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Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.e., synchronized shrinkage and growth) between networks during the scan, that we refer to as dynamic spatial network connectivity (dSNC). We find that several features of such dynamic spatial brain networks are associated with cognition, with higher dynamic variability in these networks and higher volumetric coupling between network pairs positively associated with cognitive performance. We show that these networks are modulated differently in individuals with schizophrenia versus typical controls, resulting in network growth or shrinkage, as well as altered focus of activity within a network. Schizophrenia also shows lower spatial dynamical variability in several networks, and lower volumetric coupling between pairs of networks, thus upholding the role of dynamic spatial brain networks in cognitive impairment seen in schizophrenia. Our data show evidence for the importance of studying the typically overlooked voxel‐wise changes within and between brain networks. Spatially dynamic brain networks show significant volumetric coupling with synchronized growth and shrinkage, referred to as dynamic spatial network connectivity (dSNC). Dynamic variability in such networks and coupling between network pairs are positively associated with cognitive performance, while showing negative association with schizophrenia, highlighting their possible role in cognitive impairment.

The interplay between excitation and inhibition is crucial for neuronal circuitry in the brain. Inhibitory cell fractions in the neocortex and hippocampus are typically maintained at 15 to 30%, which is assumed to be important for stable dynamics. We have studied systematically the role of precisely controlled excitatory/inhibitory (E/I) cellular ratios on network activity using mice hippocampal cultures. Surprisingly, networks with varying E/I ratios maintain stable bursting dynamics. Interburst intervals remain constant for most ratios, except in the extremes of 0 to 10% and 90 to 100% inhibitory cells. Single-cell recordings and modeling suggest that networks adapt to chronic alterations of E/I compositions by balancing E/I connectivity. Gradual blockade of inhibition substantiates the agreement between the model and experiment and defines its limits. Combining measurements of population and single-cell activity with theoretical modeling, we provide a clearer picture of how E/I balance is preserved and where it fails in living neuronal networks.

This magnetoencephalography (MEG) study addresses (i) how Friedreich ataxia (FRDA) affects the sub‐second dynamics of resting‐state brain networks, (ii) the main determinants of their dynamic alterations, and (iii) how these alterations are linked with FRDA‐related changes in resting‐state functional brain connectivity (rsFC) over long timescales. For that purpose, 5 min of resting‐state MEG activity were recorded in 16 FRDA patients (mean age: 27 years, range: 12–51 years; 10 females) and matched healthy subjects. Transient brain network dynamics was assessed using hidden Markov modeling (HMM). Post hoc median‐split, nonparametric permutations and Spearman rank correlations were used for statistics. In FRDA patients, a positive correlation was found between the age of symptoms onset (ASO) and the temporal dynamics of two HMM states involving the posterior default mode network (DMN) and the temporo‐parietal junctions (TPJ). FRDA patients with an ASO <11 years presented altered temporal dynamics of those two HMM states compared with FRDA patients with an ASO > 11 years or healthy subjects. The temporal dynamics of the DMN state also correlated with minute‐long DMN rsFC. This study demonstrates that ASO is the main determinant of alterations in the sub‐second dynamics of posterior associative neocortices in FRDA patients and substantiates a direct link between sub‐second network activity and functional brain integration over long timescales. This magnetoencephalography (MEG) study addresses (i) how Friedreich ataxia (FRDA) affects the sub‐second dynamics of resting‐state brain networks, (ii) the main determinants of their dynamic alterations, and (iii) how these alterations are linked with FRDA‐related changes in resting‐state functional brain connectivity (rsFC) over long timescales. Transient brain network dynamics was assessed using hidden Markov modeling (HMM). In FRDA patients, a positive correlation was found between the age of symptoms onset (ASO) and the temporal dynamics of two HMM states involving the posterior default mode network (DMN) and the temporo‐parietal junctions (TPJ). The temporal dynamics of the DMN state also correlated with minute‐long DMN rsFC. FRDA patients with an ASO <11 years presented altered temporal dynamics of those two HMM states.

Während qualitative Ansätze in der sozialen Netzwerkanalyse florieren, sind Forschungsprozesse und insbesondere die Datenanalyse zumeist von einem strukturalen netzwerkanalytischen Paradigma geprägt. Zudem existieren unzureichend qualitativ-interpretative Ansätze zur Untersuchung sozialer Netzwerkdaten. Um diese Forschungslücke zu schließen, entwerfen und explizieren wir ein qualitatives Analyseverfahren, das auf dem Cultural Turn der sozialen Netzwerkanalyse aufbaut und sowohl subjektive Deutungsmuster als auch historisch/prozessuale Konfigurationen erfassen soll. Wir formulieren eine biografische netzwerkanalytische Perspektive, in der wir die Entwicklung eines sozialen Netzwerkes in der Lebensgeschichte analysieren. Am Beispiel einer Fallstudie aus einem Forschungsprojekt zu transnationalen sozialen Unterstützungsnetzwerken älterer Migrant*innen in Perth explizieren wir das Verfahren der biografisch rekonstruktiven Netzwerkanalyse (BRNA). BRNA ist ein kooperativ entwickeltes analytisches Verfahren der Erhebung und der Auswertung sozialer Netzwerkdaten. Bei der BRNA-Datenerhebung werden das narrativ-biografische Interview und ego-zentrische Netzwerkkarten trianguliert. Bei der Datenanalyse folgen wir biografisch-rekonstruktiven Forschungsprinzipien und Verfahren, um die Dynamiken sozialer Netzwerke in der Lebensgeschichte zu rekonstruieren und nachzuvollziehen.

