[page 24↓]

3.  Frequency analysis in high frequency rhythmic myoclonus

The analysis of the coherence between scalp EEG and surface EMG has shown promise as a new tool in delineating the functional coupling between oscillatory activity in the motor cortex and that in muscle in patients with cortcial myoclonus (Brown et al, 1999). In this conditaion EEG-EMG frequency analysis may have methodological advantages in de­tecting a cortical correlate over the classical neurophysiological repertoire of back-avera­ging and the detection of a giant cortical sensory evoked potential. Many myoclonic pa­tients do not have reflex myoclonus and giant cortical evoked potentials and the identifi­cation of a cortical correlate that precedes jerks in back-averages relies on the absence of myoclonic events just prior to the trigger EMG burst. Yet many patients with cortical myoclonus have rhythmic EMG bursts at relatively high frequency (Thompson et al., 1994; Brown and Marsden, 1996), especially those with minipolymyoclonus (Wilkins et al., 1985; Hallett and Wilkins, 1986), as in cortical tremor (Toro et al., 1993; Terada et al., 1997), Angelman Syndrome (Guerrini et al., 1996) or autosomal dominant cortical myoclo­nus and epilepsy (Guerrini et al., 2001). In contrast, the increased signal content with repetitive myoclonic jerks favors detection using frequency analysis. In addition, the latter technique introduces no arbitrary trigger level so that jitter is less, statistical evaluation of the results is possible and the technique is quick and automated, so that long sections of data may be analysed. Thus in a recent study, EEG-EMG coherence and a cortical correlate in the cumulant density estimate were demonstrated in eight patients with a variety of con­ditions associated with cortical myoclonus, whereas only three had a time-locked EEG cor­relate upon back-averaging (Brown et al., 1999).

Nevertheless the determination of EEG-EMG coherence still requires a relatively arte­fact free EEG recording and EEG recording itself can be difficult and time-consuming in patients with involuntary jerks, some of whom are children. There is growing evidence that corticomuscular coupling is reflected in the pattern of coherence between muscles (Farmer et al., 1993; Kilner et al., 1999). This leads us to hypothesise that EMG-EMG coherence [page 25↓]may also be used to identify pathological cortical drives to muscle, and if so, this technique may have practical advantages over the assessment of EEG-EMG coherence. Certainly there is preliminary evidence of a close correspondence between the pattern of EEG-EMG coherence and that of EMG-EMG coherence in cortical myoclonus (Brown et al., 1999).

The interpretation of the results of frequency analysis in myoclonus is, however, bede­villed by the sensitivity of this technique. This has two consequences. First, even healthy subjects may be found to have EEG-EMG and EMG-EMG coherence and corresponding features in cumulant density estimates (Halliday et al . , 1998; Kilner et al., 1999; Mima and Hallett, 1999b). To date studies have failed to address which aspects distinguish the patho­logical cortico-muscular coupling found in cortical myoclonus from the physiological state. Second, afferent activities will be detected as well as efferent discharges, but so far there has been the tacit assumption that the results of frequency analysis in patients with myo­clonus may be satisfactorily interpreted solely in terms of descending drives from the motor cortex to muscles.

Here the results of back-averaging to those of frequency analysis in patients with high frequency rhycthmic myoclonus are compared. Further, the extent to which EMG-EMG co­herence can provide a practical alternative to EEG-EMG coherence, the factors that distin­guish pathological from normal corticomuscular and intermuscular coupling and whether coupling is always the product of cortical efferent activity are systematically explored. The assessments was based on minimal interventions that would lend themselves to incorpora­tion into routine clinical neurophysiological practice and studied patients with the clinical syndrome of high frequency rhythmic myoclonus, as these are the cases that are most diffi­cult to diagnose using standard back-averaging techniques. The results confirm the clinical utility of both EEG-EMG and EMG-EMG coherence estimates in the assessment of myo­clonus, but indicate that interpretation must take into account the physiological complexity of cortical myoclonus, which does not solely involve efferent cortico-muscular pathways.


[page 26↓]

