J Neurophysiol 113: 3692–3699, 2015.
First published March 18, 2015; doi:10.1152/jn.00960.2014.
Intrinsic excitability of human motoneurons in biceps brachii versus
triceps brachii
Jessica M. Wilson,1 Christopher K. Thompson,2 Laura C. Miller,1,3 and Charles J. Heckman1,2,4
1
Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, Illinois; 2Department of
Physiology, Northwestern University, Chicago, Illinois; 3Department of Biomedical Engineering, Northwestern University,
Evanston, Illinois; and 4Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, Illinois
Submitted 1 December 2014; accepted in final form 17 March 2015
elbow extensors; elbow flexors; intrinsic excitability; motoneurons;
neuromodulation
of motoneuron behavior suggested that alphamotoneurons fire linearly in proportion to synaptic input and
are therefore passive transducers of descending motor commands. However, recent studies have shown that this is not the
case (reviewed in Binder et al. 1993; Heckman et al. 2008).
Alpha-motoneurons, in fact, possess complex membrane properties capable of nonlinear integration of synaptic inputs.
Descending serotonergic and noradrenergic projections from
brain stem nuclei are the most powerful neuromodulators of
these active membrane properties (Hounsgaard et al. 1988; Lee
and Heckman 1999). Monoamines from these projections increase intrinsic motoneuron excitability by depolarizing the
resting potential and hyperpolarizing the voltage threshold for
spike activation, as well as decreasing the duration of the
postspike hyperpolarization (Fedirchuk and Dai 2004; Taka-
EARLY STUDIES
Address for reprint requests and other correspondence: J. M. Wilson, Dept.
of Physical Therapy and Human Movement Sciences, Northwestern Univ., 645
N. Michigan Ave., Suite 1100, Chicago, IL 60611 (e-mail: jessmwilson@u.
northwestern.edu).
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hashi and Berger 1990; White and Fung 1989). They also
increase motoneuron excitability through the facilitation of
persistent inward sodium and calcium currents (PICs), which
produce a prolonged depolarization in the cell known as a
plateau potential (Bennett et al. 1998; Collins et al. 2002;
Hounsgaard et al. 1984). The subsequent increase in neuronal
excitability caused by the plateau potential is capable of amplifying synaptic input as much as fivefold (Lee and Heckman
2000) and allows the motoneuron to manifest unique firing
properties. One of these properties includes a counterclockwise hysteresis in the frequency-current relationship, in
which the amount of current required to keep a motoneuron
firing is significantly lower than the amount of current
required to recruit the neuron initially (Lee and Heckman
1998a, 1998b).
PICs provide a motoneuron-specific method of prolonged
excitation without requiring additional descending drive from
supraspinal structures. It has therefore been hypothesized that
PICs are most useful for antigravity muscles or those otherwise
involved in posture, which must be activated for extended
periods of time (Hounsgaard et al. 1988; Kiehn and Eken
1997). Previous electrophysiological studies in the ventral
spinal cord of the decerebrate cat and the neonatal rat show that
PICs are larger in the extensor pools than in the flexor pools of
the limbs (Cotel et al. 2009; Hounsgaard et al. 1988). However,
this difference has not been shown yet in humans.
