Understanding how different networks relate to each other is key for understanding complex systems. We introduce an intuitive yet powerful framework to disentangle different ways in which networks can be (dis)similar and complementary to each other. We decompose the shortest paths between nodes as uniquely contributed by one source network, or redundantly by either, or synergistically by both together. Our approach considers the networks\' full topology, providing insights at multiple levels of resolution: from global statistics to individual paths. Our framework is widely applicable across scientific domains, from public transport to brain networks. In humans and 124 other species, we demonstrate the prevalence of unique contributions by long-range white-matter fibers in structural brain networks. Across species, efficient communication also relies on significantly greater synergy between long-range and short-range fibers than expected by chance. Our framework could find applications for designing network systems or evaluating existing ones.

Social Network Analysis (SNA) is a modeling technique and analytical approach well-suited for identifying and examining the structural features of supply networks and the patterns of connections between members within the network. This paper aims to present a systematic review and bibliometric analysis by investigating network structural properties and metrics in supply chain management (SCM) research. The approach involved combining a systematic literature review with a bibliometric analysis, forming a two-part methodology to examine 113 articles published between 2008 and 2023 in 62 journals. Our systematic thematic analysis reveals how SCM researchers have applied SNA techniques in terms of the reported node-level and network-level structural metrics, including network configuration description metrics, centrality measures, supply network subgroups, and models of supply network structure and formation. We identify the gaps in the existing body of literature and propose potential directions for future research. By quantitatively analyzing, classifying, and visualizing bibliographic data of previous studies, this paper provides further insights into the application of network structural properties in SCM research. Furthermore, our findings contribute to a deeper understanding of the significance of the supply network\'s relational structure and configuration. Considering the disruptions to global supply chains caused by the COVID-19 pandemic and the Russo-Ukrainian War, our findings can contribute to a better understanding of strategic supply network design.

The sensory cortices of the brain exhibit large-scale functional topographic organization, such as the tonotopic organization of the primary auditory cortex (A1) according to sound frequency. However, at the level of individual neurons, layer 2/3 (L2/3) A1 appears functionally heterogeneous. To identify if there exists a higher-order functional organization of meso-scale neuronal networks within L2/3 that bridges order and disorder, we used in vivo two-photon calcium imaging of pyramidal neurons to identify networks in three-dimensional volumes of L2/3 A1 in awake mice. Using tonal stimuli, we found diverse receptive fields with measurable colocalization of similarly tuned neurons across depth but less so across L2/3 sublayers. These results indicate a fractured microcolumnar organization with a column radius of ∼50 µm, with a more random organization of the receptive field over larger radii. We further characterized the functional networks formed within L2/3 by analyzing the spatial distribution of signal correlations (SCs). Networks show evidence of Rentian scaling in physical space, suggesting effective spatial embedding of subnetworks. Indeed, functional networks have characteristics of small-world topology, implying that there are clusters of functionally similar neurons with sparse connections between differently tuned neurons. These results indicate that underlying the regularity of the tonotopic map on large scales in L2/3 is significant tuning diversity arranged in a hybrid organization with microcolumnar structures and efficient network topologies.

UNASSIGNED: For patients in early coma after cardiopulmonary resuscitation (CPR), quantitative electroencephalogram (EEG) and brain network analysis was performed to identify relevant indicators of awakening.
UNASSIGNED: A prospective cohort study was conducted on comatose patients after CPR in the neuro-critical care unit. The included patients received clinical evaluation. The bedside high-density (64-lead) EEG monitoring was performed for visual grading and calculation of power spectrum and brain network parameters. A 3-month prognostic assessment was performed and the patients were dichotomized into the awakening group and the unawakening group.
UNASSIGNED: A total of 25 patients were included. The awakening group had higher GCS score, more slow wave pattern and reactive EEG than the unawakening group (P = 0.003, P < 0.001, P < 0.001, respectively). Compared with the unawakening group, (1) the awakening group had significantly higher absolute and relative θ power and slow/fast band ratio of the whole brain (P < 0.05), (2) the awakening group had stronger connection based on coherence, phase synchronization, phase lag index and cross-correlation (P < 0.05), (3) the awakening group had higher small-worldness, clustering coefficient and average path length based on graph theory (P < 0.05).
UNASSIGNED: The power spectrum and brain network characteristics in patients in early coma after CPR have predictive value for recovery.