3.1.  Patients and methods

3.1.1. Patients and healthy subjects

Nine patients (mean age 43 years; range 14-80 years) with jerks due to a variety of non-progressive syndromes associated which cortical myoclonus (table 3.1) were examined. All had high-frequency, low-amplitude myoclonus consistent with minipolymyoclonus (Wil­kins et al., 1985). Case 9 also had some additional infrequent and less regular larger ampli­tude jerks. Three patients of a pedigree (cases 1-3) had multifocal myoclonus in relation to the recently described and genetically defined syndrome of autosomal-dominant cortical re­flex myoclonus and epilepsy linked to chromosome 2 (Guerrini et al., 2001). Three patients (cases 4-6) had Angelman syndrome with different genetic defects (one with 15q11-13 deletion [case 4], one with uniparental disomy for chromosome 15 [case 5], and one with UBE3A mutation [case 6]). They exhibited continuous multifocal, high frequency myo­clonic jerks associated with dystonic limb posturing as previously described in this syn­drome (Guerrini et al. , 1996). One patient (case 7) had myoclonus in relation with Lennox-Gastaut-syndrome. In one patient (case 8) the diagnosis of familial cortical tremor was made. Cortical tremor is a type of minipolymyoclonus consisting of cortical reflex myoclo­nus, often associated with epilepsy and posture and/or action induced jerks at high-frequen­cies and low amplitudes showing the neurophysiological features of cortical myoclonus (Ikeda et al., 1990; Toro et al., 1993; Terada et al., 1997). The last patient (case 9) had myoclonus related to coeliac disease. More than half of the patients (cases 1, 2, 4, 6, and 7) had epilepsy with focal and/or generalised seizures besides cortical myoclonus. All patients except two (cases 3 and 8) received a variety of antimyoclonic and/or antiepileptic medica­tion with different modes of action at the time of the neurophysiological examination. Ten healthy subjects (mean age: 40, range: 27-74) were also studied.


[page 27↓]

Table 3.1. Patient’s clinical details

case

sex

age

features of myoclonus

epilepsy

clinical syndrome

drugs

1

f

47

multifocal, rest<posture

yes

autosomal-dominant cortical reflex myoclonus and epilepsy

VPA, PRM

2

f

71

multifocal, rest<posture

yes

autosomal-dominant cortical reflex myoclonus and epilepsy

PB, DPH, ESM

3

m

80

multifocal, rest<posture

1 seizure

autosomal-dominant cortical reflex myoclonus and epilepsy

None

4

f

17

multifocal, rest>posture

yes

Angelman syndrome

VPA, CLB

5

m

21

multifocal, rest>posture

no

Angelman syndrome

CLB, Pir

6

f

14

multifocal, rest>posture

yes

Angelman syndrome

VPA

7

m

26

multifocal, rest>posture

yes

Lennox-Gastaut syndrome

VPA, CBZ

8

m

45

forearm, hand, posture

no

cortical tremor

None

9

f

68

multifocal, posture, action-induced

no

coeliac disease

CZP, LEV, Pir, L-Dopa

3.1.2. EEG and EMG recording

Surface EMG and EEG were recorded with 9mm diameter silver-silver chloride elec­trodes. We opted for a bipolar EEG derivation rather than a Laplacian derivative, as the lat­ter requires considerably more channels, limiting its utility in the setting of a routine clini­cal neurophysiological service. Both montages avoid the use of a common reference al­though bipolar electrodes may degrade phase information (Mima and Hallett, 1999b). Elec­trodes were positioned according to the 10-20 system at C3-F3 and C4-F4. EMG was re­corded bilaterally from deltoid, finger extensor and intrinsic hand muscles (APB and 1DI). EMG electrodes were placed 2 cm apart on the muscle belly (except for intrinsic hand mus­cles where one electrode was sited over the metacarpo-phalangeal joint). EMG and EEG were bandpass-filtered at 16-300 and 0.53-300 Hz, respectively. The high pass filter for EMG was chosen so as to limit movement artefact. Signals were amplified and digitised with 12-bit resolution by a CED 1401 analogue-to-digital converter. The sampling rate was 1000 Hz. Signals were displayed and stored on a PC by a software package (CED Spike 3). [page 28↓] Patients were recorded either at rest (cases 4-7), so that no voluntary drive to muscles was present, or while posturing voluntarily (shoulder abduction, wrist extension, thumb adduc­tion; cases 1-3, 8, 9). Record lengths averaged 183 ±21 s (SEM). Within individual sub­jects data lengths were kept fixed. Healthy subjects were asked to co-activate recorded muscles over 4 periods of about 60 seconds. Sixty-180s rest was given between co-activa­tions. Total data lengths used were fixed at 200 s in healthy subjects.

3.1.3. Analysis

Frequency Analysis

EEG-EMG and EMG-EMG coherence were analysed as outlined in chapter II. The dis­crete Fourier transform and parameters derived from it 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. For comparison between groups, muscles and across signals the area under the curve of transformed coherence was calcula­ted for each subject over the band at which coherence was significant. Data were then pooled for each group and the 95% confidence limits of the mean calculated.

Phase was assessed only where coherence was significant and extended over at least 5 data points. The constant time lag between the 2 signals was calculated from the slope of the phase estimate after a line had been fitted by linear regression, but only if a linear rela­tionship accounted for > 80% of the variance. In some instances coherence and phase spec­tra appeared to consist of more than one component. In these cases the limits of individual components were defined by the turning points of the best-fit second or third order poly­nomial fitted to all contiguous plotted points at which coherence was significant. The poly­nomials accounted for > 80% of the variance and had > 8 data points per model order.