Because we are unable to record directly from individual
motoneurons in humans, the magnitude of PICs must be
measured indirectly by studying PIC-specific motor unit firing
behaviors. The challenge of this approach is in determining
whether changes in the firing behavior of a motor unit are due
to its intrinsic excitability or due to changes in descending
drive. Kiehn and Eken (1997) used paired-motor unit recordings to show that during slow isometric contractions a lowthreshold motor unit can be used as an indicator of descending
drive to the motoneuron pool. Therefore, changes in the firing
behavior of higher-threshold motor units that are not also
reflected in the low-threshold unit can be attributed to the
intrinsic behavior of those motor units. Gorassini and colleagues (Gorassini et al. 1998, 2002) furthered this approach
during ramp contractions. They recorded from pairs of motor
units and measured the difference in firing rate at recruitment
and derecruitment of the higher-threshold unit (named the test
unit) in terms of the firing rate of the lower-threshold unit
(named the control unit), producing a value known as the
delta-F (⌬F). After specific constraints on the selection of
motor unit pairs are employed, the ⌬F is an estimation of the
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Wilson JM, Thompson CK, Miller LC, Heckman CJ. Intrinsic
excitability of human motoneurons in biceps brachii versus triceps
brachii. J Neurophysiol 113: 3692–3699, 2015. First published March
18, 2015; doi:10.1152/jn.00960.2014.—The intrinsic excitability of
spinal motoneurons is mediated in part by the presence of persistent
inward currents (PICs), which amplify synaptic input and promote
self-sustained firing. Studies using animal models have shown that
PICs are greater in extensor motoneurons over flexor motoneurons,
but this difference has not yet been demonstrated in humans. The
primary objective of this study was to determine whether a similar
difference exists in humans by recording from motor units in biceps
and triceps brachii during isometric contractions. We compared firing
rate profiles of pairs of motor units, in which the firing rate of the
lower-threshold “control” unit was used as an indicator of common
drive to the higher-threshold “test” unit. The estimated contribution of
the PIC was calculated as the difference in firing rate of the control
unit at recruitment versus derecruitment of the test unit, a value known
as the delta-F (⌬F). We found that ⌬F values were significantly higher
in triceps brachii (5.4 ⫾ 0.9 imp/s) compared with biceps brachii
(3.0 ⫾ 1.4 imp/s; P ⬍ 0.001). This difference was still present even
after controlling for saturation in firing rate of the control unit, rate
modulation of the control unit, and differences in recruitment time
between test and control units, which are known to contribute to ⌬F
variability. We conclude that human elbow flexor and extensor motor
units exhibit differences in intrinsic excitability, contributing to different neural motor control strategies between muscle groups.
INTRINSIC EXCITABILITY IN HUMAN FLEXORS VS. EXTENSORS
METHODS
Ten healthy adults (2 women, 8 men, aged 66.9 ⫾ 6.2 yr) participated in the study. They were asked to abstain from caffeine for 12
h before the experiment to remove any possible effects of caffeine on
PICs (Walton et al. 2002, 2003). All procedures were performed in
accordance with the Declaration of Helsinki and were approved by the
Institutional Review Board at Northwestern University. All subjects
gave written informed consent prior to participation in the study.
Experimental arrangement. Participants were seated in a Biodex
chair (Biodex Medical Systems, Shirley, NY) with their dominant
arm fixed in 75° shoulder abduction, 45° shoulder flexion (horizontal adduction from the frontal plane), 90° elbow flexion, 15°
pronation, and a neutral wrist and finger posture. The participant’s
shoulder and waist were secured to the chair with straps to
minimize auxiliary movements of the trunk. The forearm and hand
were encased in a fiberglass cast and coupled via a weight-bearing
ring-mount interface to a 6 degree-of-freedom load cell (model
45E15A, JR3, Woodland, CA).
Experimental procedures. Participants were asked to generate maximum voluntary torque (MVT) for both elbow flexion and elbow
extension. MVT was calculated as the average of three maximum
torque values within 10% of one another without the last repetition
being the greatest. Visual elbow flexion/extension torque feedback
was given through a computer monitor in front of the apparatus, and
participants were given vigorous verbal encouragement through the
duration of the MVT measurements.
For experimental trials used to calculate ⌬F, participants completed
slow trapezoid contractions in elbow flexion and in elbow extension,
each to a target on the computer screen representing 10 –15% MVT.
Each trial lasted for 40 s and began with 5 s of baseline measurements
while the subject was relaxed. Participants were then instructed to flex
or extend at the elbow slowly and smoothly such that they reached the
target within 10 s, to hold their position at the target for 5 s, and to
slowly relax the muscle over another 10 s. The trial then ended with
a final 10 s of data collection. A sample trial is shown in Fig. 1A.
Subjects were explicitly instructed to relax the muscle being used
during the relaxation phase. To ensure proper pacing of the movement, the experimenter counted down the seconds out loud through
each trial.
The participants completed two or three practice trials before data
collection to familiarize themselves with the task. Between trials,
participants were given a 1-min break, and they were asked to produce
brief contractions back and forth with both agonist and antagonist, to
ensure muscle quiescence before the start of the next trial (McPherson
et al. 2008). Trials were visually inspected for quality and were
discarded if the target was reached too quickly or too slowly, if the
ascending and descending phases of the contraction were not equal to
and opposite from one another, or if there were sudden increases or
decreases in the torque profile during the ascending or descending
arms (Udina et al. 2010). Fifty-eight of 77 (75%) collected trials were
used for analysis based on the quality of the torque trace as well as
clarity of decomposition.
