Modular Network Research Articles

Age-related differences of the time-varying features in the brain functional connectivity and cognitive aging.

Brain functional modular organization changes with age. Considering the brain as a dynamic system, recent studies have suggested that time-varying connectivity provides more information on brain functions. However, the spontaneous reconfiguration of modular brain structures over time during aging remains poorly understood. In this study, we investigated the age-related dynamic modular reconfiguration using resting-state functional MRI data (615 participants, aged 18-88 years) from Cam-CAN. We employed a graph-based modularity analysis to investigate modular variability and the transition of nodes from one module to another in modular brain networks across the adult lifespan. Results showed that modular structure exhibits both linear and nonlinear age-related trends. The modular variability is higher in early and late adulthood, with higher modular variability in the association networks and lower modular variability in the primary networks. In addition, the whole-brain transition matrix showed that the times of transition from other networks to the dorsal attention network were the largest. Furthermore, the modular structure was closely related to the number of cognitive components and memory-related cognitive performance, suggesting a potential contribution to flexibility cognitive function. Our findings highlighted the notable dynamic characteristics in large-scale brain networks across the adult lifespan, which enhanced our understanding of the neural substrate in various cognitions during aging. These findings also provided further evidence that dedifferentiation and compensation are the outcomes of functional brain interactions.

Whole-brain modular dynamics at rest predict sensorimotor learning performance

Abstract Neural measures that predict cognitive performance are informative about the mechanisms underlying cognitive phenomena, with diagnostic potential for neuropathologies with cognitive symptoms. Among such markers, the modularity (subnetwork composition) of whole-brain functional networks is especially promising, due to its longstanding theoretical foundations and recent success in predicting clinical outcomes. We used functional magnetic resonance imaging to identify whole-brain modules at rest, calculating metrics of their spatio-temporal dynamics before and after a sensorimotor learning task on which fast learning is widely believed to be supported by a cognitive strategy. We found that participants’ learning performance was predicted by the degree of coordination of modular reconfiguration, and the strength of recruitment and integration of networks derived during the task itself. Our findings identify these whole-brain metrics as promising network-based markers of cognition, with relevance to basic neuroscience and the potential for clinical application.

Responses of soil properties and foliar traits of a dominant tree species Pseudotsuga sinensis to rocky desertification in a karst ecosystem

Rocky desertification is a prevalent environmental challenge across karst landscapes that hinders regional sustainable development. Understanding how soil properties and plant traits respond to increasing levels of rocky desertification is crucial for developing effective restoration strategies. Here, we investigated soil characteristics and intraspecific leaf stoichiometric traits of Pseudotsuga sinensis across a gradient of rocky desertification in a karst ecosystem. We employed principal component analysis to assess changes in soil properties and leaf chemical traits and used leaf trait network analysis to explore shifts in trait network topology. Our results revealed that severe desertification sites had soil with high bulk density and pH, and low levels of available nitrogen, phosphorus, magnesium, as well as soil organic carbon. Leaf stoichiometry shifted markedly along the gradient, with elevated nitrogen limitation and cation imbalance in degraded sites. Trait network analysis showed that network diameter and modularity increased while connectance decreased with rocky desertification, indicating reduced integration among traits. On the other hand, higher adaptation capacity to environmental harshness under moderate level of rocky desertification was also observed. These findings suggest that both soil properties and leaf traits would experience substantial shifts across rocky desertification gradient, with increasing functional fragmentation and nutrient limitation as degradation intensifies. Based on our results, we propose management strategies targeted to different levels of rocky desertification to mitigate its negative impacts and promote ecosystem recovery.