[page 29↓]Back-averaging

Back-averaging was performed off-line in Spike 2. EMG was rectified and myoclonic EMG bursts identified using a level of 100 μ V (sufficient to exclude volume conduction or mains artefact) to produce a series of digital events. EEG and EMG signals were then re-aligned to these events and averaged. The dominant frequency of cortico-muscular coup­ling was determined from the interval between the peak positivities of the largest serial cortical correlates in the back-averaged contralateral EEG. The interval with which EEG lead or lagged EMG was determined as the latency of the peak positivity in the contralate­ral EEG with respect to the time of the digital events, after subtraction of the latency of the EMG response with respect to the same events. This was only performed where a single positive-negative cortical correlate was unequivocally larger (> 10%) than others.

3.2. Results

3.2.1. Raw EMG

The raw EMG consisted of rhythmic EMG bursts of short duration with minimal or no pre-innervation between myoclonic bursts. Thus in each case the rectified EMG level between myoclonic bursts was less than 40 μ V in over 95% of interburst intervals. Fig. 3.1A is a representative example of the signal recorded in a patient with autosomal-domi­nant cortical reflex myoclonus and epilepsy linked to chromosome 2 (case 3). Brief myo­clonic bursts are evident at a frequency of ~13-15 Hz.

3.2.2. Frequency analysis

Fig. 3.1B-E illustrates the results of frequency analysis in the same patient as above. Coherence between APB and the contralateral motor cortex is significant at frequencies [page 30↓]between 6 and 19 Hz (Fig. 3.1C) with a linear phase slope between the two signals (Fig. 3.1D) and a delay of 15.7 ms with EEG preceding EMG. The corresponding cumulant density estimate is shown in Fig. 3.1E.

Fig. 3.1: Frequency analysis in case 3. (A) Left scalp EEG and EMG from right-sided deltoid, finger exten­sors and APB. EMG shows myoclonic bursts at high frequency (~13-15 Hz). (B) Autospectrum of right APB and F3-C3. (C) Coherence between right APB and F3-C3 showing exaggerated EEG-EMG coherence in the range 6 - 19 Hz. The thin horizontal line is the 95% confidence level. (D) Phase between right APB and F3-C3. The thin lines either side of the phase estimate (thick line) are the 95% confidence levels. EEG precedes EMG. Regression analysis gave a time lag between the two signals of 15.7 ms (± 2.8 ms 95% confidence limits). (E) Cumulant density function showing a negative EEG deflection with a peak about 15 ms before the EMG. EMG was the input. Thin horizontal lines are 95% confidence levels.

[page 31↓]Fig. 3.2, taken from the same patient, gives an example of the typical difference between EEG-EMG and EMG-EMG coherence for both proximal and distal muscles. The data are drawn from the same recording. While the frequency at which coherence peaks remains constant, centered around 13 Hz, the frequency content is broader and the extent of cohe­rence is higher for EMG-EMG coherence than EEG-EMG coherence.

Fig. 3.2: Comparison of frequency content and degree of coherence between EEG-EMG and EMG-EMG for proximal (A) and distal (B) muscle pairs in case 3.

EEG-EMG coherence

Fig. 3.3A-C summarises the area of significant transformed coherence between EEG and EMG in the spectra from individual subjects for deltoid, finger extensors and intrinsic hand muscles. Both right and left sided muscles are included. Note the logarithmic scale. Transformed coherences showed substantial overlap between patients and controls. Thus on an individual basis EEG-EMG coherence using a simple bipolar montage had limited sensitivity.

Nevertheless there were clear differences at the group level. Fig 3.3D summarises the mean transformed coherence area and its 95% confidence limits across all patients and all healthy subjects for the different muscles. The mean transformed coherence area in the pa­tient group is higher by a factor of 3 to 9 compared to normal values (Fig. 3.3D). Note too that coherences are considerably higher for distal muscles.


[page 32↓]

Fig. 3.3: Areas of transformed EEG-EMG coherence, taken from above the 95% confidence level, in each pa­tient for deltoid (A), finger extensor (B) and intrinsic hand muscles (C). Transformed coherence is plotted on a log scale; overlap is evident between individual patients and healthy subjects. (D) Transformed coherence areas averaged across patients and healthy subjects (asterixed) with 95%-confidence level of each mean.