Data collection and analysis. Orthogonal forces and torques
generated at the forearm-load cell interface in the x, y, and z planes
were recorded via the load cell and converted into elbow flexion
and extension torques with custom MATLAB software employing
a Jacobian-based algorithm (The MathWorks, Natick, MA).
Torque measurements were digitized at a sampling rate of 1,024
Hz and smoothed with an acausal moving average filter with a
250-ms window.
Intramuscular EMG in the long head of biceps brachii and the
lateral head of the triceps brachii were recorded with custom bipolar
fine-wire steel electrodes with 1-mm recording surfaces (221-28SS730, Jari Electrode Supply, Gilroy, CA). Each bipolar unit had barb
lengths for the two wires of 1 mm and 2.5 mm. Two electrodes were
inserted into each muscle. The signals from each electrode were
band-pass filtered (300-10,000 Hz) and amplified (⫻1-10k) (DAM50
Bio-Amplifier, World Precision Instruments, Sarasota, FL) before
digitization at 10,240 Hz (OT Bioelettronica USB2, Turin, Italy).
Because torque and intramuscular EMG signals had to be collected on
separate computers, a brief 0- to 5-V voltage pulse was generated at
the beginning of each trial and recorded by both computers as a
reference point for off-line synchronization.
Intramuscular EMG recordings were collected with OTBiolab
software (Fig. 1, B and C) (version 1.7.4735.19, OT Bioelettronica)
and were decomposed into motor unit spike times with EMGlab
software (McGill et al. 2005). Spike times were then converted into
instantaneous firing rates by calculating the reciprocal of the interspike interval.
To estimate the influence of PICs in biceps versus triceps, ⌬F
values were calculated for every possible pair of motor units in a
given trial (Fig. 1D) for which the control unit was of a lower
threshold and fired during both recruitment and derecruitment of the
test unit. In roughly 10% of motor unit pairs per trial the test unit
continued to fire after the derecruitment of the control unit, and these
pairs were not included. The instantaneous firing rates of both motor
units were smoothed by fitting fifth-order polynomials (Gorassini et
al. 2002), and the ⌬F value was calculated on the polynomials by
taking the difference between the values of the control unit at
recruitment and derecruitment of the test unit.
The ⌬F method relies on four assumptions: 1) that the PIC is
activated before or at recruitment in an all-or-nothing manner; 2) that
the firing rate of the control unit varies smoothly in proportion to
synaptic input so that it can therefore be used as an indicator of that
input; 3) that the processing of synaptic input is similar between the
test and control units; and 4) that the test and control units share a
similar common drive (Gorassini et al. 1998, 2004). To evaluate
common drive, the polynomials of the test and control units were
plotted against one another in a rate-rate plot. Linear regression was
used to calculate the slope of the rate-rate plot as well as the
coefficient of determination (r2). Pairs of motor units were included in
further analysis only if they had an r2 ⱖ 0.7, indicating that ⱖ70% of
the rate modulation of the test unit could be accounted for by the
control unit (Udina et al. 2010; Vandenberk and Kalmar 2014).
Since the ⌬F is calculated as the difference in firing rate of the test
unit relative to the control unit, the maximum ⌬F value a pair of motor
units can produce is limited by the rate modulation of the control unit.
Therefore, it is possible for a control unit with minimal rate modulation to produce a ⌬F value that underestimates the true influence of
PICs within the test unit. The rate modulation of the control unit was
calculated as the difference between the maximum and minimum of
the control unit polynomial, only during the time frame in which the
test unit is firing (Stephenson and Maluf 2011). To account for the
possibility of control unit saturation causing an underestimation of
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magnitude of the PIC in the test unit. The ⌬F calculation has
been validated in animal models (Bennett et al. 2001), and it
has shown appropriate scaling when monoaminergic drive has
been artificially increased with amphetamine in humans (Udina
et al. 2010).
