Connectivity modulations induced by reaching and grasping movements

Functional neuroimaging enables the assessment of the brain function in both rest and active conditions. While traditional functional connectivity studies focus on determining distributed patterns of brain activity, the analysis of pair-wise correlations in the time series associated to brain regions allows a paradigm shift to graph theory making available a whole set of parameters for the analysis of the functional network. Then, the study of the properties of the networks as well as of their modulations can be performed in the space of the so-identified features potentially leading to the detection of condition-specific (static or dynamic) fingerprints. Following this guideline, this study is a first attempt to using graph-based measures for capturing task-specific signatures of a reach&grasp movement. The weighted clustering coefficient (CW), characteristic path length (SW) and small-worldness (SW) were considered and performance was assessed against classical measures (eventrelated (de)synchronization). Neurophysiological data were collected through high-density EEG and a stereophotogrammetric system was used for capturing the onset and end of the movement. Though not reaching statistical significance, these preliminary results witness the modulation of the function network due to reach&grasp and provide evidence in favour of the possibility of capturing such a modulation through graph-based properties. This would allow to shed light on the movement-induced reorganization of the network, which has a clear translational impact for the assessment of the recovery of patients after acute stroke.