University of Groningen
The spread of muscle fiber conduction velocity
Lange, Friedhelm
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Lange, F. (2009). The spread of muscle fiber conduction velocity: increasing scope and usability
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Chapter 1
Introduction
The neurophysiology of healthy muscle - i.e. what physiological processes make muscles contract - is quite well understood and several
(non)invasive methods exist to measure aspects of muscle neurophysiology in vivo (see e.g. Basmajian and DeLuca (1985)). Clinically, one of
the most interesting aspects of muscle physiology is the speed at which
electrical signals (the action potentials) travel along the sarcolemma of
muscle fibers.
In short, muscle fiber contraction is triggered by the initiation of an action potential at the end-plate region. Subsequently, this action potential
propagates along the muscle fiber as the result of a repetitive sequence
of (voltage gated) ion channel opening and closing, resulting in membrane depolarization and repolarization. The velocity at which these action potentials propagate is referred to as muscle fiber conduction velocity (MFCV). The actual velocity depends on multiple factors such as the
resting potential of the muscle fiber membrane (which is controlled by the
activity of different ion channels), the metabolic state of the muscle (as a
result of factors such as energy depletion and tissue pH), the extracellular
resistance and the fiber diameter. Muscle fiber diameter can be investigated directly at an anatomical level using microscopic investigation of
muscle tissue obtained by biopsy. The finding of atrophy or hypotrophy
(muscle diameter below normal limits) is generally seen as a marker for
neuromuscular disease or disuse (e.g. due to immobilization). On the
other hand, since fiber diameters correlate linearly with conduction ve9
locity (i.e. action potentials propagate faster along thicker muscle fibers),
low muscle fiber conduction velocities can indicate muscle fiber atrophy
or hypotrophy. In this sense, MFCV can provide information about muscle
architecture without the need for a biopsy. The relative contribution of low,
normal and high conduction velocities is expressed in the combination of
the mean and the range or spread of muscle fiber conduction velocities
and can for example be summarized as the fast/slow ratio. The work in
this thesis focuses on improvements in the estimation of this spread of
muscle fiber conduction velocity using both invasive (needle) and noninvasive (surface) electromyographic (EMG) measurements. The aim of
these studies is to increase usability of MFCV spread in scientific and
clinical investigations.
A brief historical overview of muscle fiber conduction velocity estimation
The first description of human MFCV measurements was given by Denslow and Hassett (1943). They used two needle electrodes positioned
between 0.5 to 7 cm apart along the direction of muscle fibers during voluntary activation of the muscle (just enough to activate a motor unit near
the electrode) and determined the conduction velocity of motor unit action potentials. A serious drawback of their method was that the position
of the endplate zone was not taken into consideration; retrospectively the
results did not reflect the true MFCV. In 1955 Buchthal also used needle electrodes but applied intramuscular electrical stimulation (Buchthal
et al., 1955). He placed three to five concentric needles along the muscle fiber direction and was thereby able to identify the action potential
generated by the stimulated fibers at multiple points. Several years later,
Stålberg (1966) studied the conduction velocity of human muscle fibers
after voluntary activation, using needle electrodes with multiple leads.