The purpose of this study was to investigate PICs in flexor
and extensor muscles of the upper limb in humans by comparing ⌬F values in an elbow flexor (biceps brachii) and an elbow
extensor (triceps brachii) muscle. The animal models discussed
above suggest that the triceps, as an extensor muscle, will have
a higher ⌬F than the biceps, as a flexor muscle. However,
because humans have bipedal rather than quadrupedal locomotion, the relative lack of postural demand on the muscles of the
upper limb in maintaining antigravity trunk support may lessen
the need for the difference in intrinsic excitability observed in
animals.
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INTRINSIC EXCITABILITY IN HUMAN FLEXORS VS. EXTENSORS
the ⌬F, pairs of motor units in which the rate modulation of the
control unit fell within 0.5 imp/s of the calculated ⌬F for that pair
were removed from analysis.
It has been shown that there is a relationship between ⌬F and the
difference in recruitment time between motor unit pairs, in which a
difference in recruitment time less than ⬃2 s between test and control
units yields a smaller or even negative ⌬F value (Stephenson and
Maluf 2011). This 2-s limit has been attributed to the length of time
required for the PIC to activate (Bennett et al. 1998, 2001), as the
recruitment of a test unit before full activation of the control unit PIC
compromises the accuracy of the control unit as an indicator of
synaptic drive. To control for this, we also calculated difference in
recruitment time as the difference in time between the first spikes of
the test and control units.
Statistical analysis. Values are presented as means ⫾ SD. The
mean ⌬F was calculated first for each muscle in each participant, and
then the mean ⌬F for each across participants was calculated. Normality of the data was evaluated with the Shapiro-Wilk test. Unpaired
Student’s t-tests were used to compare ⌬F values across muscles and
the mean firing rates of the control and test units (Udina et al. 2010).
A one-factor ANCOVA was conducted to determine statistically
significant differences between ⌬F values for biceps and triceps,
controlling for recruitment time difference as a covariate. A similar
ANCOVA was conducted to control for rate modulation. Statistical
significance was set at P ⫽ 0.05.
RESULTS
To estimate the relative contribution of PICs in the modulation of elbow flexor and extensor motor units, we recorded
the discharge rates of pairs of motor units from the long head
of biceps brachii and the lateral head of triceps brachii in 10
healthy participants during isometric trapezoid contractions.
Each participant had two or three usable trials for each muscle
group, and motor unit yield ranged from 2 to 12 per trial
(average motor unit yield: 6.3 ⫾ 2.4 for biceps, 5.7 ⫾ 2.4 for
triceps). We then evaluated the data set more vigorously by
controlling for two factors: 1) saturation of rate modulation of
the control unit and 2) difference in recruitment time between
the test and control units. Overall, we found that there were
greater ⌬F values in triceps motor units than in biceps motor
units.
One hundred fifty-two unit pairs in biceps and 328 unit pairs
in triceps were used to calculate ⌬F values. Of these, 112 unit
pairs in biceps and 208 unit pairs in triceps had an r2 ⱖ 0.7.
The group mean ⌬F value for biceps (3.0 ⫾ 1.4) was comparable to those seen in other human studies (Mottram et al.
2009) and significantly lower than that of triceps (5.4 ⫾ 0.9,
P ⬍ 0.001). The mean ⌬F per participant is shown in Fig. 2, A
and B, for biceps and triceps, respectively.
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Fig. 1. Decomposition of intramuscular EMG from triceps brachii during a single elbow extension trial. The participant made an isometric elbow extension up
to 10% of maximum voluntary torque (MVT) (A, top). The intramuscular EMG signal (A, bottom) was then decomposed into its constituent motor units (example
within the black box enlarged in B and high-pass filtered at 500 Hz; individual motor unit templates shown in C). ⌬F values were then calculated for all possible
pairwise unit comparisons (D) by fitting 5th-order polynomials to the instantaneous firing rates of the test and control units and taking the difference in firing
rate of the polynomial for the lower-threshold control unit (shown here as MU1, black traces) at recruitment and derecruitment of the polynomial for the
higher-threshold test unit (shown as MUs 2, 3, and 4, gray traces). Rate-rate plots were generated by plotting the firing rate of the control unit vs. the firing rate
of the test unit (D, insets). Only pairs with a coefficient of determination ⱖ0.7 were included.
