Spatial organisation of the mesoscale connectome: A feature influencing synchrony and metastability of network dynamics.

Significant research has investigated synchronisation in brain networks, but the bulk of this work has explored the contribution of brain networks at the macroscale. Here we explore the effects of changing network topology on functional dynamics in spatially constrained random networks representing mesoscale neocortex. We use the Kuramoto model to simulate network dynamics and explore synchronisation and critical dynamics of the system as a function of topology in randomly generated networks with a distance-related wiring probability and no preferential attachment term. We show networks which predominantly make short-distance connections smooth out the critical coupling point and show much greater metastability, resulting in a wider range of coupling strengths demonstrating critical dynamics and metastability. We show the emergence of cluster synchronisation in these geometrically-constrained networks with functional organisation occurring along structural connections that minimise the participation coefficient of the cluster. We show that these cohorts of internally synchronised nodes also behave en masse as weakly coupled nodes and show intra-cluster desynchronisation and resynchronisation events related to inter-cluster interaction. While cluster synchronisation appears crucial to healthy brain function, it may also be pathological if it leads to unbreakable local synchronisation which may happen at extreme topologies, with implications for epilepsy research, wider brain function and other domains such as social networks.

Nonoptimal component placement of the human connectome supports variable brain dynamics.

Neural systems are shaped by multiple constraints, balancing region communication with the cost of establishing and maintaining physical connections. It has been suggested that the lengths of neural projections be minimized, reducing their spatial and metabolic impact on the organism. However, long-range connections are prevalent in the connectomes across various species, and thus, rather than rewiring connections to reduce length, an alternative theory proposes that the brain minimizes total wiring length through a suitable positioning of regions, termed component placement optimization. Previous studies in nonhuman primates have refuted this idea by identifying a nonoptimal component placement, where a spatial rearrangement of brain regions in silico leads to a reduced total wiring length. Here, for the first time in humans, we test for component placement optimization. We show a nonoptimal component placement for all subjects in our sample from the Human Connectome Project (N = 280; aged 22-30 years; 138 females), suggesting the presence of constraints-such as the reduction of processing steps between regions-that compete with the elevated spatial and metabolic costs. Additionally, by simulating communication between brain regions, we argue that this suboptimal component placement supports dynamics that benefit cognition.

Spatial multi-scaled chimera states of cerebral cortex network and its inherent structure-dynamics relationship in human brain.

Human cerebral cortex displays various dynamics patterns under different states, however the mechanism how such diverse patterns can be supported by the underlying brain network is still not well understood. Human brain has a unique network structure with different regions of interesting to perform cognitive tasks. Using coupled neural mass oscillators on human cortical network and paying attention to both global and local regions, we observe a new feature of chimera states with multiple spatial scales and a positive correlation between the synchronization preference of local region and the degree of symmetry of the connectivity of the region in the network. Further, we use the concept of effective symmetry in the network to build structural and dynamical hierarchical trees and find close matching between them. These results help to explain the multiple brain rhythms observed in experiments and suggest a generic principle for complex brain network as a structure substrate to support diverse functional patterns.

[Rapid screening and confirmation of 415 pesticide residues in red cabbages by liquid chromatography-quadrupole-time of flight-mass spectrometry].

