Brain, Vol. 122, No. 3, 461-472,
March 1999
© 1999 Oxford University Press
Article |
Coherent cortical and muscle discharge in cortical myoclonus
1 National Hospital for Neurology and Neurosurgery, 2 MRC Human Movement and Balance Unit, Institute of Neurology, London and 3 Division of Neuroscience and Biomedical Systems, University of Glasgow, UK
Correspondence to:
Dr P. Brown, MRC Human Movement and Balance Unit, Institute of Neurology, Queen Square, London WC1N 3BG, UK
 |
Abstract |
|---|
 Top Abstract IntroductionPatients and methodsResultsDiscussionReferences |
|---|
There is increasing evidence in man that the cortical drive to motor neurons is rhythmic. This oscillatory drive may be exaggerated in patients with cortical myoclonus. Spectral analysis of surface bipolar EEG and EMG activity was performed in eight such patients. Only three cases had evidence of giant cortical evoked potentials or a cortical correlate on back-averaging at the time of study. In six subjects, significant coherence between contralateral and vertex EEG and EMG was observed in ranges similar to that previously reported for normal subjects (15–30 and 30–60 Hz). Three out of these six subjects also had significant coherence at higher frequencies (up to 175 Hz). All eight patients had a correlate in the cumulant density estimate between EEG and contralateral EMG. EMG lagged EEG by about 14, 25 and 35 ms for the muscles of the forearm, hand and foot, respectively. These delays were estimated from the slope of the phase curves and the timing of the peaks in the cumulant density estimates, and are appropriate for conduction in fast pyramidal pathways. The results provide clear evidence of a cortical drive synchronizing muscle discharge over a broad range of frequencies in patients with cortical myoclonus. Fourier analysis is a promising technique in the diagnosis and investigation of such patients.
myoclonus; coherence; electroencephalogaphy
1DI = first dorsal interrosseus muscles; MEG = magnetoencephalographic activity
|
Introduction |
|---|
|
TopAbstractIntroductionPatients and methodsResultsDiscussionReferences |
|---|
Single motor units may synchronize their discharges over a short time-scale during isometric contractions in man (Datta and Stephens, 1990
; Farmer et al., 1993a, b). There is increasing evidence that short-term synchronization is associated with rhythmicities in the cortical drive to muscle (Farmer et al., 1993a; Conway et al., 1995; Salenius et al., 1997; Brown et al., 1998). Patients with cortical myoclonus have involuntary muscle jerks due to the synchronous discharge of many motor units. The timing relationships between motor units in cortical myoclonus may reflect abnormal high-frequency oscillations in the EEG of these subjects (Brown and Marsden, 1996). The study of patients with cortical myoclonus may therefore provide a rare insight into the rhythmic influence that the sensorimotor cortex has on human muscle discharge, particularly with regard to the range of frequencies involved and the phase relationship between cortex and muscle. In the present study we describe for the first time the application of frequency domain analysis to the study of cortical myoclonus. We demonstrate that the cortical drive to muscle has a complex rhythmic organization over a frequency band that may extend beyond 100 Hz. At the same time, we show that this method of analysis has major advantages over the established time domain technique of back-averaging.
|
Patients and methods |
|---|
|
TopAbstractIntroductionPatients and methodsResultsDiscussionReferences |
|---|
Eight patients with suspected cortical myoclonus were studied as part of their routine clinical investigation. The clinical details and basic electrophysiological findings are summarized in Table 1
. All the patients had multifocal action myoclonus most prominent in the distal limb, with a mean duration of myoclonic EMG bursts of <50 ms. Patients with clinical evidence of corticobasal degeneration, multiple system atrophy or the jerking stiff limb syndrome were excluded. Cases 2, 5, 6, 7 and 8 had the syndrome of progressive myoclonic ataxia, defined as myoclonus and progressive cerebellar ataxia, with frequent seizures and little or no cognitive dysfunction.
|
Surface EMG and EEG were recorded with 9-mm diameter silver–silver chloride electrodes. A bipolar montage (C3–F3, Cz–Fz and C4–F4) was used. EMG and EEG were bandpass-filtered at 16–300 and 5.3–300 Hz, respectively. High- and low-pass filters were first- and second-order, respectively. Signals were amplified and digitized with 12-bit resolution by a CED 1401 analogue-to-digital converter. Recordings were sampled at 625 Hz. Signals were displayed and stored on a PC microcomputer by a software package (CED Spike 2). EMG and EEG activity were also recorded with an FM tape recorder.
