Ansitions around the Gamma parameter plane (phase transitions) are of interest

APD356 web Greater detail regarding the process for computing PLV might be identified elsewhere [9, 28]. The modularity index supplies a sense of your subdivision of your network into non-overlapping groups of nodes that operate together within a "community." This metric assesses self-emerging structuring with the network by maximizing thenumber of within-group edges, and minimizing the amount of between-group edges. As a result, modularity is a statistic that quantifies the degree to which the network could possibly be subdivided into such clearly delineated groups. As an example, Figure 3B shows the contrast in modularity (green vs. cyan node colors) as the connectivity differs between the upper and reduced physique and changes frame by frame. This really is better appreciated in Figure four unfolding the walking session more than time, frame by frame inside the 30 min stroll period, title= s12687-015-0238-0 too as helping us determine synergies (see explanation on synergies under). The clustering coefficient another metric that we are able to use would be the clustering coefficient, i.e., the fraction of triangles about a node, or equivalently, the fraction of node's neighbors which might be neighbors of one another. As an example, in Figure 3B the size of the circles at each and every node is given by the clustering coefficient value. Substantial circles indicate nodes whose neighbors are neighbors to each and every othe.Ansitions on the Gamma parameter plane (phase transitions) are of interest in this framework. In this sense, noise is adaptive when it serves transitions from spontaneous and random to well-structured and systematic or periodic states, nevertheless it is detrimental when it stagnates in the spontaneous random levels detectable through our analyses.Fourth Layer of Data (Peripheral Network Visualization)Across the joints of the body, we examine the time series of joint angular velocities and get the phase locking values (PLV). PLV is often a statistic employed to quantify the phase coupling between two biological nonlinear signals in time-series, which include time-series of electroencephalographic signals (Gentili et al., 2009; Aydore et al., 2013). Inside the present study, PLV was employed to quantify the amount of coupling (phase synchrony) in the time series of angular velocity values amongst each and every among the list of 14 joints and all of the other folks. Particularly, the PLV nears 1 in situations exactly where the instantaneous phases title= s12936-015-0787-z of the two joints' angular velocities time series are synchronized. Conversely, if they may be unsynchronized the PLV tends to 0. Greater detail with regards to the procedure for computing PLV may be found elsewhere [9, 28]. Right here we obtained the 14 joints x 14 joints PLV matrix each 240 frames (240Hz sampling resolution of the sensors). We made use of a high threshold of synchronization value (0.85) to create a binary matrix. Entries in the original PLV frame that have been above or equal towards the threshold were set to 1 and those under the threshold were set to 0 (see Figure 3A). When we collect the network dynamically evolving frame by frame, we apply tools in the brain connectivity toolbox (Sporns, 2011, 2012) to visualize the temporal profiles of emerging modules and connectivity patterns across this peripheral network of rotational title= JVI.00652-15 joints. Under are some indexes that we use in these plots. The connectivity index is given by the degree of each node (joint), that is, the amount of links connecting the node to other nodes in the network.