Brain Dynamics during Sleep, the Worm Connectome, and eQTL Networks: the PLOS Comp Biol September Issue
Check out our highlights from the PLOS Computational Biology September issue:
A Thalamocortical Neural Mass Model of the EEG during NREM Sleep and Its Response to Auditory Stimulation
Sleep plays a pivotal role in the consolidation of memory. Recent studies suggest that different sleep stages are responsible for the consolidation of different types of memory. To better understand the changes in neuronal dynamics between the different sleep stages, neural mass models are a valuable tool, as they relate directly to the large-scale dynamics measured by an EEG. Michael Schellenberger Costa, Arne Weigenand and colleagues present a model of the sleeping thalamocortical system. Their study shows that a neural mass model incorporating few key mechanisms is sufficient to reproduce the complex brain dynamics observed during sleep.
The C. elegans Connectome Consists of Homogenous Circuits with Defined Functional Roles
How can we understand the function of gigantic complex networks such as the brain, based on connectivity data alone? Alon Zaslaver and colleagues use the available full connectome of a nematode and apply new approaches to find that the neural network is made of structurally homogeneous neural circuits. These sets of neurons also appear in defined regions of the network where they may provide valuable functional roles such as signal integration and synchronization.
Bipartite Community Structure of eQTLs
Large-scale studies have identified thousands of genetic variants associated with different phenotypes. Expression quantitative trait locus (eQTL) analysis associates the compendium of genetic variants with expression levels of individual genes, providing the opportunity to link those variants to functions. But the complexity of those associations has caused most analyses to focus solely on genetic variants immediately adjacent to the genes they may influence. John Platig and colleagues describe a method that embraces the complexity, representing all variant-gene associations as a bipartite graph.
Header Image Credit: Fried et al.