1Division of Neuroscience and Biomedical
Systems,
Halliday, David M.,
Bernard A. Conway,
Simon F. Farmer, and
Jay R. Rosenberg.
Load-Independent Contributions From Motor-Unit Synchronization to
Human Physiological Tremor.
J. Neurophysiol. 82: 664-675, 1999.
This study describes two
load-independent rhythmic contributions from motor-unit synchronization
to normal physiological tremor, which occur in the frequency ranges
1-12 Hz and 15-30 Hz. In common with previous studies, we use
increased inertial loading to identify load-independent components of
physiological tremor. The data consist of simultaneous recordings of
tremor acceleration from the third finger, a surface electromyogram
(EMG), and the discharges of pairs of single motor units from the
extensor digitorum communis (EDC) muscle, collected from 13 subjects,
and divided into 2 data sets: 106 records with the finger unloaded and
84 records with added mass from 5 to 40 g. Frequency domain
analysis of motor-unit data from individual subjects reveals the
presence of two distinct frequency bands in motor-unit synchronization,
1-12 Hz and 15-30 Hz. A novel Fourier-based population analysis
demonstrates that the same two rhythmic components are present in
motor-unit synchronization across both data sets. These frequency
components are not related to motor-unit firing rates. The same
frequency bands are present in the correlation between motor-unit
activity and tremor and between surface EMG activity and tremor,
despite a significant alteration in the characteristics of the tremor
with increased inertial loading. A multivariate analysis demonstrates
conclusively that motor-unit synchronization is the source of these
contributions to normal physiological tremor. The population analysis
suggests that single motor-unit discharges can predict an average of
10% of the total tremor signal in these two frequency bands. Rectified surface EMG can predict an average of 20% of the tremor; therefore within our population of recordings, the two components of motor-unit synchronization account for an average of 20% of the total tremor signal, in the frequency ranges 1-12 Hz and 15-30 Hz. Our results demonstrate that normal physiological tremor is a complex signal containing information relating to motor-unit synchronization in
different frequency bands, and lead to a revised
definition of normal physiological tremor during low force postural
contractions, which is based on using both the tremor spectra and the
correlation between motor-unit activity and tremor to characterize the
load-dependent and the load-independent components of tremor. In
addition, both physiological tremor and rectified EMG emerge as
powerful predictors of the frequency components of motor-unit synchronization.
The performance of maintained voluntary postural tasks in healthy
humans is accompanied by small fluctuations in limb position, referred
to as physiological tremor. Normal physiological tremor is a complex
signal resulting from interactions between several mechanical and
neural factors (Elble 1986 Irregularities in motor-unit firing provide the main source of
perturbation to a limb during voluntary postural contractions. For an
unrestrained limb segment, this stochastic input will excite the limb
at its natural resonant frequency (Stiles and Randall 1967 Several different mechanisms have been proposed for the
load-independent components of physiological tremor. After examination of tremor spectra estimated from force records obtained during isometric pinch grip tasks, Allum et al. (1978) Pairs of motor units recorded from active muscles during maintained
voluntary contractions in humans exhibit a tendency toward synchronized
firing, which is thought to reflect the presence of a common
presynaptic input to the motoneuron pool (Bremner et al.
1991 The present study therefore investigates the relationship between the
two frequency bands present in motor-unit synchronization (1-12 Hz and
15-30 Hz) and normal physiological tremor. Our aims are
1) to clarify the role of motor-unit firing rate in the
generation of physiological tremor, 2) to assess whether
there is any relationship between the rhythmic components of motor-unit
synchronization and physiological tremor, 3) to
investigate whether this relationship exhibits any load dependency, and
4) to determine whether similar information is available
using EMG instead of single motor-unit discharges, and in particular if
surface EMG can predict the rhythmic components in motor-unit
synchronization resulting from rhythmic inputs to motoneuron pool
during maintained voluntary contractions (Farmer et al.
1993 To distinguish between load-dependent and load-independent components
of tremor, the complete data are subdivided into two sets: records
obtained without additional inertial loading, and records obtained with
increased inertial loading. We apply a novel Fourier-based population
analysis (Amjad et al. 1997 Preliminary accounts of the present results are published in
Conway et al. (1994) Subjects and recording procedures
Simultaneous recordings of third finger tremor, pairs of
motor-unit spike trains, and a surface electromyogram (EMG) from EDC
were made from 13 healthy adult subjects (12 male, 1 female, age range
22-60 yr), with informed consent from each subject and local ethical
committee approval (West ethical committee, Greater Glasgow Health
Board). The tremor signal was derived from an accelerometer fixed to
the distal phalanx of the unsupported middle finger, while the subject
was seated with the forearm pronated and the other fingers, wrist, and
forearm supported and immobilized by a custom-designed rigid
polypropylene cast. Two single motor units were recorded from a pair of
concentric needle electrodes (Medelec DFC25) inserted into the middle
finger portion of the EDC muscle. The bipolar surface EMG signal was
obtained from a pair of Ag/AgCl electrodes placed ~20 mm apart on
either side of the needles.
The accelerometer output was amplified and fed to a data collection
interface for digitizing. The bandwidth of this signal is determined by
the characteristics of the accelerometer (Entran EGAX-5), which has a
flat frequency response from DC to above 200 Hz. The surface EMG signal
was filtered (3-500 Hz), and amplified (×1,000) before digitizing.
The needle electrode signals were amplified and band-pass filtered
before being passed through window discrimination devices; the
transistor-transistor logic (TTL) pulses output from these were fed to
the digital input of the data collection device.
Acceleration and surface EMG signals were sampled at 1-ms intervals,
and motor-unit spike times were recorded with a 1-ms sampling interval.
Experimental protocol
During data collection the subject was asked to extend and
maintain the middle finger in a horizontal position. The positions of
the two needle electrodes were adjusted to obtain stable recordings from separate repetitively firing motor units. Single motor units were
identified by their spike shape, which was continually monitored on a
digital storage oscilloscope. Once stable pairs of motor units were
obtained, successive records were collected, initially with the finger
unloaded, then subsequently with small weights attached to the distal
end of the finger. The weights were added in 5-g increments, ranging
from 5 to 40 g. The addition of weights was continued until stable
recordings were no longer possible from the identified motor-unit pair,
either through contamination from newly recruited units or the
cessation of repetitive firing.
We have used small incremental changes in limb inertia; Randall
and Stiles (1964) Analytic methods
As a consequence of different conduction delays in nerve and
muscle, the peak in the time domain correlation between motor-unit pairs was not always centered at zero lag. The range of peak latencies for individual records is taken to reflect these conduction delays, and
delays due to interelectrode spacing (Bremner et al.
