Cortical correlates of the basic and first harmonic frequency of Parkinsonian tremor

Raethjen, Jan; Govindan, Rathinaswamy B.; Muthuraman, M.; Kopper, F.; Volkmann, Jens; Deuschl, Günther

doi:10.1016/j.clinph.2009.06.028

Cited by 53 publications

(44 citation statements)

References 24 publications

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“…In agreement with previous findings on corticomuscular synchronization (for review, see Mima and Hallett (1999a); Salenius and Hari (2003)), the proposed statistical test improves the detection of the significant areas of correlation at 10 Hz and 20 Hz between EEG and EMG in the timefrequency plane. Significant corticomuscular interactions were found around 20 Hz in agreement with the literature (see Conway et al (1995); Kilner et al (2000); Salenius et al (1997)) but also around 10 Hz, a finding that is less common but nonetheless reported in several studies in healthy subjects (see Feige et al (2000); Marsden et al (2001)) and in Parkinson's patients (see (Raethjen et al (2009)). …”

Section: Discussion On the Analysis Of Corticomuscular Interactionssupporting

confidence: 84%

A new statistical test based on the wavelet cross-spectrum to detect time–frequency dependence between non-stationary signals: Application to the analysis of cortico-muscular interactions

Bigot¹,

Longcamp²,

Maso³

et al. 2011

NeuroImage

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The study of the correlations that may exist between neurophysiological signals is at the heart of modern techniques for data analysis in neuroscience. Wavelet coherence is a popular method to construct a time-frequency map that can be used to analyze the time-frequency correlations between two time series. Coherence is a normalized measure of dependence, for which it is possible to construct confidence intervals, and that is commonly considered as being more interpretable than the wavelet cross-spectrum (WCS). In this paper, we provide empirical and theoretical arguments to show that a significant level of wavelet coherence does not necessarily correspond to a significant level of dependence between random signals, especially when the number of trials is small. In such cases, we demonstrate that the WCS is a much better measure of statistical dependence, and a new statistical test to detect significant values of the cross-spectrum is proposed. This test clearly outperforms the limitations of coherence analysis while still allowing a consistent estimation of the time-frequency correlations between two non-stationary stochastic processes. Simulated data are used to investigate the advantages of this new approach over coherence analysis. The method is also applied to experimental data sets to analyze the time-frequency correlations that may exist between electroencephalogram (EEG) and surface electromyogram (EMG).

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Section: Discussion On the Analysis Of Corticomuscular Interactionssupporting

confidence: 84%

A new statistical test based on the wavelet cross-spectrum to detect time–frequency dependence between non-stationary signals: Application to the analysis of cortico-muscular interactions

Bigot¹,

Longcamp²,

Maso³

et al. 2011

NeuroImage

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“…On the contrary, the fact that there was no systematic bias in the error between the analytical and experimental results across all measurements is incompatible with a neural drive to the muscles reflecting a superimposition of the output of two oscillators. Although the results cannot rule out the presence of a second neural oscillator at twice the tremor frequency projecting to the motor neurons, as previously indicated by analysis of EEG-EMG coherence [17], the findings suggest that its relative contribution to the output of the motor neurons (neural drive to the muscle) would in any case be low ( figure 5). Alternatively, the relative timing between the two oscillations needs to be perfectly regular, which seems unlikely, given the experimental inter-burst interval variability (figure 8).…”

Section: Discussionmentioning

confidence: 66%

“…Therefore, spectral characteristics of tremulous movements presumably reflect specific neural mechanisms for muscle control, although the central neural networks from which tremor in ET and PD originates are currently not fully understood [15,16]. Recently, it has been suggested that two separate neural oscillators projecting to the muscle via different pathways are responsible for the spectral peaks at the base tremor frequency and at twice that frequency, respectively [17,18]. This may provide a possible explanation for the observed differences across ET and PD patients as it implies that the amplitude of spectral peaks at integer multiples of the tremor frequency reflects the relative strength of two central oscillatory networks rather than the harmonics of one input at the base frequency.…”

