RESUMO
Galaxies in the Universe are distributed in a web-like structure characterized by different large-scale environments: dense clusters, elongated filaments, sheetlike walls and under-dense regions, called voids1-5. The low density in voids is expected to affect the properties of their galaxies. Indeed, previous studies6-14 have shown that galaxies in voids are, on average, bluer and less massive, and have later morphologies and higher current star formation rates than galaxies in denser large-scale environments. However, it has never been observationally proved that the star formation histories (SFHs) in voids are substantially different from those in filaments, walls and clusters. Here we show that void galaxies have had, on average, slower SFHs than galaxies in denser large-scale environments. We also find two main SFH types present in all the environments: 'short-timescale' galaxies are not affected by their large-scale environment at early times but only later in their lives; 'long-timescale' galaxies have been continuously affected by their environment and stellar mass. Both types have evolved more slowly in voids than in filaments, walls and clusters.
RESUMO
BACKGROUND: Recent years have witnessed an upsurge in the development of methods for estimating current source densities (CSDs) in the neocortical tissue from their recorded local field potential (LFP) reflections using microelectrode arrays. Among these, methods utilizing linear arrays work under the assumption that CSDs vary as a function of cortical depth; whereas they are constant in the tangential direction, infinitely or in a confined cylinder. This assumption is violated in the analysis of neuronal activity propagating along the neocortical sheet, e.g. propagation of alpha waves or cortical spreading depression. NEW METHOD: Here, we developed a novel mathematical method (waveCSD) for CSD analysis of LFPs associated with a planar wave of neocortical neuronal activity propagating at a constant velocity towards a linear probe. RESULTS: Results show that the algorithm is robust to the presence of noise in LFP data and uncertainties in knowledge of propagation velocity. Also, results show high level of accuracy of the method in a wide range of electrode resolutions. Using in vivo experimental recordings from the rat neocortex, we employed waveCSD to characterize transmembrane currents associated with cortical spreading depressions. COMPARISON WITH EXISTING METHOD(S): Simulation results indicate that waveCSD has a significantly higher reconstruction accuracy compared to the widely-used inverse CSD method (iCSD), and the regularized kernel CSD method (kCSD), in the analysis of CSDs originating from propagating neuronal activity. CONCLUSIONS: The waveCSD method provides a theoretical platform for estimation of transmembrane currents from their LFPs in experimental paradigms involving wave propagation.