Whither lung EIT: Where are we, where do we want to go and what do we need to get there?

Breathing moves volumes of electrically insulating air into and out of the lungs, producing conductivity changes which can be seen by electrical impedance tomography (EIT). It has thus been apparent, since the early days of EIT research, that imaging of ventilation could become a key clinical application of EIT. In this paper, we review the current state and future prospects for lung EIT, by a synthesis of the presentations of the authors at the 'special lung sessions' of the annual biomedical EIT conferences in 2009-2011. We argue that lung EIT research has arrived at an important transition. It is now clear that valid and reproducible physiological information is available from EIT lung images. We must now ask the question: How can these data be used to help improve patient outcomes? To answer this question, we develop a classification of possible clinical scenarios in which EIT could play an important role, and we identify clinical and experimental research programmes and engineering developments required to turn EIT into a clinically useful tool for lung monitoring.

A decaying factor accounts for contained activity in neuronal networks with no need of hierarchical or modular organization

The mechanisms responsible for containing activity in systems represented by networks are crucial in various phenomena, for example, in diseases such as epilepsy that affect the neuronal networks and for information dissemination in social networks. The first models to account for contained activity included triggering and inhibition processes, but they cannot be applied to social networks where inhibition is clearly absent. A recent model showed that contained activity can be achieved with no need of inhibition processes provided that the network is subdivided into modules (communities). In this paper, we introduce a new concept inspired in the Hebbian theory, through which containment of activity is achieved by incorporating a dynamics based on a decaying activity in a random walk mechanism preferential to the node activity. Upon selecting the decay coefficient within a proper range, we observed sustained activity in all the networks tested, namely, random, Barabasi-Albert and geographical networks. The generality of this finding was confirmed by showing that modularity is no longer needed if the dynamics based on the integrate-and-fire dynamics incorporated the decay factor. Taken together, these results provide a proof of principle that persistent, restrained network activation might occur in the absence of any particular topological structure. This may be the reason why neuronal activity does not spread out to the entire neuronal network, even when no special topological organization exists.