1991 The framework and notation for the analysis of individual records is
that set out in Halliday et al. (1995b) In assessing the average strength of the four pairwise interactions
within each population of recordings, we use the novel population
analysis technique of pooled coherence (Amjad et al. 1997 Coherence and pooled coherence functions provide normative measures of
linear association on a scale from 0 to 1 (Amjad et al.
1997 In this study we also use estimates of first-order partial coherence
functions to assess whether a third process (or predictor) accounts for
the relationship between two other processes (Halliday et al.
1995b Summary of the data sets
The present results are based on a total of 190 records obtained
from 101 motor-unit pairs during 21 experiments performed on 13 subjects. The recordings were made from the earliest recruited, fatigue-resistant motor units displaying repetitive firing. For the
present analysis these records were split into two data sets. The first
data set, data set one, consists of 106 records obtained with the middle finger unloaded. The second, data set two,
consists of the remaining 84 records obtained with the finger loaded.
The average duration for the 190 records was 89 s (range, 20-180 s).
The mean discharge rate for the 212 motor units in data set
one was 11.9 spikes/s (range, 7.5-18.8 spikes/s), the mean
coefficient of variation (c.o.v.) was 0.31 (range, 0.11-0.75). The
corresponding 106 acceleration records had an average RMS value of 6.1 cm/s2 (range, 1.1-37.0
cm/s2).
The mean discharge rate for the 168 motor units in data set
two was 12.4 spikes/s (range, 6.3-18.2 spikes/s), the mean c.o.v. was 0.32 (range, 0.12-0.85). The corresponding 84 acceleration records
had an average RMS value of 8.1 cm/s2 (range,
1.9-34.1 cm/s2). The average load was 11.6 g (range, 5-40 g). The number of records at each load was 5 g, 30 records; 10 g, 26 records; 15 g, 13 records; 20 g, 8 records; 25 g, 3 records; 30 g,2 records; 35 g, 1 record; and 40 g, 1 record.
Figure 1 illustrates a 3-s segment of
data from one subject, along with estimates of the autospectra of the
four signals, for one record of duration 100 s from this subject,
with the finger unloaded. Log plots of the point process spectral
estimates for the two motor units,
ABSTRACT
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
; Elble and Koller
1990
; Lakie et al. 1986
; Stiles
1980
; Stiles and Randall 1967
). It contains different components that can be characterized by the presence of
distinct frequency components in the estimated spectrum of a tremor
signal (Halliday and Redfearn 1956
; Stiles and
Randall 1967
), which have been categorized into two types. One
type is due to the natural resonant frequency of the limb segment and is sensitive to inertial loading, with increased loading decreasing the
resonant frequency (Randall and Stiles 1964
;
Robson 1959
; Stiles and Randall 1967
). In
the case of hand extensor muscles, this mechanical resonance component
has been termed the mechanical reflex component of tremor
(Stiles 1980
). The frequency range for this component of
postural finger tremor measured about the metacarpophalangeal joint is
in the range of 15-30 Hz for the unloaded finger (Stiles and
Randall 1967
). For tremor about the wrist and elbow joints the
ranges of frequencies reported for this component of tremor are 8-12
Hz (Elble and Randall 1978
; Lakie et al.
1986
) and 3-5 Hz (Fox and Randall 1970
),
respectively. The second type of frequency component in normal
physiological tremor is load independent, i.e., the frequency is
unchanged by inertial loading or alterations in limb stiffness. Such
load-independent tremor components have been reported in the frequency
range of 8-12 Hz for postural tremor from the unrestrained finger
(Halliday and Redfearn 1956
; Stiles and Randall
1967
), and during isometric contractions (Elble and
Randall 1976
). A smaller load-independent component at 30-40
Hz in unrestrained finger tremor recordings has also been reported
(Amjad et al. 1994
). Load-independent features of tremor
are often considered to have a central neuronal origin (Elble
and Koller 1990
).
), generating the load-dependent component of tremor. Any other mechanical perturbation to the limb, either external or internal
(i.e., arterial pressure pulse), will contribute to the magnitude of
this load-dependent component of tremor.
concluded that a 6- to 12-Hz peak they observed in tremor spectra
resulted from the unfused twitches of late recruited motor units. This
work supports the earlier view of Marshall and Walsh
(1956)
on tremor that normal physiological tremor reflects only
motor-unit firing rates and the biomechanical properties of the
musculoskeletal system (Hömberg et al. 1986
). In
contrast, other studies examining the relationship between motor-unit
activity and tremor have proposed alternative explanations for the
load-independent frequency components of tremor. Elble and
Randall (1976)
examined the correlation between motor-unit
firing and finger tremor in force records, and between surface
electromyographic (EMG) activity and force tremor. They observed a
preferential amplitude modulation of extensor digitorum communis (EDC)
surface EMG in the frequency range 8-12 Hz, and correlation between
surface EMG and tremor, single motor-unit activity and tremor, and
motor-unit pairs in the same frequency range. They attributed this to a
central modulation of motor-unit firing in the frequency range 8-12
Hz, which was independent of muscle force and motor-unit firing rate.
In low force contractions, Conway et al. (1995a)
observed load-independent components in the correlation between single
motor-unit discharges in EDC and postural finger tremor in acceleration
records in the frequency bands 1-12 Hz and 15-30 Hz, which they
attributed to frequency components of motor-unit synchronization.
McAuley et al. (1997)
observed load-independent
correlations between first dorsal interosseous (1DI) EMG and tremor
acceleration during index finger abduction against elastic loads at
contraction strengths up to 50% maximum voluntary contraction (MVC) in
three distinct frequency ranges centred at 10, 20, and 40 Hz, which
they attributed to reflect rhythmic activity generated in central
neural oscillators.
; Buchtal and Madsen 1950
; Datta and
Stephens 1990
; Dietz et al. 1976
; Farmer
et al. 1997
; Milner-Brown et al. 1975
). Examination in the frequency domain of motor-unit synchronization in
humans has revealed distinct rhythmic components, observed as
correlations over the frequency ranges 1-12 Hz and 15-30 Hz between
the spike timings in motor-unit pairs recorded from 1DI, and between
1DI and second dorsal interossi (2DI) during isometric contractions
(Farmer et al. 1993
) and in pairs of motor units recorded from within EDC (Conway et al. 1995a
) during
postural contractions. Three previous studies, employing time domain
measures, have investigated the relationship between the level of
motor-unit synchronization and normal physiological tremor. Two studies
in first dorsal interosseous (1DI) found no significant relationship between motor-unit synchronization and the peak-to-peak magnitude (Dietz et al. 1976
) or root-mean-square (RMS) magnitude
(Semmler and Nordstrom 1995
, 1998
) of
tremor in force records. Similarly, Logigian et al.