INTRINSIC EXCITABILITY IN HUMAN FLEXORS VS. EXTENSORS
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Influence of control unit rate modulation on ⌬F. Twentythree percent of biceps pairs and 46% of triceps pairs fit the
criterion of rate modulation saturation and were discarded. The
resulting data pool consisted of 86 motor unit pairs from biceps
and 111 motor unit pairs from triceps, yielding an average ⌬F
of 2.7 ⫾ 1.5 and 5.4 ⫾ 1.2 for biceps and triceps, respectively.
As with the full data set, the difference in ⌬F group means
between biceps and triceps was significantly different (P ⬍
0.001). Figure 2 shows the mean ⌬F per subject after removal
of pairs in which the control unit fit the saturation criterion.
After removal of motor unit pairs exhibiting saturation in
rate modulation, smoothed rate modulation of the remaining
control units was plotted against ⌬F for each muscle, with each
data point representing a motor unit pair across all participants
(Fig. 3). There was a positive relationship between ⌬F and
smoothed rate modulation of the control unit for data from both
biceps and triceps; however, the slope of this relationship was
Fig. 3. ⌬F vs. smoothed rate modulation of the control unit in biceps (Œ, dashed
line) and triceps (, solid line). Each point represents a single unit pair
irrespective of subject. An ANCOVA showed a significant main effect of
muscle (P ⬍ 0.001) and rate modulation (P ⬍ 0.001) on ⌬F with a significant
interaction (P ⬍ 0.001).
significantly higher for triceps than for biceps (biceps: y ⫽
0.22x ⫹ 0.49, triceps: y ⫽ 0.56x ⫺ 0.11, P ⬍ 0.001).
Influence of recruitment time difference on ⌬F. ⌬F values
for biceps and triceps are shown as a function of recruitment
time difference in Fig. 4. There was a significant effect of
muscle on ⌬F when controlling for recruitment time difference
(P ⬍ 0.001). There was also a significant effect of recruitment
time difference (P ⬍ 0.001), but the interaction between
muscle and recruitment time difference was not significant
(P ⫽ 0.23).
DISCUSSION
Here we provide evidence that, in humans, there is a greater
contribution of intrinsic properties to the excitability of extensor motor units than flexor motor units in the upper limb,
demonstrated by a larger ⌬F value in the triceps brachii
Fig. 4. ANCOVA examining the relationship between ⌬F and difference in
recruitment time between test and control units in biceps (Œ, dashed line) and
triceps (, solid line). Each point represents a single unit pair irrespective of
subject. A similar analysis using mean ⌬F and recruitment time differences for
each subject still yields a significant main effect of muscle (P ⬍ 0.001) but a
nonsignificant main effect of recruitment time difference (P ⫽ 0.13) with no
significant interaction.
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Fig. 2. Average ⌬F for each subject in biceps (A) and triceps (B). Black bars denote ⌬F with motor unit pairs showing a coefficient of determination ⬎0.7; hatched
bars denote ⌬F with the same criteria after removal of unit pairs that show possible saturation of the control unit. There were no significant changes in average
⌬F when controlling for saturation in biceps (P ⫽ 0.6) or triceps (P ⫽ 0.9) compared with the first criterion. Error bars denote SE.
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INTRINSIC EXCITABILITY IN HUMAN FLEXORS VS. EXTENSORS
Effects of recruitment time difference on ⌬F. To assume
linearity in changes in firing rate between test and control units,
the PIC of the control unit must be fully activated before the
test unit is recruited. If the test unit is recruited before the
control unit PIC has activated, the ⌬F of the test unit will be an
underestimation of its true value. In humans, prior studies have
shown that ⌬F decreases if the difference in recruitment time
is ⬍2 s (Gorassini et al. 2002; Stephenson and Maluf 2011). In
the cat, small and negative ⌬F values were also observed as
recruitment time difference decreased, but relatively large ⌬F
values were also observed, resulting in an increase in variability but no change in the mean ⌬F (Powers et al. 2008).
Previous studies have attempted to control for this variable by
using motor unit pairs that have a recruitment time difference
⬎1–2 s (Mottram et al. 2009; Udina et al. 2010). However, in
vitro recordings have shown that motoneurons may activate
PICs within a smaller time frame (Li et al. 2004). By only
conducting analysis on motor unit pairs with a recruitment time
difference ⬎2 s, subsets of valid motor unit pairs in which the
control unit exhibits early PIC activation may be inadvertently
excluded (Li et al. 2004).