Section: Introductionmentioning

confidence: 99%

One central oscillatory drive is compatible with experimental motor unit behaviour in essential and Parkinsonian tremor

et al. 2015

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Objective. Pathological tremors are symptomatic to several neurological disorders that are difficult to differentiate and the way by which central oscillatory networks entrain tremorogenic contractions is unknown. We considered the alternative hypotheses that tremor arises from one oscillator (at the tremor frequency) or, as suggested by recent findings from the superimposition of two separate inputs (at the tremor frequency and twice that frequency). Approach. Assuming one central oscillatory network we estimated analytically the relative amplitude of the harmonics of the tremor frequency in the motor neuron output for different temporal behaviors of the oscillator. Next, we analyzed the bias in the relative harmonics amplitude introduced by superimposing oscillations at twice the tremor frequency. These findings were validated using experimental measurements of wrist angular velocity and surface electromyography (EMG) from 22 patients (11 essential tremor, 11 Parkinson's disease). The ensemble motor unit action potential trains identified from the EMG represented the neural drive to the muscles. Main results. The analytical results showed that the relative power of the tremor harmonics in the analytical models of the neural drive was determined by the variability and duration of the tremor bursts and the presence of the second oscillator biased this power towards higher values. The experimental findings accurately matched the analytical model assuming one oscillator, indicating a negligible functional role of secondary oscillatory inputs. Furthermore, a significant difference in the relative power of harmonics in the neural drive was found across the patient groups, suggesting a diagnostic value of this measure (classification accuracy: 86%). This diagnostic power decreased substantially when estimated from limb acceleration or the EMG. Signficance. The results indicate that the neural drive in pathological tremor is compatible with one central network providing neural oscillations at the tremor frequency. Moreover, the regularity of this neural oscillation varies across tremor pathologies, making the relative amplitude of tremor harmonics a potential biomarker for diagnostic use.

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“…The computation process was the same as in previous cortico-muscular coherence studies [25–27]. Following segmentation of the data stream into 70 segments of 1-s duration, the segment was windowed with a 1-s Hamming window (90% overlap), and a Fourier transform was used.…”

Section: Methodsmentioning

confidence: 99%

Functional recovery from chronic writer’s cramp by brain-computer interface rehabilitation: a case report

et al. 2014

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BackgroundDystonia is often currently treated with botulinum toxin injections to spastic muscles, or deep brain stimulation to the basal ganglia. In addition to these pharmacological or neurosurgical measures, a new noninvasive treatment concept, functional modulation using a brain-computer interface, was tested for feasibility. We recorded electroencephalograms (EEGs) over the bilateral sensorimotor cortex from a patient suffering from chronic writer’s cramp. The patient was asked to suppress an exaggerated beta frequency component in the EEG during hand extension.ResultsThe patient completed biweekly one-hour training for 5 months without any adverse effects. Significant decrease of the beta frequency component during handwriting was confirmed, and was associated with clear functional improvement.ConclusionThe current pilot study suggests that a brain-computer Interface can give explicit feedback of ongoing cortical excitability to patients with dystonia and allow them to suppress exaggerated neural activity, resulting in functional recovery.

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Cortical correlates of the basic and first harmonic frequency of Parkinsonian tremor

Cited by 53 publications

References 24 publications

A new statistical test based on the wavelet cross-spectrum to detect time–frequency dependence between non-stationary signals: Application to the analysis of cortico-muscular interactions

A new statistical test based on the wavelet cross-spectrum to detect time–frequency dependence between non-stationary signals: Application to the analysis of cortico-muscular interactions

One central oscillatory drive is compatible with experimental motor unit behaviour in essential and Parkinsonian tremor

Functional recovery from chronic writer’s cramp by brain-computer interface rehabilitation: a case report

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