Nicotine addiction is a neuropsychiatric disorder with dysfunction in cortices as well as white matter (WM). The nature of the functional alterations in WM remains unclear. The small-world model can well characterize the structure and function of the human brain. In this study, we utilized the small-world model to compare the WM functional connectivity between 62 nicotine addiction participants (called the discovery sample) and 66 matched healthy controls (called the control sample). We also recruited an independent sample comprising 32 nicotine addicts (called the validation sample) for clinical application. The WM functional network data at the network level showed that the nicotine addiction group revealed decreased small-worldness index (σ) and normalized clustering coefficient (γ) compared with healthy controls. For clinical application, the small-world topology of WM functional connectivity could distinguish nicotine addicts from healthy controls (classification accuracy=0.59323, p = 0.0464). We trained abnormal small-world properties on the discovery sample to identify the severity of nicotine addiction, and the identification was successfully applied to the validation sample (classification accuracy=0.65625, p = 0.0106). Our neuroimaging findings provide direct evidence for WM functional changes in nicotine addiction and suggest that the small-world properties of WM function could be qualified as potential biomarkers in nicotine addiction.

The theoretical analysis of maritime accidents is a hot topic, but the time characteristics and dynamics of maritime accidents time series are still unclear. It is difficult to draw a clear conclusion from the cause analysis, so the accident is difficult to be predicted. To bridge this gap, this research analyzes the characteristics and evolution mechanism of maritime accidents time series from the perspective of complex network theory. The visual graph algorithm is used to model the complex network of maritime accidents data in 22 jurisdictions of the Yangtze River, map the time series into a complex network, and reveal the time characteristics and dynamics of maritime accidents time series based on the complex system theory. In the empirical analysis, degree distribution, clustering coefficient and network diameter are used to analyze the characteristics of time series. The results show that the degree distribution of maritime accidents time series network presents power-law characteristics in the macro and micro levels, which shows that the maritime accidents time series is scale-free. In addition, according to the clustering coefficient and network diameter, maritime accidents time series in the Yangtze River has the characteristics of small-world and hierarchical structure. The research of this manuscript shows that the occurrence of maritime accidents is not random events and does not follow specific patterns but presents the characteristics of complex systems, and this phenomenon is common. The analysis of maritime accidents time series by complex network theory can provide theoretical support for maritime traffic safety management.

Covering neuroimaging evidence has demonstrated that epileptic symptoms are associated with the disrupted topological architecture of the brain network. Infantile spasms (IS) as an age-specific epileptic encephalopathy also showed abnormal structural or functional connectivity in specific brain regions or specific networks. However, little is known about the topological alterations of whole-brain functional networks in patients with IS. To fill this gap, we used the graph theoretical analysis to investigate the topological properties (whole-brain small-world property and modular interaction) in 17 patients with IS and 34 age- and gender-matched healthy controls. The functional networks in both groups showed efficient small-world architecture over the sparsity range from 0.05 to 0.4. While patients with IS showed abnormal global properties characterized by significantly decreased normalized clustering coefficient, normalized path length, small-worldness, local efficiency, and significantly increased global efficiency, implying a shift toward a randomized network. Modular analysis revealed decreased intra-modular connectivity within the default mode network (DMN) and fronto-parietal network but increased inter-modular connectivity between the cingulo-opercular network and occipital network. Moreover, the decreased intra-modular connectivity in DMN was significantly negatively correlated with seizure frequency. The inter-modular connectivity between the cingulo-opercular and occipital network also showed a significant correlation with epilepsy frequency. Together, the current study revealed the disrupted topological organization of the whole-brain functional network, which greatly advances our understanding of neuronal architecture in IS and may contribute to predict the prognosis of IS as disease biomarkers.

