Resting-brain functional connectivity predicted by analytic measures of network communication.

The complex relationship between structural and functional connectivity, as measured by noninvasive imaging of the human brain, poses many unresolved challenges and open questions. Here, we apply analytic measures of network communication to the structural connectivity of the human brain and explore the capacity of these measures to predict resting-state functional connectivity across three independently acquired datasets. We focus on the layout of shortest paths across the network and on two communication measures-search information and path transitivity-which account for how these paths are embedded in the rest of the network. Search information is an existing measure of information needed to access or trace shortest paths; we introduce path transitivity to measure the density of local detours along the shortest path. We find that both search information and path transitivity predict the strength of functional connectivity among both connected and unconnected node pairs. They do so at levels that match or significantly exceed path length measures, Euclidean distance, as well as computational models of neural dynamics. This capacity suggests that dynamic couplings due to interactions among neural elements in brain networks are substantially influenced by the broader network context adjacent to the shortest communication pathways.

Blue light activates a specific protein kinase in higher plants.

Blue light mediates the phosphorylation of a membrane protein in seedlings from several plant species. When crude microsomal membrane proteins from dark-grown pea (Pisum sativum L.), sunflower (Helianthus annuus L.), zucchini (Cucurbita pepo L.), Arabidopsis (Arabidopsis thaliana L.), or tomato (Lycopersicon esculentum L.) stem segments, or from maize (Zea mays L.), barley (Hordeum vulgare L.), oat (Avena sativa L.), wheat (Triticum aestivum L.), or sorghum (Sorghum bicolor L.) coleoptiles are illuminated and incubated in vitro with [gamma-(32)P]ATP, a protein of apparent molecular mass from 114 to 130 kD is rapidly phosphorylated. Hence, this system is probably ubiquitous in higher plants. Solubilized maize membranes exposed to blue light and added to unirradiated solubilized maize membranes show a higher level of phosphorylation of the light-affected protein than irradiated membrane proteins alone, suggesting that an unirradiated substrate is phosphorylated by a light-activated kinase. This finding is further demonstrated with membrane proteins from two different species, where the phosphorylated proteins are of different sizes and, hence, unambiguously distinguishable on gel electrophoresis. When solubilized membrane proteins from one species are irradiated and added to unirradiated membrane proteins from another species, the unirradiated protein becomes phosphorylated. These experiments indicate that the irradiated fraction can store the light signal for subsequent phosphorylation in the dark. They also support the hypothesis that light activates a specific kinase and that the systems share a close functional homology among different higher plants.

Using Pareto optimality to explore the topology and dynamics of the human connectome.

Graph theory has provided a key mathematical framework to analyse the architecture of human brain networks. This architecture embodies an inherently complex relationship between connection topology, the spatial arrangement of network elements, and the resulting network cost and functional performance. An exploration of these interacting factors and driving forces may reveal salient network features that are critically important for shaping and constraining the brain's topological organization and its evolvability. Several studies have pointed to an economic balance between network cost and network efficiency with networks organized in an 'economical' small-world favouring high communication efficiency at a low wiring cost. In this study, we define and explore a network morphospace in order to characterize different aspects of communication efficiency in human brain networks. Using a multi-objective evolutionary approach that approximates a Pareto-optimal set within the morphospace, we investigate the capacity of anatomical brain networks to evolve towards topologies that exhibit optimal information processing features while preserving network cost. This approach allows us to investigate network topologies that emerge under specific selection pressures, thus providing some insight into the selectional forces that may have shaped the network architecture of existing human brains.

Generative models of the human connectome.

The human connectome represents a network map of the brain's wiring diagram and the pattern into which its connections are organized is thought to play an important role in cognitive function. The generative rules that shape the topology of the human connectome remain incompletely understood. Earlier work in model organisms has suggested that wiring rules based on geometric relationships (distance) can account for many but likely not all topological features. Here we systematically explore a family of generative models of the human connectome that yield synthetic networks designed according to different wiring rules combining geometric and a broad range of topological factors. We find that a combination of geometric constraints with a homophilic attachment mechanism can create synthetic networks that closely match many topological characteristics of individual human connectomes, including features that were not included in the optimization of the generative model itself. We use these models to investigate a lifespan dataset and show that, with age, the model parameters undergo progressive changes, suggesting a rebalancing of the generative factors underlying the connectome across the lifespan.