Small-world Properties Research Articles

MAPT rs17649553 T allele is associated with better verbal memory and higher small-world properties in Parkinson’s disease

Currently, over 90 genetic loci have been found to be associated with Parkinson’s disease (PD) in genome-wide association studies, nevertheless, the effects of these genetic variants on the clinical features and brain structure of PD patients are largely unknown. This study investigated the effects of microtubule-associated protein tau (MAPT) rs17649553 (C>T), a genetic variant associated with reduced PD risk, on the clinical manifestations and brain networks of PD patients. We found MAPT rs17649553 T allele was associated with better verbal memory in PD patients. In addition, MAPT rs17649553 significantly shaped the topology of gray matter covariance network and white matter network. Both the network metrics in gray matter covariance network and white matter network were correlated with verbal memory, however, the mediation analysis showed that it was the small-world properties in white matter network that mediated the effects of MAPT rs17649553 on verbal memory. These results suggest that MAPT rs17649553 T allele is associated with higher small-world properties in structural network and better verbal memory in PD.

The anatomy of a population-scale social network

Large-scale human social network structure is typically inferred from digital trace samples of online social media platforms or mobile communication data. Instead, here we investigate the social network structure of a complete population, where people are connected by high-quality links sourced from administrative registers of family, household, work, school, and next-door neighbors. We examine this multilayer social opportunity structure through three common concepts in network analysis: degree, closure, and distance. Findings present how particular network layers contribute to presumably universal scale-free and small-world properties of networks. Furthermore, we suggest a novel measure of excess closure and apply this in a life-course perspective to show how the social opportunity structure of individuals varies along age, socio-economic status, and education level.

Evidence of neuroplasticity with brain-computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis.

Brain-computer interface (BCI) has been widely used for functional recovery after stroke. Understanding the brain mechanisms following BCI intervention to optimize BCI strategies is crucial for the benefit of stroke patients. Forty-six patients with upper limb motor dysfunction after stroke were recruited and randomly divided into the control group or the BCI group. The primary outcome was measured by the assessment of Fugl-Meyer Assessment of Upper Extremity (FMA-UE). Meanwhile, we performed resting-state functional magnetic resonance imaging (rs-fMRI) in all patients, followed by independent component analysis (ICA) to identify functionally connected brain networks. Finally, we assessed the topological efficiency of both groups using graph-theoretic analysis in these brain subnetworks. The FMA-UE score of the BCI group was significantly higher than that of the control group after treatment (p = 0.035). From the network topology analysis, we first identified seven subnetworks from the rs-fMRI data. In the following analysis of subnetwork properties, small-world properties including γ (p = 0.035) and σ (p = 0.031) within the visual network (VN) decreased in the BCI group. For the analysis of the dorsal attention network (DAN), significant differences were found in assortativity (p = 0.045) between the groups. Additionally, the improvement in FMA-UE was positively correlated with the assortativity of the dorsal attention network (R = 0.498, p = 0.011). Brain-computer interface can promote the recovery of upper limbs after stroke by regulating VN and DAN. The correlation trend of weak intensity proves that functional recovery in stroke patients is likely to be related to the brain's visuospatial processing ability, which can be used to optimize BCI strategies. The trial is registered in the Chinese Clinical Trial Registry, number ChiCTR2000034848. Registered 21 July 2020.

Enhanced functional connectome of cerebellum in chronic insomnia patients.

Functional abnormalities of the cerebellum have been found to be closely associated with chronic insomnia (CI). However, whether there are abnormalities in the topology of the functional connectome of the cerebellum in these patients is still unknown. This study aimed to investigate topological abnormalities of the cerebellar functional connectome in individuals with CI. We used resting-state functional magnetic resonance imaging (rs-fMRI) and graph-theoretic analysis to construct a functional connectivity matrix and extract topological property features of the cerebellar functional connectome in patients with CI. We examined global and nodal topological property changes in the cerebellar functional connectome in 102 patients with CI (CI group) and 101 healthy participants without insomnia symptoms (HC group) to determine the differences between groups. Correlations between the topological properties of the cerebellar functional connectome and clinical assessments were calculated to confirm the differences between groups. The cerebellar functional connectome of both CI and HC patients exhibited small-world properties. The CI group showed higher standardized clustering coefficients at the global properties and higher betweenness centrality in the cerebellar Crus II vermis region at the nodal properties compared with participants in the HC group. However, the topological properties of cerebellar functional connectome abnormalities in the CI group were not significantly different from those in clinical assessments. Our findings suggest that the abnormal global and nodal topological properties of the cerebellar functional connectome are associated with CI and could serve as an important biomarker for CI.

