the training of F.H., C.G., and D.B., and for providing
the equipment needed for the muscle volume measurements. The
authors also thank Dr Glen Lichtwark for introducing us to the
freehand 3D US technique.
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