Data analysis
The EEG, denoted by x, and unrectified EMG, denoted by y, were assumed to be realizations of stationary zero-mean time series. The principal statistical tool used for data analysis in this study was the discrete Fourier transform and parameters derived from it, all of which were estimated by dividing the records into a number of disjoint sections of equal duration (1024 data points), and estimating spectra by averaging across these discrete sections (Halliday et al., 1995). In the frequency domain, estimates of the autospectrum of the EEG, fxx(
), and EMG, fyy (), were constructed, along with estimates of coherence, |Rxy()|2. Timing information between the EEG and EMG signals was calculated from the phase spectrum fxy(), defined as the argument of the cross-spectrum: fxy() = arg[fxy()]. In the time domain, the cumulant density function, qxy(u), with EMG as reference, was estimated from the cross-spectrum, fxy(), via an inverse Fourier transform. Confidence intervals for all parameters were estimated (for further details see Halliday et al., 1995). It should be noted that we used unrectified EMG in this study, although the coherence spectra and phase slopes (but not intercepts) calculated from rectified EMG were similar. The difference in the intercepts relates to the biphasic nature of the unrectified EMG.
|
Results |
|---|
|
TopAbstractIntroductionPatients and methodsResultsDiscussionReferences |
|---|
Basic electrophysiology
Figure 1A
illustrates an example of the raw EEG and EMG recorded in Case 5. Brief myoclonic bursts are evident in the EMG from the extensor muscles of the left forearm. These are preceded by rather variable positive–negative wave complexes, maximal over the contralateral hemisphere. Only Cases 1 and 4 had giant cortical somatosensory evoked potentials, and only Case 4 had a C-reflex (Table 1
). Cases 1, 4 and 5 had time-locked EEG correlates preceding action jerks by a short interval (Table 1). An example of the cortical correlate obtained through back (jerk-locked) averaging is shown in Fig. 1B. The remaining patients had no such correlate on the date of study, although Cases 2, 3 and 6 had had such a feature when studied 2–8 years previously. Failure to record a cortical correlate was attributed partly to the high frequency and irregularity of the myoclonic jerks. Successful back-averaging requires the absence of EMG activity for a period of 
100 ms prior to the EMG burst used to trigger the collection of EEG (to be sure that EEG correlates precede rather than follow EMG events in the time domain).
|
Frequency domain analysis
Four principal measures were derived through application of the Fourier methods: autospectra, coherence spectra, phase and cumulant density functions. Representative examples of these measures are shown in Fig. 2
, which is derived from frequency analysis of the same signals as Fig. 1, and in Figs 3 and 4. The results in each subject are summarized in Table 2.
|
|
|
|
Cases 1, 3, 4 and 5 had regular action jerks at low frequency, apparent in raw EMG records and power spectra of EMG activity. Despite this, only Cases 3 and 4 had significant coherence between the contralateral EEG and EMG at the frequency of these jerks (Table 2
). Examples of the absence and presence of significant coherence between EEG and EMG at a frequency corresponding to the repetition rate of the myoclonic jerks are shown in Figs 2 and 3, respectively.
Significant coherence between EEG and EMG was seen in all subjects at frequencies >15 Hz (Table 2). In Cases 7 and 8 the coherence was confined to the frequency range 15–30 Hz. In Cases 1, 2 and 6 coherence was seen in the ranges 15–60, 30–60 and 15–90 Hz, respectively. In Cases 4 and 5 it encompassed the range 15–150 Hz. Coherence was most extensive in Case 3, in whom there was coherence between EEG and EMG from 30 to 175 Hz.
Figures 2 and 3 illustrate the findings in patients with coherence at very high frequency between EEG and involuntary muscle activity in the upper (Case 5) and lower (Case 4) limbs, respectively. High-frequency coherence was best seen in Cz–Fz, although in Case 3 (not illustrated) more modest high-frequency coherence was also seen over the motor areas contralateral (but not ipsilateral) to the EMG. In contrast, Fig. 4 is taken from the results in Case 1, in whom coherence between EEG and hand was confined to the frequency ranges 15–30 and 30–60 Hz.
