EFFECTS OF FATIGUE ON MOTOR UNIT FIRING RATE VERSUS
RECRUITMENT THRESHOLD RELATIONSHIPS
MATT S. STOCK, MS, TRAVIS W. BECK, PhD, and JASON M. DEFREITAS, MS
Department of Health and Exercise Science, University of Oklahoma, 1401 Asp Avenue, Norman, Oklahoma 73019-6081, USA
Accepted 1 August 2011
ABSTRACT: Introduction: The purpose of this study was to
examine the influence of fatigue on the average firing rate versus recruitment threshold relationships for the vastus lateralis
(VL) and vastus medialis. Methods: Nineteen subjects performed ten maximum voluntary contractions of the dominant leg
extensors. Before and after this fatiguing protocol, the subjects
performed a trapezoid isometric muscle action of the leg extensors, and bipolar surface electromyographic signals were
detected from both muscles. These signals were then decomposed into individual motor unit action potential trains. For each
subject and muscle, the relationship between average firing rate
and recruitment threshold was examined using linear regression
analyses. Results: For the VL, the linear slope coefficients and
y-intercepts for these relationships increased and decreased,
respectively, after fatigue. For both muscles, many of the motor
units decreased their firing rates. Conclusion: With fatigue,
recruitment of higher threshold motor units resulted in an
increase in slope for the VL.
Muscle Nerve 45: 100–109, 2012
Edwards1
defined fatigue as ‘‘the failure to maintain the required or expected force.’’ As addressed
by Weir et al.,2 identifying the precise mechanisms
underlying the decline in force is complicated by
the fact that multiple processes are involved (e.g.,
calcium release, changes in reflex function, motor
unit recruitment, etc.). They also argued that muscle fatigue is highly dependent on task-specific factors and cannot be explained with a single model.2
Furthermore, several investigators have suggested
that fatigue could be due to central or peripheral
factors, with central and peripheral fatigue occurring proximal and distal to the neuromuscular
junction, respectively.3–5
Many studies have examined the effects of fatigue on motor unit recruitment, derecruitment,
and firing rates during constant-force isometric
muscle actions.6–15 Several of these investigations
have demonstrated that, as a muscle is progressively more fatigued, the ability to maintain a constant force is accomplished, at least in part, by the
recruitment of additional motor units, which often
results in an increase in surface electromyographic
(EMG) amplitude.6–9 In addition, several studies
have reported a decline in motor unit firing rates
Abbreviations: ANOVA, analysis of variance; EMG, electromyography;
MVC, maximum voluntary contraction; PD, Precision Decomposition; PPS,
pulses per second; RF, rectus femoris; VL, vastus lateralis; VM, vastus
medialis
Key words: decomposition, electromyography, force, isometric,
motor control
Correspondence to: M. S. Stock; e-mail: mattstock@ou.edu
C 2011 Wiley Periodicals, Inc.
V
Published online in Wiley
DOI 10.1002/mus.22266
100
Motor Unit Fatigue
Online
Library
(wileyonlinelibrary.com).
as a muscle becomes fatigued.9–13 Adam and De
Luca6,7 examined motor unit firing rates for the
vastus lateralis (VL) during prolonged submaximal
isometric muscle actions. Specifically, the subjects
were required to hold a force level corresponding
to 50% of the predetermined maximum voluntary
contraction (MVC), followed by a decrease to 20%
MVC, which was then held for 50 seconds. Their
results show that motor unit firing rates first
increased and then decreased, which they7
believed may explain some of the differences in firing rate responses reported in previous studies.13,14
In addition, they reported that the recruitment
threshold of motor units declined throughout the
contraction series.6
For nearly three decades, many of the studies
by De Luca and colleagues have focused on EMG
signal decomposition.16–21 The long-term goal of
these investigations was to eventually develop a
fully automatic system capable of separating the
surface EMG signal into its constituent motor unit
action potential trains, thereby allowing researchers to study the firing rates of individual motor
units. As a result of recent improvements,19,20 the
Precision Decomposition (PD) algorithm is now
applicable to surface EMG signals and does not
require assistance from an expert operator.
