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Logo of jphysiolThe Journal of Physiology SiteMembershipSubmissionJ Physiol
J Physiol. Feb 1, 2004; 554(Pt 3): 871–878.
Published online Nov 28, 2003. doi:  10.1113/jphysiol.2003.051235
PMCID: PMC1664802

The cerebral oscillatory network of voluntary tremor

Abstract

It has recently been shown that resting tremor in Parkinson's disease is associated with oscillatory neural coupling in an extensive cerebral network comprising a cerebello–diencephalic–cortical loop and cortical motor, somatosensory and posterior parietal areas contralateral to the tremor hand. The aim of the present study was to investigate whether this oscillatory brain network exclusively reflects a pathophysiological state in parkinsonian resting tremor or whether it constitutes a fundamental feature of physiological motor control. We investigated cerebro-muscular and cerebro-cerebral coupling in 11 healthy subjects imitating typical antagonistic parkinsonian tremor. We recorded brain activity with a 122-channel whole-head neuromagnetometer and surface EMGs of the forearm extensor. Analysis of cerebro-muscular and cerebro-cerebral coherence revealed oscillatory coupling in the same brain structures that comprise the oscillatory network of parkinsonian resting tremor. Interestingly, similar to parkinsonian resting tremor, cerebro-cerebral coherences often showed a significant peak at twice the simulated tremor frequency. The most striking differences between parkinsonian patients, as investigated in a previous study and healthy subjects imitating the antagonistic resting tremor were a reduction of the coupling between primary sensorimotor cortex and a diencephalic structure – most likely the thalamus – and an enhancement of the coupling between premotor and primary sensorimotor cortex. Our results indicate that the coupling of oscillatory activity within a cerebello–diencephalic–cortical loop constitutes a basic feature of physiological motor control. Thus, our data are consistent with the hypothesis that parkinsonian resting tremor involves oscillatory cerebro-cerebral coupling in a physiologically pre-existing network.

It has been suggested that alterations within physiological oscillatory networks comprising different brain structures could result in pathological tremors such as those in Parkinson's disease (PD) or in Essential Tremor (ET) (Volkmann et al. 1996; Farmer, 1998; Burkhard et al. 2002; for review see McAuley & Marsden, 2000; McAuley, 2003). Tremor amplitude and frequency in voluntarily simulated tremor did not differ from pathological tremors in PD and ET (Burkhard et al. 2002). The authors suggested that all tremors – voluntarily produced or pathological – may be influenced by the same central oscillations. This hypothesis has been supported by similarities shown between the brain structures involved in pathological tremor and in voluntary movements in PD patients (Duffau et al. 1996).

Current theories on the origin of parkinsonian resting tremor stress the pivotal role of central oscillators (for review see McAuley & Marsden, 2000; Deuschl et al. 2001; McAuley, 2003). Thalamus or basal ganglia are the most likely sites for a central oscillator and could be predicted to generate tremor-related activity (Hua et al. 1998). Intracortical recordings in animals as well as in humans undergoing neurosurgery suggest that PD oscillations in the tremor frequency arise from 3–6 Hz oscillatory activity in the thalamus. This is probably due to altered basal ganglia input (Rothwell, 1998; Brown et al. 2001; for review see McAuley & Marsden, 2000; McAuley, 2003). As a consequence, thalamic oscillations could be transmitted to the premotor and primary motor cortex resulting in resting tremor. Actually, it has been demonstrated that the primary senorimotor cortex is involved in tremor generation in both ET (Hellwig et al. 2001) and PD (Volkmann et al. 1996; Timmermann et al. 2003). Contrarily, Halliday et al. (2000) could not replicate these findings in patients with ET by using magnetoencephalography (MEG).

In recent MEG studies it has been demonstrated that resting tremor in PD is associated with rhythmic oscillations in a cerebral network (Volkmann et al. 1996; Timmermann et al. 2003). Timmermann et al. (2003) demonstrated that this network comprises contralateral primary motor cortex, secondary somatosensory cortex, posterior-parietal cortex, lateral as well as mesial premotor areas, ipsilateral cerebellum, and a diencephalic structure, most likely the thalamus (Timmermann et al. 2003). Coupling between these areas has been shown in the tremor frequency (3–6 Hz) but primarily in the double tremor frequency band.