We study the contact process in the regime of small infection rates on finite scale-free networks with stationary dynamics based on simultaneous updating of all connections of a vertex. We allow the update rates of individual vertices to increase with the strength of a vertex, leading to a fast evolution of the network. We first develop an approach for inhomogeneous networks with general kernel and then focus on two canonical cases, the factor kernel and the preferential attachment kernel. For these specific networks, we identify and analyse four possible strategies how the infection can survive for a long time.We show that there is fast extinction of the infection when neither of the strategies is successful, otherwise there is slow extinction and the most successful strategy determines the asymptotics of the metastable density as the infection rate goes to zero. We identify the domains in which these strategies dominate in terms of phase diagrams for the exponent describing the decay of the metastable density.

The seasonal outbreaks of influenza infection cause globally respiratory illness, or even death in all age groups. Given early‐warning signals preceding the influenza outbreak, timely intervention such as vaccination and isolation management effectively decrease the morbidity. However, it is usually a difficult task to achieve the real‐time prediction of influenza outbreak due to its complexity intertwining both biological systems and social systems. By exploring rich dynamical and high‐dimensional information, our dynamic network marker/biomarker (DNM/DNB) method opens a new way to identify the tipping point prior to the catastrophic transition into an influenza pandemics. In order to detect the early‐warning signals before the influenza outbreak by applying DNM method, the historical information of clinic hospitalization caused by influenza infection between years 2009 and 2016 were extracted and assembled from public records of Tokyo and Hokkaido, Japan. The early‐warning signal, with an average of 4‐week window lead prior to each seasonal outbreak of influenza, was provided by DNM‐based on the hospitalization records, providing an opportunity to apply proactive strategies to prevent or delay the onset of influenza outbreak. Moreover, the study on the dynamical changes of hospitalization in local district networks unveils the influenza transmission dynamics or landscape in network level.

From an evolutionary economics perspective, knowledge networks are self-organizing systems. Therefore, studying changes of these systems requires an understanding of how such changes are influenced by both the behaviors and characteristics of key individual actors and the network structure. We apply this perspective to a network of investigators (i.e. lead scientists) and a sample of 9543 Phase 2 cancer clinical trials during the period 2002–2012, in order to examine the structure and explore the dynamics of the clinical trial network. Using temporal exponential random graph models, we examine whether preferential attachment, multi-connectivity, or homophily drive the formation of new collaborative relations to knowledge translators - i.e. investigators with basic and clinical research knowledge. Our results suggest that despite some increased connectivity over time the network remains fragmented due to the considerably growing number of investigators in the network. This fragmentation limits opportunities for knowledge transfer to advance clinical trials. We find that homophily in research fields and investigators’ country of affiliation and heterophily in terms of publication output promote the formation of ties to knowledge translators. We find also that multi-connectivity increases the probability of tie formation with knowledge translators while preferential attachment reduces this probability.

Statistical methods for dynamic network analysis have advanced greatly in the past decade. This article extends current estimation methods for dynamic network logistic regression (DNR) models, a subfamily of the Temporal Exponential-family Random Graph Models, to network panel data which contain missing data in the edge and/or vertex sets. We begin by reviewing DNR inference in the complete data case. We then provide a missing data framework for DNR families akin to that of Little and Rubin (2002) or Gile and Handcock (2010a). We discuss several methods for dealing with missing data, including multiple imputation (MI). We consider the computational complexity of the MI methods in the DNR case and propose a scalable, design-based approach that exploits the simplifying assumptions of DNR. We dub this technique the “complete-case” method. Finally, we examine the performance of this method via a simulation study of induced missingness in two classic network data sets.

We propose a two-step methodology for exploring the temporal characteristics of a network. First, we construct a graph time series, where each snapshot is the result of a temporal whole-graph embedding. The embedding is carried out using the degree, Katz and betweenness centralities to characterize first and higher order proximities among vertices. Then a principal component analysis is performed over the collected temporal graph samples, which exhibits eigengraphs, graphs whose temporal weight variations model the sampled graph series. Analysis of the temporal timeline of each of the main eigengraphs reveals moments of importance in terms of structural graph changes. Parameters such as the dimension of the embeddings and the number of temporal samples are explored. Two case studies are presented: a Bitcoin subgraph, where findings are cross-checked by looking at the subgraph behavior itself, and the Enron email network, which allows us to compare our findings with prior studies. In both cases, the proposed methodology successfully identified temporal structural changes in the graph evolution.

A superkine variant of interleukin-2 with six site mutations away from the binding interface developed from the yeast display technique has been previously characterized as undergoing a distal structure alteration which is responsible for its super-potency and provides an elegant case study with which to get insight about how to utilize allosteric effect to achieve desirable protein functions. By examining the dynamic network and the allosteric pathways related to those mutated residues using various computational approaches, we found that nanosecond time scale all-atom molecular dynamics simulations can identify the dynamic network as efficient as an ensemble algorithm. The differentiated pathways for the six core residues form a dynamic network that outlines the area of structure alteration. The results offer potentials of using affordable computing power to predict allosteric structure of mutants in knowledge-based mutagenesis.