Figure 2 shows the EEG/EMG coherence with the corresponding cumulant density estimates in Case 5. It can be seen that the largest range of frequencies with significant coherence is present for vertex recordings (Fig. 2B). Sensorimotor cortex recordings contalateral to the active forearm show the coherence to be more restricted to the range 15–30 Hz (Fig. 2C). Despite the presence of a small time domain component (Fig. 2E), no clear EEG/EMG coherence is present for ipsilateral sensorimotor cortex recordings (Fig. 2A). It is of interest that this subject also demonstrated a double-peaked correlate in the back-averaged EEG (Fig. 1B). These peaks were separated by ~9 ms (equivalent to a frequency of ~110 Hz) and were larger over the vertex than laterally. It should be noted that the coherence is a normalized measure of the linear association between two signals at a given frequency. It is derived from the cross-spectrum through division by the product of the autospectra of the two signals. Thus, the coherence spectrum is not directly comparable with either the back-average or cumulant density, which are directly related to the absolute amplitude of the signal. This distinction is highlighted in Fig. 2, where coherence is greatest between the midline EEG and EMG (Fig. 2B), although the cortical correlate is biggest over the EEG channel contralateral to the active limb (Figs 2G and 1B). Conversely, the high-frequency activity evident in coherence spectra may be of very small amplitude relative to activities of lower frequency, and therefore may not be evident in cumulant density estimates or back-averages (compare, for example, the coherence spectrum and cumulant density in Fig. 3D and H).
It seems unlikely that the shape information contained in the EEG correlate (as opposed to periodicities in the timing of correlates) contributed much to the pattern of EEG/EMG coherence. Thus, there was little difference in the shape of the EEG correlate in Figs 3H and 4H, and yet the corresponding coherence spectra in Figs 3D and 4E were quite different, particularly with respect to the degree of coherence at high frequency. The same is true of the cumulant density estimates and coherence over the midline and contralateral cerebral hemisphere in Fig. 3. Conversely, the principal features evident in the different coherence spectra were not due to variations in the shape the EMG potentials, as shown on the right-hand side of Fig. 3. Here, the EMG signal used to the left of Fig. 3 was reanalysed by passing the analogue EMG signal through a level detector. In this analysis only information pertaining to the timing of EMG events was preserved. The resulting coherence (Fig. 3E) was very similar to that in Fig. 3D. The range of frequencies represented remained between 15 and 150 Hz. Thus coherence spectra such as that in Fig. 3D provide strong evidence that high frequencies in the EEG may affect the timing of myoclonic muscle discharges (see also Brown and Marsden, 1996).
Time domain representations (cumulant density function) were derived from the frequency domain measures through application of the inverse Fourier transform. All eight Cases were found to have a time-locked EEG correlate over the contralateral motor cortex or vertex preceding muscle activity in the cumulant. In contrast, back-averaging techniques using the same signals revealed such a feature in only three patients (Table 1).
The principal feature in the EEG/EMG cumulants was a sharp downward deflection followed by an upward deflection. The downward defection occurred at negative lag when the EMG was used as the reference, indicating that there was a time-locked component in the EEG that preceded the EMG burst. The timing of this component was appropriate for conduction in fast pyramidal pathways (Rothwell et al., 1991). Thus it was around 14, 20–26 and 35 ms for muscles of the forearm, hand and foot, respectively (Table 2). It is of interest that the phase delays for any given subject were similar at all frequencies at which coherence was present. For example in subject 4 (Fig. 3F), in whom a broad spread of frequencies were coherent, the phase delay was constant from 15 to 150 Hz, corresponding to a lag of ~35 ms. This suggests that the pyramidal tract and spinal motor neurons transmit information faithfully at a variety of frequencies.
