Challenges and recommendations for high quality research using electronic health records.

Harnessing Real World Data is vital to improve health care in the 21st Century. Data from Electronic Health Records (EHRs) are a rich source of patient centred data, including information on the patient's clinical condition, laboratory results, diagnoses and treatments. They thus reflect the true state of health systems. However, access and utilisation of EHR data for research presents specific challenges. We assert that using data from EHRs effectively is dependent on synergy between researchers, clinicians and health informaticians, and only this will allow state of the art methods to be used to answer urgent and vital questions for patient care. We propose that there needs to be a paradigm shift in the way this research is conducted - appreciating that the research process is iterative rather than linear. We also make specific recommendations for organisations, based on our experience of developing and using EHR data in trusted research environments.

Directed Abelian sandpile with multiple downward neighbors.

We study the directed Abelian sandpile model on a square lattice, with K downward neighbors per site, K>2. The K=3 case is solved exactly, which extends the earlier known solution for the K=2 case. For K>2, the avalanche clusters can have holes and side branches and are thus qualitatively different from the K=2 case where avalanche clusters are compact. However, we find that the critical exponents for K>2 are identical with those for the K=2 case, and the large-scale structure of the avalanches for K>2 tends to the K=2 case.

Homological scaffolds of brain functional networks.

Networks, as efficient representations of complex systems, have appealed to scientists for a long time and now permeate many areas of science, including neuroimaging (Bullmore and Sporns 2009 Nat. Rev. Neurosci. 10, 186-198. (doi:10.1038/nrn2618)). Traditionally, the structure of complex networks has been studied through their statistical properties and metrics concerned with node and link properties, e.g. degree-distribution, node centrality and modularity. Here, we study the characteristics of functional brain networks at the mesoscopic level from a novel perspective that highlights the role of inhomogeneities in the fabric of functional connections. This can be done by focusing on the features of a set of topological objects-homological cycles-associated with the weighted functional network. We leverage the detected topological information to define the homological scaffolds, a new set of objects designed to represent compactly the homological features of the correlation network and simultaneously make their homological properties amenable to networks theoretical methods. As a proof of principle,we apply these tools to compare resting state functional brain activity in 15 healthy volunteers after intravenous infusion of placebo and psilocybin-the main psychoactive component of magic mushrooms. The results show that the homological structure of the brain's functional patterns undergoes a dramatic change post-psilocybin, characterized by the appearance of many transient structures of low stability and of a small number of persistent ones that are not observed in the case of placebo.