P11.05.B LONGITUDINAL MODIFICATIONS OF GRADIENTS AND GRAPH-THEORY MEASURES OF FUNCTIONAL MRI CONNECTIVITY IN GLIOMA PATIENTS AS POTENTIAL MARKERS OF POST-SURGICAL LONG-TERM COGNITIVE OUTCOME

Abstract BACKGROUND Integrating connectomics and functional brain activity into the study of brain tumors is among neuro-oncology’s challenges. Functional MRI (fMRI) studies showed that gliomas are integrated with brain networks’ activity, exerting effects beyond their margins. We investigated longitudinal modifications of fMRI gradients and graph-theory parameters in glioma patients, assessing their role in cognitive prognostication. MATERIAL AND METHODS Diffuse non-enhancing glioma patients underwent fMRI and cognitive assessments (OCS-Bridge) before, within 72h after, 3 and 12 months after surgery. Cognitive data were normalized and averaged into a global score. Differences between the last and preoperative scores were computed (“cognitive difference”). fMRI data underwent preprocessing, cortical parcellation, computation of timeseries, connectivity matrices, gradients and graph-theory measures. To quantify gradient dispersion, we computed a circularity metric (“CM”) of the hull perimeter of the first two gradients’ distribution. T-tests, partial correlations (correcting for volume and surgery radicality) and linear regression (regressors: age, volume, preoperative cognition, surgery radicality) were used to investigate associations with cognitive difference. RESULTS 17 patients (22-56 years, 8 females) were included, 4 had long-term cognitive worsening. Postoperative circularity metric, especially of the healthy intra- and inter-hemispheric connectivity (“HIIHC”), was higher than the preoperative (0.43 vs 0.32, t=2.4, p=0.03). Month3-postoperative and month3-preoperative differences in HIIHC CM were higher in patients with compared to those without cognitive worsening (0.33 vs -0.06, t=6.9, p<0.0001 and 0.27 vs 0.009, t=4.3, p=0.001). Both were negative predictors of the cognitive difference (the first: R=-0.94 with p=0.0002; β=-1.1 with p=0.001 and regression model’s p=0.003; the second: R=-0.76 with p=0.006; β=-1.1 with p=0.02 and model’s p=0.05). Month3-preoperative network modularity difference correlated with the second metric (R=-0.72, p=0.01) and the cognitive difference (R=0.67, p=0.02), while the preoperative modularity with the first metric (R=0.77, p=0.014). Similar results were obtained using a predetermined number of modules (Yeo’s 7 networks). Month12-preoperative difference in healthy intra-hemispheric connectivity CM also correlated with cognitive difference (R=-0.91, p=0.002). CONCLUSION Functional gradients seem dispersed after glioma surgery. Their recovery or worsening at month 3 could predict opposite long-term cognitive outcomes, putatively due to connectivity changes mainly involving the healthy hemisphere. Low preoperative network modularity and its increase at month 3 are associated with such gradient dispersion. Cognitive rehabilitation might be beneficial in the early post-surgical phase.

Biocontrol agents transform the stability and functional characteristics of the grape phyllosphere microenvironment.

The spread of grape leaf diseases has a negative impact on the sustainable development of agriculture. Diseases induced by Uncinula necator significantly affect the quality of grapes. Bacillus biocontrol agents have been proven effective in disease management. However, limited research has been conducted on the impact of biocontrol agents on the assembly and potential functions of plant phyllosphere microbial communities. This study used high-throughput sequencing combined with bioinformatics analysis and culture omics technology for analysis. The results showed that biocontrol bacteria B. subtilis utilized in this study can significantly reduce the disease index of powdery mildew (p<0.05); concurrently, it exhibits a lower disease index compared to traditional fungicides. A comprehensive analysis has revealed that biocontrol bacteria have no significant impact on the diversity of phyllosphere fungi and bacteria, while fungicides can significantly reduce bacterial diversity. Additionally, biocontrol agents can increase the complexity of fungal networks and enhance the degree of modularity and stability of the bacterial network. The results also showed that the biocontrol agents, which contained a high amount of B. subtilis, were able to effectively colonize the grapevine phyllosphere, creating a microenvironment that significantly inhibits pathogenic bacteria on grape leaves while enhancing leaf photosynthetic capacity. In conclusion, biocontrol agents significantly reduce the grape powdery mildew disease index, promote a microenvironment conducive to symbiotic microorganisms and beneficial bacteria, and enhance plant photosynthetic capacity. These findings provide a basis for promoting biocontrol agents and offer valuable insights into sustainable agriculture development.