Coherence between co-activated muscles
In three subjects simultaneous EMG recordings were obtained from upper limb muscles co-activated by the myoclonic jerks. Coherence between motor units recorded from different muscles is an expression of the frequency content of the common presynaptic input to the two sets of motor neurons (Farmer et al., 1993b). Coherence analysis of EMGs co-activated by the myoclonus revealed correlation between a wide range of frequencies. In particular, the coherence between different muscles revealed the frequency of repetition of the myoclonic jerks and expressed the range of frequencies detected between the EEG and EMG. Figure 5 shows an example of coherence between forearm extensor muscles and first dorsal interrosseus (1DI) muscles in Case 5. Significant coherence is present at the frequency of the jerks (~5 Hz) and in the frequency range 20–180 Hz. The corresponding coherence between the EEG and left forearm extensor muscles is shown in Fig. 2. Although not identical, the EEG–muscle coherence encompasses the same range of frequencies expressed between the two EMGs. Thus coherence between the EMGs of muscles co-activated by the myoclonic volley reveals the frequency content of the descending common input. Time-domain analysis of the data revealed the expected highly synchronous EMG bursts. The peak in Fig. 5E is offset from time zero by ~6 ms, indicating that the more proximal forearm extensor muscle discharges slightly earlier than 1DI. This lag corresponds to the approximate difference in motoneuron–muscle conduction time between the two muscles. Thus, it may be inferred that, at the level of the spinal cord, the presynaptic drive to the forearm extensor muscles and 1DI motor neurons resulting from the myoclonic discharge is synchronous.
|
|
Discussion |
|---|
|
TopAbstractIntroductionPatients and methodsResultsDiscussionReferences |
|---|
We have shown that the cortical drive to muscle has a complex rhythmic organization extending over several frequency bands in patients with cortical myoclonus. This cortical drive is reflected in both the EEG–EMG coherence and, importantly, in the coherence between the EMGs of different muscles, further validating the latter as a measure of the common presynaptic drive to motor neurons. In addition, our results demonstrate that frequency domain analysis has significant advantages over the established time domain technique of back-averaging, and may therefore have diagnostic potential in myoclonic disorders.
Coherence between EEG and EMG at frequencies in excess of 60 Hz
Perhaps the most remarkable finding was the presence of coherence between EEG and EMG at frequencies of >60 Hz in four subjects. A previous study of cortical myoclonus demonstrated periodicities of comparable frequency in the precise timing of EMG discharges (Brown and Marsden, 1996), and here it is shown that these periodicities are driven by the motor cortex. Thus, with jerks in the hand, coherence between EEG and muscle is greatest over the vertex and contralateral hemisphere, and the phase lag between EEG and EMG is appropriate for conduction in fast pyramidal pathways. Cumulant density functions confirmed the presence of positive EEG waves of high frequency preceding EMG discharges in some patients. Although there are no reports of coherence between scalp-recorded EEG and EMG at such high frequencies in healthy subjects, activity of comparable frequency has been recorded in pyramidal tract fibres in the lightly anaesthetized cat (Adrian and Morruzzi, 1939) and the unanaesthetized cat (Calma and Arduini, 1954) in response to peripheral stimulation, and is a consistent feature of activity in cortical neurons projecting to the pyramidal tract during voluntary movement in subhuman primates (Evarts, 1965; Porter, 1985). The present results are important in showing that this high-frequency efferent activity may be found in man and is synchronized across many neurons. This must be the case as large populations of cortical neurons must be synchronized to give a surface-recorded EEG wave. Similarly, many motor units must be synchronously activated to give the myoclonic jerks, which are often large (Brown and Marsden, 1996). How this comes about may be best understood by drawing an analogy with the CA1 region of the rat hippocampus (Buzsaki et al., 1992). Inputs to this region set off fast (200 Hz) oscillations in field potentials, with the discharge of pyramidal cells time-locked to the negative peaks of these oscillations.
It is important to note that volume conduction of EEG discharges from the ipsilateral hemisphere could contribute to high-frequency coherence. Such ipsilateral discharges might arise from transcallosal spread, which takes ~10 ms between cortical representations of the distal upper limb (Brown et al., 1991). However, this does not seem to account for the coherences in the present cases, as any volume conduction would be expected to be two-way, and yet there was no high-frequency coherence over the ipsilateral motor areas in Cases 3 and 5. Moreover, transcallosal spread would be expected to be more rapid between the cortical representations of the lower limb, leading to coherence at even higher frequencies. However, the upper limit of the high-frequency coherence seen in leg jerks in Case 4 was no higher than that picked up in the upper limb jerks of Cases 3 and 5.
Finally, the period (<16 ms for frequencies of >60 Hz) of these high-frequency rhythmicities in brain and muscle and the similarity of the frequencies seen in the hand and foot argue against a simple re-afferance mechanism. Combined afferent and efferent loop times from the hand and foot would be ~45 and 90 ms, respectively.
