Functional embedding predicts the variability of neural activity

Abstract

Neural activity is irregular and unpredictable, yet little is known about why this is the case and how this property relates to the functional architecture of the brain. Here we show that the variability of a region's activity systematically varies according to its topological role in functional networks. We recorded the resting-state electroencephalogram (EEG) and constructed undirected graphs of functional networks. We measured the centrality of each node in terms of the number of connections it makes (degree), the ease with which the node can be reached from other nodes in the network (efficiency) and the tendency of the node to occupy a position on the shortest paths between other pairs of nodes in the network (betweenness). As a proxy for variability, we estimated the information content of neural activity using multiscale entropy analysis. We found that the rate at which information was generated was largely predicted by centrality. Namely, nodes with greater degree, betweenness, and efficiency were more likely to have high information content, while peripheral nodes had relatively low information content. These results suggest that the variability of regional activity reflects functional embedding.

Keywords: centrality; connectivity; degree; efficiency; entropy; functional integration; variability.

Figure 1

Multiscale entropy curves. Values of S_E are summed across temporal scales and the spatial distribution is shown in (A). The complete multiscale entropy (MSE) curves for two representative channels (marked by black circles), showing S_E at each level of coarse-graining, are displayed in (B). Error bars indicate standard errors of the mean.

Figure 2

Multiscale entropy and functional embedding. Top row: scatter plots and regression lines depict the relationship between S_E and centrality across all electrodes (both have been integrated across temporal scales). Middle row: the correlation coefficient between S_E and each of the three network measures is plotted as a function of temporal scale. Bottom row: Scalp distributions for the three types of centrality. The measures were calculated for functional connectivity graphs at each time scale and then integrated across scales as a summary measure.