Frontotemporal dementia (FTD) is a spectrum of clinical syndromes that affects personality, behavior, language, and cognition. The current diagnostic criteria recognize three main clinical subtypes: the behavioral variant of FTD (bvFTD), the semantic variant of primary progressive aphasia (svPPA), and the non-fluent/agrammatic variant of PPA (nfvPPA). Patients with FTD display heterogeneous clinical and neuropsychological features that highly overlap with those presented by psychiatric syndromes and other types of dementia. Moreover, up to now there are no reliable disease biomarkers, which makes the diagnosis of FTD particularly challenging. To overcome this issue, different studies have adopted metrics derived from magnetic resonance imaging (MRI) to characterize structural and functional brain abnormalities. Within this field, a growing body of scientific literature has shown that graph theory analysis applied to MRI data displays unique potentialities in unveiling brain network abnormalities of FTD subtypes. Here, we provide a critical overview of studies that adopted graph theory to examine the topological changes of large-scale brain networks in FTD. Moreover, we also discuss the possible role of information arising from brain network organization in the diagnostic algorithm of FTD-spectrum disorders and in investigating the neural correlates of clinical symptoms and cognitive deficits experienced by patients.

UNASSIGNED: The efficacy and prognosis of major depressive disorder (MDD) are limited by its heterogeneity. MDD with melancholic features is an important subtype of MDD. The present study aimed to reveal the white matter (WM) network changes in melancholic depression.
UNASSIGNED: Twenty-three first-onset, untreated melancholic MDD, 59 non-melancholic MDD patients and 63 health controls underwent diffusion tensor imaging (DTI) scans. WM network analysis based on graph theory and support vector machine (SVM) were used for image data analysis.
UNASSIGNED: Compared with HC, small-worldness was reduced and abnormal node attributes were in the right orbital inferior frontal gyrus, left orbital superior frontal gyrus, right caudate nucleus, right orbital superior frontal gyrus, right orbital middle frontal gyrus, left rectus gyrus, and left median cingulate and paracingulate gyrus of MDD patients. Compared with non-melancholic MDD, small-worldness was reduced and abnormal node attributes were in right orbital inferior frontal gyrus, left orbital superior frontal gyrus and right caudate nucleus of melancholic MDD. For correlation analysis, the 7th item score of the HRSD-17 (work and interest) was positively associated with increased node betweenness centrality (aBC) values in right orbital inferior frontal gyrus, while negatively associated with the decreased aBC in left orbital superior frontal gyrus. SVM analysis results showed that abnormal aBC in right orbital inferior frontal gyrus and left orbital superior frontal gyrus showed the highest accuracy of 81.0% (69/83), the sensitivity of 66.3%, and specificity of 85.2% for discriminating MDD patients with or without melancholic features.
UNASSIGNED: There is a significant difference in WM network changes between MDD patients with and without melancholic features.

Every blood vessel is lined by a single layer of highly specialized, yet adaptable and multifunctional endothelial cells. These cells, the endothelium, control vascular contractility, hemostasis, and inflammation and regulate the exchange of oxygen, nutrients, and waste products between circulating blood and tissue. To control each function, the endothelium processes endlessly arriving requests from multiple sources using separate clusters of cells specialized to detect specific stimuli. A well-developed but poorly understood communication system operates between cells to integrate multiple lines of information and coordinate endothelial responses. Here, the nature of the communication network has been addressed using single-cell Ca2+ imaging across thousands of endothelial cells in intact blood vessels. Cell activities were cross-correlated and compared to a stochastic model to determine network connections. Highly correlated Ca2+ activities occurred in scattered cell clusters, and network communication links between them exhibited unexpectedly short path lengths. The number of connections between cells (degree distribution) followed a power-law relationship revealing a scale-free network topology. The path length and degree distribution revealed an endothelial network with a “small-world” configuration. The small-world configuration confers particularly dynamic endothelial properties including high signal-propagation speed, stability, and a high degree of synchronizability. Local activation of small clusters of cells revealed that the short path lengths and rapid signal transmission were achieved by shortcuts via connecting extensions to nonlocal cells. These findings reveal that the endothelial network design is effective for local and global efficiency in the interaction of the cells and rapid and robust communication between endothelial cells in order to efficiently control cardiovascular activity.