(1988)
concluded that there was no relationship between
motor-unit synchronization in the wrist extensor muscles and the RMS
value of normal physiological wrist tremor. These studies have led to
the consensus view that, under normal conditions, motor-unit
synchronization does not contribute to physiological tremor
(Allum et al. 1978
; Dietz et al. 1976
;
Freund 1983
; Logigian et al. 1988
;
Semmler and Nordstrom 1995
, 1998
). In
contrast, the recent observations on the rhythmic nature of processes
generating motor-unit synchronization during weak contractions
(Farmer et al. 1993
) and the correspondence between the
frequency bands associated with motor-unit synchronization and the 1- to 12-Hz and 15- to 30-Hz load-independent components of physiological
tremor (Conway et al. 1995a
) suggest that the role of
motor-unit synchronization in physiological tremor should be reassessed.
).
) to characterize the
correlation structure across all records in each data set. The results,
which are similar to those obtained by analysis of sequential records
from an individual subject (Conway et al. 1995a
), demonstrate that normal physiological tremor is a complex signal containing information relating to motor-unit synchronization in two
distinct frequency bands.
and Conway et al.
(1995a)
.
METHODS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
demonstrated that 5-g increments were
sufficient to alter the frequency of the 15-30 Hz load-dependent
component of physiological tremor. Stiles and Randall
(1967)
demonstrated systematic changes in the tremor
characteristics with incremental loading using 5-g increments. In
addition using small incremental loading allows recordings to be made
from the earliest recruited fatigue-resistant motor units.
; Kakuda et al. 1992
). Before frequency
domain analysis, we corrected for these delays using a temporal
alignment procedure, in which the timings of one spike train of each
pair were adjusted by a constant time offset, which was chosen such
that the resultant peak in the time domain correlation between each
pair of spike trains was centered at zero lag. The offset was an
integer multiple of the sampling interval, and varied between 0 and 9 ms. In their study of motor-unit pairs recorded from within intrinsic
(1st, 2nd, and 4th dorsal interossei) and extrinsic (extensor pollicis brevis, extensor digitorum, and flexor digitorum) hand muscles, Bremner et al. (1991)
obtained a range of 0-8 ms for
the peak offset.
. The
acceleration signal, denoted by x, and full-wave rectified
EMG, denoted by y, are assumed to be realizations of
stationary zero mean time series. The two motor-unit spike trains,
motor unit 0 and motor unit 1, are assumed to be
realizations of stationary stochastic point processes, and are denoted
by N0 and
N1, respectively (Conway et al.
1993
). All four processes are assumed to satisfy a mixing condition, whereby sample values widely separated in time are independent (Brillinger 1981
). Autospectra of these four
signals are denoted by fxx(
),
fyy(
), f00(
), and
f11(
), respectively, an estimate is
identified by the inclusion of the ^ symbol, e.g.,
xx(
). In the frequency domain, the
correlation between two processes is assessed through the use of
coherence functions (Brillinger 1981
; Halliday et
al. 1995b
; Rosenberg et al. 1989
). The estimated coherence function between the two motor units is denoted by
|
10(
)|2.
Other pairwise interactions we consider are between motor units and
tremor,
|
x0(
)|2
and
|
x1(
)|2,
and between rectified EMG and tremor,
|
xy(
)|2
(Halliday et al. 1995b
). Motor-unit synchronization has
traditionally been studied in the time domain (e.g., Bremner et
al. 1991
). In the present study, correlation between
motor unit 0 and motor unit 1 is assessed in the
time domain through the use of a second-order cumulant density
function, the estimate of which is denoted by
10(u). Cumulant densities
provide a general measure of statistical dependence between random
processes and will assume the value zero if the processes are
independent (Halliday et al. 1995b
). Complete details of
the analytic framework, including estimation techniques, procedures for
construction of confidence limits, and a detailed analysis of a single
record drawn from the present data are given in Halliday et al.
(1995b)
.
). This provides a single measure that summarizes the
correlation structure across all data sets in a population. The use of
pooled coherence overcomes the problems associated with the more usual examination and presentation of individual records, which may be
misleading, by characterizing features that are not representative of
the larger population (Fetz 1992
).
; Brillinger 1981
; Halliday et al.
1995b
; Rosenberg et al. 1989
). We can therefore
interpret the appropriate coherence and pooled coherence estimates as
providing an estimate of the contribution from motor-unit or surface
EMG activity to the tremor signal. This provides a measure of the
percentage of the tremor signal, at each frequency, which can be
accounted for by motor-unit or surface EMG signals, allowing us to
quantify frequency components of normal physiological tremor related to
rhythmic neural activity, both for individual records and complete populations.
; Rosenberg et al. 1989
,
1998
). In situations where three processes exhibit
significant pairwise coherence estimates over the same range of
frequencies, first-order partial coherence estimates can be used to
test the hypothesis that common frequency components are involved in
all of the pairwise correlations (Halliday et al. 1995b
;
Rosenberg et al. 1989
). For the present data we examine
the first-order partial coherence between the two motor units using the
tremor or the rectified EMG as the predictor. The partial coherence
between the two motor units using the tremor as predictor is denoted by
|R10/x(
)|2;
with EMG as predictor it is denoted by
|R10/y(
)|2.
If motor-unit synchronization contributes to physiological tremor, then
the tremor signal, x, will be able to predict the components of
|R10(
)|2.
In this case the estimated partial coherence,
|
10/x(
)|2,
will show a clear reduction in magnitude when compared with the
ordinary coherence estimate between the motor units,
|
10(
)|2,
at the frequency components where motor-unit synchronization contributes to physiological tremor. Similar comments apply for interpreting the estimated partial coherence with EMG as predictor, |
10/y(
)|2.
