Network Level Research Articles

Current techniques of neuroimaging, including electrical devices, are either of low spatiotemporal resolution or invasive, impeding multiscale monitoring of brain activity at both single-cell and network levels. Overcoming this issue is of great importance to assess the brain's computational ability and for neurorehabilitation projects that require real-time monitoring of neurons and concomitant network activities. Currently, that information could be extracted from functional MRI when combined with mathematical models. Novel combinations of measurement techniques that enable quantitative and long-lasting recording at both single cell and network levels will enable to correlate the MRI data and single cell activity to refine those models. Here, we report the fabrication and validation of ultra-thin, optically transparent, and flexible subcortical microelectrode arrays for combining functional MRI and multisite single-spike recordings. The sensing devices demonstrate both fMRI transparency at 4.7 T and high electrophysiological performance, and thus appear as a promising candidate for simultaneous multiscale neurodynamic measurements.

Hemiplegic shoulder pain (HSP), a common complication of stroke, has a reported incidence of 34%-85% and a complex pathophysiology. This study aimed to explore differences in brain network topological properties between patients with and without HSP after stroke using resting-state functional magnetic resonance imaging. Fifty patients with hemiplegia after stroke were recruited and divided into two groups based on the presence of HSP. Resting-state functional magnetic resonance imaging data were acquired, and GRETNA was applied to calculate both global and regional network topological properties. Group differences were assessed between patients with and without HSP. At the global network level, both groups demonstrated a clear small-world organization (small-worldness index > 1). Compared with the non-pain group, patients with HSP showed significantly lower global efficiency (0.1733 ± 0.0047 vs. 0.1765 ± 0.0044; P = 0.02) and higher characteristic path length (0.3224 ± 0.0152 vs. 0.3170 ± 0.0089; P = 0.001). At the regional network level, patients with HSP showed reduced nodal degree centrality and nodal local efficiency in the opercular part of the right inferior frontal gyrus and in the orbital gyrus (P < 0.05). They also exhibited decreased nodal betweenness centrality in the right pallidum, left dorsolateral superior frontal gyrus, triangular part of the left inferior gyrus, and left Rolandic operculum (P < 0.05). Conversely, nodal betweenness centrality was increased in the left thalamus, right parahippocampal gyrus, left inferior occipital gyrus, and left angular gyrus (P < 0.05). The topological properties of brain networks in patients with HSP have shifted toward a weaker small-world organization. Nodal alterations were primarily concentrated in the executive control network, default mode network, basal ganglia, and language network, regions associated with pain processing and emotional regulation. These findings provide new insights into the central mechanisms of HSP after stroke and suggest potential neural targets for future research and therapeutic intervention.

Motion detection is a primary task required for robotic systems to perceive and navigate in their environment. Proposed in the literature bioinspired neuromorphic Time-Difference Encoder (TDE-2) combines event-based sensors and processors with spiking neural networks to provide real-time and energy-efficient motion detection through extracting temporal correlations between two points in space. However, on the algorithmic level, this design leads to a loss of direction-selectivity of individual TDEs in textured environments. In the present work, we propose an augmented 3-point TDE (TDE-3) with additional inhibitory input that makes TDE-3 direction-selectivity robust in textured environments. We developed a procedure to train the new TDE-3 using backpropagation through time and surrogate gradients to linearly map input velocities into an output spike count or an Inter-Spike Interval (ISI). Using synthetic data, we compared training and inference with spike count and ISI with respect to changes in stimuli dynamic range, spatial frequency, and level of noise. ISI turns out to be more robust toward variation in spatial frequency, whereas the spike count is a more reliable training signal in the presence of noise. We conducted an in-depth quantitative investigation of optical flow coding with TDE and compared TDE-2 vs. TDE-3 in terms of energy efficiency and coding precision. The results show that at the network level, both detectors show similar precision (20° angular error, 88% correlation with the truth of the ground). However, due to the more robust direction selectivity of individual TDEs, the TDE-3 based network spikes less and is hence more energy efficient. Reported precision is on par with model-based methods but the spike-based processing of the TDEs provides allows more energy-efficient inference with neuromorphic hardware. Additionally, we also employed TDE-2 and TDE-3 to estimate ego-motion and showed results competitive with those achieved by neural networks with 1.5 × 10 5 parameters.

