EEG Oscillations Are Modulated in Different Behavior-Related Networks during Rhythmic Finger Movements

Abstract

Sequencing and timing of body movements are essential to perform motoric tasks. In this study, we investigate the temporal relation between cortical oscillations and human motor behavior (i.e., rhythmic finger movements). High-density EEG recordings were used for source imaging based on individual anatomy. We separated sustained and movement phase-related EEG source amplitudes based on the actual finger movements recorded by a data glove. Sustained amplitude modulations in the contralateral hand area show decrease for α (10-12 Hz) and β (18-24 Hz), but increase for high γ (60-80 Hz) frequencies during the entire movement period. Additionally, we found movement phase-related amplitudes, which resembled the flexion and extension sequence of the fingers. Especially for faster movement cadences, movement phase-related amplitudes included high β (24-30 Hz) frequencies in prefrontal areas. Interestingly, the spectral profiles and source patterns of movement phase-related amplitudes differed from sustained activities, suggesting that they represent different frequency-specific large-scale networks. First, networks were signified by the sustained element, which statically modulate their synchrony levels during continuous movements. These networks may upregulate neuronal excitability in brain regions specific to the limb, in this study the right hand area. Second, movement phase-related networks, which modulate their synchrony in relation to the movement sequence. We suggest that these frequency-specific networks are associated with distinct functions, including top-down control, sensorimotor prediction, and integration. The separation of different large-scale networks, we applied in this work, improves the interpretation of EEG sources in relation to human motor behavior.

Significance statement: EEG recordings provide high temporal resolution suitable to relate cortical oscillations to actual movements. Investigating EEG sources during rhythmic finger movements, we distinguish sustained from movement phase-related amplitude modulations. We separate these two EEG source elements motivated by our previous findings in gait. Here, we found two types of large-scale networks, representing the right fingers in distinction from the time sequence of the movements. These findings suggest that EEG source amplitudes reconstructed in a cortical patch are the superposition of these simultaneously present network activities. Separating these frequency-specific networks is relevant for studying function and possible dysfunction of the cortical sensorimotor system in humans as well as to provide more advanced features for brain-computer interfaces.

Keywords: EEG source imaging; finger movements; large-scale networks; neural oscillations; sensorimotor system; spectral profiles.

Figure 1.

Task, behavior, and EEG montage. a, Time line of the movement task instruction displayed on the computer screen. b, Glove data (mean ± SEM) after alignment of trials for the slow (blue) and fast (red) tapping cadence. c, Histogram of performed movement cycles. d, EEG montage. Circles represent electrodes.

Figure 2.

Separating sustained from dynamic AE. a, Amplitude time course for β1 (18–24 Hz) frequencies during the slow movement cadence (0.66 Hz) in the left ROI. Blue represents original AE. Green represents low pass filtered “sustained AE.” Red represents high pass filtered “dynamic AE.” Magenta represents glove data, shown for comparison with dynamic AE, which are related to the movement phase. b, Same illustration as in a, but for faster movement (1.37 Hz).

Figure 3.

Dynamic and sustained AE in the right and left ROI during the slow movement cadence. a, Glove data. b–d, TF plot of dynamic (high pass filtered), sustained (low pass filtered), and original (unfiltered) source AE. e, Right and left ROI. f, Relation of dynamic AE to the movement phase, centered (0 rad) at peak displacement of the fingers. g, Frequency spectra for sustained (bottom) and MPA (top) for the left/right ROI in magenta/green. Selected frequency ranges marked in gray shaded regions. Amplitudes in dB.

Figure 4.

Dynamic and sustained AE in the right and left ROI during the fast movement cadence. a, Glove data. b–d, TF plot of dynamic (high pass filtered), sustained (low pass filtered), and original (unfiltered) source AE. e, Right and left ROI. f, Relation of dynamic AE to the movement phase, centered (0 rad) at peak displacement of the fingers. g, Frequency spectra for sustained (bottom) and MPA (top) for the left/right ROI in magenta/green. Selected frequency ranges marked in gray shaded regions. Amplitudes in dB.

Figure 5.

EEG source images of the slow movement cadence in selected frequency ranges (left to right column). a, EEG sources of sustained ERD/ERS during movements are illustrated in blue/yellow-red. b, EEG sources of movement MPAs are illustrated as modulation magnitude in red to white. Amplitudes in dB.

Figure 6.

EEG source images of the fast movement cadence in selected frequency ranges (left to right column). a, EEG sources of sustained ERD/ERS during movements are illustrated in blue/yellow-red. b, EEG sources of movement MPAs are illustrated as modulation magnitude in red to white. Amplitudes in dB.

Figure 7.

Amplitude comodulation between cortical areas. Left, Visual areas are comodulated with the left sensorimotor area (seed region in red) during visual instruction. Right, Subcortical regions are comodulated with the right sensorimotor area (seed region in red) during slow, but prefrontal areas during fast movement cadences.