According to De Luca and Nawab,21 the ability
to decompose surface EMG signals was made possible by combining their PD approach with the artificial intelligence–based Integrated Processing and
Understanding of Signals concept, originally
described by Lesser et al.22 According to Nawab
et al.,20 these types of algorithms are widely
applied in other fields, and are effective due to
their use of a knowledge base of adaptable ‘‘rules’’
and ‘‘cases.’’ However, the PD III algorithm and
the ‘‘reconstruct-and-test’’ procedure introduced by
Nawab et al.20 were recently called into question.23
Without going into great detail, Farina and
Enoka23 were not convinced that the data reported
recently by De Luca and Hostage24 portrayed an
accurate assessment of motor unit behavior, as
they indicated that the reconstruct-and-test procedure had not yet been adequately validated.23 In
their rebuttal, De Luca and Nawab21 noted that
both De Luca et al.19 and Nawab et al.20 validated
their algorithms with variations of the original twosource test18 and that, in both studies, the average
accuracy of the PD algorithm was >92% when
MUSCLE & NERVE
January 2012
compared with separate sensors. The reader was
then reminded that the reconstruct-and-test procedure uses ‘‘…synthetic surface EMG signals for
which we know the action potential shapes and the
firing times of all involved motor units throughout
the signal.’’21 De Luca and Nawab21 concluded
their letter by stating that accuracy assessments
must be specific to each decomposed EMG signal,
and that understanding ‘‘…how well a decomposition algorithm functions under artificial test conditions provides no assurance that it works well on a
specific real EMG signal….’’
In a recent study by De Luca and Hostage,24
linear regression analyses were used to examine
the relationship between the average firing rates of
motor units and their recruitment thresholds during constant-force isometric muscle actions of the
VL, tibialis anterior, and first dorsal interosseous.
By examining force levels corresponding to 20%,
50%, 80%, and 100% MVC, they were able to study
changes in the linear regression lines for these
relationships. In agreement with the ‘‘onion skin’’
phenomenon demonstrated earlier by De Luca
et al.25 and De Luca and Erim,26 both individual
subject and grouped data showed inverse relationships across all force levels, indicating that the lowthreshold motor units consistently maintained the
highest average firing rates. As displayed in their
Figures 2 and 3,24 the increase in firing rates and
the recruitment of additional motor units at
higher force levels resulted in slight increases in
the linear slope coefficients (i.e., the slopes
became less negative). Even at the 100% MVC
force level, however, the inverse relationships
between the average firing rates of motor units
and their recruitment thresholds were maintained.
Although data are limited on the sensitivity of
the linear regression line for the average firing
rate versus recruitment threshold relationship, the
results from De Luca and Hostage24 suggest that
these linear slope coefficients and y-intercepts may
be useful for studying changes in motor control
during the course of an intervention (e.g., strength
training, stretching, and fatigue). As just one
example, if a researcher were to hypothesize that
strength training results in an increase in the average firing rates for only the high-threshold motor
units, after the training program, one would
expect to observe an increase in the linear slope
coefficients (i.e., flatter slopes) for these relationships with no change in the y-intercepts. Alternatively, an increase in the mean y-intercept with no
change for the mean linear slope coefficient would
be interpreted as an increase in the average firing
rates for all of the observed motor units. Previous
studies6,7 examining the average firing rates of
motor units throughout the fatigue process have
Motor Unit Fatigue
reported that the onion skin property was maintained; however, the purpose of this study was to
examine the average firing rate versus recruitment
threshold relationships for the VL and vastus medialis (VM) before and immediately after a fatiguing
protocol of the dominant leg extensors.
METHODS
Twelve healthy men (mean 6 SD: age,
22.1 6 1.4 years; body weight, 78.9 6 10.4 kg) and
7 healthy women (age, 21.6 6 1.2 years; body
weight, 65.4 6 13.1 kg) volunteered to participate
in this study. Each subject completed a pre-exercise health and exercise status questionnaire,
which indicated no current or recent (within the
past 6 months) neuromuscular or musculoskeletal
problems. The study was approved by the university institutional review board for human subjects,
and all participants signed an informed consent
form prior to testing.
Subjects.
Familiarization Session. At a minimum of 48 h
prior to data collection, the subjects participated
in a familiarization session to become acquainted
with the equipment and to minimize the influence
of learning on the study’s dependent variables.
The purpose of the familiarization session was for
the subjects to become comfortable performing
multiple unilateral isometric MVCs of the dominant (based on kicking preference) leg extensors.