This implies that resting tremor in PD might be based on altered oscillations within a pre-existing cerebral network. The aim of our study was to investigate whether the oscillatory network of PD resting tremor demonstrated by Timmermann et al. (2003) reflects a pathophysiological state in parkinsonian resting tremor or whether it constitutes a fundamental feature of motor control.

Methods

Subjects and protocol

We recorded neuromagnetic activity in 11 healthy subjects (mean age 28.9 ± 2.4 years, range 21–41 years; 2 left-handed, 9 right-handed) while they imitated typical 3–6 Hz parkinsonian resting tremor. Subjects had no neurological deficits and were naive with regard to the experiment's purpose. All subjects received their instructions by watching a video showing a patient suffering from a typical parkinsonian resting tremor. Subjects performed the task with the dominant hand continuously for 5 min. All subjects gave their written informed consent prior to the experiment. The study was performed with the approval of the local ethics committee and was in accordance with the Declaration of Helsinki.

Data collection

We recorded neuromagnetic activity with a helmet-shaped 122-channel whole-head neuromagnetometer (Neuromag™) in a magnetically shielded room while subjects imitated the PD resting tremor. Simultaneously, muscle activity was registered with surface EMG placed on the extensor digitorum communis (EDC) and on the flexor digitorum longus (FDL) muscle of the dominant hand. MEG and EMG signals were recorded with a bandpass filter of 0.03–330 Hz digitized with 1011 Hz and stored digitally for off-line analysis. Eye blinks were controlled by vertical EOG and contaminated epochs were excluded from further data analysis.

We determined the exact position of the head with respect to the sensor-array by measuring magnetic signals from four coils placed on the scalp. High-resolution T1-weighted magnetic resonance images (MRI) were obtained from each subject. Three anatomical landmarks (nasion, preauricular points left and right) were localized in each individual and used for the alignment of the MRI and MEG coordinate system. EMG signals were high-pass filtered at 60 Hz and rectified offline.

Data analysis

In order to identify the sources within the brain which are coupled to the surface EMG we used a recently developed analysis tool, DICS (Dynamic Imaging of Coherent Sources) (Gross et al. 2001), which employs a spatial filter and a realistic head model. DICS provides a topographic map of cerebro-muscular and cerebro-cerebral coherence. Coherence is a normalized measure quantifying dependencies in the frequency domain. Values can range between 0, indicating complete independence of two signals, and 1, representing a perfectly linear relationship (for details see Schnitzler et al. 2000). With this tool, coherence can be calculated even for sources in deep brain structures. In order to achieve this, the signal-to-noise ratio has to be enhanced (i) by averaging data over the whole measurement period, (ii) by calculating the coherence in a narrow frequency band, and, in particular, (iii) by using a spatial filter.

The brain areas with the strongest coherence to the EMG signal at tremor and at double tremor frequency were detected and defined as reference regions for further coherence analysis between brain areas. After identifying a brain source coherent with the reference region the exact localization in three-dimensional space was determined. For subsequent analysis the strongest local maxima in the cerebro-cerebral coherence map were used. Coherence spectra between all combinations of identified areas and between all areas and EMG were computed with a resolution of 0.98 Hz. The frequencies of coherence above the 95% confidence level were identified. We calculated the confidence limit for cerebro-muscular coupling according to Halliday et al. (1995). Cerebro-cerebral coherence confidence limits were computed from surrogate data by randomly shuffling the original time courses, thereby maintaining frequency content but destroying all real coherence.

We used coherence to identify cerebro-muscular and cerebro-cerebral functional coupling. Unfortunately, significant coherence between two signals may also occur if both signals simply share a common input from a third source. An additional partial coherence analysis allows distinguishing between direct functional coupling and a common input as the cause for significant coherence. Partial coherence (Halliday et al. 1995; Mima et al. 2000; Ohara et al. 2001) represents coherence between two signals after eliminating a possible common input from a third signal. This analysis indicates how much of a coupling between two signals (e.g. brain areas) can be explained by independent coupling of both signals with a third signal (e.g. an EMG). In a first step, we calculated the cerebro-muscular partial coherence. Using this analysis – which provides information on ‘true cerebro-muscular coupling’– we were able to calculate to what extent coupling between different brain areas and EMG can be explained by a common input of M1/S1. In a second step, we calculated cerebro-cerebral partial coherence, which allows an assessment of the extent to which coherence between identified brain areas and M1/S1 could be explained by a common input from the muscles. Consequently, this analysis allows an estimation of ‘true cerebro-cerebral coupling’.