Blood transcriptome profiling reveals distinct gene networks induced by mRNA vaccination against COVID-19.

Messenger RNA (mRNA) vaccines represent a new class of vaccines that has been shown to be highly effective during the COVID-19 pandemic and that holds great potential for other preventative and therapeutic applications. While it is known that the transcriptional activity of various genes is altered following mRNA vaccination, identifying and studying gene networks could reveal important scientific insights that might inform future vaccine designs. In this study, we conducted an in-depth weighted gene correlation network analysis of the blood transcriptome before and 24h after the second and third vaccination with licensed mRNA vaccines against COVID-19 in humans, following a prime vaccination with either mRNA or ChAdOx1 vaccines. Utilizing this unsupervised gene network analysis approach, we identified distinct modular networks of co-varying genes characterized by either an expressional up- or downregulation in response to vaccination. Downregulated networks were associated with cell metabolic processes and regulation of transcription factors, while upregulated networks were associated with myeloid differentiation, antigen presentation, and antiviral, interferon-driven pathways. Within this interferon-associated network, we identified highly connected hub genes such as STAT2 and RIGI and associated upstream transcription factors, potentially playing important regulatory roles in the vaccine-induced immune response. The expression profile of this network significantly correlated with S1-specific IgG levels at the follow-up visit in vaccinated individuals. Those findings could be corroborated in a second, independent cohort of mRNA vaccine recipients. Collectively, results from this modular gene network analysis enhance the understanding of mRNA vaccines from a systems immunology perspective. Influencing specific gene networks could lead to optimized vaccines that elicit augmented vaccine responses.

Sex Differences in Hierarchical and Modular Organization of Functional Brain Networks: Insights from Hierarchical Entropy and Modularity Analysis.

Existing studies have demonstrated significant sex differences in the neural mechanisms of daily life and neuropsychiatric disorders. The hierarchical organization of the functional brain network is a critical feature for assessing these neural mechanisms. But the sex differences in hierarchical organization have not been fully investigated. Here, we explore whether the hierarchical structure of the brain network differs between females and males using resting-state fMRI data. We measure the hierarchical entropy and the maximum modularity of each individual, and identify a significant negative correlation between the complexity of hierarchy and modularity in brain networks. At the mean level, females show higher modularity, whereas males exhibit a more complex hierarchy. At the consensus level, we use a co-classification matrix to perform a detailed investigation of the differences in the hierarchical organization between sexes and observe that the female group and the male group exhibit different interaction patterns of brain regions in the dorsal attention network (DAN) and visual network (VIN). Our findings suggest that the brains of females and males employ different network topologies to carry out brain functions. In addition, the negative correlation between hierarchy and modularity implies a need to balance the complexity in the hierarchical organization of the brain network, which sheds light on future studies of brain functions.

Modular Co-attention Networks in Nepali Visual Question Answering Systems

Visual question answering (VQA) has been regarded as a challenging task requiring a perfect blend of computer vision and natural language processing. As no dataset was available to train such a model for the Nepali language, a new dataset was developed during the research by translating the VQAv2 dataset. Then the dataset consisting of 202,577 images and 886,560 questions was used to train an attention-based VQA model. The dataset consists of yes/no, counting, and other questions with primarily one-word answers. Modular Co-attention Network (MCAN) was applied to the visual features extracted using the Faster RCNN framework and question embeddings extracted using the Nepali GloVe model. After co-attending the visual and language features for a few cascaded MCAN layers, the features are fused to train the whole network. During evaluation, an overall accuracy of 69.87% was obtained with 81.09% accuracy in yes/no type questions. The results surpassed the performance of models developed for Hindi and Bengali languages. Overall, novel research has been done in the Nepali Language VQA domain paving the way for further advancements.