Graph analysis of resting state functional brain networks and associations with cognitive outcomes in survivors of pediatric brain tumor

Survivors of pediatric brain tumors often live with long-term cognitive difficulties related to brain changes associated with the tumor itself as well as treatments such as radiation therapy. The present study used graph theory to examine functional network properties in this population and whether graph metrics relate to core cognitive skills: attention, working memory, and processing speed. 31 survivors and 31 matched controls completed neuropsychological testing and functional magnetic resonance imaging. Neuroimaging was preprocessed and spatially constrained ICA was completed, followed by calculation of area under the curve values of graph metrics. Results revealed a significant difference such that brain tumor survivors exhibited less small-world properties. This was found to be related to working memory, such that less small-worldness in the network was related to poorer performance. Furthermore, hub regions appear to be particularly vulnerable to disruption. Comparison to results of microstructural network analysis from a similar sample suggest functional connectivity graph metrics provide different and complementary information and additional post-hoc analyses are also discussed. These findings reveal that survivors of pediatric brain tumor indeed display significant differences in functional brain networks that are quantifiable by graph theory and build a foundation to better understand how metrics such as small-worldness can be used to predict long-term cognitive outcomes in adulthood. Ongoing neuroimaging research may play a part in precision medicine determining treatment protocols and interventions for pediatric brain tumor patients.

Diagnosis of Chronic Musculoskeletal Pain by Using Functional Near-Infrared Spectroscopy and Machine Learning.

Chronic pain (CP) has been found to cause significant alternations of the brain's structure and function due to changes in pain processing and disrupted cognitive functions, including with respect to the prefrontal cortex (PFC). However, until now, no studies have used a wearable, low-cost neuroimaging tool capable of performing functional near-infrared spectroscopy (fNIRS) to explore the functional alternations of the PFC and thus automatically achieve a clinical diagnosis of CP. In this case-control study, the pain characteristics of 19 chronic pain patients and 32 healthy controls were measured using fNIRS. Functional connectivity (FC), FC in the PFC, and spontaneous brain activity of the PFC were examined in the CP patients and compared to those of healthy controls (HCs). Then, leave-one-out cross-validation and machine learning algorithms were used to automatically achieve a diagnosis corresponding to a CP patient or an HC. The current study found significantly weaker FC, notably higher small-worldness properties of FC, and increased spontaneous brain activity during resting state within the PFC. Additionally, the resting-state fNIRS measurements exhibited excellent performance in identifying the chronic pain patients via supervised machine learning, achieving F1 score of 0.8229 using only seven features. It is expected that potential FC features can be identified, which can thus serve as a neural marker for the detection of CP using machine learning algorithms. Therefore, the present study will open a new avenue for the diagnosis of chronic musculoskeletal pain by using fNIRS and machine learning techniques.

Brokers in beneficial ownership: A network approach

We investigate beneficial ownership networks from 1999 to 2018 and find the presence of the small-world phenomenon, which fosters the participation of shareholders and public firms in beneficial ownership events. The small-world phenomenon is also associated with the presence of brokers, which are nodes that bridge shareholders with other shareholders and targets. Moreover, the beneficial ownership network is shaped by star nodes, that is, nodes that have many adjacent nodes, most of which are not connected to each other. Although star nodes affect the presence of the small-world phenomenon, there is weak evidence in support of the association of star nodes with the participation of shareholders and public firms in beneficial ownership events or with the probability of brokers' presence. Furthermore, we find that cash-on-hand and firm size seem to have the most significant impact on the probability of brokers' presence in the beneficial ownership network.

Research on Biodiesel in the National Institute of Science and Technology for Energy and Environment: An Analysis through Scientometry, and Complex and Social Networks

The National Institute of Science and Technology for Energy and Environment (INCT E&A) is a virtual network of scientific-institutional cooperation focused on research for energy sustainable use and environmental preservation. This article aimed to identify production and articulations of researchers affiliated to INCT E&A in biodiesel research in the period from 2009 to 2020, based on scientometric, and social and complex network analysis. Altogether 76 articles involving biodiesel theme were analysed and a network of 212 authors from 48 institutions with a high level of collaboration was identified. Network topological properties were calculated, making it possible to find central researchers and affirm that it presents the small world phenomenon. It was also possible to observe that scientific production by the researchers occurred especially between the years 2009 and 2014. After this period, biodiesel theme stopped attracting INCT E&A researcher’s attention, demobilizing a network at a moment when Brazil chose biofuels as a decarbonizing strategy for transport matrix through the National Biofuels Policy (RenovaBio).