Coherence between EEG and EMG in the 30–60 Hz band
Six out of eight of the cases demonstrated coherence between EEG and EMG in this band, with a phase lag between EEG and muscle appropriate for conduction in fast pyramidal pathways (Rothwell et al., 1991). There has been increasing interest in this frequency of oscillatory activity at both the cortical (Singer, 1993) and the motoneuron (Brown, 1997) level. In particular, this is the frequency range of the Piper rhythm of muscle, which is now recognized as a feature of both movement and tonic, particularly strong, contractions (Piper, 1912; Hill, 1921; Hagbarth et al., 1983; Brown, 1997; McAuley et al., 1997). Although coherence between scalp-recorded EEG and EMG has been reported only exceptionally in this band (Halliday et al., 1998), recent magnetoencephalographic (MEG) recordings have demonstrated consistent coherence between the cortical signal from the motor cortex and EMG activity in the Piper band during strong contractions and movement in healthy subjects (Brown et al., 1998). It is likely that an exaggeration of this normal rhythmic drive plays an important role in the involuntary muscle activity seen in patients with cortical myoclonus. As such, one might predict that coherence between cortex and muscle in the 30–60 Hz band would be particularly prominent in those myoclonic jerks occurring during movement or strong tonic contractions.
It remains unclear whether the Piper rhythm and oscillations of even higher frequency, which are manifest in muscle and paralleled in the motor cortex, are intrinsic to the latter or imposed from elsewhere, possibly the basal ganglia and/or the cerebellum. It is therefore of interest that pathological changes in patients with cortical myoclonus occur in the cerebellum rather than the cerebral cortex (Hauw et al., 1986), and that cerebellothalamocortical projections are characterized by activity at 40 Hz (Steriade, 1995) or even higher frequencies (Timofeev and Steriade, 1997).
Coherence between EEG and EMG in the 15–30 Hz band
Six out of the eight cases demonstrated coherence between EEG and EMG in this band, with an appropriate phase lag between EEG and muscle, although these were not necessarily the same cases as those that showed coherence in the Piper band. The coherence evident during involuntary action myoclonus between 15 and 30 Hz is likely to be an exaggeration of a parallel drive to muscle in healthy voluntary contraction (Farmer et al., 1993b). Recent MEG and EEG recordings have demonstrated coherence between the cortical signal from the motor cortex and EMG activity in this frequency band during sustained contraction in healthy subjects (Conway et al., 1995; Salenius et al., 1997; Halliday et al., 1998). Similar results have been obtained in monkeys (Murphy and Fetz, 1992; Sanes and Donoghue, 1993; Baker et al., 1997). The MEG and EEG results in man have proved difficult to interpret as cortical and muscle activity may appear to be synchronized with zero delay (Conway et al., 1995; Halliday et al., 1998). In these experiments the subjects maintained a steady, gentle isometric or postural contraction. In this situation much of the input to motor neurons depends on afferent feedback, which maintains corticomotoneuronal drive through transcortical reflex pathways (Matthews and Miles, 1988; Matthews et al., 1990). The presence of strong closed-loop feedback may lead to the synchronization (without delays in phase) of rhythmically firing neurons despite significant interneuronal conduction delays (Engel et al., 1991; Singer, 1993).
The recordings of myoclonic activity afford an opportunity to study phase relationships outside this steady state. Under such open-loop conditions, the 15–30 Hz cortical drive to muscle is associated with a phase lag appropriate for conduction in fast pyramidal pathways. The function of this descending drive is unclear. Baker et al. (1997) and Halliday et al. (1998) considered it a function of the motor cortex acting in a `holding' postural mode, and it remains to be seen if the coherence between cortex and muscle in the 15–30 Hz band is particularly represented in those myoclonic jerks occurring during sustained posture as opposed to action.
It is important to note that although, under some circumstances, the EEG activity recorded over the motor cortex may be coherent with muscle activity in healthy subjects in the 15–30 Hz band, the strength of this coherence is considerably less than that seen in our patients with cortical myoclonus (Halliday et al., 1998).