The primary objective was to evaluate the impact of the Community-Hospital-Family Interactive Management (CHFIM) Model on social isolation in elderly patients undergoing maintenance hemodialysis. Secondary objectives included assessing its effects on social support, loneliness, depression, and family function. A total of 160 elderly maintenance hemodialysis patients from the Blood Purification Center of Taixing People's Hospital between July 2023 and March 2024 were selected as the study subjects. Using a controlled trial design, the patients were divided into a control group (n = 80) and an intervention group (n = 80). The control group received routine care, while the intervention group received Community-Hospital-Family Interactive Management. The social network level, social support level, loneliness, and depression levels of the two groups were compared. After the intervention, the intervention group showed significant improvements in social network level (primary outcome) and social support, along with significant reductions in loneliness and depression (secondary outcomes) compared to the control group (p < 0.05). The CHFIM Model effectively reduces social isolation in elderly maintenance hemodialysis patients, promoting their physical and mental health. Chinese Clinical Trial Registry, ChiCTR2500107611.

Effects of dibenzazepine compounds on Nav1.2 channels and neuronal network activity: A systematic comparison.

Sigma-1-targeting multimodal compound HBK-15 reverses memory deficits and restores hippocampal plasticity under NMDA hypofunction.

Sickle cell disease (SCD) is an inherited blood disorder caused by a mutation in the beta-globin gene, resulting in chronic complications, including cognitive decline-particularly in executive functions. Neuroimaging studies have identified structural and functional abnormalities associated with SCD; however, the directionality of information flow between brain networks and how disruptions in these interactions contribute to cognitive deficits remains poorly understood. This study employed Granger causality (GC) analysis to investigate effective connectivity and information flow between brain regions and resting-state networks using ultra-high-field 7T MRI in adult patients with SCD (n = 51) and age-, sex-, and race-matched controls (n = 44). We first performed a whole-brain network analysis, followed by an examination of specific brain regions within the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN), and ventral attention network (VAN). For each analysis, we computed both the magnitude and directionality of information flow to capture the strength and directional influence of connectivity between brain regions. While patients with SCD exhibited a higher magnitude of information flow compared to controls, this difference was only statistically significant when computed at the brain region level, not at the resting-state network level. In terms of directionality, afferent flow from DAN and VAN to ECN was significantly greater in patients with SCD than in controls. Subtype analysis revealed that patients with severe SCD demonstrated significantly higher magnitude of information flow than those with mild SCD and controls. We also observed subtype-specific differences in afferent flow to ECN: mild SCD patients showed significant flow from VAN, while severe SCD patients showed significant flow from DAN. Additionally, multiple regression analyzes assessing correlations between information flow and cognitive performance showed that controls had higher R2 values than patients with SCD, suggesting reduced network efficiency in SCD. This study is the first to apply GC-based effective connectivity analysis in SCD, revealing unique pathways of information exchange in patients with SCD, potentially as compensatory mechanisms for disease-related structural and functional disruptions. These findings provide novel insights into how SCD impacts brain network organization and cognitive function, emphasizing the importance of investigating network-level dynamics in this population.

Consciousness resonation in eco-environment balance: Initiation of and response to black carbon emissions from the Arctic shipping routes.

This study analyzes how Community-Based Tourism (CBT) has been formed and implemented in Gohan-eup, Jeongseon-gun—a former mining community that has sought survival and recovery in the post-mining era. CBT is a form of sustainable tourism that aims to minimize negative impacts of tourism development while maximizing local benefits, characterized by locally driven, resident-led tourism initiatives. Focusing on the successful case of a community-operated village hotel, this research explores the formation and key factors behind the effective implementation of CBT. The study adopts an instrumental case study approach using qualitative data from literature review, participant observation, and in-depth interviews with key stakeholders. The findings reveal that the emergence of capable leaders and active resident support were key formation factors. Crucial success factors included the community's self-organization process, the development of collective efficacy, collaborative governance, and the internalization of external human resources. Based on these findings, a “Successful CBT Implementation Model” is proposed, emphasizing the role of social and institutional support at the individual, organizational, and network levels. This study refines the conceptual understanding of CBT, offers an analytical framework for its multi-layered implementation mechanisms. Practically, it empirically validates the effectiveness of resident-led tourism policies, providing strategic and policy implications for promoting CBT as a sustainable local development model.