The subjects also performed several trapezoid isometric muscle actions of the leg extensors. Specifically, the subjects were required to linearly
increase isometric leg extension force from 0% to
50% MVC over a period of 4 s. They then held the
force constant at 50% MVC for 12 s, followed by a
linear decrease in force from 50% to 0% MVC in 4
s. They were provided with a visual template of
their force production during the trapezoid muscle
action. Practicing these muscle actions helped the
subjects perform smooth linear increases and
decreases in force during data collection.
Isometric Testing and Fatigue Protocol. After the
familiarization session, the subjects returned to the
laboratory for the data collection trial. Upon arrival, they were seated in a custom-built chair
designed for lower body isometric strength testing.
Furthermore, the subjects were strapped tightly to
the chair with a Velcro strap around the abdomen
and were instructed to remain seated during all
data collection procedures. After a brief warm-up,
they performed two 3-s unilateral MVCs of the leg
extensors separated by 3 min of rest at a joint
angle of 120 between the thigh and the leg. A
tension/compression load cell (Model SSM-AJ-500;
Interface, Scottsdale, Arizona) was attached to an
ankle cuff to allow for measurement of force
MUSCLE & NERVE
January 2012
101
production. The highest force from the two
attempts was used as the MVC value. After determination of the isometric MVC, the subjects performed a trapezoid isometric muscle action of the
leg extensors using the same force template as that
for the familiarization session (i.e., increased isometric leg extension force from 0% to 50% MVC
in 4 s, held the force constant for 12 s, and
decreased increased isometric leg extension force
from 50% to 0% MVC in 4 s). All subjects were
instructed to maintain their force output as close
as possible to the target force.
Immediately after performing the trapezoid isometric muscle action, the subjects performed a fatiguing protocol that involved ten 10-s isometric
MVCs of the dominant leg extensors, with 10 s of
rest between each MVC (i.e., 10 s on, 10 s off).
They were given verbal encouragement to produce
as much force as possible during each MVC. After
this protocol, they performed a 3-s isometric MVC
to measure strength when the quadriceps femoris
muscles were in the fatigued state. Immediately after this MVC, they performed a second trapezoid
isometric muscle action in the same manner as the
first. However, the same absolute force level (i.e.,
50% of the fresh muscle MVC) was used for the
first and second trapezoid muscle actions.
EMG Signal Detection and Processing. Eight separate bipolar surface EMG signals were detected
from the VL and VM (i.e., four signals per muscle)
during the trapezoid isometric actions. For each
muscle, the signals were detected with a surface
array EMG sensor (Delsys, Inc., Boston, Massachusetts) that consists of five pin electrodes. Four of
the five electrodes are arranged in a square (interelectrode distance 5.6 mm), with the fifth electrode in the center of the square and at a distance
of 3.6 mm from all other electrodes. (For detailed
information regarding the surface EMG sensors
used in this study, refer to the Methods section
and Fig. 1 in the study by Nawab et al.20)
Prior to detecting any EMG signals, the skin
over both muscles was shaved and cleansed with
rubbing alcohol. The surface EMG sensors were
then placed over the belly of the VL and VM, and
fixed with adhesive tape (Fig. 1). The reference
electrode was placed over the patella. All analog
EMG signals were low-pass (fourth-order Butterworth, 24 dB/octave slope, 9500-HZ cut-off) and
high-pass (second-order Butterworth, 12 dB/octave
slope, 100-HZ cut-off) filtered prior to sampling at
a rate of 20,000 samples/s. The digitized EMG signals were then digitally band-pass filtered with an
eighth-order Butterworth filter (24 dB/octave on
both the high- and low-pass slopes, cut-off frequencies of 250 and 2000 HZ). The four separate filtered EMG signals from each muscle then served
102
Motor Unit Fatigue
FIGURE 1. Example of the surface EMG sensor placements.
[Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
as the input to the PD III algorithm. This algorithm was designed specifically for decomposing
surface EMG signals into their constituent motor
unit action potential trains. These trains were then
used to calculate a mean firing rate curve for each
detected motor unit. All mean firing rate curves
were then smoothed with a 1-s Hanning filter and
selected from the portions where the mean firing
rates remained relatively constant (Fig. 2). Thus,
none of the motor units analyzed in this study
were selected from portions of the firing rate curve
that corresponded to an increase or decrease in
force production. Once all of the bipolar EMG signals for this study were decomposed, the accuracy
level for each motor unit was assessed using the
reconstruct-and-test procedure. Only motor units
that could be decomposed with >85.0% accuracy
were included for analysis. Each motor unit’s
recruitment threshold was calculated as the relative
force level (% MVC) when the first firing
occurred.
