Neural synchrony within the motor system: what have we learned so far?

Abstract

Synchronization of neural activity is considered essential for information processing in the nervous system. Both local and inter-regional synchronization are omnipresent in different frequency regimes and relate to a variety of behavioral and cognitive functions. Over the years, many studies have sought to elucidate the question how alpha/mu, beta, and gamma synchronization contribute to motor control. Here, we review these studies with the purpose to delineate what they have added to our understanding of the neural control of movement. We highlight important findings regarding oscillations in primary motor cortex, synchronization between cortex and spinal cord, synchronization between cortical regions, as well as abnormal synchronization patterns in a selection of motor dysfunctions. The interpretation of synchronization patterns benefits from combining results of invasive and non-invasive recordings, different data analysis tools, and modeling work. Importantly, although synchronization is deemed to play a vital role, it is not the only mechanism for neural communication. Spike timing and rate coding act together during motor control and should therefore both be accounted for when interpreting movement-related activity.

Keywords: corticospinal coherence; information processing; motor control; motor cortex; movement; movement disorders; neural synchronization; oscillations.

Figure 1

Evidence for independent modulations in cortical power and corticospinal synchronization? EEG and hand muscle EMG were recorded during a pre-cued reaction time task with either the left or right hand as response hand (van Wijk et al., 2009). During the interval between pre-cue and stimulus, which required static force production, significant beta band coherence between the EMG and sensors overlying the contralateral motor cortex was observed (left panels). The same electrodes showed a brief cue-related drop in spectral power (middle panels). Looking at the different time courses for power, corticospinal coherence, and corticospinal relative phase uniformity [also referred to as “phase-locking index” (Mardia, ; Lachaux et al., 1999)], the modulations in the pre-cue–stimulus interval could be decomposed into a slow, sustained modulation and the linearly superimposed brief cue-related drop (right panels). Remarkably, corticospinal phase uniformity only showed a sustained modulation, suggesting that the cue-related drop was not transferred to the spinal cord. On the other hand, corticospinal coherence explicitly depends on spectral power and was hence unable to discriminate between the different modulations in cortical and corticospinal synchronization. Alternatively, the cue-related drop might originate from nearby cortical sources that do not have projections to the spinal cord. For more details, see van Wijk et al. (2008).

Figure 2

Volume conduction complicates the interpretation of connectivity patterns estimated from MEG or EEG recordings. Data were recorded using MEG with axial gradiometers and group results for the beta band (20–25 Hz) are shown (see also, van Wijk et al., 2012). Top row: activity during bimanual force production alone is not very informative. Neighboring sensors show strong relative phase uniformity as they pick up activity of common sources. Second row: contrasting movement with resting state yields the characteristic movement-related power decrease over motor cortices. In addition, two clusters of increased connectivity are evident that seem to be located in between the locations with largest power suppression. However, the phase lag index reveals that all pair-wise connections with non-zero or non-pi phase difference are distributed randomly over the scalp. Hence, one cannot rule out that the increased local connectivity is caused by volume conduction. Third row: for unimanual movement there even seems to be increased interhemispheric coupling. But again it is difficult to discern whether these connections express true in-phase synchronization. Bottom row: by contrast, a transformation to planar gradients reveals a strong decrease in connectivity overlying motor areas that coincides with a drop in power. This means that, due to less beta activity, the estimated relative phase uniformity between neighboring sensors is weaker compared to resting state. Increases in power and connectivity are indicated in red, decreases in blue. Only the strongest connections are shown.