Using electroencephalography to study functional coupling between cortical activity and electromyograms during voluntary contractions in humans

Halliday, David M.; Conway, B.A.; Farmer, Simon F.; Rosenberg, J.R.

doi:10.1016/s0304-3940(97)00964-6

Cited by 351 publications

(272 citation statements)

References 11 publications

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“…Magnetoencephalography (MEG) and electroencephalography (EEG) studies have shown that the β-CMC is derived from the primary motor cortex (M1) and drives the muscle activities of the limbs and fingers through spinal motoneurons (MEG: Conway et al, 1995;Salenius et al, 1996Salenius et al, , 1997Brown et al, 1998;Gross et al, 2000) (EEG: Halliday et al, 1998;Mima et al, 2000). In our previous MEG study (Maezawa et al, 2014c), in addition to finding that the β-CMC for the thumb occurred over the contralateral hemisphere, we also found that the CMC for the tongue was detected at two different frequency bands (the β band and a low-frequency band at 2-10 Hz) over both hemispheres during isometric tongue protrusion for each side of the tongue.…”

Section: Introductionmentioning

confidence: 99%

Cortico-muscular synchronization by proprioceptive afferents from the tongue muscles during isometric tongue protrusion

et al. 2016

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Tongue movements contribute to oral functions including swallowing, vocalizing, and breathing. Fine tongue movements are regulated through efferent and afferent connections between the cortex and tongue. It has been demonstrated that cortico-muscular coherence (CMC) is reflected at two frequency bands during isometric tongue protrusions: the beta (β) band at 15-35 Hz and the low-frequency band at 2-10 Hz. The CMC at the β band (β-CMC) reflects motor commands from the primary motor cortex (M1) to the tongue muscles through hypoglossal motoneuron pools. However, the generator mechanism of the CMC at the low-frequency band (low-CMC) remains unknown. Here, we evaluated the mechanism of low-CMC during isometric tongue protrusion using magnetoencephalography (MEG). Somatosensory evoked fields (SEFs) were also recorded following electrical tongue stimulation. Significant low-CMC and β-CMC were observed over both hemispheres for each side of the tongue.Time-domain analysis showed that the MEG signal followed the electromyography signal for low-CMC, which was contrary to the finding that the MEG signal preceded the electromyography signal for β-CMC. The mean conduction time from the tongue to the cortex was not significantly different between the low-CMC (mean, 80.9 ms) and SEFs (mean, 71.1 ms). The cortical sources of low-CMC were located significantly posterior (mean, 10.1 mm) to the sources of β-CMC in M1, but were in the same area as tongue SEFs in the primary somatosensory cortex (S1). These results reveal that the low-CMC may be driven by proprioceptive afferents from the tongue muscles to S1, and that the oscillatory interaction was derived from each side of the tongue to both hemispheres. Oscillatory proprioceptive feedback from the tongue muscles may aid in the coordination of sophisticated tongue movements in humans.

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Section: Introductionmentioning

confidence: 99%

Cortico-muscular synchronization by proprioceptive afferents from the tongue muscles during isometric tongue protrusion

et al. 2016

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“…During sustained contractions, primary motor cortex shows oscillations in alpha (8 12 Hz) and beta (15 30 Hz) bands (Murthy and Fetz, 1992;Sanes and Donoghue, 1993;Baker et al, 2003). Although oscillations in both frequency bands are e↵ectively carried down the corticospinal tract (Baker et al, 2003), most studies using sustained contractions find that only beta-band oscillations are coherent between motor cortex and muscle activity Halliday et al, 1998;Baker et al, 1997;Gross et al, 2000). Corticomuscular beta-band coherence is most prominent during tonic muscle contractions and disappears during movement (Baker et al, 1997;Riddle and Baker, 2006;Kilner et al, 2000;Baker et al, 1999) and beta-band activity is enhanced when higher precision is required Kristeva-Feige et al, 2002;Witte et al, 2007;Gilbertson et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

The reorganization of corticomuscular coherence during a transition between sensorimotor states

2014

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Recent research suggests that neural oscillations in di↵erent frequency bands support distinct and sometimes parallel processing streams in neural circuits. Studies of the neural dynamics of human motor control have primarily focused on oscillations in the beta band (15 30 Hz). During sustained muscle contractions, corticomuscular coherence is mainly present in the beta band, while coherence in the alpha (8 12 Hz) and gamma (30 80 Hz) bands has not been consistently found. Here we test the hypothesis that the frequency of corticomuscular coherence changes during transitions between sensorimotor states. Corticomuscular coherence was investigated in twelve participants making rapid transitions in force output between two targets. Corticomuscular coherence was present in the beta band during sustained contractions but vanished before movement onset, being replaced by transient synchronization in the alpha and gamma bands during dynamic force output. Analysis of the phase spectra suggested a time delay from muscle to cortex for alpha-band coherence, by contrast to a time delay from cortex to muscle for gamma-band coherence, indicating a↵erent and e↵erent corticospinal interactions respectively. Moreover, alpha and gamma -band coherence revealed distinct spatial topologies, suggesting di↵erent generative mechanisms. Coherence in the alpha and gamma bands was almost exclusively confined to trials showing a movement overshoot, suggesting a functional role related to error correction. We interpret the dual-band synchronization in the alpha and gamma bands as parallel streams of corticospinal processing involved in parsing prediction errors and generating new motor predictions.

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“…The frequency or spectral contents of such signals display important information and have been used to evaluate the physiological and functional state of the nervous systems. Fourier analysis has been used extensively in studying the spectra of neurophysiological signals and in recent years interests have been focused on the study of synchronised phenomena between two or more events, such as the synchronisation between brain areas [2,31] and correlation between electroencephalogram (EEG), electromyogram (EMG) and magnetoencephalogram (MEG) [9,21,22,7]. In general, the investigated signals or data are assumed to be stationary and the estimates of the individual and cross spectra are calculated to obtain the coherence estimate.…”

Section: Introductionmentioning

confidence: 99%

Detecting time-dependent coherence between non-stationary electrophysiological signals—A combined statistical and time–frequency approach

Zhan

Halliday

Jiang

et al. 2006

Journal of Neuroscience Methods

112

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Various time-frequency methods have been used to study the time-varying properties of non-stationary neurophysiological signals. In the present study, a time-frequency coherence using continuous wavelet transform (CWT) together with its confidence intervals are proposed to evaluate the correlation between two non-stationary processes. A systematic comparison between approaches using CWT and short-time Fourier transform (STFT) is carried out. Simulated data are generated to test the performance of these methods when estimating time-frequency based coherence. Surprisingly and in contrast to the common belief, the coherence estimation based upon CWT does not always supersede STFT. We suggest that a combination of STFT and CWT would be most suitable for analysing non-stationary neural data. In both frequency and time domains, methods to test whether there are two coherent signals presented in recorded data are presented. Our approach is then applied to the electroencephalogram (EEG) and surface electromyogram (EMG) during wrist movements in healthy subjects and the local field potential (LFP) and surface EMG during resting tremor in patients with Parkinson's disease. A software package including all results presented in the current paper is available at

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Using electroencephalography to study functional coupling between cortical activity and electromyograms during voluntary contractions in humans

Cited by 351 publications

References 11 publications

Cortico-muscular synchronization by proprioceptive afferents from the tongue muscles during isometric tongue protrusion

Cortico-muscular synchronization by proprioceptive afferents from the tongue muscles during isometric tongue protrusion

The reorganization of corticomuscular coherence during a transition between sensorimotor states

Detecting time-dependent coherence between non-stationary electrophysiological signals—A combined statistical and time–frequency approach

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