Functional magnetic resonance imaging study on anxiety and depression disorders induced by chronic restraint stress in rats

Persistent and negative stress stimulation is one of the most important factors leading to anxiety and depression in individuals, and it can negatively affect the normal function and structure of brain-related regions. However, the maladaptive changes of brain neural networks in anxiety and depression induced by chronic stress have not been explored in detail. In this study, we analyzed the changes in global information transfer efficiency, stress related blood oxygen level dependent (BOLD)- and diffusion tensor imaging (DTI)- signals and functional connectivity (FC) in rat models based on resting-state functional magnetic resonance imaging (rs-fMRI). The results showed that compared to control group, rats treated with chronic restraint stress (CRS) for 5 weeks had reconstructed the small-world network properties. In addition, CRS group had increased coherence and activity in bilateral Striatum (ST_R & L), but decreased coherence and activity in unilateral (left) Frontal Association Cortex (FrA_L) and unilateral (left) Medial Entorhinal Cortex (MEC_L). DTI analysis and correlation analysis confirmed the disrupted integrity of MEC_L and ST_R & L and their correlation to anxiety- and depressive-liked behaviors. Functional connectivity further showed these regions of interest (ROI) had decreased positive correlations with several brain areas, respectively. Our study comprehensively revealed the adaptive changes of brain neural networks induced by chronic stress and emphasized the abnormal activity and functional connectivity of ST_R & L and MEC_L in the pathological condition.

Integrated analysis of topoisomerase I inhibitors from flavonoids of Scutellaria baicalensis Georgi using bioaffinity ultrafiltration UPLC-TripleTOF MS/MS, molecular docking and target-based multiple complex networks

Scutellaria baicalensis Georgi (SBG) has been widely used as medical plant in East Asia with remarkable anti-cancer activity. However, the underlying mechanisms are still confused. In this study, an integrated analysis was conducted to screen topoisomerase I (topo I) inhibitors from flavonoids of SBG and investigate the anti-cancer mechanisms, containing bioaffinity ultrafiltration UPLC-ESI-TripleTOF-MS/MS, molecular docking, and multiple complex networks. The SBG extract exhibited notable cytotoxic activity on Hela cells. Five flavonoids were identified as potential topo I inhibitors, including skullcapflavone II, wogonin, chrysin, oroxylin A, and tenaxin I. Their ESI-MS/MS spectra showed that RDA reaction and neutral molecule loss were the main fragment patterns. Docking results demonstrated that π–π interaction and the formation of hydrogen bond contributed most to their binding with topo I. The selected compounds, related target proteins and pathways were integrated into target-based multiple complex networks, which consisted of three subnetworks. Statistical and topological analysis of these networks revealed a series of characteristics, including scale-free property with power-law degree distribution, Poisson degree distribution, and small-world property. Chrysin, wogonin, and oroxylin A exhibited as main active components with much higher degree values. Chemical carcinogenesis-receptor activation (hsa05207) was considered as critical pathway due to remarkable centrality indexes. Additionally, potential synergistic effect of wogonin and chrysin was observed on the conversion of supercoiled DNA to relaxed forms. These results improved current understanding of flavonoid-rich plants on the treatment of cancer. Moreover, the multi-disciplinary approach provided a new strategy for the research of natural products from medical plants.

Modulation of Brain Network Topological Properties in Knee Osteoarthritis by Electroacupuncture in Rats.

