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Predicting diseases with Wikipedia, how the brain modifies memories, and hypersynchrony: the PLOS Comp Biol November Issue

Here are our highlights from November’s PLOS Computational Biology.

Predicting Diseases with Wikipedia

Effective and timely disease surveillance is a critical component of prevention and mitigation strategies that can save lives. Nicholas Generous and colleagues have proposed a new approach for detecting and monitoring epidemics based on freely available Wikipedia article access logs. The authors’ proof-of-concept experiments suggest that Wikipedia is a broadly effective data source for predicting the present, as well as forecasting outbreaks up to 28 days in advance. The approach could help to overcome some of the key gaps in existing traditional and internet-based techniques.

How the Brain Modifies Memories

Rendering of Parkin's opening conformers resulting from molecular dynamics simulations. Image Credit: Thomas R. Caulfield
Rendering of Parkin’s opening conformers resulting from molecular dynamics simulations.
Image Credit: Thomas R. Caulfield

When do we modify old memories, and when do we create new ones? Samuel J. Gershman and colleagues suggest that the question can be answered statistically. When sensory data change gradually over time, the brain infers that the environment has slowly been evolving, and the current representation of the environment (an existing memory trace) is updated. In contrast, abrupt changes indicate transitions between different structures, leading to the formation of new memories. The authors use a new model of statistical inference to show that humans use temporal discontinuities in the structure of the environment to determine when to form new memory traces.

Hypersynchronous Neural Activity

In the study of neurological disorders, a number of approaches have been used to study the mechanisms of seizure activity. John R. Terry and colleagues have developed a new computational modelling framework to explain the interplay between local dynamics and global networks in the emergence of hypersynchronous neural activity. By applying this framework to collected data sets from people with idiopathic generalized epilepsy, the authors demonstrate that brain networks of people with epilepsy have a much greater tendency to hypersynchronize than do brain networks of people without epilepsy. This finding demonstrates a critical role for network structure in the tendency to have seizures.

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