Connector hubs in semantic network contribute to creative thinking.

Semantic memory offers a rich repository of raw materials (e.g., various concepts and connections between concepts) for creative thinking, represented as a semantic network. Similar to other networks, the semantic network exhibits a modular structure characterized by modules with dense internal connections and sparse connections between them. This organizational principle facilitates the routine storage and retrieval of information but may impede creativity. The present study investigated the effect of hub concepts with varying connection patterns on creative thinking from the perspective of a modular structured semantic network. By analyzing a large-scale semantic network, connector hubs (C-hubs) and provincial hubs (P-hubs) were identified based on their intra- and intermodule connections. These hubs were used as cue words in the alternative uses task, a widely used measure of creative thinking. Across four experiments, behavioral and neural evidence indicated that C-hubs facilitate the generation of more novel and remote ideas compared to P-hubs. However, this effect is predominantly observed in the early stage of the creative thinking process, involving changes in brain activation and functional connectivity in core regions of the default mode network and the frontoparietal network, including the dorsolateral prefrontal cortex, angular gyrus, and precuneus. Neural findings suggest that the superior performance of C-hubs relies on stronger interactions between automatic spreading activation, controlled semantic retrieval, and attentional regulation of salient information. These results provide insight into how concepts with varying semantic connection patterns facilitate and constrain different stages of the creative thinking process through the modular structure of semantic network. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Developmental dynamics of brain network modularity and temporal co-occurrence diversity in childhood

ObjectiveBrain development during childhood involves significant structural, functional, and connectivity changes, reflecting the interplay between modularity, information interaction, and functional segregation. This study aims to understand the dynamic properties of brain connectivity and their impact on cognitive development, focusing on temporal co-occurrence diversity patterns. MethodsWe recruited 481 children aged 6 to 12 years from the Healthy Brain Network database. Functional MRI data were used to construct dynamic functional connectivity matrices with a sliding window approach. Modular structures were identified using multilayer network community detection, and the Dagum Gini coefficient decomposition technique, which uniquely allows for multi-faceted exploration of modular temporal co-occurrence diversities, quantified these diversities. Mediation analysis assessed the impact on small-world properties. ResultsTemporal co-occurrence diversity in brain networks increased with age, especially in the default mode, frontoparietal, and salience networks. These changes were driven by disparities within and between communities. The small-world coefficient increased with age, indicating improved information processing efficiency. To validate the impact of changes in spatiotemporal interaction disparities during childhood on information transmission within brain networks, we used mediation analysis to verify its effect on alterations in small-world properties. ConclusionThis study highlights the critical developmental changes in brain modularity and spatiotemporal interaction patterns during childhood, emphasizing their role in cognitive maturation. These insights into neural mechanisms can inform the diagnosis and intervention of developmental disorders.

Dynamic Reorganization Patterns of Brain Modules after Stroke Reflecting Motor Function.

Advancements in neuroimaging technologies have significantly deepened our understanding of the neural physiopathology associated with stroke. Nevertheless, the majority of studies ignored the characteristics of dynamic changes in brain networks. The relationship between dynamic changes in brain networks and the severity of motor dysfunction after stroke needs further investigation. From the perspective of multilayer network module reconstruction, we aimed to explore the dynamic reorganization of the brain and its relationship with motor function in subcortical stroke patients. We recruited 35 healthy individuals and 50 stroke patients with unilateral limb motor dysfunction (further divided into mild-moderate group and severe group). Using dynamic multilayer network modularity analysis, we investigated changes in the dynamic modular reconfiguration of brain networks. Additionally, we assessed longitudinal clinical scale changes in stroke patients. Correlation and regression analyses were employed to explore the relationship between characteristic dynamic indicators and impairment and recovery of motor function, respectively. We observed increased temporal flexibility in the Default Mode Network (DMN) and decreased recruitment of module reconfiguration in the Attention Network (AN), Sensorimotor Network (SMN), and DMN after stroke. We also observed reduced module loyalty following stroke. Additionally, correlation analysis showed that hyper-flexibility of the DMN was associated with better lower limb motor function performance in stroke patients with mild-to-moderate impairment. Regression analysis indicated that increased flexibility within the DMN and decreased recruitment coefficient within the AN may predict good lower limb function prognosis in patients with mild to moderate motor impairment. Our study revealed more frequent modular reconfiguration and hyperactive interaction of brain networks after stroke. Notably, dynamic modular remodeling was closely related to the impairment and recovery of motor function. Understanding the temporal module reconfiguration patterns in multilayer networks after stroke can provide valuable information for more targeted treatments.