Statistical Analyses. For each subject and muscle,
the relationship between average firing rate and
recruitment threshold was examined using linear
regression analyses (Fig. 3). The resulting mean
linear slope coefficients and y-intercepts in the
fresh and fatigued state were then compared using
paired-samples t-tests for both the VL and VM. The
average firing rate of all motor units detected by
the decomposition algorithm in 10% increments
(i.e., 0–10%, 10–20%, etc.) was analyzed for fresh
and fatigued conditions using independent-samples t-tests. A one-way repeated-measures analysis of
variance (ANOVA) was used to examine the isometric MVC data. When appropriate, follow-up
analyses included a Bonferroni post hoc
MUSCLE & NERVE
January 2012
FIGURE 2. Average firing rate plots for the vastus lateralis of Subject F (see Fig. 4) prior to the fatiguing protocol (ten 10-s isometric
maximum voluntary contractions of the leg extensors). The solid black line shows the leg extension force production, and the remaining curves demonstrate average firing rates across time for each of the motor units that were decomposed during this particular muscle action. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
comparisons. The intraclass correlation coefficient
(model 2,1) for the isometric force data in this
study was 0.965, with no significant difference
between the MVC values from the familiarization
session and the data collection trial.27 An alpha
level of 0.05 was used for all statistical analyses.
FIGURE 3. Regression lines obtained from the average firing rate versus recruitment threshold relationships for the vastus lateralis
and vastus medialis before (fresh muscle) and after (fatigued muscle) the fatiguing protocol (ten 10-s isometric maximum voluntary
contractions of the leg extensors). Data are from all muscle actions for each of the 19 subjects. Note that the variance in the regression lines appears greater for the vastus medialis than for the vastus lateralis for both fresh and fatigued conditions.
Motor Unit Fatigue
MUSCLE & NERVE
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103
Table 1. Number of motor units detected by the decomposition algorithm from the vastus lateralis for each subject as well as the linear
slope coefficients (PPS/% MVC) and y-intercepts (PPS) for the relationship between average firing rate and recruitment threshold for fresh
versus fatigued muscle.
Vastus lateralis fresh muscle
Subject
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
Mean
SD
Vastus lateralis fatigued muscle
Motor units
Slope coefficient
y-int.
Motor units
Slope coefficient
y-int.
20
22
30
26
26
23
32
29
19
28
22
16
26
29
28
33
30
22
23
25.5
4.6
–0.490
–0.348
–0.217
–0.592
–0.772
–0.563
–0.382
–0.192
–0.417
–0.725
–0.328
–0.584
–0.504
–0.373
–0.260
–0.322
–0.292
–0.317
–0.302
–0.420
0.165
32.8
32.2
18.0
28.4
32.4
32.9
34.0
27.2
22.8
40.5
27.0
31.8
32.6
35.5
20.4
26.3
33.5
18.8
30.7
29.4
6.0
21
22
30
27
17
28
27
31
21
29
22
23
27
30
28
32
31
34
23
26.5
4.6
–0.223
–0.453
–0.210
–0.299
–0.106
–0.363
–0.404
–0.227
–0.209
–0.280
–0.426
–0.393
–0.417
–0.275
–0.457
–0.203
–0.214
–0.308
–0.244
–0.301
0.102
20.7
29.8
23.4
27.5
26.2
29.8
33.7
25.5
18.1
23.6
24.2
28.3
34.4
31.2
23.6
22.1
28.7
25.3
24.1
26.3
4.3
RESULTS
As displayed in Tables 1 and 2 for the VL and VM,
the mean 6 SD number of motor units detected
by the PD III algorithm prior to the fatiguing protocol was 25.5 6 4.6 and 23.7 6 7.3, respectively.
When examined after the fatiguing protocol, the
mean 6 SD number of motor units detected was
26.5 6 4.6 for the VL and 24.6 6 7.1 for the VM.
Tables 1 and 2 also show individual subject results
for the linear slope coefficients and y-intercepts for
the relationships between average firing rate and
recruitment thresholds for fresh and fatigued
Table 2. Number of motor units detected by the decomposition algorithm from the vastus medialis for each subject as well as the linear
slope coefficients (PPS/% MVC) and y-intercepts (PPS) for the relationship between average firing rate and recruitment threshold for fresh
vs. fatigued muscle.