Osteoarthritis is a chronic, ongoing disease that affects patients, and pain is considered a key factor affecting patients, but the brain changes during the development of osteoarthritis pain are currently unclear. In this study, we used electroacupuncture (EA) to intervene the rat model of knee osteoarthritis and analyzed the changes in topological properties of brain networks using graph theory. Sixteen SD rat models of right-knee osteoarthritis with anterior cruciate ligament transection (ACLT) were randomly divided into electroacupuncture intervention group and control group. The electroacupuncture group was intervened on Zusanli (ST36) and Futu (ST32) for 20 min each time, five times a week for 3 weeks, while the control group was applied sham stimulation. Both groups were measured for pain threshold. The small-world properties and node properties of the brain network between the two groups after the intervention were statistically analyzed by graph theory methods. The differences are mainly in the changes in node attributes between the two groups, such as degree centrality, betweenness centrality, and so on in different brain regions (P<0.05). Both groups showed no small-world characteristics in the brain networks of the two groups. The mechanical thresholds and thermal pain thresholds were significantly higher in the EA group than in the control group (P<0.05). The study demonstrated that electroacupuncture intervention enhanced the activity of nodes related to pain circuit and relieved pain in osteoarthritis, which provides a complementary basis for explaining the effect of electroacupuncture intervention on pain through graphical analysis of changes in brain network topological properties and helps to develop an imaging model for pain affected by electroacupuncture.

LSW-Net: Lightweight Deep Neural Network Based on Small-World properties for Spine MR Image Segmentation.

Segmenting spinal tissues from MR images is important for automatic image analysis. Deep neural network-based segmentation methods are efficient, yet have high computational costs. To design a lightweight model based on small-world properties (LSW-Net) to segment spinal MR images, suitable for low-computing-power embedded devices. Retrospective. A total of 386 subjects (2948 images) from two independent sources. Dataset I: 214 subjects/779 images, all for disk degeneration screening, 147 had disk degeneration, 52 had herniated disc. Dataset II: 172 subjects/2169 images, 142 patients with vertebral degeneration, 163 patients with disc degeneration. 70% images in each dataset for training, 20% for validation, and 10% for testing. T1- and T2-weighted turbo spin echo sequences at 3 T. Segmentation performance of LSW-Net was compared with four mainstream (including U-net and U-net++) and five lightweight models using five radiologists' manual segmentations (vertebrae, disks, spinal fluid) as reference standard. LSW-Net was also deployed on NVIDIA Jetson nano to compare the pixels number in segmented vertebrae and disks. All models were evaluated with accuracy, precision, Dice similarity coefficient (DSC), and area under the receiver operating characteristic (AUC). Pixel numbers segmented by LSW-Net on the embedded device were compared with manual segmentation using paired t-tests, with P < 0.05 indicating significance. LSW-Net had 98.5% fewer parameters than U-net but achieved similar accuracy in both datasets (dataset I: DSC 0.84 vs. 0.87, AUC 0.92 vs. 0.94; dataset II: DSC 0.82 vs. 0.82, AUC 0.88 vs. 0.88). LSW-Net showed no significant differences in pixel numbers for vertebrae (dataset I: 5893.49 vs. 5752.61, P = 0.21; dataset II: 5073.42 vs. 5137.12, P = 0.56) and disks (dataset I: 1513.07 vs. 1535.69, P = 0.42; dataset II: 1049.74 vs. 1087.88, P = 0.24) segmentation on an embedded device compared to manual segmentation. Proposed LSW-Net achieves high accuracy with fewer parameters than U-net and can be deployed on embedded device, facilitating wider application. 2. 1.

Altered brain functional networks after Quchi (LI 11) acupuncture: An EEG analysis.

As a unique traditional Chinese medicine therapy, the central effect of acupuncture has received increasing attention. Functional brain networks can provide connectivity information among brain regions. The study goal is to explore the regulatory effect of acupuncture on the brain functional network. This paper analyzes the electroencephalography (EEG)-based power spectrum and brain functional network elicited by acupuncture at Quchi (LI 11). The power spectrum results showed that acupuncture at LI 11 decreased the energy in the alpha frequency, mainly in the central region, left parietal lobe, left temporal lobe and left frontal lobe. Moreover, functional brain networks converted from the magnitude-squared coherence matrix in the alpha band are reconstructed. The results show that acupuncture did not alter the basic properties of the brain functional connection network. During acupuncture, the average node degree, average clustering coefficient, and small-world property of the brain functional connection network decreased after acupuncture compared with that before it. However, the average characteristic path length increased after acupuncture compared with before. Acupuncture at LI 11 altered the brain's electrical activity. In the meantime, this acupuncture reduced the network's internal connectivity and information transfer efficiency.