RESULTS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
00(
) and
11(
), are shown in Fig. 1, B and C, along with the asymptotic value for a
random discharge with the same mean rates and upper and lower 95%
confidence limits (see Halliday et al. 1995b
). The
dominant feature in each estimate is a single spectral peak, at 12.6 Hz
for motor unit 0 and ~10 Hz for motor unit 1, these represent the mean rate of discharge of the motor units (12.6 spikes/s and 9.9 spikes/s) and illustrate that the periodic firing of
each motor unit is the dominant rhythmic component identifiable in each
spectral estimate. Motor unit 0 has a more regular discharge
(c.o.v. 0.18) than motor unit 1 (c.o.v. 0.27), resulting in
a more clearly defined spectral peak and a small harmonic component
around 25 Hz in Fig. 1B. Log plots of the time series
spectral estimates of the acceleration record,
xx(
), and the rectified EMG,
yy(
) are illustrated in Fig. 1,
D and E. The 95% confidence interval for these
estimates is indicated by the solid vertical line in the top
right of each graph, these lines provide a scale bar against which
to assess the significance of local fluctuations. The tremor spectrum
(Fig. 1D) contains three distinct frequency bands. The
dominant mechanical resonance component is centered around 20 Hz;
smaller neurogenic components around 12 and 30 Hz are visible
(Amjad et al. 1994
; Stiles and Randall
1967
). The rectified EMG spectrum (Fig. 1E) has two
dominant peaks around 12 and 20-30 Hz, which we interpret to reflect
increased power due to preferential grouping of motor-unit activity
(i.e., motor-unit synchronization) in these two frequency ranges
(Halliday et al. 1995b
).
View larger version (34K):
[in a new window]
Fig. 1.
Tremor, motor-unit, and electromyographic (EMG) data. A:
a 3-s segment of data from 1 subject, illustrating raw surface EMG
(top), the tremor acceleration signal
(middle) and the times of occurrence of motor-unit
spikes shown as vertical lines (bottom). Log plots of
estimated auto spectra of (B) motor unit
0, 00(
), and
C, motor unit 1,
11(
). Dashed horizontal lines
represent the asymptotic value of each estimate; solid horizontal lines
represent the upper and lower 95% confidence limits. Log plots of
estimated auto spectra of (D) tremor signal,
xx(
), and (E)
rectified surface EMG signal,
yy(
). Solid vertical lines at
the top right represent the 95% confidence interval for
each estimate.
Pooled coherence and cumulant estimates as population measures of motor-unit synchronization
Figure 2 shows the estimated
coherence
|10(
)|2,
and cumulant density,
10(u),
between three individual motor-unit pairs drawn from data set
one (unloaded). These examples, which all have the same record
duration of 100 s and are taken from different subjects, illustrate the
range of strengths of motor-unit synchronization and intersubject
variability present within this data set. The first example (Fig. 2,
A and B), estimated from the same data as
illustrated in Fig. 1, has the strongest coupling, with significant coherence in two distinct frequency bands, and an estimated cumulant with a clearly defined central peak. The other two examples illustrate progressively weaker correlation, seen as a reduced magnitude in
coherence estimates, and less distinct central peaks in cumulant estimates; a similar range and variation in the strength of motor-unit synchronization is present in data set two (not shown). We
can use pooled coherence analysis to summarize the correlation
structure across all 106 records in data set one and across
all 84 records in data set two. These pooled coherence
estimates are illustrated in Fig. 3,
A and B, respectively. This population analysis
across all available data results in the same correlation structure as observed for individual records, namely significant coherence in the
two frequency bands, 1-12 Hz and 15-30 Hz. This result is in
agreement with that of Farmer et al. (1993)
, who studied motor-unit synchronization in human 1DI and 2DI muscles during isometric contractions. In their study of motor-unit coupling Farmer et al. (1993)
used a histogram to represent the
proportion of motor-unit pairs with significant coherence in individual
estimates at each frequency. Similarly constructed histograms are shown in Fig. 3C for data set one, and Fig.
3D for data set two, showing the proportion of
coherence estimates with values above the 95% confidence limit (based
on the assumption of independence and represented by the horizontal
dashed line in Fig. 2, A, C, and E), at each
frequency. These histograms indicate the presence of two distinct
frequency bands corresponding to the peaks in the pooled coherence
estimates. Pooled coherence has the advantage of further providing a
measure of the strength of coupling at each frequency.
|
|
Figure 3, E and F, illustrates the corresponding
pooled cumulant density estimate and confidence limits for data
sets one and two, respectively; the binwidth is 1 ms.
These estimates have a well-defined time course, with a central peak of
around 14 ms in width. Histograms counting the proportion of individual
cumulant density estimates with values above the upper 95% confidence
limit (top solid line in Fig. 2, B, D, and F) in
each bin, are shown in Fig. 3G for data set one,
and Fig. 3H for data set two. These histograms
indicate that, after the temporal alignment process, over 95% of the
individual cumulant density estimates in each data set have a
significant peak at zero lag, indicating a high prevalence of
synchronization in both data sets. In their study of motor-unit
synchronization in EDC, dorsal and palmer interossei, and flexor
digitorum superficialis Bremner et al. (1991) observed significant peaks in 88% of records.
Pooled coherence estimates for motor unit to tremor correlations and surface EMG to tremor correlations
Pooled coherence estimates were constructed between each motor
unit and the tremor, and between the surface EMG and tremor for
data sets one and two, providing a measure of the
average contribution to the tremor from motor-unit and surface EMG
activity in each data set. In accordance with the assumption of
independent experiments (Amjad et al. 1997), the motor
unit to tremor interactions are considered separately (because the
tremor signal is common to each motor-unit pair). The choice of label
(0 or 1) for each motor unit was specified by the
channel on which it was recorded. Including the pooled motor-unit
coherence estimates illustrated in Fig. 3, four pooled coherence
estimates are used to characterize the interaction between motor units,
EMG, and tremor. All four estimates for data set one are
shown in Fig. 4. Histograms of the mean
firing rates of the motor-unit discharges are shown above the coherence
estimates in Fig. 4, A-C. The histogram in Fig. 4A is for all 212 motor-unit discharges. Those in Fig. 4,
B and C are for the 106 discharges designated as
motor unit 0 and motor unit 1, respectively. The
corresponding pooled coherence estimates and firing rate histograms for
data set two are shown in Fig. 5.