Vastus medialis fresh muscle
Subject
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
Mean
SD
104
Vastus medialis fatigued muscle
Motor units
Slope coefficient
y-int.
Motor units
Slope coefficient
y-int.
20
28
21
37
18
20
24
17
25
23
14
9
33
33
33
24
28
17
27
23.7
7.3
–0.393
–0.343
–0.445
–0.799
–0.371
–0.316
–0.649
–0.709
–0.227
–0.654
–0.297
–0.808
–0.455
–0.382
–0.297
–0.450
–0.326
–0.164
–0.383
–0.446
0.189
34.0
32.7
24.3
40.9
25.0
20.4
52.6
51.7
21.2
39.1
27.8
42.7
30.6
29.0
24.2
31.6
29.8
19.2
26.3
31.7
9.7
17
31
28
39
24
22
21
21
26
31
15
14
36
17
21
19
25
32
28
24.6
7.1
–0.484
–0.385
–0.293
–0.271
–0.190
–0.387
–0.460
–0.205
–0.128
–0.629
–0.118
–0.167
–0.665
–0.122
–0.325
–0.455
–0.276
–0.368
–0.472
–0.337
0.164
40.9
33.7
23.8
31.9
20.0
18.1
38.1
26.3
16.8
37.8
19.9
26.8
41.9
22.0
24.5
32.5
31.1
28.0
27.2
28.5
7.7
Motor Unit Fatigue
MUSCLE & NERVE
January 2012
FIGURE 4. An example of the relationships between average
firing rate and recruitment threshold for 1 subject (Subject F)
before (fresh) and after (fatigued) the fatiguing protocol (ten
10-s isometric maximum voluntary contractions of the leg
extensors) for the vastus lateralis.
muscle for the VL and VM, respectively. Figure 4
shows an example of these relationships for 1 subject (Subject F) for the VL. Figures 5 and 6 show
the mean 6 SD linear slope coefficients and
y-intercepts for the average firing rate versus
recruitment threshold relationships before (fresh
muscle) and after (fatigued muscle) the fatiguing
protocol for the VL and VM, respectively.
The results from the paired-samples t-test indicate that there was a significant increase in the linear slope coefficients (i.e., they became less negative) due to the fatiguing protocol for the VL, but
FIGURE 5. (a) Mean 6 SD average firing rate versus recruitment threshold slope coefficient (PPS/% MVC) before (fresh
muscle) and after (fatigued muscle) the fatiguing protocol (ten
10-s isometric maximum voluntary contractions of the leg extensors) for the vastus lateralis. *Statistically significant increase in
the linear slope coefficient for this relationship due to the fatiguing protocol. (b) Mean 6 SD average firing rate versus recruitment threshold relationship y-intercept (PPS) before and after
the fatiguing protocol. *Statistically significant decrease in the
y-intercept due to the fatiguing protocol.
Motor Unit Fatigue
FIGURE 6. (a) Mean 6 SD average firing rate versus recruitment threshold slope coefficient (PPS/% MVC) before (fresh
muscle) and after (fatigued muscle) the fatiguing protocol (ten
10-s isometric maximum voluntary contractions of the leg extensors) for the vastus medialis. (b) Mean 6 SD average firing
rate versus recruitment threshold relationship y-intercept (PPS)
before and after the fatiguing protocol.
not for the VM. For the VL, the fatiguing protocol
resulted in a significant decrease in the y-intercepts
for these relationships. However, for the VM, the
decrease in the y-intercepts was not significant. Figures 7 and 8 show histograms of average firing rate
as a function of recruitment threshold before
(fresh muscle) and after (fatigued muscle) the fatiguing protocol for the VL and VM, respectively.
The fatiguing protocol resulted in a significant
decrease in average firing rate for all motor units
with recruitment thresholds corresponding to 30–
40% of the fresh muscle MVC for the VL, as well
as 0–10%, 10–20%, and 50–60% of the fresh muscle MVC for the VM. Finally, the results from the
one-way repeated-measures ANOVA indicate that
the fatiguing protocol resulted in a significant
decrease in unilateral isometric leg extension
strength (see Fig. 9 for pairwise differences). On
average, the fatiguing protocol resulted in an
18.6% reduction in MVC values (mean 6 SD: fresh
MVC, 841.3 6 228.8 N; fatigued MVC, 684.9 6
181.3 N). Table 3 shows the individual-subject unilateral isometric leg-extension strength values for
both conditions (i.e., fresh MVC and fatigued
MVC).