To calculate MEG–EMG phase differences both signals were filtered with a narrow band-pass filter around the peak frequency of cerebro-muscular coherence. In order to separate phase and amplitude the Hilbert transform was applied to the filtered MEG and EMG signals. The instantaneous phase differences between the MEG and EMG signal at times of maximum amplitude were averaged. The resulting mean phase lag allowed calculation of cerebro-muscular delay (for details see Schnitzler et al. 2000; Gross et al. 2000).

Results

All 11 subjects experienced no difficulties in simulating antagonistic resting tremor. Figure 1A shows the typical antagonistic tremor pattern with alternating bursts of EDC and FDL EMG. As we could find no differences between cerebro-muscular coupling of EDC and FDL activity, only EDC-related data will be reported. Power spectral analysis of the surface EMG revealed a peak at tremor frequency (3.9 ± 0.1 Hz; mean ±s.e.m.) and a second peak at double tremor frequency (8.1 Hz ± 0.1 Hz; mean ±s.e.m.) in 10 subjects (Fig. 1A). In one subject no peak in the double tremor frequency was detected; in another subject no peak at tremor frequency was observed. Power amplitude was 491.9 ± 92.9 μV at tremor frequency and 331.3 ± 94.4 μV at double tremor frequency (mean ±s.e.m.).

Figure 1
Analysis of cerebro-muscular coherence in a representative subject imitating parkinsonian resting tremor with the dominant right hand

We found peaks in the same frequency ranges in the coherence between MEG sensors covering contralateral sensorimotor cortex and EDC (Fig. 1B). In five subjects coherence was strongest at tremor frequency and in six subjects at double tremor frequency. Consistently, in all subjects the source with the strongest coherence to EMG in both frequency bands was localized in the primary sensorimotor hand area contralateral to the imitating hand (S1/M1) (Fig. 1C). Significant coupling between EDC and S1/M1 was observed in all 11 subjects. In 9 individuals significant coupling was observed at tremor frequency and in 10 subjects at double tremor frequency. Mean strength of coherence was 4.8 ± 0.9% at tremor frequency and 13.5 ± 2.6% at double tremor frequency.

The distribution of phase differences between S1/M1 and EMG showed peaks at –340 ± 15.1 deg (corresponding to a time difference of –47.6 ± 1.9 ms), –156.4 ± 10.8 deg (–21.7 ± 2.1 ms), 139.4 ± 7.7 deg (19.4 ± 1.1 ms) and 318.2 ± 13.0 deg (44.2 ± 1.8 ms) (mean ±s.e.m.). Negative time values indicate that EMG activity leads cortical activity. Correspondingly, positive values indicate that cortical activity leads EMG activity. These data imply that this activation originates in the primary motor cortex (M1) due to motor command as well as in the primary somatosensory cortex (S1) due to somatosensory feedback. Calculation of cerebro-cerebral coherence to sensorimotor cortex consistently revealed a number of other areas. We found significant coherences between S1/M1 contralateral to the imitating hand and the premotor cortex, supplementary motor area (SMA/CMA), secondary somatosensory cortex (S2) and posterior-parietal cortex (PPC) contralateral and the cerebellum ipsilateral to the imitating hand. Identified cerebral sources were also coherent to the EMG of the forearm extensor (Fig. 2). Cerebro-muscular coupling was observed at tremor and at double tremor frequency. We found significant coupling between EDC and cerebellum in 10 subjects (9 at tremor frequency and 3 at double tremor frequency), between EDC and the diencephalic structure in 7 subjects (5 at tremor frequency and 5 at double tremor frequency), and between EDC and premotor cortex in 11 subjects (9 at tremor frequency and 6 at double tremor frequency). Since analysis of the cerebro-muscular partial coherence showed no significant differences, cerebro-muscular coupling could not be explained as an independent coupling of the cerebral sources and EDC with the source in S1/M1.