|
|
Several points emerge from this population analysis of motor-unit
pairs, surface EMG, and tremor. The first point is the similarity between the frequency bands present in these pooled coherence estimates, taken across subjects, and those present in single records
from individual subjects (see also Amjad et al. 1997, Figs. 2, 5, 7, and 8; Conway et al. 1995a
, Fig. 1;
Halliday et al. 1995b
, Fig. 4). A characteristic feature
of the four pooled coherences for both data sets is the presence of the
same two distinct frequency bands, 1-12 Hz and 15-30 Hz, in each
estimate. The correlation between single motor units and tremor
therefore occurs at the same frequencies as the correlation between
motor-unit pairs (Conway et al. 1995a
). Within each data
set, the peak in the superimposed firing rate histograms lies largely
between these two frequency bands, thus these frequencies do not in
general correspond to the firing rate of the motor units (Conway
et al. 1995a
). For both data sets, the pooled coherence
estimate between surface EMG and tremor (Figs. 4D and
5D) is greater in magnitude than those for the motor
unit to tremor estimates (Fig. 4, B and C
and Fig. 5, B and C), which in turn are
greater than the motor-unit coherences (Figs. 4A and
5A). The rectified surface EMG emerges as a powerful
predictor of that motor-unit activity that is correlated with the
tremor signal (Figs. 4D and 5D). The
pooled coherence estimates between single motor-unit discharges and
tremor have a peak magnitude of between 0.04-0.1 (Figs. 4,
B and C, and 5, B and
C), which indicates that, on average, a single
motor-unit discharge can account for between 4 and 10% of the tremor
signal in the two frequency bands 1-12 Hz and 15-30 Hz. The rectified EMG, which samples the activity across a number of active motor units,
can account for 20% of the tremor signal in the two frequency bands
1-12 Hz and 15-30 Hz (Figs. 4D and 5D).
The presence of the same two frequency bands in the above pooled
coherence estimates for both loaded and unloaded data sets, despite the
increased inertial loading associated with data set two,
implies that these two frequency bands represent load-independent contributions to tremor. To establish that these frequency bands reflect load independent features of the data, it has to be established that the increased inertial loading in data set two
produced a significant alteration in the tremor characteristics as
predicted by the model for the relationship between added mass,
m, and the frequency, , of the dominant mechanical
resonance component of finger tremor:
1/
m
(Stiles and Randall 1967
). The average mass applied in
the 84 records was 11.6 g (range, 5-40 g). The mean RMS value of
the tremor increased from 6.1 cm/s2 in data set
one, to 8.1 cm/s2 in data set two.
The RMS values do not give any indication of the dominant frequency
components in the tremor records. The estimated pooled spectra for each
data set can be used to determine whether the frequency components in
the tremor across each population have been altered by the increased
inertial loading. Because the range of RMS values for individual tremor
records was 1.1-37 cm/s2, each tremor signal was scaled to
have the same RMS value across each data set, 6.1 or 8.1 cm/s2; otherwise the pooled tremor spectra would be
dominated by the records with the largest RMS tremor. Figure
6 illustrates estimates of these pooled
tremor spectra for each data set. The frequency of the dominant peak
decreases from 23 to 17 Hz with inertial loading, which is comparable
with that reported for individual subjects (Stiles and Randall
1967
, Figs. 3 and 4). This alteration in the mechanical
resonance component of tremor in the unsupported finger establishes the
load independence of the distinct peaks in the pooled coherences
illustrated in Figs. 4 and 5. The pooled EMG spectra for each data set
(calculated by same approach of standardizing the variance across each
data set) are illustrated in Fig. 6B. These have the
same form as that for a single subject (Fig. 1E), with
power concentrated around 12 and 25 Hz, which we interpret to reflect
frequency components due to motor-unit synchronization across the two
populations of data.
|
Load-independent contributions from motor-unit synchronization to physiological tremor; partial coherence analysis using tremor, and surface EMG as predictors of motor-unit synchronization
The above observations that motor-unit to motor-unit coherences and motor-unit to tremor coherences occur in the same frequency bands suggests that the processes responsible for motor-unit synchronization are transmitted to the muscle as a tension or length change and consequently may be observed as a component of the tremor signal. This can be directly tested through the application of partial coherences, in which the tremor signal is used as a predictor of the motor-unit correlation. If the tremor signal contains frequency components due to motor-unit synchronization, then the partial coherence between motor-unit pairs, with the tremor signal as predictor, will not be significant over the range of frequencies associated with the two components of motor-unit synchronization.
The EMG to tremor coherences, which also involve the same two load-independent frequency bands, suggest that surface EMG can be used as a predictor of motor-unit correlation, in which case the pooled partial coherence between the motor-unit pairs using the surface EMG as predictor will not be significant over the two frequency bands associated with motor-unit correlation.
For data set one, the estimated pooled partial coherence
with the tremor as predictor,
|10/x(
)|2,
is shown in Fig. 7A. The
estimate with rectified EMG as predictor, |
10/y(
)|2,
is shown in Fig. 7B. The corresponding estimates for
data set two are shown in Fig. 7, C and
D, respectively. In each graph the original estimate of
pooled motor-unit coherence,
|
10(
)|2,
is shown as a dotted line, this allows a direct comparison of the
pooled partial coherence with the pooled ordinary coherence.
|
Considering first the 15- to 30-Hz frequency band with the tremor as
predictor, the partial coherence estimates (Fig. 7, A and
C, solid lines) exhibit no consistent significant features in this frequency range. Therefore all frequency components in the 15- to 30-Hz frequency range present in the motor-unit synchronization are
transmitted to the tremor signal, allowing it to accurately predict the
motor-unit synchronization in this frequency range. We can therefore
conclude that there is a significant contribution to physiological
tremor resulting from motor-unit synchronization in this frequency
range. Although a similar conclusion can be drawn for the 1- to 12-Hz
component, the effect is more complex. The pooled partial coherence
estimates () exhibit only a part reduction in magnitude of the
significant features present in the original pooled coherence estimates
(· · ·). A partial reduction can occur for two reasons:
1) the 1- to 12-Hz component of motor-unit synchronization
does not contribute as strongly to physiological tremor as the 15- to
30-Hz component, or 2) the contribution may involve linear
and nonlinear interactions, possibly reflecting different mechanisms.
Partial coherence functions are based on the assumption of linearity
and cannot detect a nonlinear interaction (Halliday et al.
1995b
; Rosenberg et al. 1989
,
1998
). On the basis of the present analysis, we cannot
distinguish between either of these mechanisms. The partial reduction
in the range 1-12 Hz does, however, indicate that there is a component
of physiological tremor in the range 1-12 Hz, which can be attributed
to this lower frequency component of motor-unit synchronization. With
the exception of a small residual component in the pooled partial
coherence around 22-28 Hz in data set one (Fig.
