Connectivity modulations induced by reach&grasp movements: a multidimensional approach

Abstract

Reach&grasp requires highly coordinated activation of different brain areas. We investigated whether reach&grasp kinematics is associated to EEG-based networks changes. We enrolled 10 healthy subjects. We analyzed the reach&grasp kinematics of 15 reach&grasp movements performed with each upper limb. Simultaneously, we obtained a 64-channel EEG, synchronized with the reach&grasp movement time points. We elaborated EEG signals with EEGLAB 12 in order to obtain event related synchronization/desynchronization (ERS/ERD) and lagged linear coherence between Brodmann areas. Finally, we evaluated network topology via sLORETA software, measuring network local and global efficiency (clustering and path length) and the overall balance (small-worldness). We observed a widespread ERD in α and β bands during reach&grasp, especially in the centro-parietal regions of the hemisphere contralateral to the movement. Regarding functional connectivity, we observed an α lagged linear coherence reduction among Brodmann areas contralateral to the arm involved in the reach&grasp movement. Interestingly, left arm movement determined widespread changes of α lagged linear coherence, specifically among right occipital regions, insular cortex and somatosensory cortex, while the right arm movement exerted a restricted contralateral sensory-motor cortex modulation. Finally, no change between rest and movement was found for clustering, path length and small-worldness. Through a synchronized acquisition, we explored the cortical correlates of the reach&grasp movement. Despite EEG perturbations, suggesting that the non-dominant reach&grasp network has a complex architecture probably linked to the necessity of a higher visual control, the pivotal topological measures of network local and global efficiency remained unaffected.

Figure 1

Event-related desynchronization in δ, θ, α, β bands and the relative signrank-maps comparing Movement vs. Rest. The “+” marks indicate electrodes with a significant rest-move difference (the figure was obtained with Matlab R2018 https://it.mathworks.com/products.html).

Figure 2

Connectivity matrices in Rest and Mov conditions averaged across subjects in δ, θ, α, β frequency bands. Each matrix element provides the color-coded LLC value between Brodmann areas as indicated by the colour bar. The upper left quadrant of the matrices depicts LLC among Brodmann’areas of the left hemisphere and the lower right quadrant shows LLC among areas of the right hemisphere (the figure was obtained with Matlab R2018 https://it.mathworks.com/products.html).

Figure 3

Significant FC links (LLC scores) derived from groups comparison. The figures denote the edges that reduced the strength of connectivity to each node during the movement compared to the resting condition (p < 0.05, corrected for multiply comparisons) (the figure was obtained with the BrainNet Viewer 1.7 http://www.nitrc.org/projects/bnv/).

Figure 4

The figure sums up the main changes of cortical activity in α band induced by reach&grasp movement. In the sections A and B of the figure we show, both in Rest and Mov conditions, respectively for the right and left reach&grasp movement: (1) ERD/ERS averaged maps, (2) functional connectivity averaged matrices, (3) Brodmann areas with decreased lagged linear coherence during the reach&grasp movement.

Figure 5

(A) The sagittal view of a subject in his initial position before performing the task. The spheres on the anatomical landmarks represent the passive reflective spherical markers which, in combination with infrared cameras, allow the motion capture. The subject wears an EEG cap. (B) Subject’s modelling according to the RAB kinematic protocol and the position of the cylinder marker.

Figure 6

Speed of the wrist during the reach&grasp cycle. The red line indicates the 5% threshold used for marking the starting and ending timepoints of the movement.