Low-frequency coherence between EEG and muscle
Four of the patients had regular series of EMG bursts with a frequency of <10 Hz during action myoclonus. However, only two of these subjects had significant coherence between EEG and EMG at the frequency of rhythmic jerks. This variability of the cortical and muscle locking is also seen in the pathological tremors. Patients with parkinsonian tremor demonstrate coherence between MEG and EMG at frequencies corresponding to that of the tremor (Volkmann et al., 1996), although MEG–EMG coherence has not been demonstrated at higher frequencies. Patients with essential tremor demonstrate the reverse picture, with coherence between MEG and EMG at 15–30 Hz, the same frequency range as is associated with steady muscle contraction in healthy subjects. However, in essential tremor the tremor frequency does not seem to be expressed in the MEG–EMG correlation (Halliday et al., 1997). Thus different movement disorders show differing frequencies of cortical–EMG coupling and may differ in the extent to which the pathological frequency of the tremor, be it essential, Parkinsonian or myoclonic, involves the motor cortical neurons.
Diagnostic use of frequency domain analysis techniques
Hitherto, the time domain analysis technique of back-averaging has, together with the presence of giant cortical sensory evoked potentials, formed the principle method for diagnosing myoclonus of cortical origin. Such physiological characterization may have implications for aetiology and treatment (Brown, 1995). In the present study only three of the eight patients had evidence of a time-locked EEG correlate preceding action jerks, as determined by back-averaging. A further three subjects had no EEG correlate at the time of study, but had been shown to have such a correlate during earlier studies. In contrast, a time-locked EEG correlate preceding muscle activity was evident in the cumulant in all eight cases. Fourier analysis may have several advantages over traditional diagnostic techniques. High-frequency myoclonic discharges do not preclude analysis, no arbitrary trigger level has to be chosen so that jitter is less, statistical evaluation of results is possible and the technique is quick and automated, so that long sections of data may be analysed. The result is that a cortical correlate may be demonstrated using coherence analysis in patients in whom classical back-averaging has failed to demonstrate such a feature.
Conclusion
The present study has identified several frequency bands in which there may be coherence between EEG and EMG in cortical myoclonus. The importance of this is threefold. First, these activities are likely to represent pathological exaggerations of physiological central rhythmicities relating to movement. Two such activities have recently been found in the human motor cortex, in the frequency bands 15–30 and 30–60 Hz, and the present results suggest that more remain to be identified. Secondly, the precise pattern of coherence in myoclonic patients may have potential diagnostic value in distinguishing between different pathological processes. Finally, it remains to be seen whether frequency-domain analysis is able to demonstrate a cortical correlate to the myoclonic activity in conditions such as corticobasal degeneration, where existing techniques have failed to show such a feature.
|
Acknowledgments |
|---|
We wish to thank Professor C. D. Marsden for referring many of the patients included in this study and Mr P. Asselman for his technical assistance.
|
References |
|---|
|
TopAbstractIntroductionPatients and methodsResultsDiscussionReferences |
|---|
Adrian ED, Morruzzi G. Impulses in the pyramidal tract. J Physiol (Lond) 1939; 97: 153–99.
Baker SN, Olivier E, Lemon RN. Coherent oscillations in monkey motor cortex and hand muscle EMG show task-dependent modulation. J Physiol (Lond) 1997; 501: 225–41.
Nhatia KP, Brown P, Gregory R, Lennox GG, Manji H, et al. Progressive myoclonic ataxia associated with coeliac disease. Brain 1995; 118: 1087–93.
Brown P. Myoclonus: a practical guide to drug therapy. CNS Drugs 1995; 3: 22–9.[Web of Science]
Brown P. Muscle sounds in Parkinson's disease. Lancet 1997; 349: 533–5.[Web of Science][Medline]
Brown P, Marsden CD. Rhythmic cortical and muscle discharge in cortical myoclonus. Brain 1996; 119: 1307–16.
Brown P, Day BL, Rothwell JC, Thompson PD, Marsden CD. Intrahemispheric and interhemispheric spread of cerebral cortical myoclonic activity and its relevance to epilepsy. Brain 1991; 114: 2333–51.
Brown P, Salenius S, Rothwell JC, Hari R. The cortical correlate of the Piper rhythm in man. J Neurophysiol 1998; 80: 2911–17.
Buzsaki G, Horvath Z, Urioste R, Hetke J, Wise K. High-frequency network oscillation in the hippocampus. Science 1992; 256: 1025–7.
Calma I, Arduini A. Spontaneous and induced activity in pyramidal units. J Neurophysiol 1954; 17: 321–35.
Conway BA, Halliday DM, Farmer SF, Shahani U, Maas P, Weir AI, Rosenberg JR. Synchronization between motor cortex and spinal motoneuronal pool during the performance of a maintained motor task in man. J. Physiol (Lond) 1995; 489: 917–24.