The present results demonstrating a significant main effect
of recruitment time difference on ⌬F are in agreement with
previous literature (Stephenson and Maluf 2011). There was
also a significant main effect of muscle, controlling for differences in recruitment time, reflecting the greater values of ⌬F in
the triceps than the biceps. However, there was no significant
muscle ⫻ recruitment time difference interaction, suggesting
that the effects of muscle and recruitment time difference
contribute independently to the ⌬F.
Functional implications. Because we cannot explain higher
⌬F values in triceps through differential effects in recruitment
time difference, recruitment thresholds, or saturation in firing
rate of the control unit, these data support the hypothesis that
extensor motor units are subject to different motor control
principles than flexor units. There are several intrinsic mechanisms that could produce a difference in ⌬F between extensors and flexors. We speculate that these might include differences in the number of monoaminergic receptors, in the density
of L-type CaV1.3 channels that produce the PIC, or in the
number of monoaminergic boutons onto extensor versus flexor
motoneurons. Any of these could facilitate PIC activation
and/or increase the amplitude of the PIC, leading to higher ⌬F
values and differences in the slope of the ⌬F/control rate
modulation relationship. Recent studies have shown that the
densities of serotonergic and noradrenergic axon terminals in
the neck muscles of the cat are 2.3-fold and 1.4-fold greater in
extensor motoneurons compared with flexors, suggesting facilitated PICs by an increase in monoaminergic effects (Maratta
et al. 2015). Simulation studies have suggested that spikefrequency adaptation may also contribute to ⌬F values, especially during trapezoid ramps like those used in the present
study (Revill and Fuglevand 2011), but visual inspection of
units indicates very little difference in changes in firing rate
between biceps and triceps during the plateau portion of the
contraction. Furthermore, data from both computer simulations
and human investigations indicate that a 5-s plateau would only
contribute ⬍0.5 imp/s to a ⌬F value (Revill and Fuglevand
2011; Vandenberk and Kalmar 2014), which is not enough to
fully explain the difference we have observed between muscles. It is possible that other aspects of intrinsic motoneuron
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compared with biceps brachii. These data are consistent
with previous studies demonstrating higher neuronal excitability in the extensors than the flexors of the decerebrate cat
(Hounsgaard et al. 1988) as well as the neonatal rat preparation (Cotel et al. 2009).
The interpretation and limitations of the ⌬F method have
been subject to scrutiny by several groups, who concluded that
the ⌬F value is subject to a high degree of variability, in part
from mechanisms other than PICs. Factors that can increase the
variability of ⌬F include the choice of control unit, the difference in recruitment time between test and control units, the
maximum rate modulation of the control unit, the possible
effects of spike frequency adaptation, and the presence and
timing of secondary range firing (Powers et al. 2008; Revill
and Fuglevand 2011; Stephenson and Maluf 2011; Vandenberk
and Kalmar 2014).
Effects of rate modulation of control unit on ⌬F. The
positive correlation between ⌬F values and the amount of rate
modulation in the control unit has been presented previously
(Stephenson and Maluf 2011; Udina et al. 2010). Stephenson
and Maluf (2011) speculated that this correlation reflects
graded synaptic action of the PIC, as opposed to an “all or
none” mechanism. Another interpretation is that the ⌬F is
restricted by the maximum rate modulation of the control unit,
raising the possibility that units with limited rate modulation
are providing underestimations of the true magnitude of the
PIC, particularly if the firing rate of the control unit saturates.
Rate modulation of the control unit has been calculated in two
ways: as an absolute range of the smoothed firing rate for the
entire time the control unit is recruited (Udina et al. 2010) and
as the range of the smoothed firing rate limited specifically to
the time frame in which the test unit is firing (Stephenson and
Maluf 2011). We chose to use the latter, although calculating
rate modulation with either method showed that rate modulation in triceps was greater than that in biceps.
When motor unit pairs in which the maximum rate modulation during test unit firing fell within 0.5 imp/s of the ⌬F
value were eliminated, a significant difference in ⌬F between
biceps and triceps remained. Furthermore, the ⌬F values within
either muscle group were not significantly different when
comparing values from the full data set and values with
saturated control units removed. In fact, the group mean values
were virtually identical. Therefore, these data suggest that it is
unlikely that saturation in rate modulation substantially impacts ⌬F values.
