GAT: Graph Analysis Toolbox for Brain Networks Figure 11. Degree distributions. The log-log plot of cumulative degree distributions of ALL (A) and CON (B) networks thresholded at Dmin. The solid line indicates the exponentially truncated power-law curve fitted to the cumulative degree distribution of the networks (dotted line). The estimated exponent was 1.19 for ALL and 1.27 for CON, the cut-off degree was 2.65 for the ALL and 2.52 for the CON network. These parameters resulted in R-square value of 0.97 for both distributions (value close to one represents a good fit). doi:10.1371/journal.pone.0040709.g011 imaging studies involving ALL survivors that reported a diffuse pattern of microstructural white matter damage [35,37,89]. Regional network measures. The observed between-group differences in regional network measures did not survive after correction for multiple comparisons. However, the uncorrected results were in line with previous findings and thus we consider the following to be exploratory. Several regions in the prefrontal cortex showed smaller betweenness centrality in the ALL network compared to CON. Nodes with higher centrality in the network identify regions that have the potential to participate in a large number of functional interactions [90]. A number of neuropsy- chological studies have shown a subtle long-term neurocognitive deficits, specifically in cognitive functions subserved by prefrontal cortex, in survivors of childhood ALL (see [41,42] for a review). Previous neuroimaging studies reported reduced white matter connectivity between frontal and occipital regions [34,89], decreased white matter structure and volume in frontal regions [37,38,91], and reduced fractional anisotropy in frontal white matter structure [37] in ALL survivors. Decreased regional white matter volume in prefrontal regions was also associated with decreased performance on neuropsychological measures in this population [91]. Paakko and colleagues [92] reported that ALL patients with white matter changes more often had impairment of attention and cognitive functions subserved by the frontal regions. These reports might explain the observed lack of centrality in prefrontal regions in the ALL network in our study suggesting an impaired functional interaction between frontal regions and the rest of the brain network. Network hubs. The identified hubs in the CON network are consistent with the results of previous graph-theoretical analysis involving healthy subjects [12,14]. The CON network showed more number of central hubs located in the prefrontal cortex compared to ALL. The observed lack of highly central hubs in the prefrontal cortex in the ALL network is in line with the results of group-differences in regional network measures and suggests network alterations involving regions critical for the executive functions in ALL. Random failure and targeted attack analysis. The AUC of the random failure curve was significantly lower in the ALL network compared to CON suggesting the overall lower resilience of the ALL network in response to random failure. In addition, the resilience of the ALL network was lower in response to targeted attack but the overall between-group difference was not significant. This observation is consistent with the results of global network measures suggesting that the ALL network is more regularized relative to CON network. Networks with more regularization are less resilient to random failure and show reduced resilience to pathologies [20]. A regularized network, compared with a small- world network, does not have highly connected hubs and thus fails to integrate various modules in the event of network fragmentation as a result of cascaded random failures. Network modularity. While both the networks had the same number of modules, the degree of network modularity was significantly smaller in the ALL network. The smaller network modularity in the ALL network corroborates the observed higher path length and lower clustering in the ALL network and suggests a reduction in the balance between network segregation and integration in ALL. Degree distribution. The degree distribution of both networks followed an exponentially truncated power law distribu- tion suggesting a network with many regions having small number of connections and a few regions having large number of connections (hubs). The cutoff degree was around 3 for both the networks, consistent with previous reports [13,20]. GAT Features GAT provides a GUI framework to facilitate the investigation of organization of brain networks without requiring the users to have knowledge of Matlab or programming. In addition, GAT has a number of unique features that other available graph-analysis packages lack. First, unlike other graph-analysis packages that mainly focused on extraction of network measures and/or visualization, the unique feature of the GAT is that it integrates the processes of ROI extraction, network construction, regional and global network analysis, network comparison, hub analysis, random failure and targeted attack simulation and network visualization. Second, GAT facilitates analyzing between-group differences in global network measures, regional network proper- PLoS ONE | www.plosone.org 13 July 2012 | Volume 7 | Issue 7 | e40709