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.

Modulations of Cortical Power and Connectivity in Alpha and Beta Bands during the Preparation of Reaching Movements

“…Furthermore, the selected ROIs were based on the Desikan–Killiany atlas, and some of them englobe large portions of the cortex. In particular, as concerning the sensorimotor areas, we did not specifically consider the primary motor cortex (M1), supplementary motor areas (SMA), and premotor cortex (PMC), which are small regions in the sensorimotor cortex, and deemed to be core motor areas, largely investigated in connectivity studies [ 23 , 34 , 35 ]. Rather, we preferred to consider larger areas (likely englobing the previous core areas) also due to the use of a template head model for cortical source estimation, rather than individual head models.…”

“…The latter have a coarser spatial resolution, but their high temporal resolution allows connectivity to be characterized in specific frequency bands, thus examining how brain interactions are associated with different, functionally relevant brain rhythms. In EEG-based studies, patterns of connectivity related to motor tasks are often analyzed in alpha and beta bands (e.g., see [ 17 , 18 , 19 , 20 ]), although connectivity in other spectral ranges (gamma, i.e., >30 Hz, delta, i.e., 1–4 Hz, theta, i.e., 4–8 Hz) is sometimes investigated too (e.g., see [ 21 , 22 , 23 ]). In the following section, some results of connectivity studies (both fMRI- and M/EEG-based) are delineated.…”

“…Despite the intense research and the large amount of collected findings, some aspects remain under-investigated. In particular, although reaching is a key component of motor actions that allow humans to interact with the environment, only a limited number of studies have examined EEG-based connectivity in reaching tasks [ 23 , 34 , 35 ]. Chung et al [ 34 ] investigated EEG oscillatory activity and directed connectivity (via dynamic causal modeling) during the execution of visually-guided ballistic arm movement, and compared the effect of high vs. low visual gain.…”

“…The study focused on two cortical areas (left sensorimotor and medial parietal), considered the movement and post-movement phase, and characterized connectivity differences between movements in the two visual conditions. Caliandro et al [ 23 ] analyzed scalp ERD/ERS during the execution of reach and grasp movements, and quantified changes in the source-level connectivity network over the whole cortex during movement compared to rest; they used a non-directed connectivity measure (lagged coherence), and applied a graph analysis to evaluate the ‘small world-ness’ property of the network. From MEG data, Yeom et al [ 35 ] applied a time-window shifting approach to explore changes in brain connectivity with motor states, from movement preparation to movement execution; non-directed connections (using mutual information) were estimated between motor-related cortical regions, and graph-based degree centralities were computed to identify network hubs.…”

“…From MEG data, Yeom et al [ 35 ] applied a time-window shifting approach to explore changes in brain connectivity with motor states, from movement preparation to movement execution; non-directed connections (using mutual information) were estimated between motor-related cortical regions, and graph-based degree centralities were computed to identify network hubs. While these studies of course provide relevant results, they suffer from the limitations that either non-directed metrics of connectivity were used to investigate couplings among several widely distributed regions [ 23 , 35 ] or directional metrics were used to investigate the coupling between two brain regions only [ 34 ]. Thus, a description is desired about the frequency-specific changes in directional-dependent interactions evoked by a reaching task within a large network of task-related areas, including occipital (visual), parietal, and fronto-central cortices.…”