Connectomes provide neuronal wiring diagrams and allow for investigating the detailed synaptic morphology of each connection. In the visual system of Drosophila, T5 cells are the primary motion-sensing neurons in the OFF-pathway and receive dendritic input from the excitatory Tm1, Tm2, Tm4, Tm9 and the inhibitory CT1 neurons. This connectivity, however, has not yet been investigated with respect to polyadic synapses known to be abundant in the fly nervous system. In this study, we use the FlyWire database and identify that Tm and CT1 cells wire on T5a dendrites via eight polyadic synapse types. We then explore the distribution of the different synapse types on T5a dendrites and find differences in their spatial patterns. Finally, we show that the polyadic morphology is setting a directional wiring architecture at the T5 network level. Our work showcases the directionality that polyadic synapses introduce in T5 connectivity.

The use of network survey vehicles (NSVs) for the functional evaluation of highway assets has become industry practice around the world. However, owing to the lack of adequate quality control in pavement condition assessment at the network level, substantial variability in roughness measurement can be observed attributed to interchangeable usage of multiple operators, varying segment lengths, presence of local anomalies, and so forth. This study assesses the repeatability and reproducibility of international roughness index (IRI) measurements computed for a wide range of segment lengths and IRI run averages across multiple NSVs. Fifteen runs of IRI measurements were conducted using three NSVs along a six km section of National Highway 44. The results showed that the variability in IRI measurements reduced as the number of runs and the segment length under consideration were increased. Consequently, there exist a combination of measurement mitigation strategies that can be considered by agencies to meet quality assurance standards. The results also demonstrated variability in the repeatability results across NSVs as well as lack of agreement in IRI measurements for different segment lengths. The differential impact of the data collection parameters on IRI measurement is highlighted through a heteroscedastic linear regression framework. Additional analyses explored the impact of following marked paths on run-to-run variability, as well as the impact of structural joints on IRI estimates. Some strategies for determining critical limits for repeatability and reproducibility are also discussed.

The article presents a systematic review of heat-and-power integration methods into chemical-technological processes, focusing on enhancing energy efficiency, reducing green-house gas emissions and optimizing thermal resource utilization. The study investigates key directions for waste heat recovery, including the use of flue gases, superheated and secondary steam, heat from domestic and industrial wastewater, and the energy of excess pressure in gas distribution systems. Special attention is given to technologies of cogeneration, heat recovery, and the application of the heat pumps and exchangers across various industrial sectors. It is shown that flue gases offer high potential for thermal reuse due to their elevated temperatures and prevalence in heat-intensive operations. At the same time, limitations such as uneven heating and high transport costs are noted. The study discusses the advantages and shortcomings of secondary steam in processes like evaporation, distillation, drying, heating, and electricity generation. It further explores the integration potential in sewage systems, highlighting how thermal recovery at component, building, network and treatment plant levels can reduce environmental impact and reclaim energy losses. However, challenges such as heat exchanger fouling present significant barriers to implementation. The article also addresses energy losses due to pressure reduction in gas pipelines, underlining the potential of turboexpander technologies. The analysis concludes that thermal energy integration plays a strategic role in reducing resource intensity and enhancing the sustainability of industrial systems. Among the evaluated solutions, the recovery of flue gas heat emerges as the most promising due to its efficiency and technological feasibility, although other approaches may be more appropriate depending on industry-specific conditions. The practical value of the study lies in providing a theoretical basis for the implementation of energy-saving measures in industry through a systematized approach to thermal flow integration.