7A,
), comparison of this analysis for data set
one and data set two reveals identical results. Therefore contributions over a similar frequency range are made to
physiological tremor in both the unloaded (data set 1) and loaded (data set 2) populations. This reduction in the
partial coherence estimates compared with the ordinary coherence
estimates in Fig. 7, A and C, indicates that
common frequency components are involved in the population correlations
and establishes the rhythmic components of motor-unit synchronization
as the source of the 1- to 12-Hz and 15- to 30-Hz contributions from
motor-unit activity to the tremor.
The pooled partial coherence estimates using the rectified surface EMG as predictor (Fig. 7, B and D) are similar to those using the tremor signal (Fig. 7, A and C). Therefore the rectified surface EMG is as efficient a predictor of motor-unit synchronization as the tremor signal.
![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Relationship between physiological tremor and motor-unit activity
This study considers the relationship between physiological tremor
and motor-unit activity in a number of recordings taken across subjects
performing the same postural task under two conditions (unloaded and
loaded). The pooled spectral estimates (Fig. 6) indicate the relative
power (variance) of the tremor at each Fourier frequency and provide a
concise description of the characteristics of the tremor for all
records in each condition. These pooled spectral estimates exhibit the
same load dependency as those obtained from individual subjects
(Stiles and Randall 1967). In both conditions the
dominant spectral component, which can be taken as an indication of the
dominant tremor rhythm, is the load-dependent mechanical resonance
component. This has an average frequency of 23 Hz for the unloaded data
set and 17 Hz for the loaded data set. These frequencies overlap with
the 15- to 30-Hz band of motor-unit synchronization, therefore the
load-independent contribution to physiological tremor from rhythmic
motor-unit activity in this frequency range cannot be identified from
examination of tremor spectra. The pooled coherence estimates (Figs. 4,
B and C, and 5, B and C),
however, provide an estimate of the contribution to the tremor from
motor-unit activity, at each frequency. These indicate that a single
motor-unit discharge can account for between 4 and 10% of the tremor
signal in the two frequency bands 1-12 Hz and 15-30 Hz. In some
subjects coherence estimates between single motor units and tremor for single records had values up to 0.4, indicating that, in some cases, an
individual motor unit (and all those correlated with it) can predict up
to 40% of the tremor signal at these frequencies. Examples of
coherence estimates between single motor units and tremor for single
records drawn from the present data sets are shown in Conway et
al. (1995a)
, Halliday et al. (1995b)
, and
Amjad et al. (1997)
. A natural question to ask of this
analysis is "are the effects uniform across subjects?" In other
words, is pooled coherence an appropriate tool to apply to the present
data? As part of the analysis, we estimated the pooled motor unit to
motor-unit coherence for each subject (by combining all records from
each subject) and plotting histograms of the peak coherence in the 1- to 12-Hz and 15- to 30-Hz frequency bands for each subject. These
histograms were well fit by a normal distribution (assessed by
constructing normal probability plots and summary statistics for each
set of values) indicating a range of values distributed around a
central value. Therefore for the present data, intersubject variability
leads to a range of strengths of motor-unit synchronization, which
appears normally distributed. Under these conditions, pooled coherence
is an appropriate tool to estimate the average strength of correlation
at each frequency across subjects. In addition, the presence of the
same two load-independent frequency bands can be inferred from
coherence estimates between pairs of motor units, and between single
motor units and tremor from single subjects (Conway et al.
1995a
), as in the present analysis across subjects.
The pooled coherence estimates between the rectified surface EMG and
tremor (Figs. 4D and 5D) have a larger magnitude
and indicate that on average, the rectified surface EMG can account for
20% of the tremor signal in the two frequency bands 1-12 Hz and
15-30 Hz. This increase in magnitude over the pooled coherence for a
single motor-unit discharge may be due to the surface EMG containing
information representing the discharge of many motor units. Thus the
surface EMG that samples a pool of the active motor units in EDC is
able to account for more of the tremor signal in the two frequency
bands at which the motor-unit discharges are correlated. In some
subjects coherence estimates between rectified EMG and tremor for
single records had values up to 0.7, indicating that the surface EMG
can account for 70% of the tremor signal in these frequency bands.
These examples were generally obtained from subjects who had stronger
motor-unit synchronization. An example of coherence between rectified
surface EMG and tremor is given in Halliday et al.
(1995b).
Using a similar approach based on coherence estimates, Padsha
and Stein (1973) estimated that surface EMG activity in finger extensor muscles could account for ~11% of the tremor activity at
frequencies up to 10 Hz. Our pooled coherence estimates between surface
EMG and tremor consider the relationship between motor-unit activity
and tremor over a wider range of frequencies, and suggest that, on
average, ~20% of the tremor signal in the frequency bands 1-12 Hz
and 15-30 Hz can be accounted for by EMG activity in a single muscle.
Relationship between physiological tremor and motor-unit synchronization
We have demonstrated that correlation between motor-unit activity
and tremor occurs in the same two frequency bands as the correlation
between motor units (Figs. 4 and 5). Partial coherence analysis
demonstrates that motor-unit synchronization is the source of the
rhythmic contributions to normal physiological tremor in these two
frequency bands (Fig. 7). Previous studies on motor-unit synchronization and tremor used a time domain approach based on comparison of the magnitude of the central peak in the
cross-correlation histogram with either peak-to-peak tremor magnitude
(Dietz et al. 1976) or RMS tremor magnitude
(Logigian et al. 1988
; Semmler and Nordstrom
1995
, 1998
). It is therefore difficult to
reconcile these previous studies of the relationship between motor-unit synchronization and tremor with the present analysis, which is based on
a frequency domain approach. A frequency domain analysis may be more
suited to charcterize the relationship between the different rhythmic
components of physiological tremor and motor-unit synchronization. The
present study demonstrates, using a statistically rigorous protocol
(Amjad et al. 1997
; Halliday et al.
1995b
), that two distinct rhythmic components of normal
physiological tremor are related to motor-unit synchronization. We
therefore conclude that motor-unit synchronization contributes to
normal physiological tremor, and not just to cases of enhanced tremor as claimed in Freund (1983)
.