Fig 3.4 compares the distribution of transformed EEG-EMG coherence across frequen­cies for the different muscles. For this purpose the individual spectra, rather than the cumu­lative area, have been pooled. Healthy subjects only show a discrete peak in the pooled spectra for the forearm and intrinsic hand muscles centred around 15 Hz. This is slightly


[page 33↓]

Fig. 3.4: Averaged transformed EEG-EMG coherence spectra in patients and normal subjects for deltoid (A), finger extensor (B) and intrinsic hand muscles (C). The level of averaged transformed coherence is higher in paients compared to healthy subjects in all three muscles (bold lines), but with overlap of the 95%-confidence li­mits of the mean (thin lines) bet­ween the two groups.


[page 34↓]

lower than in a previous study of physiological cortico-muscular coupling (Brown et al., 1998) and may be partly due to the use of EEG (which is affected by the low-pass filter characteristics of the skull and scalp) rather than magnetoencephalograpy in the current study. The frequency range in the present study is comparable to that found in other elec­troencephalographic studies of corticomuscular coherence (Mima and Hallett, 1999b), where coherence in the alpha band is not uncommon (Mima and Hallet, 1999a). Patients also had a peak in the pooled spectrum for deltoid, while their mean transformed coherence area was mostly above the 95% confidence limits for the healthy subjects in all three mus­cles. The peak frequency was similar in the different spectra, although peaks were broader in the patient group, where there was also a subsidiary peak at just above 30 Hz in the spec­tra for distal muscles.

EMG-EMG coherence

Similar analyses were performed for EMG-EMG coherence. Fig. 3.5 A and B summari­se the area of significant transformed coherence between finger extensor and deltoid and between finger extensor and intrinsic hand muscle EMG in the spectra from individual sub­jects. Transformed coherences show overlap between patients and controls, although com­pared to EEG-EMG coherence (Fig. 3.3) this overlap is modest.

[page 35↓]Fig: 3.5

Fig. 3.5: Areas of transformed EMG-EMG coherence, taken from above the 95% confidence level, in each patient for deltoid-finger extensor (A) and finger ex­tensor-intrinsic hand muscles (B). Trans­formed coherence is plotted on a log scale. In comparison to respective trans­formed EEG-EMG coherences (fig 3) there is less overlap between patients and healthy subjects. (C) Transformed cohe­rence areas averaged across patients and healthy subjects (asterixed) with 95%-confidence level of each mean.

The differences were even clearer at the group level. Fig 3.5 C summarises the mean transformed coherence and its 95% confidence limits across all patients and all healthy subjects for the different muscle pairs. The mean transformed coherence in the patient group is very much greater than in normals and greater than the mean transformed cohe­rence between EEG and muscles in the patients (Fig 3.3D). Note too that coherences are considerably higher for the distal muscle pair.

[page 36↓]Fig 3.6 compares the distribution of transformed EMG-EMG coherence across frequen­-

Fig. 3.6: Averaged transformed EMG-EMG coherence spectra in pa­tients and normal subjects for del­toid-finger extensor (A) and finger extensor-intrinsic hand muscles (B). The level of averaged transformed coherence is higher in patients com­pared to normals, but unlike fig 4 there is no overlap of the 95%-confi­dence limits of the mean (thin lines) between the two groups over the 6-30 Hz band. Data were smoothed with a 3 point moving average.

cies for the different muscle pairs. Healthy subjects only show a peak in the pooled spectra for the distal muscle pair. Patients have a peak in both proximal and distal muscle pairs and transformed coherences for both are clearly above the 95% confidence limits of the mean for the healthy subjects. The peak frequency in the spectrum for finger extensor-intrinsic hand muscles is similar in controls and patients, although the peak is broader in the patient [page 37↓]group.

In every single patient the coherence between muscle pairs, be they proximal or distal, as measured by the area of transformed coherence exceeded the coherence between the EEG and respective muscles. In addition, EMG-EMG coherence between finger extensor and intrinsic hand muscles was able to establish abnormal coupling in every case, whereas abnormal coupling could only be demonstrated in less than 90 % of cases of EEG-EMG coherence (Table 3.2).

Table 3.2.

 

back-averaging

frequency analysis

 
 

cortical correlate

frequency

[Hz]

temporal delay

signif.