Differential spatial responses and assembly mechanisms of soil microbial communities across region-scale Taiga ecosystems

Different soil microbial communities play distinct key roles in regulating forest ecosystem processes and functions. However, the differences in spatial variability and assembly mechanisms of various taiga forest soil microbial taxa remain poorly understood. Here, we assessed the spatial patterns of bacterial and fungal communities, their assembly processes, and the influencing factors in taiga forest ecosystems in Xinjiang, China. A significant distance decay pattern was observed in the similarity of bacterial and fungal communities, with bacterial communities exhibiting a more pronounced pattern than fungal communities. Stochastic and deterministic processes governed together to drive soil bacterial community assembly, whereas stochastic processes dominated fungal community assembly. The coexistence networks revealed that the interactions of bacterial and fungal networks in the four regions are primarily based on interspecies symbiosis, with fungal coexistence networks demonstrating greater stability than bacterial networks. Additionally, the study identified a positive relationship between the modularity of bacterial networks and dispersal limitation. Analysis of environmental factors revealed that soil pH primarily affects the characteristics and assembly mechanisms of bacterial communities, while vegetation conditions primarily affect fungal diversity and composition, with other unconsidered environmental variables influencing the fungal community assembly process. This study emphasized the distinct ways in which bacteria and fungi respond to environmental factors and interspecies interactions. Our results suggested that distinct restoration measures should be implemented for bacteria and fungi in future conservation efforts for forest soil microorganisms.

Community organization and network complexity and stability: contrasting strategies of prokaryotic versus eukaryotic microbiomes in the Bohai Sea and Yellow Sea.

Unraveling the effects of spatial gradients on microbiome assembly and association is a challenging topic that remains understudied in the coastal ecosystem. Here, we aimed to investigate the effects of spatial variation on the network complexity and stability of plankton microbiomes in the Bohai Sea and Yellow Sea. These seas serve as spawning and nursery grounds for economically important fisheries valued at billions of dollars annually. Environmental heterogeneity structures microbial communities into distinct spatial patterns, leading to complex direct/indirect relationships and broader ecological niches of bacterioplankton compared to microeukaryotic communities. Interestingly, salinity gradients positively influenced the richness of rare subgroups of bacterioplankton, while the rare microeukaryotic subgroups showed an opposite trend. Abundant subgroups of prokaryotic/eukaryotic microbiomes exhibited greater environmental niche breadth and lower phylogenetic distance compared to the rare subgroups. Stochastic processes contributed greatly to microbiome dynamics, and deterministic processes governed the bacterioplankton organization with a lower phylogenetic turnover rate. Compared to microeukaryotes, bacterioplankton exhibit higher network modularity, complexity, and robustness and lower fragmentation, and vulnerability. These observations offer vital insights into the anti-interference ability and resistance of plankton microbiomes in response to environmental gradients in terms of organization and survival strategy as well as their adaptability to environmental disturbances.IMPORTANCEAn in-depth understanding of community organization and stability of coastal microbiomes is crucial to determining the sustainability of marine ecosystems, such as the Bohai Sea and Yellow Sea. Distinct responses between prokaryotic and eukaryotic microbiomes to spatial heterogeneity were observed in terms of geographical distribution, phylogenetic distance, niche breadth, and community assembly process. Environmental variations are significantly correlated with the dynamics of rare eukaryotic plankton subcommunities compared to prokaryotic plankton subcommunities. Deterministic processes shaped prokaryotic plankton community organization with a lower phylogenic turnover rate. Rare subgroups had noticeably higher phylogenetic distance and lower niche breadth than the corresponding abundant subgroups. Prokaryotic microbiomes had higher molecular network complexity and stability compared to microeukaryotes. Results presented here show how environmental gradients alter both the geographical characteristics of the microbial organization in coastal seas and also their co-occurrence network complexity and stability and thus have critical implications for nutrient and energy cycling.