Benign paroxysmal positional vertigo (BPPV) is the most common type of peripheral vertigo, which is caused by dislodged calcium carbonate crystals entering the semicircular canals. Previous research suggested multiple brain regions are involved in the central mechanism of BPPV, but the contribution of brain networks to this disorder has not yet been explained. The study aimed to investigate resting-state functional connectivity (FC) differences in BPPV patients at level of brain networks to provide support for the central mechanism of this disorder. Sixty-eight patients with BPPV and 60 matched healthy subjects were recruited in the cross-sectional study. FC of intra-and inter-network from all participants were detected using independent component analysis. Regions with altered intra-network connectivity were set as seeds to explore whole-brain FC in BPPV. Then the correlations were analyzed between abnormal FC and vestibular function. Increased intra-network FC were found within auditory network, cerebellar network (CN), executive control network and salience network (SN) in BPPV compared to controls (p<0.05, cluster-level, FWE corrected). Inter-network FC alterations were shown in auditory network, basal ganglia network, default mode network (DMN) and SN in BPPV (p<0.01, FDR corrected). FC differences of inferior parietal lobule to superior frontal gyrus and precuneus, together between cerebellum VIII-X and VI were observed between two groups (p<0.05, cluster-level, FDR corrected). Additional analyses presented positive correlation between FC within CN and vertigo intensity (p=0.039, FDR corrected). BPPV is characterized by specific alterations of intra-and inter-network connectivity across several cortical and subcortical regions, which are relevant to the vestibulocerebellar dysfunction in varying degrees. Furthermore, CN connectivity may become a potential neuroimaging target for adjunct clinical intervention of BPPV. BPPV＝ benign paroxysmal positional vertigo; FC＝ functional connectivity; ICA＝ independent component analysis; RSN＝ resting-state network; IC＝ independent component; IPL＝ inferior parietal lobule; DMN＝ default mode network; SN＝ salience network; CN＝ cerebellar network.

Epilepsy is primarily defined by the repetitive occurrence of seizures, but the full impact of seizures extends beyond these episodic events. Seizures themselves cause changes at the cellular, network, and systemic levels in individual patients with epilepsy and may contribute to the progressive nature of the disease in some patients. Seizures may have an impact on brain structure and function over time. There is also a high societal and economic burden associated with persistent seizures, including major impacts on quality of life, activities of daily living, and productivity. Therefore, seizure freedom with no or minimal side effects should be the key goal in the treatment of epilepsy, with the potential to reduce both disease progression and the societal and economic impacts of the disease. Physicians have a responsibility to address the key obstacles to early seizure freedom, including optimizing initial treatment strategies, minimizing treatment delays, overcoming therapeutic nihilism, and improving medication adherence. PLAIN LANGUAGE SUMMARY: Epilepsy involves repeated seizures which can cause changes in the brain over time and affect memory, thoughts, and everyday activities. Repeated seizures place a heavy burden on individuals, families, and society, affecting their quality of life, independence, and work. The main goal of epilepsy treatment should be the elimination of all seizures with few or no side effects. To achieve this, doctors should act quickly, choose the right treatments early, avoid treatment delays, and support patients in taking their medications.

Purpose The performance dilemma of megaprojects remains a challenge. However, existing research on megaproject performance (MP) mostly ignored the role of network embeddedness. In particular, the effects of dual network embeddedness across distinct organizational levels – defined as the simultaneous embeddedness of project participants within project-level networks (PNE) and participants' parent companies within industry-level networks (INE) – remain underexplored, leaving the unclear influencing mechanism of MP. To bridge this gap, this study draws on network embeddedness theory and resource orchestration theory, proposing a conceptual model to explore how dual network embeddedness influences MP, with a particular focus on the mediating roles of project routine capability (PRC) and project innovative capability (PIC). Design/methodology/approach This study applies the triangulation approach, analyzing questionnaire data by partial least-squares structural equation modeling and substantiating findings with semi-structured interviews based on a megaproject case. Findings The results indicate that INE affects MP positively and significantly, whereas the direct impact of PNE on MP is verified only in public construction projects. Besides, PRC and PIC serve as mediating roles linking dual network embeddedness to MP. Originality/value The findings clarify the discrepancy impacts of dual network embeddedness on MP, identifying INE as the primary driver compared to PNE, which reveals the varying explanatory power of network embeddedness theory for MP. Additionally, the findings uncover the vital mediating roles of project capabilities, extending the applicability of resource orchestration theory in megaprojects by exploring the intrinsic impacts of network embeddedness on MP. This study also provides practical implications for project stakeholders on improving MP through network embeddedness and capability development.

