Coherent neural representation of hand speed in humans revealed by MEG imaging

By K. Jerbi, Jean-Philippe Lachaux, K. N’diaye, Dimitrios Pantazis, Richard M. Leahy, Line Garnero, Sylvain Baillet. Proceedings of the National Academy of Sciences of the United States of America, 2007

298 citations recorded by the source corpus.

The spiking activity of single neurons in the primate motor cortex is correlated with various limb movement parameters, including velocity. Recent findings obtained using local field potentials suggest that hand speed may also be encoded in the summed activity of neuronal populations. At this macroscopic level, the motor cortex has also been shown to display synchronized rhythmic activity modulated by motor behavior. Yet whether and how neural oscillations might be related to limb speed control is still poorly understood. Here, we applied magnetoencephalography (MEG) source imaging to the ongoing brain activity in subjects performing a continuous visuomotor (VM) task. We used coherence and phase synchronization to investigate the coupling between the estimated activity throughout the brain and the simultaneously recorded instantaneous hand speed. We found significant phase locking between slow (2- to 5-Hz) oscillatory activity in the contralateral primary motor cortex and time-varying hand speed. In addition, we report long-range task-related coupling between primary motor cortex and multiple brain regions in the same frequency band. The detected large-scale VM network spans several cortical and subcortical areas, including structures of the frontoparietal circuit and the cerebello–thalamo–cortical pathway. These findings suggest a role for slow coherent oscillations in mediating neural representations of hand kinematics in humans and provide further support for the putative role of long-range neural synchronization in large-scale VM integration. Our findings are discussed in the context of corticomotor communication, distributed motor encoding, and possible implications for brain–machine interfaces.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/52dfd7b3a7ea4ecc400b335e4b7f680d602d02a6

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In Cognitive Neuroscience

Citations in the corpus

Review of the BCI Competition IV (2012, unknown): “, 2010; Wang and Makeig, 2010), and MEG (Jerbi et al., 2007; Waldert et al., 2008; Bradberry et al., 2009; Wang et al., 2010).”
Measuring functional connectivity in MEG: A multivariate approach insensitive to linear source leakage (2012, methodology): “…(EEG)) to measure connectivity has been shown by a large number of studies (Gow et al., 2008; Gross et al., 2001, 2002; Ioannides et al., 2000; Jerbi et al., 2007; Nolte et al., 2004, 2008; Ramnania et al., 2004; Schlögl and Supp, 2006; Schoffelen and Gross, 2009; Tass et al., 1998).”
Frequency-dependent functional connectivity within resting-state networks: An atlas-based MEG beamformer solution (2012, unknown): “Various modifications of linear estimators have been used to reconstruct the time-series for a large number of locations (de Pasquale et al., 2010; Ghuman et al., 2011; Harle et al., 2004; Jerbi et al., 2007; Lin et al., 2004), for a set of cortical patches (e.”
Source connectivity analysis with MEG and EEG (2009, methodology): “This phase synchronization analysis has been used in source connectivity analysis to complement traditional co- herence analysis [Jerbi et al., 2007; Kujala et al., 2007; Lin et al., 2004].”
Inter-Brain Synchronization during Social Interaction (2010, unknown): “The theta synchronization was found between the two right parietal regions but not in motor regions, although theta band is involved in the encoding of low level parameters of hand movements such as position [59–61] and speed [62].”
Action understanding and active inference (2011, unknown): “” (Jerbi et al. 2007). Evidence for localised oscillations or coherence during writing (or writing observation) is sparse; however, Butz et al. (2006) were able to show that “coherence between cortical sources and muscles appeared primarily in the frequency of writing movements (3–7Hz), while cohere”
Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy. (2015, unknown): “The MEG data segments used for the purpose of the current analysis were extracted from the pre-stimulus baseline of a visuomotor MEG experiment (Jerbi et al., 2007b), and each trial was assigned one of 2 (or of 4) arbitrary labels for the 2-class (or 4-class) classiﬁcation .”
Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives (2014, unknown): “3-D velocity of hand movement from the very low frequency SMRs [45]–[47].”