Ared for every single edge the

The absolute model error is positivelyPLOS Computational Biology | DOI:10.1371/journal.pcbi.1005025 August 9,10 /Modeling Functional Connectivity: From DTI to EEGcorrelated using the betweenness centrality (r = 0.58, n = 66, p .0001) as shown in Fig 3C. A equivalent indicator of a nodes connectedness in the network will be the sum of all connection strengths of that node. Also for this metric, we find a linear relationship among the total connection strength of a node along with the model error (r = 0.35, n = 66, p .005). Also, the dependence among the model error plus the eigenvalue centrality, which measures how effectively a node is linked to other network nodes [64], was evaluated (r = 0.26, n = 66, p .05). The regional clustering coefficient, which quantifies how frequently the neighbors of a single node are neighbors to every other [65], did not show significant relations with all the nearby model error (r = 0.06, n = 66, p = .65).Ared for every edge the model error together with the fiber distance (Fig 3A). The typical fiber distance between connected ROIs was negatively correlated using the logarithm of the local model error of each and every connection (r = -0.32, n = 2145, p .0001). A equivalent dependence was calculated among Euclidean distance between ROI locations and local model error (r = -0.33, n = 2145, p .0001). Both results indicate that the SAR model performed worse in simulating FC for closer ROIs in topographic space (measured in fiber lengths) and Euclidean space (measured as distance involving ROI areas). This could be attributed to a greater variance inside the SC and empirical FC matrices for close ROIs (as shown in supporting S2 Fig). The empirical structural and functional connectivity are each dependent around the interregional distance between nodes with higher connectivity for short-range connections and decrease connectivity for long-range connections [61, 62]. As a result, we also calculate the model functionality of our reference process following regressing out the distance between regions. The remaining partial correlation involving modeled and empirical functional connectivity is r = 0.36 following regressing out the euclidean distance. A similar partial correlation r = 0.38 was calculated just after removing the effect of fiber distance. We further evaluated the performance in relation to certain node L Disorders 2013, 14:48 http://www.biomedcentral.com/1471-2474/14/Page ten ofneed to acknowledge qualities and averaged the errors of all edges per node. The node efficiency in terms of model error is shown in Fig 3BD dependent on diverse node qualities. First, we looked in the influence of ROI size on the model error. We hypothesized that resulting from bigger sample sizes and more precise localization, the model error would be smaller for significant ROIs. As expected, the model error for each ROI is negatively correlated with all the corresponding size of the ROI (r = -0.37, n = 66, p .005) as shown in Fig 3B. Then we hypothesized, that because of the sparseness of SC, some ROIs inside the SC possess a pretty higher connectedness compared to functional information, top to a bigger model error. To address this aspect we calculated quite a few graph theoretical measures that assess the regional connectedness in distinct approaches and related this towards the typical model error. As a 1st measure we calculated for each and every node the betweenness centrality, defined as the fraction of all shortest paths within the network that pass via a given node [63].