A Low-Cost Sensor Network for Monitoring Peatland.

Peatlands across the world are vital carbon stores. However, human activities have caused the degradation of many sites, increasing their greenhouse gas emissions and vulnerability to wildfires. Comprehensive monitoring of peatlands is essential for their protection, tracking degradation and restoration, but current techniques are limited by cost, poor reliability and low spatial or temporal resolution. This paper covers the research, development, deployment and performance of a resilient and modular multi-purpose wireless sensor network as an alternative means of monitoring peatlands. The sensor network consists of four sensor nodes and a gateway and measures temperature, humidity, soil moisture, carbon dioxide and methane. The sensor nodes transmit measured data over LoRaWAN to The Things Network every 30 min. To increase the maximum possible deployment duration, a novel datastring encoder was implemented which reduced the transmitted datastring length by 23%. This system was deployed in a New Forest (Hampshire, UK) peatland site for two months and collected more than 7500 measurements. This deployment demonstrated that low-cost sensor networks have the potential to improve the temporal and spatial resolution of peatland emission monitoring beyond what is achievable with traditional monitoring techniques.

Modular stochastic configuration network with attention mechanism for soft measurement of water quality parameters in wastewater treatment processes

Due to the complex and multi-distributed nature of wastewater treatment process data, stochastic configuration networks (SCNs) face difficulties such as large network structure and complex construction process when dealing with data modeling tasks. To address these challenges, this paper proposes a modular stochastic configuration network with attention mechanism, which integrates the advantages of modular partitioning, attention mechanism, and SCN. The model employs a fuzzy mean clustering strategy to simplify and decompose the complex multi-case and multi-distribution nonlinear modeling task, thereby reducing the modeling complexity of a single model. Furthermore, the model utilizes a stochastic configuration algorithm to learn the subtasks and construct corresponding sub-networks. The output weights are then assigned to each sub-network based on the attention mechanism to obtain the model’s final output. The validity of the proposed method is validated through two benchmark experiments. Then the model is applied to measure effluent ammonia nitrogen. The results indicate that compared with the classical randomized and modularized network models, the proposed model exhibits certain advantages in terms of learning accuracy and generalization performance.

Modular Network Architectures for Enhanced Interpretability and Efficient Knowledge Transfer in Machine Learning

Modular Network Architectures for Enhanced Interpretability and Efficient Knowledge Transfer in Machine Learning

Minimal model for reservoir computing

A minimal model for reservoir computing is studied. We demonstrate that a reservoir computer exists that emulates given coupled maps by constructing a modularised network. We describe a possible mechanism for collapses of the emulation in the reservoir computing by introducing a measure of finite scale deviation. Such transitory behaviour is caused by either (i) an escape from a finite-time stagnation near an unstable chaotic set, or (ii) a critical transition driven by the effective parameter drift. Our approach reveals the essential mechanism for reservoir computing and provides insights into the design of reservoir computer for practical applications.