The rapid increase in internet users has driven the development of WiFi networks, which play a crucial role in providing secure internet access, especially within Industry 4.0 and Industry 5.0 environments that rely on efficient data exchange. Penetration testing (pentest) is a vital approach for auditing and evaluating the security level of WiFi networks. Several frameworks such as PTES, PETA, and ISSAF are often used as references, although only a few are explicitly designed for WiFi networks. This study proposes a modification of the PTES framework to better align with the security characteristics of WiFi networks by providing relevant solution recommendations. The integration of the Boyer-Moore algorithm is employed as an efficient method to identify solutions for detected vulnerabilities. The implementation of this framework is demonstrated through testing the suggestion process, which produces solution recommendations based on vulnerabilities found during the pentest. The Boyer-Moore algorithm exhibits high efficiency in generating recommendations with a response time of 0.0000087 seconds.

Abstract Modern astronomical surveys deliver immense volumes of transient detections, yet distinguishing real astrophysical signals (for example, explosive events) from bogus imaging artefacts remains a challenge. Convolutional neural networks are effectively used for real versus bogus classification; however, their reliance on opaque latent representations hinders interpretability. Here we show that large language models (LLMs) can approach the performance level of a convolutional neural network on three optical transient survey datasets (Pan-STARRS, MeerLICHT and ATLAS) while simultaneously producing direct, human-readable descriptions for every candidate. Using only 15 examples and concise instructions, Google’s LLM, Gemini, achieves a 93% average accuracy across datasets that span a range of resolution and pixel scales. We also show that a second LLM can assess the coherence of the output of the first model, enabling iterative refinement by identifying problematic cases. This framework allows users to define the desired classification behaviour through natural language and examples, bypassing traditional training pipelines. Furthermore, by generating textual descriptions of observed features, LLMs enable users to query classifications as if navigating an annotated catalogue, rather than deciphering abstract latent spaces. As next-generation telescopes and surveys further increase the amount of data available, LLM-based classification could help bridge the gap between automated detection and transparent, human-level understanding.

BackgroundPatients’ sexual harassment against nurses is a worldwide phenomenon. Some forms occur on a daily to weekly basis. Despite the known high prevalence and its negative consequences, there is still a lack of evidence-based measures to prevent patients’ sexual harassment against nurses. Given the complexity of the problem, multidimensional interventions are required.ObjectiveThe main objective of the StopSH project is to develop an evidence-based, complex intervention package to prevent patients’ sexual harassment against nurses and minimize its negative consequences for the acute care sector in the German-speaking part of Switzerland.MethodsThis project is an intervention development study with a multimethod design. It involves the participative development and testing of a complex intervention package in one to two Swiss hospitals as practice partners. The project is carried out in four project phases. First, a systematic scoping review will be conducted to identify and map existing interventions aimed at preventing sexual harassment of nurses or minimizing its consequences. The review will include interventions at the individual, organizational, and network levels of nurses. The problem and needs analysis form the second phase, where a cross-sectional web-based survey will be carried out among nurses in one to two partner hospitals. The aim is to assess the prevalence, forms, and perceived consequences of sexual harassment, as well as existing and desired strategies or support structures. The results will inform the development of the intervention package. As a third phase, a complex intervention package will be codeveloped using a participatory action research approach, based on the findings from the first two phases. This process will involve nurses, hospital management, human resources, and other relevant stakeholders to ensure contextual relevance and feasibility. Finally, during a feasibility assessment, the developed intervention package will be implemented and tested on two to three test wards within the partner hospitals. The mixed methods feasibility study will assess the acceptability, practicality, and preliminary effects of the intervention. Survey data, as well as contextual and observational data, will be collected.ResultsThe project was launched in February 2024 and is scheduled to last for 5 years. As of August 2025, this project is in phase 2. Data collection is ongoing. The StopSH project is expected to develop and test a complex intervention package for the prevention of patients’ sexual harassment against nurses. This intervention package is predicted to reduce the prevalence and negative effects of sexual harassment against nurses.ConclusionsThe results of this project will provide important guidance for nurses, but also for their employers, and as such can contribute to the long-term reduction of sexual harassment against nurses. It lays the foundation for the development and adaptation of interventions in further nursing settings and other health care professions.International Registered Report Identifier (IRRID)DERR1-10.2196/71425