Network Model with Scale-Free, High Clustering Coefficients, and Small-World Properties

Networks are prevalent in real life, and the study of network evolution models is very important for understanding the nature and laws of real networks. The distribution of the initial degree of nodes in existing classical models is constant or uniform. The model we proposed shows binomial distribution, and it is consistent with real network data. The theoretical analysis shows that the proposed model is scale-free at different probability values and its clustering coefficients are adjustable, and the Barabasi-Albert model is a special case of p = 0 in our model. In addition, the analytical results of the clustering coefficients can be estimated using mean-field theory. The mean clustering coefficients calculated from the simulated data and the analytical results tend to be stable. The model also exhibits small-world characteristics and has good reproducibility for short distances of real networks. Our model combines three network characteristics, scale-free, high clustering coefficients, and small-world characteristics, which is a significant improvement over traditional models with only a single or two characteristics. The theoretical analysis procedure can be used as a theoretical reference for various network models to study the estimation of clustering coefficients. The existence of stable equilibrium points of the model explains the controversy of whether scale-free is universal or not, and this explanation provides a new way of thinking to understand the problem.

Network science approach for identifying disruptive elements of an airline

Currently, flight delays are common and they propagate from an originating flight to connecting flights, leading to large disruptions in the overall schedule. These disruptions cause massive economic losses, affect airlines’ reputations, waste passengers’ time and money, and directly impact the environment. This study adopts a network science approach for solving the delay propagation problem by modeling and analyzing the flight schedules and historical operational data of an airline. We aim to determine the most disruptive airports, flights, flight-connections, and connection types in an airline network. Disruptive elements are influential or critical entities in an airline network. They are the elements that can either cause (airline schedules) or have caused (historical data) the largest disturbances in the network. An airline can improve its operations by avoiding delays caused by the most disruptive elements. The proposed network science approach for disruptive element analysis was validated using a case study of an operating airline. The analysis indicates that potential disruptive elements in a schedule of an airline are also actual disruptive elements in the historical data and they should be considered to improve operations. The airline network exhibits small-world effects and delays can propagate to any part of the network with a minimum of four delayed flights. Finally, we observed that passenger connections between flights are the most disruptive connection type. Therefore, the proposed methodology provides a tool for airlines to build robust flight schedules that reduce delays and propagation.

Altered brain structural and functional connectivity in cannabis users

Cannabis is one of the most used and commodified illicit substances worldwide, especially among young adults. The neurobiology mechanism of cannabis is yet to be identified particularly in youth. The purpose of this study was to concurrently measure alterations in brain structural and functional connectivity in cannabis users using resting-state functional magnetic resonance images (rs-fMRI) and diffusion-weighted images (DWI) from a group of 73 cannabis users (age 22–36, 19 female) in comparison with 73 healthy controls (age 22–36, 14 female) from Human Connectome Project (HCP). Several significant differences were observed in local structural/functional network measures (e.g. degree and clustering coefficient), being prominent in the insular and frontal opercular cortex and lateral/medial temporal cortex. The rich-club organization of structural networks revealed a normal trend, distributed within bilateral frontal, temporal and occipital regions. However, minor differences were found between the two groups in the superior and inferior temporal gyri. Functional rich-club nodes were mostly located within parietal and posterior areas, with minor differences between the groups found mainly in the centro-temporal and parietal regions. Regional network measures of structural/functional networks were associated with times used cannabis (TUC) in several regions. Although the structural/functional network in both groups showed small-world property, no differences between cannabis users and healthy controls were found regarding the global network measures, showing no association with cannabis use. After FDR correction, all of the significant associations between network measures and TUC were found to be insignificant, except for the association between degree and TUC within the presubiculum region. To recap, our findings revealed alterations in local topological properties of structural and functional networks in cannabis users, although their global brain network organization remained intact.

Altered Resting-State Functional Networks in Nondialysis Patients with Stage 5 Chronic Kidney Disease: A Graph–Theoretical Analysis