Based on the relation between fiber diameter and conduction velocity
he found a decrease of mean conduction velocity in disuse atrophy. In a
condition with increased fiber diameter (i.e. hypertrophic muscle fibers in
acromegaly) he discovered high mean conduction velocities. Furthermore, Stålberg elaborated on ideas with respect to the repolarization
10
phase (the velocity recovery function, which is beyond the scope of this
thesis). In 1970 Lindstrøm and coworkers performed MFCV estimation
using surface instead of invasive EMG techniques for the first time (Lindstrom et al., 1970). Their findings were based on frequency domain analysis techniques. The MFCV distribution in the time domain was investigated by Troni et al (1983) using direct needle stimulation and recording. They found that the distribution of MFCVs follows a Gaussian distribution in healthy muscle. By the end of the 1980s Arendt-Nielsen and
Zwarts gave a comprehensive overview of then existing invasive and surface EMG techniques to estimate MFCV (Arendt-Nielsen and Zwarts,
1989). At that point in time researchers had also developed several
necessary analytical algorithms to analyze surface EMG in order to estimate (mean) MFCV, such as zero-crossing, spike-triggered averaging
and cross-correlation techniques. In 1996 Haig and coworkers (Haig
et al., 1996) reviewed the clinical relevance of surface EMG techniques
and concluded, that, based on the available literature between 1964 and
1994, surface EMG had no clear-cut use in clinical diagnosis. The scope
of their investigation was the diagnosis of peripheral neurological disease
by means of MFCV determination, using both surface and needle EMG
techniques. At that time, the available literature only demonstrated that
surface EMG may be used to detect fasciculations. However, they expected that future developments may change this, predicting that surface EMG together with specialized computer signal processing methods
may prove to be clinically useful, but possibly only in combination with
needle examination. One of the technical developments that indeed improved clinical use of surface EMG methods, even without the necessity of using needle EMG, was the development of high density surface
EMG, using densely packed electrode arrays with small (in the order
of a few millimeters) inter-electrode distance and up to 128 electrodes
(summarized by Roeleveld and Stegeman (2002)). Zwarts and coworkers (Zwarts et al., 2000) described the progress in the use of surface
EMG - from single bipolar EMG recordings, via linear arrays to multichannel two-dimensional electrode arrays - showing that the use of the latter
allows to extract information on motor unit properties (e.g. motor unit
potential amplitude, duration, firing rate), traditionally only accessible by
needle EMG. However, they still emphasize that further research is nec-
11
essary, especially regarding analysis techniques. Very recently Meekins
and coworkers (2008) reviewed the use of surface electromyography in
the diagnosis and study of neuromuscular disorders. They concluded
that surface EMG is useful to detect the presence of some neuromuscular disorders but can not differentiate between myopathic and neuropathic
disorders. They based their conclusions on published studies that used
either small array surface EMG technique (van der Hoeven et al., 1994) or
high-density surface EMG techniques (Beekvelt et al., 2006; Drost et al.,
2001, 2004a,b).
(Dis)advantages of current techniques for MFCV estimation
The above shows that to determine MFCV basically two techniques are
available: invasive (needle) and noninvasive (surface) EMG techniques.
In invasive techniques needles are inserted through the skin into the
muscle to pick up the extracellular electrical activity resulting from action potentials propagating along muscle fibers. The conduction velocity can then be measured by first stimulating muscle fibers electrically
and picking up the activity at some distance from the stimulation point
or by recording the electrical activity resulting from voluntary activation
of muscles. Stimulated invasive measurement of MFCV has the advantage that the subject does not have to voluntarily activate the muscle.
This is of great advantage in patient studies when paralyzed muscles or
denervated fibers in otherwise normally innervated muscles need to be
investigated. Furthermore, stimulation parameters are fully controlled - in
contrast to voluntary activation - which facilitates comparison of investigations at different sites of the muscle or at different moments in time. To facilitate more detailed evaluation of MFCV high-density surface EMG can
be used. This allows the application of spacial filtering techniques and
algorithms for enhanced identification of different motor units involved in
generating the EMG signal, including their separate conduction velocities (Drost et al., 2006).
12
Clinical measurement and significance of MFCV
spread
From the physiological point of view, the motor unit can be seen as the
elementary functional unit of muscles. A motor unit consists of a variable
amount of muscle fibers that are all innervated by one α-motor neuron.
The motor unit conduction velocity (mu-CV) then is the collective or common conduction velocity of all muscle fibers that belong to that specific
motor unit. The mu-CV depends on the type of motor unit (e.g. fatigue
resistant versus non-fatigue resistant) and its metabolic state. The muCV can be determined by using surface electrodes to record the EMG.
One example is to use three electrodes mounted in line on a rigid array
and configured as two bipolar channels. After positioning this array in
parallel with muscle fibers two time-shifted surface EMG signals can be
acquired. From the time shift between these signals the mean conduction
velocity can be determined. However, the surface EMG signal theoretically also contains information concerning the spread of the conduction
velocities of each of the activated muscle fibers. The MFCV spread could
therefore be an interesting measure for demonstrating changes of MFCV
in (a part of) the motor unit action potentials (MUAPs) during exercise.
Furthermore, as mentioned before, the MFCV has a linear relationship with the fiber diameter (Blijham et al., 2006). Relatively high or low
MFCV can therefore provide an indication for hypertrophy and atrophy,
respectively. Atrophy or hypotrophy can occur secondary to diseases of
the muscle, the axon or the motor neuron itself, as shown in patients
with Amyotrophic Lateral Sclerosis, plexus brachialis injury (van der Hoeven et al., 1993a) or patients with myositis (Blijham et al., 2004). In
these cases, the diagnostic significance relies on the ratio of the fastest
to the slowest MFCV (fast/slow ratio, F/S ratio). Based on this principle,
this technique could be used in the scientific investigation and diagnostic
work-up of all diseases in which (clinically silent) diameter changes could
be expected.