EEG-EMG coherence

peak

frequency

[Hz]

temporal

delay

EEG-EMG coherence >95%-CL of control group*

EMG-EMG coherence >95%-CL of control group*

Deltoid

50%

17.6 ± 3.8

31%

88%

14.4 ± 3

63% (31%)

88%

94%

finger extensor

77%

16.9 ± 2.4

56%

100%

14.1 ± 2.4

94% (67%)

89%

100%

intrinsic hand muscles

75%

16.3 ± 2

42%

100%

14.4 ± 2.6

83% (67%)

75%

 

deltoid-finger extensor

 

 

94%

finger extensor-intrinsic hand muscles

 

 

100%

above 95%-confidence limit
at least 5 contiguous data points in the frequency of significant coherence with p<0.05
*at least 2 data points above the highest point of upper 95%-confidence limit irrespective of frequency


[page 38↓]

Phase

Hand muscles

The temporal difference between EEG and EMG calculated from phase spectra demon­strated a uniform pattern in the intrinsic hand muscles. EEG systematically lead EMG by 8.3 to 18.8 ms (Fig 3.7A). The mean EEG lead was 14.8 ± 2.3 ms (95% confidence limits), being shorter than the mean MEP latency in active 1DI following transcutaneous magnetic stimulation of the motor cortex of 20.4 (Eisen and Shtybel, 1990). A comparable pattern was seen for the temporal difference between forearm extensor and intrinsic hand muscle EMG. The former lead by 3.3 to 11.2 ms (Fig. 3.7B). The mean lead of the forearm exten­sors was 6.7 ± 1.4 ms and was therefore compatible with the difference of 5.2 ms between MEP latencies for forearm extensors and 1DI following TMS of the motor cortex in normal controls (Eisen and Shtybel, 1990).

Proximal muscles

In contrast, phase differences between EEG and EMG for more proximal muscles and between deltoid and finger extensor EMG were more complicated. In some cases EEG lead EMG but in others EMG lead EEG (Fig. 3.7C-E). The latter suggests an afferent drive from muscle to cortex. Although the picture varied between subjects, phase estimates concurred within each subject in every case in whom estimates were available for deltoid and forearm extensors on the same side. Thus, phase estimates were of the same sign in cases 2, 5 and 9 (see fig. 3.7C and D). This suggests that variations in the temporal difference between EEG and proximal muscles across patients were due to physiological differences rather than chance. In some subjects EEG-EMG coupling for proximal muscles was dominated by a corticomuscular efferent drive as with 1DI, whereas in others coupling was dominated by an afferent drive from the periphery.

When delays were compatible with an efferent system they were somewhat shorter than [page 39↓]the latency of TMS induced MEPs to the respective active muscle, as was the case for the intrinsic hand muscles. For example, in the one patient (case 2) in whom EEG lead deltoid EMG, this was by 9.6 ms (fig 3.7C). In cases 2, 3 and 8, in whom EEG lead forearm extensor EMG, this was by around 10 ms (fig 3.7D).

On the other hand, when delays were compatible with an afferent system they were sel­dom consistent with the latency of evoked potentials from the region of the respective mus­cle recorded in healthy subjects. For example, in cases 5, 7 and 9 in whom deltoid EMG lead EEG this lead varied between -24.1 to -76.9 ms (fig 3.7C). In cases 1, 4, 5, 6 and 9, in whom finger extensor EMG lead EEG, the mean delay between the two signals exceeded 25 ms (fig 3.7D). EMG leads were therefore greater than the expected delay for an afferent loop, even allowing 10 or so ms for electromechanical delay (McCauley et al. , 1997). This variation in the temporal delays was not systematically related to the semiology of the myo­clonus or the clinical syndrome associated with it.

A similar pattern was observed in the temporal delay between deltoid and finger exten­sor EMG. In cases 6, 7, and cases 2 and 5 on the right, deltoid EMG lead that in the finger extensor, as would be expected for a simple efferent system. However, in cases 1, 3 and case 2 on the left, forearm extensor EMG lead by 20.1 ± 12.1 ms, compatible with an affe­rent loop, in which afferent activity from the distal upper limb drove a reflex response in deltoid.


[page 40↓]

Fig. 3.7: Phase relationships in patients for pro­ximal and distal muscles. Only patients meeting our criteria for calculation of temporal delays (see methods) are included. Horizontal lines are the 95%-confidence limit of the temporal delay in each subject. A positive sign for EEG-EMG (A, C, D) indicates that EEG leads EMG. When negative EMG leads EEG. A positive sign for EMG-EMG (B, E) indicates that the more pro­ximal muscle leads the more distal muscle. Note that for distal muscles (A, B) the phase relation indicates a dominant efferent drive between cor­tex and muscle as well between muscles. For proximal muscles (C-E) phase suggests efferent and afferent drives in individual patients.


[page 41↓]

3.2.3.  Back-averaging

Back-averaging was performed in all 9 patients (table 3.2). A back-averaged cortical correlate could be discerned in only 50to 77 % of muscles, but cortico-muscular coherence was above the significance level in 100% of cases for finger extensors and intrinsic hand muscles and in 82% of cases for deltoid. For those muscles in which back-averaging was successful the back-averaged EEG consisted of a rhythmic series of cortical correlates , and was similar in nature to the cumulant density estimate(Fig. 3.8B-D).