The positive relationship between smoothed rate modulation
of the control unit and ⌬F of the motor unit pairs not exhibiting
saturation in rate modulation was similar to that seen in
Stephenson and Maluf (2011). Interestingly, the slope of the
⌬F/control rate modulation relationship is significantly greater
in triceps than in biceps, suggesting that a given change in
synaptic input results in a greater increase in ⌬F in triceps than
in biceps. The possible contribution of saturation of the control
unit having been removed, this difference in slope may support
the hypothesis of greater PICs in triceps than in biceps, since
greater PIC activation for a given synaptic input would result
in a steeper ⌬F/rate modulation slope. Given evidence that
PICs may be activated in a gradual manner with increases in
synaptic input (Elbasiouny et al. 2006), the difference in slopes
suggests that the dynamics by which synaptic input activates
the PIC may differ between muscles.
INTRINSIC EXCITABILITY IN HUMAN FLEXORS VS. EXTENSORS
combination of both flexion and supination (ter Haar Romeny
et al. 1982, 1984; van Zuylen et al. 1988). Similarly, motor
units in triceps brachii have been shown to be recruited in
response to pronation/supination as well as extension, even
though triceps does not biomechanically contribute to the
former (van Zuylen et al. 1988). It is not known how the
multifunctionality of a muscle might contribute to its ⌬F values
during single degree-of-freedom movements. While we tried to
be consistent in recording from the lateral and medial long
head of biceps corresponding to the location of units that
respond to flexion or a combination of flexion and supination
(ter Haar Romeny et al. 1982; van Zuylen et al. 1988), it is
possible that sampling biases toward units that are also responsive to supination may contribute to the variability of ⌬F
between participants. Other sources of between-subject variability can involve differences in muscle activation strategies
including the relative contributions of synergist muscles and a
muscle’s angular range of activation, which have been shown
to vary between individuals (Buchanan et al. 1986). Considering that we did not control for the fitness level of participants,
this may also contribute to subject variability due to the
neuromuscular changes that occur with exercise, even in an
aged population (Grabiner and Enoka 1995). However, the
relationship between exercise and changes in the intrinsic
excitability of motoneurons via PIC-mediated mechanisms has
not been thoroughly studied.
An important question is whether our observations within a
single head are generalizable to the entire muscle. The short
and long heads of biceps have previously been considered as a
single entity (Buchanan et al. 1989; Cavallari and Katz 1989)
and have been shown to receive similar reflex inputs (Barry et
al. 2008; Naito et al. 1996; Riley et al. 2008). In a neutral
forearm position, the two heads show similar recruitment
thresholds and discharge rates. However, when the forearm is
supinated, the two heads of biceps show significant differences
in recruitment threshold as well as discharge rate (Harwood et
al. 2010). With regard to functional compartmentalization, the
long head has been shown to contain motor units responsive to
flexion only, while motor units in the short head tend to
respond to a combination of flexion and supination (van Zuylen
et al. 1988). In addition, motor unit discharge variability is
greater in the short head of biceps, suggestive of differences in
synaptic noise (Harwood et al. 2010). Triceps, by comparison,
has fewer studies examining interhead differences, although
van Groeningen and Erkelens (1994) did show that recruitment
thresholds of motor units are not significantly different across
the three heads of triceps, and EMG activation between triceps
heads covaries relatively closely with increasing elbow extension (Buchanan et al. 1986). Because serotonergic projections
are highly diffuse (Björkland and Skagerberg 1982), it is
unlikely that descending monoaminergic drive differs within a
muscle, but a comparison of ⌬F between muscle heads and
across different aspects of muscle functionality will be an
important extension of the present work.
The age of our participants introduces some additional
caveats in the interpretation of results. Older adults show
significantly lower recruitment thresholds and more variable
firing rates compared with younger subjects in the upper limb
(Harwood et al. 2010; Laidlaw et al. 2000; Tracy et al. 2005),
which may be a consequence of motor unit remodeling following a decrease of motor unit number with age (Roos et al.
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properties, such as the prolongation of spike afterhyperpolarization (AHP) seen in motor units in the cat (Wienecke et al.
2009), may contribute to differences in ⌬F between extensors
and flexors, but the contributions of changes in AHP to ⌬F in
humans have not been clarified.