13
Aim of the studies presented in this thesis
The spread of motor unit CVs and muscle fiber CVs can not straightforwardly be extracted from two channel surface EMG signals. We developed and here present a new method to determine the spread of MFCV
based on two channel surface EMG measurements. One of our aims was
to validate the use of this method both clinically and theoretically which
lead to our first hypothesis:
The conduction velocity of multiple motor unit action potentials in surface EMG measurements can be estimated using automatically identified
peaks in the EMG signal obtained from two spatially separated channels
that are configured in line. The resulting collection of multiple muscle
fiber conduction velocities provides a reliable measure of the mean and
spread of muscle fiber conduction velocities.
As mentioned earlier in this section, the CV of muscle fibers can also
be determined using electrical stimulation of the muscle fibers in combination with needle electrode recordings from a site further along the fiber.
Theoretically, applying this technique, one should be able to measure the
MFCV of atrophic or even denervated fibers, when voluntary activation is
no longer possible. The second part of the work presented in this thesis therefore involves the investigation of the presence of disturbances in
muscle fiber CV in clinically unsuspected muscles using invasive needle
EMG recordings, as expressed in the second hypothesis:
Invasive determination of muscle fiber conduction velocity using direct
stimulation of muscle fibers is a sensitive electrodiagnostic method and
can detect conduction abnormalities, pointing to diameter changes even
in clinically unaffected muscle.
Design of the studies presented in this thesis
Part I of this thesis focuses on surface EMG recordings. In an earlier
experiment we studied muscle fatigue and recovery after isometric maximal voluntary contractions. During recovery, an overshoot of the MFCV
was found, reaching a steady state at supernormal values after several
minutes of recovery (van der Hoeven et al., 1993b) that was long-lasting.
It was not yet clear whether such an increase can also be found at a
14
more physiological workload, or whether there is any functional significance. To answer these questions an additional series of experiments
was performed, in which variations in MFCV and EMG parameters during intermittent isometric exercise were investigated. These experiments
are described in Chapter 2.
The results of this first study illustrated the general usefulness of surface EMG recordings to determine MFCV, yet information about MFCV
spread was still lacking. The MFCV in this first surface EMG study (only)
reflected the mean conduction velocity of active (i.e. recruited) motor
units. It is known that mammalian muscles contain both fatigue-resistant
and non-fatigue-resistant motor units (Burke et al., 1973). During the
intermittent exercise studied in Chaper 2, the non-fatigue-resistant motor units theoretically will show a steeper decline in MFCV than fatigueresistant motor units. To be able to evaluate the contribution of both types
of motor units to the MFCV we designed an analysis paradigm to derive
conduction velocities of multiple (i.e. slow and fast conducting) motor
units from a two channel bipolar surface EMG signal. This technique was
called the Inter Peak Latency (IPL) method and is introduced and applied
in a fatigue study in Chapter 3.
In principle, the IPL method can have many more applications than in
fatigue studies alone. However, since it is difficult to decompose motor
unit potentials from a surface EMG signal during higher levels of force
(which might be encountered in other potential application areas for the
IPL method), we wondered whether the results from the IPL method as
applied to the in vivo EMG signals described in Chapter 3 actually reflect
the real spread of MFCVs. To answer this question, we built a computer
model to validate the results of the IPL method, at all, but higher force
levels in particular. The results of this modelling study are summarized in
Chapter 4.
Part II of this thesis describes invasive needle EMG recordings in patients in whom it is difficult to find objective measures of motor unit dysfunction at the level of the axon, the endplate zone or the muscle.
In Chapter 5 a group of patients with diabetes mellitus is evaluated.
These patients frequently develop polyneuropathy. Clinically the polyneuropathy in these patients often starts with sole or predominantly sensory
disturbance or dysfunction. We hypothesized that the invasively deter15
mined MFCV can be used to detect muscle fiber denervation atrophy,
as an early sign of motor axonal loss. Subtle signs of involvement of
the motor unit in early diabetic polyneuropathy, should then be reflected
in abnormalities in MFCV (a deviant ratio of fastest/slowest conducting
fibers). Next we extended this investigation to patients who receive botuline toxin as a treatment for torticollis spasmodica and additionally investigated whether the findings were related to age. The findings in the latter
group of patients are presented and discussed in Chapter 6.
16
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