Elble and Randall (1976) did study the relationship
between motor-unit firing and tremor in the frequency domain. They
considered tremor in force records recorded from the extended middle
finger and used forces of 200-250 g, to maximize the 8- to 12-Hz
component of tremor, which resulted in patterns of motor-unit firing
containing transient sequences of double discharges consisting of
alternating short and long intervals (see Fig. 2, Elble and
Randall 1976
). Under these conditions, they obtained coherence
estimates between both surface EMG and tremor and single motor units
and tremor, which were restricted to the frequency range 8-12 Hz. The
double discharge patterns of motor-unit firing observed by Elble
and Randall (1976)
may be a consequence of the larger force
exerted by their subjects compared with the present study. Such
motor-unit activity was not observed in our data (Fig. 1). Fox
and Randall (1970)
observed a 10-Hz peak in the spectra of
forearm tremor and rectified, filtered biceps EMG, which was only
present with the addition of 10-lb load to the wrist, and was not
present with smaller loads. In recordings of forearm tremor and biceps
and brachioradialis EMGs, made under conditions of compliant loading, Matthews and Muir (1980)
observed a large coherence at
10 Hz between the tremor and rectified, filtered EMG only during
recordings made with large force levels. In a study of the frequency
components present in tremor recordings, surface EMG and muscle
vibration in subjects making contractions of up to 50% MVC against an
elastic load, McAuley et al. (1997)
observed peaks in
coherence estimates around 10, 20, and 40 Hz between the signals. It is
therefore possible that the pattern of motor-unit synchronization may
alter when large force levels are used. Although our pooled coherence estimates extend to frequencies above 40 Hz, there is no evidence of a
distinct frequency band around 40 Hz in the motor-unit synchronization, or in the correlation between motor-unit activity and tremor (see Figs.
4 and 5) (McAuley et al. 1997
, Fig. 4). Given these
differences in motor-unit and EMG firing patterns, it is difficult to
generalize from the above results to the present study, and vice versa.
Indeed Elble and Randall (1976)
point out that their
observed pattern of motor-unit firing cannot be assumed to be present
in other muscles. Nevertheless, there are similarities between the
results of Elble and Randall and the present study: both observed a
relationship between motor-unit activity and finger tremor in a
preferred frequency range, which is not related to motor-unit firing rate.
Physiological tremor and surface EMG as predictors of motor-unit synchronization
We have demonstrated that components in normal physiological
tremor are due to motor-unit synchronization in two frequency bands.
The corollary to this result is that normal physiological tremor can be
used as a predictor of motor-unit synchronization in the two frequency
bands 1-12 Hz and 15-30 Hz. On this basis a statistical spectral
analysis undertaken using a tremor acceleration signal can be used as a
predictor of motor-unit synchronization in these frequency bands within
the muscles that contribute to the tremor (Halliday et al.
1995b).
Rectified surface EMG, when processed according to the methods in
Halliday et al. (1995b), is a powerful predictor of the presence of distinct frequency bands in motor-unit synchronization (Fig. 7, B and D). Previous studies on estimating
motor-unit synchronization from a surface EMG signal have used time
domain averaging techniques (Milner-Brown et al. 1973
),
which compare spike-triggered averages of unrectified and rectified
EMG. This approach is based on a specific model describing the relative
contributions of synchronized and background motor-unit activity to raw
and rectified EMG averages (Milner-Brown et al. 1973
).
This method has limitations in its use; the model has two major sources
of error related to the nonlinear relation between rectified and
unrectified EMG, and the index is sensitive to the background level of
surface EMG (Yue et al. 1995
). In contrast, the complete
reduction over the 15- to 30-Hz frequency range in the estimated
partial coherence between the motor units with rectified surface EMG as
predictor (Fig. 7, B and D) demonstrates that
rectified surface EMG can be used as a statistical predictor of
motor-unit synchronization in a multivariate Fourier analysis
(Halliday et al. 1995b
), under experimental conditions similar to those in the present study.
Figure 7 illustrates that tremor and surface EMG signals are equally good predictors of motor-unit synchronization. Physiological tremor and surface EMG recordings are therefore rich signals that contain information related to rhythmic components associated with motor-unit synchronization, within single muscles (EMG) and agonist muscle groups (tremor).
Relationship between unrestrained limb tremor and isometric force tremor
The present study considers tremor during postural contractions
involving position holding of the unrestrained finger against gravity.
Many previous studies have considered tremor in force recordings during
isometric contractions. Halliday and Redfearn (1956)
pointed out that the characteristics of tremor will depend on how it is
measured. The dominant mechanical resonance component in acceleration
recordings of postural tremor (Stiles and Randall 1967
)
is damped out in isometric contractions against a force transducer
(Allum et al. 1978
; Elble and Randall
1976
; Hömberg et al. 1986
). The mechanical
resonance component reflects activation of a resonant system by
motor-unit activity (Stiles and Randall 1967
), or other
external perturbations to the limb segment.
The presence of an 8- to 12-Hz load-independent component of
physiological tremor has been reported in postural tremor of the
unrestrained finger (Halliday and Redfearn 1956;
Stiles and Randall 1967
) and in force recordings during
isometric contractions (Allum et al. 1978
; Elble
and Randall 1976
). Brown et al. (1982)
observed
an 8- to 11-Hz rhythmicity in force recordings obtained under isometric
conditions and conditions of low inertial compliant loading during
thumb flexion. The 1- to 12-Hz and 15- to 30-Hz components of
motor-unit synchronization that we have described for the present data
are also present in motor-unit pairs recorded under isometric force
conditions (Farmer et al. 1993
). Although different
types of contraction (postural or isometric) have a strong effect on
the characteristics of the resultant tremor signal, the 1- to 12-Hz and
15- to 30-Hz rhythmic components of motor-unit synchronization are
present in both types of contraction at low force levels.
Allum et al. (1978) and Hömberg et al.
(1986)
have suggested that the load-independent component
observed in isometric force tremor spectra relates to the firing rate
of unfused motor units. This suggestion is based only on examination of
tremor spectra. The present study considers the correlation between a
large population of single motor-unit discharges and shows no
dependence on firing rate (Figs. 4, B and C, and
5, B and C). Examination of tremor spectra alone
will fail to reveal contributions from the 15- to 30-Hz rhythmic
component of motor-unit synchronization. Hömberg et al.
(1986)
suggest that tremor is a useful predictor of motor-unit activity and reflects firing rates of active motor units in isometric force tremor. The present data alternatively suggest that unrestrained postural tremor reflects rhythmic processes related to motor-unit synchronization within active pools of motor units, and not
asynchronous activity relating to motor-unit firing rates.