Anatomically, joint angle is an important contributor to ⌬F
values because of the sensitivity of the PIC to reciprocal
inhibition (Hyngstrom et al. 2007). According to analysis of
elbow flexor and extensor muscle fascicle excursions at different elbow flexion angles by Murray et al. (2000), having the
elbow flexed at 90° results in the long head of the biceps being
moderately stretched compared with the lateral head of the
triceps. It is possible that having the biceps relatively more
stretched than the triceps could contribute to the difference in
the ⌬F values observed between these muscles. However, it
has been shown in the cat that stretching a muscle by changing
the joint angle increases the amplitude of the PIC compared
with when the muscle is neutral or shortened. Progressive
sensory ablation provided strong evidence for the role of
reciprocal Ia inhibitory control of the PIC (Hyngstrom et al.
2007). Provided that such reciprocal inhibition exists in the
upper limb (Katz et al. 1991), a joint angle where the biceps is
stretched should not only increase the biceps PIC but would
also inhibit the triceps. If this is the case, our values of ⌬F in
the biceps may be overestimated and our values of ⌬F in the
triceps may be underestimated compared with a joint angle
where both muscles are equivalently stretched or slackened. As
such, we do not believe that the difference in ⌬F between
biceps and triceps occurs as a result of joint angle.
While differences in intrinsic excitability of flexor and
extensor muscles have also been observed in the hindlimbs of
animal models (Cotel et al. 2009; Hounsgaard et al. 1988), our
results may also be partly explained by factors specific to the
muscles of the upper limb. For example, differences in fiber
composition between muscles may lead to differences in ⌬F,
because slow motor units tend to exhibit long-lasting PICs (Lee
and Heckman 1998a, 1998b). Autopsy studies, however, suggest that the ratio of slow-twitch to fast-twitch fibers is similar
in biceps versus triceps (Elder et al. 1982). Differences in
afferent input may also contribute to differences in ⌬F. While
biceps and triceps have been shown to inhibit each other via Ia
reciprocal inhibition (Katz et al. 1991), biceps receives additional group I inhibitory afferents from brachioradialis (Barry
et al. 2008; Naito et al. 1996) and pronator teres (Naito et al.
1998). However, considering that both brachioradialis and
pronator teres are antagonistic to biceps on the pronation/
supination axis and not the flexion/extension axis, the extent to
which these muscles contribute to ⌬F during elbow flexion is
not clear (Buchanan et al. 1986). We also cannot say for certain
whether the observed difference in ⌬F also applies to the lower
limb in humans. Gorassini and colleagues (Gorassini et al.
2002) compared ⌬F values in 12 unit pairs in tibialis anterior
versus 4 unit pairs in soleus and did not find a significant
difference between muscles.
Although ⌬F values observed in biceps are similar to those
seen in previous studies (Mottram et al. 2009), there is noticeable variability in ⌬F values between individual subjects. One
source of ⌬F variability in biceps may be from the compartmentalization of function within the muscle. Since biceps
brachii acts as both a flexor and a supinator, motor units can be
recruited in response to flexion torque, supination torque, or a
3697
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INTRINSIC EXCITABILITY IN HUMAN FLEXORS VS. EXTENSORS
ACKNOWLEDGMENTS
We thank Dr. Jules Dewald for use of laboratory space, Dr. Monica
Gorassini for technical assistance, and Dr. Randy Powers for his comments on
the manuscript.
GRANTS
This work was supported by National Institutes of Health Grant NS-085331
and Training Grants T32 HD-057845 and T32 EB-009406, the Craig H.
Neilsen Foundation 260215, and the Northwestern Memorial Foundation
Parkinson’s Disease and Movement Disorders Advisory Council.
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the author(s).
AUTHOR CONTRIBUTIONS
Author contributions: J.M.W., C.K.T., and L.C.M. conception and design of
research; J.M.W. and C.K.T. performed experiments; J.M.W. analyzed data;
J.M.W., C.K.T., and C.J.H. interpreted results of experiments; J.M.W. prepared figures; J.M.W. drafted manuscript; J.M.W., C.K.T., L.C.M., and C.J.H.
edited and revised manuscript; J.M.W., C.K.T., L.C.M., and C.J.H. approved
final version of manuscript.
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