Fig 3.8: Examples of back-averaged contralateral EEG. (A) Back-ave­raged EEG (black line) of left finger extensor of case 3 fails to disclose a cortical correlate, although the cu­mulant density estimate (grey line) shows a maximal positive deflection that follows EMG onset and exceeds the 95%-confidence limits (dotted grey lines). (B) Back-average and cumulant density estimate compared in case 3 (same as Fig 1). Note that positive deflections are symmetrical and therefore the temporal diffe­rence between EEG and EMG was ambiguous with these time domain measures. In contrast, phase spectra (Fig 1 d) in the same patient clearly showed that EEG lead EMG. (C) Back-average in case 1. The peak positive deflection is ambiguous, but the oscillatory nature of the back-averaged EEG can be seen at a fre­quency of 14 Hz. (D) Unambiguous back-average in case 2. The peak positive deflection in the EEG pre­cedes EMG onset in the right finger extensors by 23 ms. Note the oscilla­tory nature of the back-averaged EEG at a frequency of 22 Hz. In each case EMG is rectified and the same data were analysed to give the back-average and cumulant density estimate.

[page 42↓]The frequency of back-averaged cortical correlates was around 16 Hz, while the peak frequency upon frequency analysis was about 14 Hz. However, there were no statistically significant difference between the peak frequency derived from back-averages and that de­rived from coherence spectra in those patients in whom both estimates were available. When areas under the curve were considered sensitivity for EMG-EMG coherence was greater than for EEG-EMG coherence (table 3.2) with a specificity which was better than values for sensitivity.

A single positive-negative EEG correlate exceeded others in peak-to-peak amplitude in a given series by > 10% in 31 to 56 % of muscles examined. Accordingly, we were only able to measure unambiguous time differences between EEG correlates and EMG onset in these patients. Examples of uninformative/negative (3.8A), ambiguous (3.8B and C)and unambiguous (3.8D) back-averages are illustrated in Fig. 3.8. Temporal differences mea­sured from unambiguous back-averages are summarised in Fig. 3.9.EEG lead EMG in the intrinsic hand muscles in all but one case. In contrast, EEG could lead or lag EMG in the forearm extensors. EEG’s lead or lag over EMG was always the same in direction in those muscles where temporal delays could be calculated from back-averages and frequency ana­lysis, suggesting that variability was physiological rather than technique dependent.

Fig. 3.9: Time lag between EEG and EMG based on back-averaged EEG where a negative sign indicates that EEG leads EMG and a positive sign suggests that the EMG signal precedes the EEG. Note that re­sults are comparable to phase estimates calculated by frequency analysis (fig. 7), although deriving partly from differ­rent individuals (asterixes indicate identi­cal cases represented in fig. 7). In particular, there is a wide variation of time lags suggestive of afferent and/or efferent conduction. Except in one case, EEG always leads EMG in the hand, while there is a mixed pattern of efferent and afferent conduction between EEG and finger extensor EMG.


[page 43↓]

3.3.  Discussion

It could be shown that the exaggerated functional cortico-muscular coupling in patients with cortical myoclonus is not only reflected in an exaggerated coherence between EEG and EMG, but also in an abnormally strong coherence between the EMGs of muscles co-activated by myoclonic jerks. In addition, the results demonstrate that the phase relation­ship between EEG and EMG and between pairs of EMG signals is complex, reflecting both efferent and afferent drives between cortex and muscles.

Clinical utility of frequency analysis in myoclonus

Hitherto the electrophysiological characterisation of cortical myoclonus has largely de­pended on the results of back-averaging, in which a positive result requires the demonstra­tion of a cortical correlate that precedes myoclonic EMG bursts. As stated in the introduc­tion, frequency analysis of myoclonic activity has several potential advantages over back-averaging when myoclonic EMG bursts are frequent as in minipolymyoclonus. This was borne out by the present study in which the symmetry of cortical correlates upon back-ave­raging meant that unambiguous estimates of the temporal difference between cortical cor­relate and myoclonic EMG could only be made for 31 to 56 % the muscles examined, and a cortical correlate could not be discerned for around 40% of muscles. In contrast, frequency analysis demonstrated abnormal EEG-EMG coherence for 75 to 89 % of muscles and was able to establish a temporal delay in the majority of cases.