An analytical method for the simultaneous rapid screening and accurate confirmation of 415 pesticide residues in red cabbages was established using liquid chromatography-quadrupole-time of flight-mass spectrometry (LC-QTOF/MS) with single acquisition. In the established method, the pesticides in red cabbage were extracted using acetonitrile-acetic acid (99∶1, v/v) and salted-out using anhydrous magnesium sulfate and sodium chloride. The resultant solution was then cleaned-up by automatic solid phase extraction using a Carbon/NH2 cartridge. The SPE cartridge was activated with 4 mL acetonitrile-toluene (3∶1, v/v) and the effluents were discarded. The resultant solution was transferred to the Carbon/NH2 cartridge, using 3×2 mL acetonitrile-toluene (3∶1, v/v) to wash the test sample concentrate bottle, and waited until the surface of the test sample concentrate liquid reached the top layer of anhydrous Na2SO4 before transferring the washing liquid to the cartridge. A 30-mL reservoir was attached to the upper part of the SPE cartridge and 25 mL acetonitrile-toluene (3∶1, v/v) was used to wash the SPE cartridge again. The eluent was evaporated in the glass tube in a water bath at 37 ℃ and shaking speed 150 r/min to reduce the volume to 0.5 mL. Nitrogen was used to dry the concentrates, and the residues were dissolved in 1.0 mL acetonitrile-water (3∶2, v/v), homogenized by ultrasonication, and passed through 0.22-µm filtering membrane before determination. The dissolved sample solution was loaded onto a ZORBAX SB-C18 column (100 mm×2.1 mm, 3.5 µm) and separated under gradient elution using 0.1% (v/v) formic acid aqueous solution containing 5 mmol/L ammonium acetate and acetonitrile as the binary mobile phase. The eluent from the column was further detected by QTOF/MS under electrospray positive ionization in the MS/MS scanning mode. A matrix-matched external calibration method was used for quantitation. By optimizing the different parameters under Auto MS/MS and All Ions MS/MS acquisition modes, the optimal conditions for All Ions MS/MS under each acquisition mode were obtained, which were then compared for selection of a better mode. The results demonstrated that the developed method can be used to accurately screen and quantify all 415 pesticides in red cabbage. The linear regression correlation coefficients (r2) for the 415 pesticides were all greater than 0.990 in the corresponding linear concentration range. In addition, the screening detection limits (SDL) of 411 pesticides were no more than 5 µg/kg, and the limits of quantification (LOQs) of 413 pesticides were no more than 10 µg/kg. At the spiked levels of LOQ, two-fold LOQ, and 10-fold LOQ, the recoveries were in the ranges of 65.7%-118.4%, 72.0%-118.8% and 70.2%-111.2%, with relative standard deviations (RSDs) in the ranges of 0.9%-19.7%, 0.2%-19.9% and 0.6%-19.9%, respectively. The method was applied to detect pesticide residues in the red cabbage samples provided by the 2019 European proficiency test project for unknown pesticide screening (EUPT-SM-11) and accurate quantitation (EUPT-FV-21). For EUPT-SM-11, all the spiked and incurred pesticides in red cabbage were qualified accurately, without false positives or false negatives. This is completely consistent with the final results published by the EU official. For EUPT-FV-21, there were 19 non-volatile pesticides that can be detected by LC-MS, which were then accurately quantitated with the corresponding pesticide standard. The results demonstrate that the proposed method is accurate and reliable. It is also rapid and time-saving, and can be used for high-throughput screening and quantitative determination of pesticide residues in cabbage. It can also be extended to other fruits and vegetable matrices.

A two-layered brain network model and its chimera state.

Based on the data of cerebral cortex, we present a two-layered brain network model of coupled neurons where the two layers represent the left and right hemispheres of cerebral cortex, respectively, and the links between the two layers represent the inter-couplings through the corpus callosum. By this model we show that abundant patterns of synchronization can be observed, especially the chimera state, depending on the parameters of system such as the coupling strengths and coupling phase. Further, we extend the model to a more general two-layered network to better understand the mechanism of the observed patterns, where each hemisphere of cerebral cortex is replaced by a highly clustered subnetwork. We find that the number of inter-couplings is another key parameter for the emergence of chimera states. Thus, the chimera states come from a matching between the structure parameters such as the number of inter-couplings and clustering coefficient etc and the dynamics parameters such as the intra-, inter-coupling strengths and coupling phase etc. A brief theoretical analysis is provided to explain the borderline of synchronization. These findings may provide helpful clues to understand the mechanism of brain functions.