DISCUSSION
The main finding from this study is that fatigue
caused a significant increase in the mean linear
slope coefficient (i.e., the slopes became less
MUSCLE & NERVE
January 2012
105
FIGURE 7. Histogram of the average firing rate (PPS) in bins
that represent 10% MVC increments for the vastus lateralis
before (fresh muscle) and after (fatigued muscle) the fatiguing
protocol (ten 10-s isometric maximum voluntary contractions of
the leg extensors). The numbers indicated outside of the data
bars represent the total number of motor units detected by the
decomposition algorithm across all subjects. The average firing
rate in each bin reflects the average across motor units from all
19 subjects. *For recruitment thresholds corresponding to 30–
40% of the fresh muscle MVC, independent-samples t-tests
indicated that the average firing rate for fresh muscle was significantly greater than that observed in the fatigued state.
negative) for the average firing rate versus recruitment threshold relationship for the VL, but not
the VM. For the VL, this increase was also accompanied by a significant decrease in the mean yintercept of the relationship. It is important to reiterate that the same absolute force level (i.e., 50%
of the fresh muscle MVC) was used for both conditions in this investigation. As a result of the decline
in leg-extension force of 18.6 6 10.8% (Table 3),
it is very likely that different motor units were
examined pre- versus postfatigue. As noted earlier,
De Luca and Hostage24 demonstrated that, when
the average firing rate versus recruitment thresh-
FIGURE 8. Histogram of the average firing rate (PPS) in bins
that represent 10% MVC increments for the vastus medialis
before (fresh muscle) and after (fatigued muscle) the fatiguing
protocol (ten 10-s isometric maximum voluntary contractions of
the leg extensors). The numbers given outside the data bars
represent the total number of motor units detected by the
decomposition algorithm across all subjects. The average firing
rate in each bin reflects the average across motor units from all
19 subjects. *For recruitment thresholds corresponding to 0–
10% MVC, 10–20% MVC, and 50–60% of the fresh muscle
MVC, independent-samples t-tests indicated that the average
firing rate for fresh muscle was significantly greater than that
observed in the fatigued state.
106
Motor Unit Fatigue
FIGURE 9. Mean 6 standard deviation unilateral isometric leg
extension strength values before (fresh MVC), during (MVC #1–
10), and after (fatigued MVC) the fatiguing protocol. Results
from one-way repeated-measures ANOVA are shown below the
graph.
old relationships were examined at 20%, 50%,
80%, and 100% MVC, the slopes became progressively flatter at higher force levels. Thus, for the
VL, the increase for the linear slope coefficients
and the decrease for the y-intercepts after the fatiguing protocol were likely related to the recruitment of higher threshold motor units. Our findings might also be an indication of the increased
drive to the motor neuron pool to compensate for
the changes in the mechanical characteristics of
the muscle.7
As shown in Tables 1 and 2, the mean 6 SD
linear slope coefficients for the average firing rate
versus recruitment threshold relationships prior to
the fatiguing protocol were 0.420 6 0.165 pulses
per second (PPS)/% MVC for the VL and 0.446
6 0.189 PPS/% MVC for the VM. As a result of
the fatiguing protocol, these values increased to
0.301 6 0.102 and 0.337 6 0.164 PPS/% MVC
for the VL and VM, respectively. To examine what
may have caused the changes in the linear slope
coefficients and y-intercepts for these relationships,
we performed independent-samples t-tests for all of
the motor units that were identified by the decomposition algorithm in 10% increments (e.g., 0–
10%, 10–20%, 20–30%, 30–40%, 40–50%, and 50–
60% MVC). For motor units of the VL with recruitment thresholds corresponding to 30–40% of the
fresh muscle MVC, the average firing rates
decreased after the fatiguing protocol (Fig. 7). For
motor units of the VM, however, a decrease in average firing rate was found for the motor units
identified with recruitment thresholds of 0–10%,
10–20%, and 50–60% of the fresh muscle MVC
(Fig. 8). Therefore, the increased linear slope coefficients and decreased y-intercepts with fatigue
were also a result of decreased average firing rates
for many of the detected motor units.