It must be stressed that back-averaging and frequency analysis emphasise different as­pects of the data. The results of frequency analysis reflect the coupling between motor cor­tex and muscle and that between muscles due to common drive averaged over time. Here we have characterised how this coupling deviates from normal in patients with high fre­quency myoclonus. All signals within a recording are analysed, so there is the methodolo­gical concern that the index of coupling reflects both myoclonic activity and any pre-inner­vation. In practice this was not a problem in our data sets, where EMG levels were very [page 44↓]low between myoclonic bursts. However, had this not been the case then only back-avera­ging would have demonstrated the cortical correlate exclusively linked to myoclonic EMG bursts. In addition, non-reflex myoclonic bursts can be relatively infrequent in some condi­tions. In these instances current frequency analysis techniques would be inappropriate as local data stationarity is not approximated and back-averaging would offer the only possi­bility of documenting a cortical origin. Thus frequency analysis of myoclonus has advan­tages when myoclonic jerks occur at high frequency, as in minipolymyoclonus, but back-averaging is the analytical technique of choice when myoclonic bursts occur at low fre­quency.

The present study also suggests that the assessment of EMG-EMG coherence may be more useful in the future than EEG-EMG coherence in the routine neurophysiological eva­luation of patients with myoclonus. As EEG is not required the technique is less time-con­suming and applicable when movement artefact or cranial EMG activity are likely to pre­vent satisfactory EEG recordings. More importantly, the technique appears to be more sen­sitive in distinguishing abnormal and normal common drives, and for this purpose the simultaneous assessment of the coherence between the forearm extensor and ipsilateral in­trinsic hand muscles can be recommended. The greater sensitivity of EMG-EMG cohe­rence may relate to the increased coherence levels seen between these signals compared to EEG and EMG. Importantly, the increased coherence levels found between EMG signals did not seem to relate to volume conduction, as coherence occurred over relatively narrow bands and did not involve zero phase delays between EMG signals. It is possible that other forms of recording of cortical activity, such as MEG or Laplacian derivatives of EEG may provide more sensitive measurements of EEG-EMG coherence, but these techniques are not universally available or are time consuming and require multiple EEG channels.

Attention should also be drawn to the question of specificity of an elevated EEG-EMG or EMG-EMG coherence with regard to other pathologies. Inflated EEG-EMG coherences have also been reported in Parkinson’s disease (Hellwig et al., 2000) and essential tremor (Hellwig et al., 2001), but here coherence is narrow band in nature and centred on a tremor frequency of < 10 Hz. Thus EEG-EMG coherence occurs at generally lower frequencies in [page 45↓] these tremor disorders, but a comparative study of coherence in tremor and high frequency rhythmic myoclonus is necessary to establish whether there is any significant overlap in the frequency of peak coherence in these entities.

Possible homology between EEG-EMG and EMG-EMG coherence

If EMG-EMG coherence is to be useful in the clinical evaluation of cortical myoclonus then it should be a faithful marker of the functional coupling between cortex and muscle. This may not necessarily be the case as the coherence between EMG signals reflects sub­cortical and spinal inputs as well as oscillatory cortical drives to α -motoneurones. In prac­tice, however, the pattern of EEG-EMG and EMG-EMG coherence was similar, suggesting that the major oscillatory influence on spinal motoneurones, at least in this pathological state, involves the sensorimotor cortex. The one notable difference between EEG-EMG and EMG-EMG coherences was the wider frequency band of the latter. However, this band was still centered on similar peak frequencies and may simply be the product of the improved signal to noise ratio and greater coherence between pairs of EMG signals.

Preferential projection of the oscillatory corticomuscular system to the distal limb

EEG-EMG coherence was greater distally than proximally in the upper limb. This was evident in patients, but also in healthy controls, where there was no detectable coherence in deltoid using bipolar EEG electrodes. It is tempting to interpret these observations as evi­dence in favor of the preferential projection of pyramidal pathways to distal upper limb muscles (Colebatch and Gandevia, 1989; Rothwell et al., 1991; Ferbert et al., 1992; Palmar and Ashby, 1992; Marsden et al., 1999; Turton and Lemon, 1999).

However, we must first consider an alternative suggestion, that it was our use of a bipo­lar EEG lead drawn form over the hand area of the motor cortex that lead to the greater co­herences for intrinsic hand muscles. Isocoherence maps of the coherence between cortical and muscle activity in studies using magnetoencephalography or surface EEG (Salenius et [page 46↓]al., 1997; Hellwig et al., 2001) and a further study examining the distribution of coherence by Mima et al. (2000b) would suggest that the source of cortical activity coupled to EMG activity is relatively focal. On the other hand the Laplacian derivations used in many of these studies have been criticized as applying an excessively high spatial filter (Srinivasan et al., 1998), and many MEG studies start from the assumption of a point source responsi­ble for activity (Salenius et al., 1997; Brown et al., 1998). Studies using electrocorticogra­phy or tomographic modelling of EEG sources suggest a much more distributed source for the cortical inputs responsible for EEG-EMG coherence even in healthy subjects (Marsden et al., 2000a; Feige et al., 2000; Ohara et al., 2000).