Neural structures relating to normal postural physiological tremor
The presence of an 8- to 12-Hz load-independent component in
physiological tremor that is correlated with motor-unit activity has
been well documented (Elble and Randall 1976;
Halliday and Redfern 1956
; Stiles and Randall
1967
). In the present study, we have observed a
load-independent component of motor-unit synchronization that includes
this frequency range and extends down to lower frequencies. However,
the lowest frequency components in this 1- to 12-Hz range are not as
strong in the pooled motor unit to tremor or pooled surface EMG to
tremor coherence estimates for both the unloaded (Fig. 4,
B-D) or loaded (Fig. 5, B-D) data sets, when
compared with other frequencies in this range. These lowest frequency
components in the motor-unit synchronization could result from common
slow rate modulation of motor-unit firing rates during the postural task. This slow trend in the mean rate will appear as an increase in
the lowest frequencies in coherence estimate between the two motor
units (e.g., see Fig. 2, A and C). Such trends in
point process interval values cannot be easily removed (unlike trends in amplitude values in time series data). Therefore the 1- to 12-Hz
component of motor-unit synchronization may contain contributions from
two different sources, one of which reflects common rate modulation of
motor-unit pairs during the maintained postural tasks and occurs at the
lowest frequency components (DeLuca et al. 1982
;
Farmer et al. 1993
). The higher frequency components in
this 1- to 12-Hz range overlaps with the previously reported 8- to
12-Hz load-independent component of physiological tremor. It is in this
frequency range that the pooled partial coherence estimates exhibit the
largest reduction in magnitude using the tremor as predictor,
indicating a clear contribution from motor-unit synchronization to the tremor.
Recent work on the origin of this 8- to 12-Hz rhythmic load-independent
component of physiological tremor has suggested the presence of an 8- to 12-Hz oscillator. A number of theories have been proposed for the
location of such an oscillator. Elble and Randall (1976)
proposed Renshaw inhibition. Llinás and co-workers have suggested
inhibition-rebound excitation oscillations in the inferior olive as the
source of these oscillations (Llinás 1984
). Other
authors have suggested the thalamus or cerebellum as structures involved in generating the 8- to 12-Hz component (for a review see
Elble and Koller 1990
). The present results demonstrate
a load-independent contribution to postural finger tremor from
motor-unit synchronization in this frequency range and provide strong
evidence in support of an 8- to 12-Hz central generator. However, the
present experiments cannot provide any evidence in favor or against any of the above origins of such an 8- to 12-Hz oscillator.
The involvement of the stretch reflex in physiological tremor remains
unclear. Early studies suggested that oscillations in the stretch
reflex were the source of the 8- to 12-Hz load-independent component of
finger tremor (Halliday and Redfearn 1956;
Lippold 1970
). The evidence against this has been well
documented (see, e.g., Elble and Koller 1990
;
Elble and Randall 1976
; Freund 1983
). In
particular, the frequency of the oscillations is independent of the
conduction time around the reflex loop; 8- to 12-Hz load-independent oscillations have been observed in tremor and EMG recordings from finger (Elble and Randall 1976
; Halliday and
Redfearn 1956
), elbow (Fox and Randall 1970
),
and foot (Mori 1973
) muscles. Several authors have,
however, suggested that oscillations in the reflex loop are a mechanism
underlying enhanced or activated larger amplitude physiological and
pathological tremor (Freund 1983
; Logigian et al.
1988
). Hagbarth and Young (1979)
demonstrated
that human muscle spindle primary endings have sufficient sensitivity
to respond to normal physiological tremor. The present results
demonstrate that 1- to 12-Hz and 15- to 30-Hz load-independent
components in motor-unit synchronization can be detected in normal
postural finger tremor, with the likely result of afferent discharges
being modulated by these components. In a study of the relationship between torque and Ia afferent in humans during weak isometric contractions, Halliday et al. (1995a)
observed a
preferential transmission of frequencies, which was not related to
motor-unit firing rates, with the torque signal modulating the afferent
discharge in the frequency range 5-11 Hz. However, Wessberg and
Vallbo (1996)
examined the timing and strength of reflex
effects during voluntary finger movements and concluded that the
stretch reflex cannot account for the 8- to 10-Hz modulation of EMG
activity observed during finger movements (Vallbo and Wessberg
1993
).
The present study provides the first direct demonstration of the
involvement of a 15- to 30-Hz component of motor-unit synchronization in normal physiological tremor, which can be predicited through the use
of a surface EMG signal (Fig. 7, B and D).
Conway et al. (1995b) demonstrated that cortical
activity recorded from the hand area of the sensorimotor cortex was
correlated with surface EMG from 1DI during a maintained contraction.
These correlations occurred mostly in the frequency range 15-30 Hz,
and they concluded that this represented a contribution to motor-unit
synchronization related to the performance of postural tasks and
suggested that these frequencies also contribute to physiological
tremor. Similar correlations have been reported between cortical
activity and biceps EMG and cortical activity and foot flexor EMG
(Salenius et al. 1997
) and between cortical activity and
wrist extensor and flexor muscles during postural tasks
(Halliday et al. 1998
). Therefore the 15- to 30-Hz
rhythmic component of motor-unit synchronization that contributes to
physiological tremor is related to rhythmic cortical activity.
Definition of normal physiological tremor
We conclude by proposing a revised definition of physiological
tremor, recorded as an acceleration signal, during low force contractions involving extension of the unrestrained finger against gravity. The traditional view of such a signal is that it contains two
rhythmic components: a mechanical resonance component (around 15-30
Hz) and a load-independent rhythmic component around 8-12 Hz
(Elble and Randall 1976; Halliday and Redfearn
1956
; Stiles and Randall 1967
). These two
rhythmic components can be identified by examination of the estimated
tremor spectrum. In the present study we have used spectral correlation
methods to identify rhythmic components in motor-unit activity that
also contribute to physiological tremor, which cannot be identified by
inspection of the tremor spectrum. Using this approach, the present
data have identified the presence of a third component in tremor during
postural contraction of finger muscles against gravity: a 15- to 30-Hz
load-independent component, which is masked by the mechanical resonance
component of the tremor, but which can be identified from correlation
between motor-unit activity and tremor. Both the load-independent
components at 8-12 Hz and 15-30 Hz reflect rhythmic components of
motor-unit synchronization and are not related to motor-unit firing
rates. Importantly, the latter is expressed at the level of the motor cortex (Conway et al. 1995b
; Halliday et al.
1998
; Salenius et al. 1997
) and may reflect a
component of the motor command associated with the performance of
postural tasks.
![]() |
ACKNOWLEDGMENTS |
---|
This research was supported by The Wellcome Trust (036928, 048128).
![]() |
FOOTNOTES |
---|
Address for reprint requests: D. M. Halliday, West Medical Building, University of Glasgow, Glasgow G12 8QQ, UK.
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 20 October 1998; accepted in final form 29 April 1999.
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|