An additional hypothesis, however, is that
motor units were recruited at lower absolute force
MUSCLE & NERVE
January 2012
Table 3. Individual subject data for the unilateral isometric leg extension strength values before and immediately
after the fatiguing protocol*
Subject
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
Mean
SD
Fresh MVC (N)
50% fresh MVC (N)
Fatigued MVC (N)
MVC percent decline
50% fresh MVC/fatigued MVC (%)
530.5
949.1
947.6
884.1
990.2
1420.2
653.3
629.1
476.5
1072.6
653.3
722.6
841.3
729.7
746.0
836.2
739.0
1080.4
1083.0
841.3
228.8
265.2
474.6
473.8
442.0
495.1
710.1
326.7
314.6
238.3
536.3
326.7
361.3
420.6
364.9
373.0
418.1
369.5
540.2
541.5
420.6
114.4
550.5
692.8
947.5
537.1
912.2
962.3
518.8
497.9
382.3
849.1
467.6
553.5
736.2
663.6
663.9
744.3
560.0
839.6
933.4
684.9
181.3
3.8
27.0
0.0
39.2
7.9
32.2
20.6
20.9
19.8
20.8
28.4
23.4
12.5
9.1
11.0
11.0
24.2
22.3
13.8
18.6
10.8
48.2
68.5
50.0
82.3
54.3
73.8
63.0
63.2
62.3
63.2
69.9
65.3
57.1
55.0
56.2
56.2
66.0
64.3
58.0
61.4
8.3
*As shown in the far right column, due to the fact that the same absolute force level (i.e., 50% of the fresh MVC) was used for the first and second
trapezoid muscle actions, the mean 6 SD absolute force level required to achieve this target force corresponded to 61.4 6 8.3% of the fatigued MVC.
levels. Adam and De Luca6 demonstrated that, during sustained isometric muscle actions of the leg
extensors, the recruitment threshold of each
motor unit declined, but the order in which they
were recruited did not change. It is important to
note that our study design did not allow us to
examine motor unit firing rates as the leg extensors progressively fatigued. Our findings indirectly
suggest, however, that, after the fatiguing protocol,
an increased number of motor units were activated
to compensate for the decline in the force production capabilities of the active motor units.
A unique aspect of this study was the simultaneous analysis of motor unit firing rates for the VL and
VM. To date, many studies have used EMG techniques in an attempt to better understand the role of
these muscles in patellofemoral pain28–33 and knee
osteoarthritis.34–36 Several of these studies have
focused on the timing of activation and/or inactivation during dynamic muscle actions.28,29,31–36 Other
investigations have examined the ability to preferentially fatigue individual muscles of the quadriceps
femoris30,37 and changes in motor unit firing rates
for the VL and VM as a result of strength and endurance training.38 Although there is considerable variance in fiber type distribution for the VL39 and
VM,40 an autopsy study by Johnson et al.41 showed
that the VM usually contains a greater percentage of
type 1 fibers than the VL. As a result, several studies
have hypothesized that the VM could exhibit a
greater degree of fatigue resistance than the VL
and/or rectus femoris (RF).42–45 These studies did
Motor Unit Fatigue
not show consistent differences in fatigue resistance
between the muscles. For example, Housh et al.45
reported that, during cycle ergometry, the EMG fatigue threshold occurred at a lower power output
for the RF than the VL, but no differences were
observed between the VL and VM. Ebersole and
Malek42 reported similar patterns of fatigue-induced
decreases in electromechanical efficiency for the VL
and VM when subjects performed 75 consecutive
maximal concentric isokinetic muscle actions.
Grabiner et al.30 also failed to provide evidence that
the VL or VM could be selectively fatigued during
sustained isometric muscle actions. Conversely,
Rainoldi et al.46 detected changes in conduction velocity for the VL, vastus medialis oblique, and vastus
medialis longus during sustained isometric muscle
actions at 60% and 80% MVC. The aforementioned
findings indicate that, for sustained muscle actions
at 80% MVC, the VL exhibited a greater decline in
conduction velocity compared with that of the vastus
medialis oblique. In our study, the fatiguing protocol resulted in a similar mean increase and decrease
for the linear slope coefficients and y-intercepts,
respectively, for both muscles. These changes were
statistically significant for the VL, but not for the
VM. This discrepancy was likely influenced by
greater intersubject variance for the linear slope
coefficients and y-intercepts for the average firing
rate versus recruitment threshold relationships for
the VM compared to that for the VL. Specifically,
the SDs for the linear slope coefficients were 0.189
and 0.164 PPS/% MVC for the VM before and after
MUSCLE & NERVE
January 2012
107
the fatiguing protocol, respectively. In contrast, the
corresponding SDs for the VL were 0.165 and 0.102
PPS/% MVC. Not only was the variance for these
slopes greater than that from the VL, but it was also
much more pronounced than in examples recently
reported by De Luca and Hostage24 for the first dorsal interosseous and tibialis anterior. In addition, as
shown in Tables 1 and 2, for Subjects A, F, M, P, Q,
and S, the fatiguing protocol resulted in an increase
in the linear slope coefficients for the VL, but no
change or a decrease (i.e., they became more negative) for the VM. Although direct statistical comparisons were not made, these results suggest that motor
units for the VM may be slightly more resistant to fatigue than those for the VL, despite the fact that
they are both innervated by the femoral nerve.