Long-term power forecasting of photovoltaic plants using artificial neural networks

Increasing the robustness of forecasting energy production models from a photovoltaic plant is crucial in investment decisions, while profitability is closely linked to its location since its energy production depends on the local climate conditions. This study aimed to mitigate uncertainty regarding the installation of photovoltaic systems by addressing the challenge of forecasting electrical energy production over the long term. Artificial Neural Network models were used for this purpose, predicting the power output of a photovoltaic plant based on the ambient temperature, cell temperature, and solar irradiance. Data recorded every minute over one year at an experimental photovoltaic plant revealed a strong correlation between energy production and the input variables. This research compared the performance of multilayer perceptron, feedforward, long short-term memory, and modular artificial neural networks architectures. Validation was carried out using mean squared error, mean absolute error, mean absolute percentage error, root mean squared error, and coefficient of determination as key metrics. Notably, the long short-term memory network demonstrated the highest accuracy in energy forecasting achieving a mean squared error of 0.0089 p.u, a mean absolute error of 0.0527 p.u, a root mean squared error of 0.0944 p.u, and a mean absolute percentage error of 49.0124 %. Nonetheless, the modular model came close to long short-term memory accuracy but with lower computational cost.

Enhanced diversity on connector hubs following sleep deprivation: Evidence from diffusion and functional magnetic resonance imaging

Sleep deprivation has been demonstrated to exert widespread and intricate impacts on the brain network. The human brain network is a modular network composed of interconnected nodes. This network consists of provincial hubs and connector hubs, with provincial hubs having diverse connectivities within their own modules, while connector hubs distribute their connectivities across different modules. The latter is crucial for integrating information from various modules and ensuring the normal functioning of the modular brain. However, there has been a lack of systematic investigation into the impact of sleep deprivation on brain connector hubs. In this study, we utilized functional connectivity from resting-state functional magnetic resonance imaging, as well as structural connectivity from diffusion-weighted imaging, to systematically explore the variation of connector hub properties in the cerebral cortex after one night of sleep deprivation. The normalized participation coefficients (PCnorm) were utilized to identify connector hubs. In both the functional and structural networks, connector hubs exhibited a significant increase in average PCnorm, indicating the diversity enhancement of the connector hub following sleep deprivation. This enhancement is associated with increased network cost, reduced modularity, and decreased small-worldness, but enhanced global efficiency. This may potentially signify a compensatory mechanism within the brain following sleep deprivation. The significantly affected connector hubs were primarily observed in both the Control Network and Salience Network. We believe that the observed results reflect the increasing demand on the brain to invest more effort at preventing performance deterioration after sleep loss, in exchange for increased communication efficiency, especially involving systems responsible for neural resource allocation and cognitive control. These results have been replicated in an independent dataset. In conclusion, this study has enhanced our understanding of the compensatory mechanism in the brain response to sleep deprivation. This compensation is characterized by an enhancement in the connector hubs responsible for inter-modular communication, especially those related to neural resource and cognitive control. As a result, this compensation comes with a higher network cost but leads to an improvement in global communication efficiency, akin to a more random-like network manner.

The contribution of movement to social network structure and spreading dynamics under simple and complex transmission.

The structure of social networks fundamentally influences spreading dynamics. In general, the more contact between individuals, the more opportunity there is for the transmission of information or disease to take place. Yet, contact between individuals, and any resulting transmission events, are determined by a combination of spatial (where individuals choose to move) and social rules (who they choose to interact with or learn from). Here, we examine the effect of the social-spatial interface on spreading dynamics using a simulation model. We quantify the relative effects of different movement rules (localized, semi-localized, nomadic and resource-based movement) and social transmission rules (simple transmission, anti-conformity, proportional, conformity and threshold rules) to both the structure of social networks and spread of a novel behaviour. Localized movement created weakly connected sparse networks, nomadic movement created weakly connected dense networks, and resource-based movement generated strongly connected modular networks. The resulting rate of spreading varied with different combinations of movement and transmission rules, but-importantly-the relative rankings of transmission rules changed when running simulations on static versus dynamic representations of networks. Our results emphasize that individual-level social and spatial behaviours influence emergent network structure, and are of particular consequence for the spread of information under complex transmission rules.This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.