Figure 2
Localization of cerebral sources and coherence spectra to right EDC muscle in another representative subject imitating the PD resting tremor with the right hand

Analysis of cerebro-cerebral coherence revealed coupling at both tremor frequency and double tremor frequency (Fig. 3). Coupling between cerebellum and diencephalon was observed in 10 subjects (4 at tremor frequency, 8 at double tremor frequency), between diencephalon and M1/S1 in 7 subjects (4 at tremor frequency, 3 at double tremor frequency), between diencephalon and premotor cortex in 9 subjects (7 at tremor frequency, 3 at double tremor frequency), and between M1/S1 and premotor cortex in 9 subjects (5 at tremor frequency, 5 at double tremor frequency). Furthermore, we observed significant coupling in the 20 Hz range between cerebellum and diencephalic structure in 8 subjects and between diencephalon and M1/S1 in 4 subjects.

Figure 3
Cerebro-cerebral coherence between different brain areas in one representative subject

Calculation of cerebro-cerebral partial coherence showed no significant differences to the coherence values indicating that cerebro-cerebral coherences could not be explained by cerebro-muscular coupling.

Discussion

We studied neuromagnetic activity during the imitation of the typical antagonistic parkinsonian resting tremor in healthy subjects. Our data demonstrate that voluntary tremor is associated with an oscillatory neuronal network comprising primary sensorimotor cortex, premotor cortex, posterior-parietal, secondary somatosensory cortex and a diencephalic structure contralateral to, and the cerebellum ipsilateral to, the imitating hand. Cerebro-cerebral as well as cerebro-muscular coupling was observed at tremor frequency and also at double tremor frequency. Calculation of partial coherences demonstrated that coherence between two signals could not be explained by independent coupling with third sources. Analysis of cerebro-muscular coupling demonstrated a highly significant coherence between EDC and primary sensorimotor cortex. These results dovetail with previous studies which demonstrated the involvement of this area in voluntary (Conway et al. 1995; Gross et al. 2000) as well as in involuntary movements (Volkmann et al. 1996; Halliday et al. 2000; Hellwig et al. 2001; Timmermann et al. 2003).

Calculation of phase lags between EDC and primary sensorimotor cortex showed peaks at about 20 and 40 ms which indicate the involvement of different pathways and/or neurones with different conduction velocities. Phase lags of about 20 ms have been frequently shown (Gross et al. 2002; Salenius et al. 2002; Timmermann et al. 2003) indicating the involvement of fast conducting pyramidal pathways in PD resting tremor (Salenius et al. 2002; Timmermann et al. 2003) as well as in voluntary action in healthy controls (Gross et al. 2002). Besides these fast corticospinal axons the pyramidal tract also consists of more slowly conducting fibres (Edgley et al. 1997). Salenius et al. (2002), investigating PD patients, showed phase lags of about 30 ms in some subjects. They suggested that PD action tremor could be associated with slowly conducting pathways, while fast conducting axons are probably involved in the generation of PD resting tremor.

In a recent study it has been shown that an oscillatory network comprising the same cerebral structures and coupling in the same frequency bands is associated with resting tremor in PD (Volkmann et al. 1996; Timmermann et al. 2003). The functional significance of the double tremor frequency is still under debate. On the one hand, double tremor frequency represents the second harmonic of the tremor frequency. Since the tremor signal in the EMG does not fit a pure sinus wave it seems plausible that harmonics or subharmonics of the tremor frequency occur. Alternatively, this frequency could represent a physiological feature of the motor system underlying the control of alternating activation of agonistic and antagonistic forearm muscles (Timmermann et al. 2003). Interestingly, it has been demonstrated that a cerebello–thalamo–cortical loop, oscillating at 8–10 Hz, underlies discontinuities in slow finger movements, which result from alternating activation of agonist and antagonist muscles (Gross et al. 2002). Finally, the fastest alternating hand movements yield a maximum at 8–10 Hz (Freund, 1983) and physiological tremor occurs with a frequency of 8–12 Hz (Deuschl et al. 1998). These findings suggest that a frequency around 10 Hz – corresponding to double the resting tremor frequency – might represent a physiological feature of the motor system. Another explanation which would account for the double tremor frequency is that alternating movements are associated with sensory feedback from the movement's forward and backwards action. Therefore, double tremor frequency might be caused by mechanoreceptor feedback.

