Stimulus type shapes the topology of cellular functional networks in mouse visual cortex.

On the timescale of sensory processing, neuronal networks have relatively fixed anatomical connectivity, while functional interactions between neurons can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed single-cell resolution electrophysiological data from the Allen Institute, with simultaneous recordings of stimulus-evoked activity from neurons across 6 different regions of mouse visual cortex. Comparing the functional connectivity patterns during different stimulus types, we made several nontrivial observations: (1) while the frequencies of different functional motifs were preserved across stimuli, the identities of the neurons within those motifs changed; (2) the degree to which functional modules are contained within a single brain region increases with stimulus complexity. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.

Stimulus-dependent functional network topology in mouse visual cortex.

Information is processed by networks of neurons in the brain. On the timescale of sensory processing, those neuronal networks have relatively fixed anatomical connectivity, while functional connectivity, which defines the interactions between neurons, can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli, which drive different neuronal activities in the network, could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed electrophysiological data from the Allen Brain Observatory, which utilized Neuropixels probes to simultaneously record stimulus-evoked activity from hundreds of neurons across 6 different regions of mouse visual cortex. The recordings had single-cell resolution and high temporal fidelity, enabling us to determine fine-scale functional connectivity. Comparing the functional connectivity patterns observed when different stimuli were presented to the mice, we made several nontrivial observations. First, while the frequencies of different connectivity motifs (i.e., the patterns of connectivity between triplets of neurons) were preserved across stimuli, the identities of the neurons within those motifs changed. This means that functional connectivity dynamically changes along with the input stimulus, but does so in a way that preserves the motif frequencies. Secondly, we found that the degree to which functional modules are contained within a single brain region (as opposed to being distributed between regions) increases with increasing stimulus complexity. This suggests a mechanism for how the brain could dynamically alter its computations based on its inputs. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.

Multi-element imaging of urinary stones by LA-ICP-MS with a homogeneous co-precipitation CaC2O4-matrix calibration standard.

Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) studies on trace element concentration and their spatial distribution in CaC2O4-matrix urinary stones are important but powerfully rely on matrix-matched external calibration. In this work, CaC2O4 precipitate CaOx-1 which was doped with Mg, Cr, Mn, Fe, Co, Cu, Zn, and Sr was prepared by the homogeneous co-precipitation method. It had a homogeneous distribution of major (RSD of 0.46%) and trace elements (RSD of 1.83-6.92%) due to the negligible concentration difference compared with that prepared by the heterogeneous co-precipitation method. Based on this, an analytical method for quantitative determination of elemental concentration in CaC2O4-matrix samples was established using CaOx-1 as a calibration standard, and the accuracy of this method was assessed by calibrating the elemental concentration in another synthetic CaC2O4 precipitate CaOx-2 with relative deviation (Dr) from - 11.43% (Mn) to 9.76% (Mg). Finally, a methodology for quantitative imaging of Mg, Cr, Mn, Fe, Co, Cu, Zn, and Sr in urinary stones via LA-ICP-MS was developed. From the elemental distributional maps, an annular texture can be found for Mg, Cu, Zn, and Sr, which corresponds to the annular white and brown texture in the real urinary stone. A homogeneous distribution of Fe and low concentrations of Cr and Co were found throughout the stone, while Mn was highly concentrated in the margin of the stone. All these results demonstrate that quantitative distribution patterns of Mg, Cr, Mn, Fe, Co, Cu, Zn, and Sr can be obtained by LA-ICP-MS using CaOx-1 as a calibration standard, which can provide potential evidence for urological and other medical studies.

Predictability of real temporal networks.

Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and function. Empirical evidence has shown that the temporal nature of links in many real-world networks is not random. Nonetheless, it is challenging to predict temporal link patterns while considering the entanglement between topological and temporal link patterns. Here, we propose an entropy-rate-based framework, based on combined topological-temporal regularities, for quantifying the predictability of any temporal network. We apply our framework on various model networks, demonstrating that it indeed captures the intrinsic topological-temporal regularities whereas previous methods considered only temporal aspects. We also apply our framework on 18 real networks of different types and determine their predictability. Interestingly, we find that, for most real temporal networks, despite the greater complexity of predictability brought by the increase in dimension, the combined topological-temporal predictability is higher than the temporal predictability. Our results demonstrate the necessity for incorporating both temporal and topological aspects of networks in order to improve predictions of dynamical processes.