Future studies should further address the control
properties for these two muscles, as well as those for
the RF.
It is important to acknowledge the methodological differences between this investigation and previous muscle fatigue studies. First, although many
investigations have examined changes in motor unit
firing rates during constant-force isometric muscle
actions,6–15 we examined the motor unit firing rate
versus recruitment threshold relationships before
and after the subjects performed multiple MVCs.
We did not directly quantify changes in recruitment
thresholds and/or firing rates for specific motor
units or statistically compare data for low- versus
high-threshold motor units. Furthermore, when
examining data from individual motor units and
multiple subjects, it is important for investigators to
carefully consider the research question and the statistical procedures necessary for its answer. Examining data for one motor unit is relatively simple, but
interpreting data from many motor units across multiple trials is much more complex. Specifically, one
must decide whether to examine the results on a
subject-by-subject basis or by group-mean data
coupled with conventional hypothesis testing and
parametric statistics. In addition to the detailed
observations by Adam and De Luca,6,7 De Luca and
Hostage24 recently compared r2-values for individual
subjects to those from grouped data for the average
firing rate versus recruitment threshold relationships. They found that the variability for these relationships increased when data from multiple subjects were combined. In terms of examining these
relationships for fresh versus fatigued conditions on
an individual-subject basis, it must be noted that, in
a few cases, the number of motor units detected by
the decomposition algorithm may have been important. For example, for Subject L (Table 2), the linear slope coefficient and y-intercept was determined
from a linear regression analysis performed on only
nine data points. Similarly, for some of the subjects
108
Motor Unit Fatigue
the distribution of the recruitment thresholds was
less than optimal, despite the fact that the algorithm
was able to accurately decompose many (i.e., >20)
motor units (Fig. 3). This finding may be explained
by the fact that, at very low force levels, the decomposition algorithm may not identify many motor
units in large muscles such as the VL and VM, especially in subjects with greater adipose tissue between
the muscle and the surface of the skin.20
In spite of the potential limitations, we presented the linear slope coefficient and y-intercept
values for both individual subjects (Tables 1 and
2) and grouped mean data (Figs. 5 and 6). For the
VL, despite a few cases in which the fatiguing protocol caused the slopes of these relationships to
decrease, many of them increased (i.e., flatter
slopes). Thus, for the VL, we are confident that
the linear slope coefficients and y-intercepts for
the
observed
relationships
increased
and
decreased, respectively, regardless of how the data
are analyzed (i.e., by individual subject or group
mean). Furthermore, as with all research studies,
statistical power is of great importance. However,
recruiting 19 subjects and analyzing the firing patterns of over 900 motor units for each muscle has
given us great confidence in the validity of our
conclusions.
The results of this investigation show that a fatiguing protocol of the dominant leg extensors
resulted in increased (i.e., less negative) linear
slope coefficients and decreased y-intercepts for
the average firing rate versus recruitment threshold relationships of the VL. The increase in the
linear slope coefficients for the VL was consistent
with the ‘‘operating point’’ concept described
recently by De Luca and Hostage,24 and suggests
that higher threshold motor units were recruited
when the muscle was in the fatigued state. Our
findings also indirectly suggest that motor units for
the VM may be slightly more resistant to fatigue
than those for the VL. Finally, when examined on
an individual-subject basis (Tables 1 and 2),
although many of the linear slope coefficients
changed after the fatiguing protocol, these relationships were all negative. In other words, even in
the fatigued state, the average firing rates for the
higher threshold motor units were never equivalent to those for the earlier recruited motor units.
The authors thank Professor Carlo J. De Luca and Dr. Paola Contessa for their helpful suggestions with this manuscript.
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