Datta AK, Stephens JA. Synchronisation of motor unit activity during voluntary contractions in man. J Physiol (Lond) 1990; 422: 397–419.
Engel AK, Konig P, Kreiter AK, Singer W. Interhemispheric synchronization of oscillatory neuronal responses in cat visual cortex. Science 1991; 252: 1177–9.
Evarts EV. Relation of discharge frequency to conduction velocity in pyramidal tract neurons. J Neurophysiol 1965; 28: 216–28.
Farmer SF, Swash M, Ingrams DA, Stephens JA. Changes in motor unit synchronisation following central nervous lesions in man. J Physiol (Lond) 1993a; 463: 83–105.
Farmer SF, Bremner FD, Halliday DM, Rosenberg JR, Stephens JA. The frequency content of common synaptic inputs to motoneurones studied during voluntary isometric contraction in man. J Physiol (Lond) 1993b; 470: 127–55.
Hagbarth KE, Jessop J, Eklund G, Wallin EU. The Piper rhythm—a phenomenon related to muscle resonance characteristics? Acta Physiol Scand 1983; 117: 263–71.[Web of Science][Medline]
Halliday DM, Rosenberg JR, Amjad AM, Breeze P, Conway BA, Farmer SF. A framework for the analysis of mixed time series/point process data—theory and application to the study of physiological tremor, single motor unit discharges and electromyograms. [Review]. Prog Biophys Mol Biol 1995; 64: 237–78.[Web of Science][Medline]
Halliday DM, Conway BA, Shahani U, Russell A, Farmer SF, Weir AI, et al. Coherence estimates between cortical activity and motor output in subjects with essential tremor. J Physiol (Lond) 1997; 501.P: 38P.
Halliday DM, Conway BA, Farmer SF, Rosenberg JR. Using electroencephalography to study functional coupling between cortical activity and electromyograms during voluntary contractions in humans. Neurosci Lett 1998; 241: 5–8.[Web of Science][Medline]
Hauw JJ, Escourelle R, Baulac M, Morel-Maroger A, Goulon M, Castaigne P. Postmortem studies on posthypoxic and post-methyl bromide intoxication: case reports. Adv Neurol 1986; 43: 201–14.[Medline]
Hill AV. The tetanic nature of the voluntary contraction in man. J Physiol (Lond) 1921; 55: 14P–6P.
Matthews PB, Miles TS. On the long-latency reflex responses of the human flexor digitorum profundus. J Physiol (Lond) 1988; 404: 515–34.
Matthews PB, Farmer SF, Ingram DA. On the localization of the stretch reflex of intrinsic hand muscles in a patient with mirror movements. J Physiol (Lond) 1990; 428: 561–77.
McAuley JH, Rothwell JC, Marsden CD. Frequency peaks of tremor, muscle vibration and electromyographic activity at 10 Hz, 20 Hz and 40 Hz during human finger muscle contraction may reflect rhythmicities of central neural firing. Exp Brain Res 1997; 114: 525–41.[Web of Science][Medline]
Murthy VN, Fetz EE. Coherent 25- to 35-Hz oscillations in the sensorimotor cortex of awake behaving monkeys. Proc Natl Acad Sci USA 1992; 89: 5670–4.
Piper HE. Elektrophysiologie menschlicher Muskeln. Berlin: Springer; 1912.
Porter R. The corticomotoneuronal component of the pyramidal tract: corticomotoneuronal connections and functions in primates. Brain Res Rev 1985; 10: 1–26.
Rothwell JC, Thompson PD, Day BL, Boyd S, Marsden CD. Stimulation of the human motor cortex through the scalp. [Review]. Exp Physiol 1991; 76: 159–200.[Web of Science][Medline]
Salenius S, Portin K, Kajola M, Salmelin R, Hari R. Cortical control of human motoneuron firing during isometric contraction. J Neurophysiol 1997; 77: 3401–5.
Sanes JN, Donoghue JP. Oscillations in local field potentials of the primate motor cortex during voluntary movement. Proc Natl Acad Sci USA 1993; 90: 4470–4.