Wireless Sensor Networks (WSNs) consist of numerous battery-powered sensor nodes that operate with limited energy, computation, and communication capabilities. Designing routing strategies that are both energy-efficient and attack-resilient is essential for extending network lifetime and ensuring secure data delivery. This paper proposes Adaptive Federated Reinforcement Learning-Hunger Games Search (AFRL-HGS), a Hybrid Routing framework that integrates multiple advanced techniques. At the node level, tabular Q-learning enables each sensor node to act as a reinforcement learning agent, making next-hop decisions based on discretized state features such as residual energy, distance to sink, congestion, path quality, and security. At the network level, Federated Reinforcement Learning (FRL) allows the sink node to aggregate local Q-tables using adaptive, energy- and performance-weighted contributions, with Polyak-based blending to preserve stability. The binary Hunger Games Search (HGS) metaheuristic initializes Cluster Head (CH) selection and routing, providing a well-structured topology that accelerates convergence. Security is enforced as a constraint through a lightweight trust and anomaly detection module, which fuses reliability estimates with residual-based anomaly detection using Exponentially Weighted Moving Average (EWMA) on Round-Trip Time (RTT) and loss metrics. The framework further incorporates energy-accounted control plane operations with dual-format HELLO and hierarchical ADVERTISE/Service-ADVERTISE (SrvADVERTISE) messages to maintain the routing tables. Evaluation is performed in a hybrid testbed using the Graphical Network Simulator-3 (GNS3) for large-scale simulation and Kali Linux for live adversarial traffic injection, ensuring both reproducibility and realism. The proposed AFRL-HGS framework offers a scalable, secure, and energy-efficient routing solution for next-generation WSN deployments.

BackgroundSleep dysfunction shares a bidirectional relationship with hallucinatory experiences, with the strongest path from sleep dysfunction to the occurrence of hallucinatory experiences. This review aimed to identify potential mechanisms through which sleep dysfunction leads to hallucinations.Study DesignA narrative review was conducted across 4 levels of explanation: phenomenology (via lived-experience accounts), psychology, neural networks, and neurophysiology.Study ResultsRelatively few studies have directly tested underlying mechanisms linking sleep dysfunction to hallucinations, particularly at the levels of neural networks and neurophysiology. There is good support for stress as a mediator between sleep dysfunction and hallucinations. Stress was a plausible mechanism across levels of explanation and was supported by sleep manipulation studies in non-clinical populations. Inflammation of the nervous system is affected by sleep loss, which in turn impacts the brain connectivity underpinning hallucinatory experiences. Lived-experience accounts identified 3 novel mechanisms, all of which are meaningful to people with lived experience of hallucinations: source monitoring, mental resilience, and reasoning skills. Quantitative studies show these mechanisms are impacted by sleep loss, but the full causal path from sleep dysfunction to hallucinations via these mechanisms requires testing.ConclusionsKey priorities for future research are to (1) test stress as a mediator in clinical populations experiencing hallucinations, with stress assessed across the levels of explanation simultaneously; (2) carry out experimental tests of novel potential mediators identified in this review (eg, source monitoring, inflammation, prefrontal cortical networks); and (3) identify potential moderators that might explain individual differences in the lived-experience accounts of the effect of sleep dysfunction on hallucinations.