This study aimed to investigate the topological characteristics of the resting-state functional network and the underlying pathological mechanism in nondialysis patients with stage 5 chronic kidney disease (CKD5 ND). Eighty-five subjects (21 patients with CKD5 ND, 32 patients with CKD on maintenance hemodialysis (HD), and 32 healthy controls (HCs)) underwent laboratory examinations, neuropsychological tests, and brain magnetic resonance imaging. The topological characteristics of networks were compared with a graph-theoretical approach, and correlations between neuropsychological scores and network properties were analyzed. All participants exhibited networks with small-world attributes, and global topological attributes were impaired in both groups of patients with CKD 5 (ND and HD) compared with HCs (p < 0.05); these impairments were more severe in the CKD5 ND group than in the HD group (p < 0.05). Compared with the HC group, the degree centrality of the CKD5 ND group decreased mainly in the basal ganglia and increased in the bilateral orbitofrontal gyrus, bilateral precuneus, and right cuneus. Correlation analysis showed that the degree of small-worldness, normalized clustering coefficients, and Montreal Cognitive Assessment (MoCA) scores were positively correlated and that characteristic path length was negatively correlated with these variables in patients with CKD5 ND. The nodal efficiency of the bilateral putamen (r = 0.53, p < 0.001 and r = 0.47, p < 0.001), left thalamus (r = 0.37, p < 0.001), and right caudate nucleus (r = 0.28, p = 0.01) was positively correlated with MoCA scores. In conclusion, all CKD5 ND patients exhibited changes in functional network topological properties and were closely associated with mild cognitive impairment. More interestingly, the topological property changes in CKD5 ND patients were dominated by basal ganglia areas, which may be more helpful to understand and possibly reveal the underlying pathological mechanisms of cognitive impairment in CKD5 ND.

Optimal Scale-Free Small-World Graphs with Minimum Scaling of Cover Time

The cover time of random walks on a graph has found wide practical applications in different fields of computer science, such as crawling and searching on the World Wide Web and query processing in sensor networks, with the application effects dependent on the behavior of the cover time: the smaller the cover time, the better the application performance. It was proved that over all graphs withNnodes, complete graphs have the minimum cover timeNlogN. However, complete graphs cannot mimic real-world networks with small average degree and scale-free small-world properties, for which the cover time has not been examined carefully, and its behavior is still not well understood. In this article, we first experimentally evaluate the cover time for various real-world networks with scale-free small-world properties, which scales asNlogN. To better understand the behavior of the cover time for real-world networks, we then study the cover time of three scale-free small-world model networks by using the connection between cover time and resistance diameter. For all the three networks, their cover time also behaves asNlogN. This work indicates that sparse networks with scale-free and small-world topology are favorable architectures with optimal scaling of cover time. Our results deepen understanding the behavior of cover time in real-world networks with scale-free small-world structure, and have potential implications in the design of efficient algorithms related to cover time.

Predicting poststroke dyskinesia with resting-state functional connectivity in the motor network.

Motor function evaluation is essential for poststroke dyskinesia rehabilitation. Neuroimaging techniques combined with machine learning help decode a patient's functional status. However, more research is needed to investigate how individual brain function information predicts the dyskinesia degree of stroke patients. We investigated stroke patients' motor network reorganization and proposed a machine learning-based method to predict the patients' motor dysfunction. Near-infrared spectroscopy (NIRS) was used to measure hemodynamic signals of the motor cortex in the resting state (RS) from 11 healthy subjects and 31 stroke patients, 15 with mild dyskinesia (Mild), and 16 with moderate-to-severe dyskinesia (MtS). The graph theory was used to analyze the motor network characteristics. The small-world properties of the motor network were significantly different between groups: (1)clustering coefficient, local efficiency, and transitivity: MtS > Mild > Healthy and (2)global efficiency: MtS < Mild < Healthy. These four properties linearly correlated with patients' Fugl-Meyer Assessment scores. Using the small-world properties as features, we constructed support vector machine (SVM) models that classified the three groups of subjects with an accuracy of 85.7%. Our results show that NIRS, RS functional connectivity, and SVM together constitute an effective method for assessing the poststroke dyskinesia degree at the individual level.

Determinants of collective failure in excitable networks.

We study collective failures in biologically realistic networks that consist of coupled excitable units. The networks have broad-scale degree distribution, high modularity, and small-world properties, while the excitable dynamics is determined by the paradigmatic FitzHugh-Nagumo model. We consider different coupling strengths, bifurcation distances, and various aging scenarios as potential culprits of collective failure. We find that for intermediate coupling strengths, the network remains globally active the longest if the high-degree nodes are first targets for inactivation. This agrees well with previously published results, which showed that oscillatory networks can be highly fragile to the targeted inactivation of low-degree nodes, especially under weak coupling. However, we also show that the most efficient strategy to enact collective failure does not only non-monotonically depend on the coupling strength, but it also depends on the distance from the bifurcation point to the oscillatory behavior of individual excitable units. Altogether, we provide a comprehensive account of determinants of collective failure in excitable networks, and we hope this will prove useful for better understanding breakdowns in systems that are subject to such dynamics.