Singer W. Synchronization of cortical activity and its putative role in information processing and learning. [Review]. Annu Rev Physiol 1993; 55: 349–74.[Web of Science][Medline]
Steriade M. Two channels in the cerebellothalamocortical system. J Comp Neurol 1995; 354: 57–70.[Web of Science][Medline]
Timofeev I, Steriade M. Fast (mainly 30–100 Hz) oscillations in the cat cerebellothalamic pathway and their synchronization with cortical potentials. J Physiol (Lond) 1997; 504: 153–68.
Volkmann J, Joliot M, Mogilner A, Ioannides AA, Lado F, Fazzini E, et al. Central motor loop oscillations in parkinsonian resting tremor revealed by magnetoencephalography. Neurology 1996; 46: 1359–70.
Received August 3, 1998. Revised September 8, 1998. Accepted October 15, 1998.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  C. L. Morgia, A. Achilli, L. Iommarini, P. Barboni, M. Pala, A. Olivieri, C. Zanna, S. Vidoni, C. Tonon, R. Lodi, et al. Rare mtDNA variants in Leber hereditary optic neuropathy families with recurrence of myoclonus Neurology, March 4, 2008; 70(10): 762 - 770. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Mastrangelo, L. Canafoglia, S. Franceschetti, C. Oppezzo, F. Mosca, F. Menni, R. Parini, C. Ciano, V. Scaioli, and F. Panzica High-Frequency Rhythmic Cortical Myoclonus in a Long-Surviving Patient With Nonketotic Hypergylcemia J Child Neurol, March 1, 2008; 23(3): 321 - 324. [Abstract] [PDF]
|
|
|
|
 |
|
 A. Sharott, P. Grosse, A. A. Kuhn, F. Salih, A. K. Engel, A. Kupsch, G.-H. Schneider, J. K. Krauss, and P. Brown Is the synchronization between pallidal and muscle activity in primary dystonia due to peripheral afferance or a motor drive? Brain, February 1, 2008; 131(2): 473 - 484. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 F. Tecchio, C. Porcaro, G. Barbati, and F. Zappasodi Functional source separation and hand cortical representation for a brain-computer interface feature extraction J. Physiol., May 1, 2007; 580(3): 703 - 721. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. J. Magill, A. Sharott, J. P. Bolam, and P. Brown Delayed synchronization of activity in cortex and subthalamic nucleus following cortical stimulation in the rat J. Physiol., August 1, 2006; 574(3): 929 - 946. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. Canafoglia, C. Ciano, F. Panzica, V. Scaioli, C. Zucca, P. Agazzi, E. Visani, G. Avanzini, and S. Franceschetti Sensorimotor cortex excitability in Unverricht-Lundborg disease and Lafora body disease Neurology, December 28, 2004; 63(12): 2309 - 2315. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. Grosse and P. Brown Acoustic Startle Evokes Bilaterally Synchronous Oscillatory EMG Activity in the Healthy Human J Neurophysiol, September 1, 2003; 90(3): 1654 - 1661. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. Grosse, R. Guerrini, L. Parmeggiani, P. Bonanni, A. Pogosyan, and P. Brown Abnormal corticomuscular and intermuscular coupling in high-frequency rhythmic myoclonus Brain, February 1, 2003; 126(2): 326 - 342. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. Guerrini, P. Bonanni, A. Patrignani, P. Brown, L. Parmeggiani, P. Grosse, P. Brovedani, F. Moro, P. Aridon, R. Carrozzo, et al. Autosomal dominant cortical myoclonus and epilepsy (ADCME) with complex partial and generalized seizures: A newly recognized epilepsy syndrome with linkage to chromosome 2p11.1-q12.2 Brain, December 1, 2001; 124(12): 2459 - 2475. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J Gross, P A Tass, S Salenius, R Hari, H-J Freund, and A Schnitzler Cortico-muscular synchronization during isometric muscle contraction in humans as revealed by magnetoencephalography J. Physiol., September 15, 2000; 527(3): 623 - 631. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. A. J. Tijssen, J. F. Marsden, and P. Brown Frequency analysis of EMG activity in patients with idiopathic torticollis Brain, April 1, 2000; 123(4): 677 - 686. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. F. Marsden, K. J. Werhahn, P. Ashby, J. Rothwell, S. Noachtar, and P. Brown Organization of Cortical Activities Related to Movement in Humans J. Neurosci., March 15, 2000; 20(6): 2307 - 2314. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A New Diagnostic Technique for Cortical Myoclonus Journal Watch Neurology, July 1, 1999; 1999(701): 11 - 11. [Full Text]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||