In considering the possibility that our use of a bipolar EEG lead over the hand area may have contributed to the apparent preferential projection of fast conducting pyramidal path­ways to distal upper limb muscles we are fortunate in having a further measure of common inputs to motoneurones that is independent of the EEG. Importantly, EMG–EMG cohe­rence in the upper limb was also greater for a distal muscle pair compared to a proximal muscle pair.

Afferent loops in proximal muscles

A previous frequency analysis of data from patients with cortical myoclonus (Brown et al., 1999) suggested that EEG consistently lead EMG. This study limited itself to conside­ration of distal upper and lower limb muscles. Our frequency analysis and back-averaging results in the distal upper limb were in accord with this. However, here it was also demon­strated that the temporal relationships between EEG and the EMG of more proximal upper limb muscles and between pairs of EMG signals from more proximal muscles is more complex, regardless of whether relationships are calculated from time or frequency domain estimates. In many patients temporal relationships were inverted so that EMG lead EEG or a distal muscle lead a proximal muscle. In these instances an afferent loop is implicated and the myoclonic bursts in such proximal muscles may be the product of a complex inter­action of cortical, subcortical and spinal influences. It is worth noting that despite the [page 47↓]differences in phase relationships between proximal and distal muscles, coherence did not involve activity over systematically different frequency bands.

It is also notable that not all patients demonstrated temporal relationships in proximal muscles suggestive of afferent loops. The consistency of findings for different proximal muscles within the same subject argues that this is likely to represent biological variation. A variability similar to ours has been reported in the phase differences between cortex and forearm muscles in the physiological action tremor of healthy subjects (Marsden et al., 2001) and in patients with tremor due to Parkinson’s disease. Here estimated phase delays between cortex and forearm muscles were widely distributed with cortex leading or lagging by as much as 76 ms (Hellwig et al., 2000; Salenius et al., 2002). The implication is that in­dividual variation in the organisation and dominance of afferent and efferent loops to upper limb muscles occurs outside the hand. Some of this individual variation may be pathologi­cal, although in our patients there seemed no consistent correlation between the variation in phase and either the semiology of the myoclonus or the presence of concomitant epilepsy or drugs. On the other hand physiological inter-individual variation in the organisation of motor pathways to proximal muscles is increasingly recognised and may underlie the varia­bility in recovery following stroke (Hamdy and Rothwell, 1998; Turton and Lemon, 1999).

The finding of prolonged delays between EMG and EEG when estimates indicated affe­rent conduction may, at least in theory, be due to conduction delays of somatosensory path­ways as documented in some myoclonic syndromes, such as Angelman’s syndrome (Guer­rini et al., 1996). However in the patients the N1-latency was within normal limits (mean 18.6 ms ± 1.6, 95% confidence limits; value not available in case 9). Neither do delays due to cortico-cortical spread of afferent triggered cortical activity (Brown et al., 1991c) seem sufficient to account for the very excessive delays found in some of the patients. One possi­bility is the involvement of afferent pathways with indirect projections to cortex.


[page 48↓]

Delays to distal muscles

Although cortical activity lead EMG in distal upper limb muscles, the sensorimotor cortex’s lead over muscle was, in our patients, slightly shorter than expected from the TMS-induced MEP latency in the respective active muscle, recorded in healthy controls. Similar observations have been made in studies of myoclonic patients using back-avera­ging (Cantello et al., 1997) and in healthy subjects regardless of whether EEG was recor­ded with bipolar electrodes as here (personal observations), Laplacian or current source de­rivations (Mima and Hallett, 1999a and b). There may be several explanations for this, in­cluding the additional synaptic delay during cortical activation by (submaximal intensity) TMS, the way in which delay is calculated from a single point rather than the whole EMG waveform in TMS and back-averaging studies and the low-pass filtering (with phase delay) of EEG by the skull (Pfurtscheller and Cooper, 1975). However, these factors are alone un­likely to explain the shorter cortical lead in the present patients as in earlier studies, using similar analytical techniques, we found that the phase differences between EEG and EMG in distal muscles were consistent with TMS-induced MEP latencies (Brown et al., 1999; Marsden et al., 2000b). Our earlier studies involved patients with large amplitude multi­focal jerks rather than high-frequency myoclonus. These differences in phase relationship could be reconciled if we were to assume mixed afferent and efferent loops to the distal muscles of the upper limbs, with activity in these loops occupying overlapping frequency bands, as for proximal muscles. In patients with large amplitude cortical myoclonic jerks the efferent system might dominate, so that phase differences mirrored closely those expec­ted from TMS. In patients with minipolymyoclonus, there may be mixed afferent and effe­rent influences on distal muscles. The latter still dominate, so that cortex still leads, but the lead will be an underestimate as it reflects two processes occurring over the same frequen­cy range.


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