Interestingly, besides the cerebro-cerebral coupling evident at tremor frequency and double tremor frequency we also found additional coupling in the 20 Hz range. It has been supposed that oscillations in this frequency band might be considered part of the pathophysiology of PD (Brown et al. 2001; Timmermann et al. 2003). Brown et al. (2001) demonstrated that in humans coupling between the subthalamic nucleus and the pallidum in the 20 Hz range depends on the level of dopaminergic activity: in PD patients levodopa treatment leads to reduced activity in the 20 Hz band in power as well as in coherence between both structures (Brown et al. 2001). Our data demonstrate that oscillatory coupling between the thalamus and other brain structures at 20 Hz does not per se serve as an explanation for the pathophysiology of PD. Since in the study of Brown et al. (2001) coupling in the 20 Hz band did not vanish after levodopa treatment one might speculate that coupling at a moderate strength is physiological, but that once a certain amount is exceeded pathological motor symptoms as in PD would occur.

As can be seen in Fig. 3 coupling between S1/M1 and PPC does not exactly occur at double tremor frequency. Therefore, one might speculate whether coupling frequency depends on the particular interacting areas involved. Since we found slight shifts of frequencies in couplings between all areas, we can rule out that this observation is a result of coupling between specific areas. The observed variability is most likely due to the fact that coherence spectra were computed with a resolution of 0.98 Hz. Therefore, shifts of the main coupling frequency up to ± 2 Hz might occur.

Even though our data demonstrate striking similarities between the oscillatory network of pathological PD tremor and voluntary tremor, we found some differences when we compared the present results directly with the results of PD tremor research by Timmermann et al. (2003). Firstly, in the healthy controls significant coupling between diencephalic structure and primary sensorimotor cortex was clearly reduced in all frequency bands compared to the PD patient group. Secondly, coupling between premotor cortex and primary sensorimotor cortex was enhanced in the healthy group but only at tremor frequency. These results imply a much lesser influence of the thalamus on the activity of the primary sensorimotor cortex in the healthy group while imitating tremor. Stronger coupling between premotor cortex and primary sensorimotor cortex in the tremor frequency indicates that the premotor cortex might drive M1 resulting in the voluntary 3–6 Hz tremor. In contrast, in the patient group the premotor cortex and M1 might be driven by deep diencephalic structures like the thalamus resulting in involuntary tremor. Interestingly, it has been demonstrated that PD is associated with higher coupling in the frequency around 6 Hz and 20 Hz between the subthalamic nucleus and the pallidum, which is diminished after levodopa treatment (Brown et al. 2001). The internal part of the globus pallidus is the major output nucleus of the basal ganglia and receives substantial input from the subthalamic nucleus (Parent & Hazrati, 1995). In monkeys treated with MPTP, synchronized oscillatory activity was demonstrated in these structures (Nini et al. 1995; Raz et al. 1996). Thus, altered oscillatory activity within thalamus and basal ganglia seems to be highly correlated with the symptoms of PD. As a consequence, this altered oscillatory activity and coupling between thalamus and basal ganglia might be transferred to the primary and premotor cortex resulting in involuntary tremor. To support this hypothesis, Llinás et al. (1999) demonstrated thalamocortical dysrhythmia in patients suffering from PD but also in other psychiatric diseases. In contrast, activation of the premotor cortex is associated with the initiation of voluntary motor action (Halsband et al. 1993, 1994).

When compiled, these results indicate that in healthy subjects the imitation of resting tremor could be caused by interaction between premotor and motor cortex while in patients with PD tremor may result from pathological oscillatory activity within a basal ganglia–thalamocortical loop. However, one has to exercise caution when comparing directly the data of the present study with those of the study by Timmermann et al. (2003) as subjects and patients were not age-matched and we did not control kinematics of the movements. Therefore, we cannot definitely rule out that the observed differences within the coupling network could be due to different kinematics of patients and healthy subjects.

To conclude, the results of our study demonstrate that the same brain areas are involved in voluntary tremor as in parkinsonian resting tremor. Therefore, our data strongly support the hypothesis that pathological tremors might be based on a physiological pre-existing cerebral oscillatory network (Duffau et al. 1996; Farmer, 1998; McAuley & Marsden, 2000; Burkhard et al. 2002; McAuley, 2003).

Acknowledgments

We would like to thank our participants for their cooperation, Erika Rädisch for technical support during MRI scans and Frank Schmitz for expert help with MEG measurements. This study was supported by Volkswagen-Stiftung (I/73240) and Deutsche Forschungsgemeinschaft (SFB 575, C4).

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