Cortical Control of Human Motoneuron Firing During Isometric Contraction

Salenius, Stephan; Portin, K.; Kajola, M.; Salmelin, Riitta; Hari, Riitta

doi:10.1152/jn.1997.77.6.3401

Cited by 501 publications

(466 citation statements)

References 24 publications

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“…Our method could however apply to cortex-muscle coherence Salenius et al, 1997Salenius et al, , 1996, corticokinmeatic coherence with active, passive or observed movements (Bourguignon et al, , 2012(Bourguignon et al, , 2011Jerbi et al, 2007;Marty et al, 2015;Piitulainen et al, 2015Piitulainen et al, , 2013aPiitulainen et al, , 2013b, and brain connectivity obtained with seed-based correlation or coherence (Brookes et al, 2012;de Pasquale et al, 2010;Hipp et al, 2012;Wens et al, 2014aWens et al, , 2014b. Also, we have demonstrated that our method of location comparison works with power-ratio maps, although it is too conservative in that context.…”

Section: Perspectivesmentioning

confidence: 97%

Contrasting functional imaging parametric maps: The mislocation problem and alternative solutions

2018

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A B S T R A C TIn the field of neuroimaging, researchers often resort to contrasting parametric maps to identify differences between conditions or populations. Unfortunately, contrast patterns mix effects related to amplitude and location differences and tend to peak away from sources of genuine brain activity to an extent that scales with the smoothness of the maps. Here, we illustrate this mislocation problem on source maps reconstructed from magnetoencephalographic recordings and propose a novel, dedicated location-comparison method. In realistic simulations, contrast mislocation was on average~10 mm when genuine sources were placed at the same location, and was still above 5 mm when sources were 20 mm apart. The dedicated location-comparison method achieved a sensitivity of~90% when inter-source distance was 12 mm. Its benefit is also illustrated on real brain-speech entrainment data. In conclusion, contrasts of parametric maps provide precarious information for source location. To specifically address the question of location difference, one should turn to dedicated methods as the one proposed here.

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Section: Perspectivesmentioning

confidence: 97%

Contrasting functional imaging parametric maps: The mislocation problem and alternative solutions

2018

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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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“…By finding the sources in an appropriate way, and analyzing the synchrony between those, we avoid the problem of spurious synchronization mentioned above, allowing for more contrast between synchronized and nonsynchronized pairs of signals, thus permitting sharper detection. Two of the methods we propose are sourceseparation algorithms based on phase synchrony, 3 applicable when one has a reference signal (Referenced Phase Analysis, RPA), or in a blind manner (Independent Phase Analysis, IPA). They are described in Section III-A and III-B, respectively.…”

Section: Introductionmentioning

confidence: 99%

Source Separation and Clustering of Phase-Locked Subspaces

Almeida

Schleimer

Bioucas-Dias

et al. 2011

IEEE Trans. Neural Netw.

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Abstract-It has been proven that there are synchrony (or phase-locking) phenomena present in multiple oscillating systems such as electrical circuits, lasers, chemical reactions, and human neurons. If the measurements of these systems cannot detect the individual oscillators but rather a superposition of them, as in brain electrophysiological signals (electo-and magneoencephalogram), spurious phase locking will be detected. Current source-extraction techniques attempt to undo this superposition by assuming properties on the data, which are not valid when underlying sources are phase-locked. Statistical independence of the sources is one such invalid assumption, as phase-locked sources are dependent. In this paper, we introduce methods for source separation and clustering which make adequate assumptions for data where synchrony is present, and show with simulated data that they perform well even in cases where independent component analysis and other well-known sourceseparation methods fail. The results in this paper provide a proof of concept that synchrony-based techniques are useful for lownoise applications.

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Cortical Control of Human Motoneuron Firing During Isometric Contraction

Cited by 501 publications

References 24 publications

Contrasting functional imaging parametric maps: The mislocation problem and alternative solutions

Contrasting functional imaging parametric maps: The mislocation problem and alternative solutions

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

Source Separation and Clustering of Phase-Locked Subspaces

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