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

Age-Related Changes in Human Anatomical and Functional Brain Networks

Thesis (Ph.D.) - Indiana University, Psychological and Brain Sciences, 2015i) The first component characterizes age-related changes in specific connections. We find that functional connections within and between intrinsic connectivity networks (ICNs) follow distinct lifespan trajectories. We further characterize these changes in terms of each ICN’s “modularity” and find that most ICNs become less modular (i.e. less segregated) with age. In anatomical networks we find that hub regions are disproportionately affected by age and become less efficiently connected to the rest of the brain. Finally, we find that, with age stronger functional connections are supported by longer (multi-step) anatomical pathways for communication. ii) The second component is concerned with characterizing age-related changes in the boundaries of ICNs. To this end we used a multi-layer variant of modularity maximization to decompose networks into modules at different organizational scales, which we find exhibit scale-specific trends with age. At coarse scales, for example, we find that modules become more segregated whereas modules defined at finer scales become less segregated. We also find that module composition changes with age, and specific areas associated with memory change their module allegiance with age. iii) In the final component we use generative models to uncover wiring rules for the anatomical brain networks. Modeling network growth as a spatial penalty combined with homophily, we find that we can generate synthetic networks with many of the same properties as real-world brain networks. Fitting this model to individuals, we show that the parameter governing the severity of the spatial penalty weakens monotonically with age and that the overall ability to reproduce realistic connectomes for older individuals suffers. These results suggest that, with age, additional constraints may play an important role in shaping the topology of brain structural networks

Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks

We investigate the relationship of resting-state fMRI functional connectivity estimated over long periods of time with time-varying functional connectivity estimated over shorter time intervals. We show that using Pearson's correlation to estimate functional connectivity implies that the range of fluctuations of functional connections over short time scales is subject to statistical constraints imposed by their connectivity strength over longer scales. We present a method for estimating time-varying functional connectivity that is designed to mitigate this issue and allows us to identify episodes where functional connections are unexpectedly strong or weak. We apply this method to data recorded from $N=80$ participants, and show that the number of unexpectedly strong/weak connections fluctuates over time, and that these variations coincide with intermittent periods of high and low modularity in time-varying functional connectivity. We also find that during periods of relative quiescence regions associated with default mode network tend to join communities with attentional, control, and primary sensory systems. In contrast, during periods where many connections are unexpectedly strong/weak, default mode regions dissociate and form distinct modules. Finally, we go on to show that, while all functional connections can at times manifest stronger (more positively correlated) or weaker (more negatively correlated) than expected, a small number of connections, mostly within the visual and somatomotor networks, do so a disproportional number of times. Our statistical approach allows the detection of functional connections that fluctuate more or less than expected based on their long-time averages and may be of use in future studies characterizing the spatio-temporal patterns of time-varying functional connectivityComment: 47 Pages, 8 Figures, 4 Supplementary Figure