The specificity and robustness of long-distance connections in weighted, interareal connectomes

Brain areas' functional repertoires are shaped by their incoming and outgoing structural connections. In empirically measured networks, most connections are short, reflecting spatial and energetic constraints. Nonetheless, a small number of connections span long distances, consistent with the notion that the functionality of these connections must outweigh their cost. While the precise function of these long-distance connections is not known, the leading hypothesis is that they act to reduce the topological distance between brain areas and facilitate efficient interareal communication. However, this hypothesis implies a non-specificity of long-distance connections that we contend is unlikely. Instead, we propose that long-distance connections serve to diversify brain areas' inputs and outputs, thereby promoting complex dynamics. Through analysis of five interareal network datasets, we show that long-distance connections play only minor roles in reducing average interareal topological distance. In contrast, areas' long-distance and short-range neighbors exhibit marked differences in their connectivity profiles, suggesting that long-distance connections enhance dissimilarity between regional inputs and outputs. Next, we show that -- in isolation -- areas' long-distance connectivity profiles exhibit non-random levels of similarity, suggesting that the communication pathways formed by long connections exhibit redundancies that may serve to promote robustness. Finally, we use a linearization of Wilson-Cowan dynamics to simulate the covariance structure of neural activity and show that in the absence of long-distance connections, a common measure of functional diversity decreases. Collectively, our findings suggest that long-distance connections are necessary for supporting diverse and complex brain dynamics.Comment: 18 pages, 8 figure

Analyzing overlapping communities in networks using link communities

One way to analyze the structure of a network is to identify its communities, groups of related nodes that are more likely to connect to one another than to nodes outside the community. Commonly used algorithms for obtaining a network’s communities rely on clustering of the network’s nodes into a community structure that maximizes an appropriate objective function. However, defining communities as a partition of a network’s nodes, and thus stipulating that each node belongs to exactly one community, precludes the detection of overlapping communities that may exist in the network. Here we show that by defining communities as partition of a network’s links, and thus allowing individual nodes to appear in multiple communities, we can quantify the extent to which each pair of communities in a network overlaps. We define two measures of community overlap and apply them to the community structure of five networks from different disciplines. In every case, we note that there are many pairs of communities that share a significant number of nodes. This highlights a major advantage of using link partitioning, as opposed to node partitioning, when seeking to understand the community structure of a network. We also observe significant differences between overlap statistics in real-world networks as compared with randomly-generated null models. By virtue of their contexts, we expect many naturally-occurring networks to have very densely overlapping communities. Therefore, it is necessary to develop an understanding of how to use community overlap calculations to draw conclusions about the underlying structure of a network

Language control is not a one-size-fits-all languages process: Evidence from simultaneous interpretation students and the n-2 repetition cost

Simultaneous interpretation is an impressive cognitive feat which necessitates the simultaneous use of two languages and therefore begs the question: how is language management accomplished during interpretation? One possibility is that both languages are maintained active and inhibitory control is reduced. To examine whether inhibitory control is reduced after experience with interpretation, students with varying experience were assessed on a three language switching paradigm. This paradigm provides an empirical measure of the inhibition applied to abandoned languages, the n-2 repetition cost. The groups showed different patterns of n-2 repetition costs across the three languages. These differences, however, were not connected to experience with interpretation. Instead, they may be due to other language characteristics. Specifically, the L2 n-2 repetition cost negatively correlated with self-rated oral L2 proficiency, suggesting that language proficiency may affect the use of inhibitory control. The differences seen in the L1 n-2 repetition cost, alternatively, may be due to the differing predominant interactional contexts of the groups. These results suggest that language control may be more complex than previously thought, with different mechanisms used for different languages. Further, these data represent the first use of the n-2 repetition cost as a measure to compare language control between groups