Specificity and stability in topology of protein networks

Neural correlates of anhedonia in young adults with subthreshold depression: A graph theory approach for cortical-subcortical structural covariance

BACKGROUND

Anhedonia is an enduring symptom of subthreshold depression (StD) and predict later onset of major depressive disorder (MDD). Brain structural covariance describes the inter-regional distribution of morphological changes compared to healthy controls (HC) and reflects brain maturation and disease progression. We investigated neural correlates of anhedonia from the structural covariance.

METHODS

T1-weighted brain magnetic resonance images were acquired from 79 young adults (26 StD, 30 MDD, and 23 HC). Intra-individual structural covariance networks of 68 cortical surface area (CSAs), 68 cortical thicknesses (CTs), and 14 subcortical volumes were constructed. Group-level hubs and principal edges were defined using the global and regional graph metrics, compared between groups, and examined for the association with anhedonia severity.

RESULTS

Global network metrics were comparable among the StD, MDD, and HC. StD exhibited lower centralities of left pallidal volume than HC. StD showed higher centralities than HC in the CSAs of right rostral anterior cingulate cortex (ACC) and pars triangularis, and in the CT of left pars orbitalis. Less anhedonia was associated with higher centralities of left pallidum and right amygdala, higher edge betweenness centralities in the structural covariance (EBSC) of left postcentral gyrus-parahippocampal gyrus and LIPL-right amygdala. More anhedonia was associated with higher centralities of left inferior parietal lobule (LIPL), left postcentral gyrus, left caudal ACC, and higher EBSC of LIPL-left postcentral gyrus, LIPL-right lateral occipital gyrus, and left caudal ACC-parahippocampal gyrus.

LIMITATIONS

This study has a cross-sectional design.

CONCLUSIONS

Structural covariance of brain morphologies within the salience and limbic networks, and among the salience-limbic-default mode-somatomotor-visual networks, are possible neural correlates of anhedonia in depression.

Dynamic microbiome diversity shaping the adaptation of sponge holobionts in coastal waters

The microbial communities associated with sponges contribute to the adaptation of hosts to environments, which are essential for the trophic transformation of benthic-marine coupling. However, little is known about the symbiotic microbial community interactions and adaptative strategies of high- and low-microbial abundance (HMA and LMA) sponges, which represent two typical ecological phenotypes. Here, we compared the 1-year dynamic patterns of microbiomes with the HMA sponge Spongia officinalis and two LMA sponge species Tedania sp. and Haliclona simulans widespread on the coast of China. Symbiotic bacterial communities with the characteristic HMA-LMA dichotomy presented higher diversity and stability in S. officinalis than in Tedania sp. and H. simulans, while archaeal communities showed consistent diversity across all sponges throughout the year. Dissolved oxygen, dissolved inorganic phosphorus, dissolved organic phosphorus, and especially temperature were the major factors affecting the seasonal changes in sponge microbial communities. S. officinalis-associated microbiome had higher diversity, stronger stability, and closer interaction, which adopted a relatively isolated strategy to cope with environmental changes, while Tedania sp. and H. simulans were more susceptible and shared more bacterial Amplicon Sequence Variants (ASVs) with surrounding waters, with an open way facing the uncertainty of the environment. Meta-analysis of the microbiome in composition, diversity, and ecological function from 13 marine sponges further supported that bacterial communities associated with HMA and LMA sponges have evolved two distinct environmental adaptation strategies. We propose that the different adaptive ways of sponges responding to the environment may be responsible for their successful evolution and their competence in global ocean change.

IMPORTANCE

During long-term evolution, sponge holobionts, among the oldest symbiotic relationships between microbes and metazoans, developed two distinct phenotypes with high- and low-microbial abundance (HMA and LMA). Despite sporadic studies indicating that the characteristic microbial assemblages present in HMA and LMA sponges, the adaptation strategies of symbionts responding to environments are still unclear. This deficiency limits our understanding of the selection of symbionts and the ecological functions during the evolutionary history and the adaptative assessment of HMA and LMA sponges in variable environments. Here, we explored symbiotic communities with two distinct phenotypes in a 1-year dynamic environment and combined with the meta-analysis of 13 sponges. The different strategies of symbionts in adapting to the environment were basically drawn: microbes with LMA were more acclimated to environmental changes, forming relatively loose-connected communities, while HMA developed relatively tight-connected and more similar communities beyond the divergence of species and geographical location.

Co-Occurrence Patterns Do Not Predict Mutualistic Interactions Between Plant and Butterfly Species

Biotic interactions are crucial for determining the structure and dynamics of communities; however, direct measurement of these interactions can be challenging in terms of time and resources, especially when numerous species are involved. Inferring species interactions from species co-occurrence patterns is increasingly being used; however, recent studies have highlighted some limitations. To our knowledge, no attempt has been made to test the accuracy of the existing methods for detecting mutualistic interactions in terrestrial ecosystems. In this study, we compiled two literature-based, long-term datasets of interactions between butterflies and herbaceous plant species in two regions of Germany and compared them with observational abundance and presence/absence data collected within a year in the same regions. We tested how well the species associations generated by three different co-occurrence analysis methods matched those of empirically measured mutualistic associations using sensitivity and specificity analyses and compared the strength of associations. We also checked whether flower abundance data (instead of plant abundance data) increased the accuracy of the co-occurrence models and validated our results using empirical flower visitation data. The results revealed that, although all methods exhibited low sensitivity, our implementation of the Relative Interaction Intensity index with pairwise null models performed the best, followed by the probabilistic method and Spearman's rank correlation method. However, empirical data showed a significant number of interactions that were not detected using co-occurrence methods. Incorporating flower abundance data did not improve sensitivity but enhanced specificity in one region. Further analysis demonstrated incongruence between the predicted co-occurrence associations and actual interaction strengths, with many pairs exhibiting high interaction strength but low co-occurrence or vice versa. These findings underscore the complexity of ecological dynamics and highlight the limitations of current co-occurrence methods for accurately capturing species interactions.

Architectural styles of curiosity in global Wikipedia mobile app readership

Intrinsically motivated information seeking is an expression of curiosity believed to be central to human nature. However, most curiosity research relies on small, Western convenience samples. Here, we analyze a naturalistic population of 482,760 readers using Wikipedia's mobile app in 14 languages from 50 countries or territories. By measuring the structure of knowledge networks constructed by readers weaving a thread through articles in Wikipedia, we replicate two styles of curiosity previously identified in laboratory studies: the nomadic "busybody" and the targeted "hunter." Further, we find evidence for another style-the "dancer"-which was previously predicted by a historico-philosophical examination of texts over two millennia and is characterized by creative modes of knowledge production. We identify associations, globally, between the structure of knowledge networks and population-level indicators of spatial navigation, education, mood, well-being, and inequality. These results advance our understanding of Wikipedia's global readership and demonstrate how cultural and geographical properties of the digital environment relate to different styles of curiosity.

Seasonal variations of microbial communities and viral diversity in fishery-enhanced marine ranching sediments: insights into metabolic potentials and ecological interactions

BACKGROUND

The ecosystems of marine ranching have enhanced marine biodiversity and ecological balance and have promoted the natural recovery and enhancement of fishery resources. The microbial communities of these ecosystems, including bacteria, fungi, protists, and viruses, are the drivers of biogeochemical cycles. Although seasonal changes in microbial communities are critical for ecosystem functioning, the current understanding of microbial-driven metabolic properties and their viral communities in marine sediments remains limited. Here, we employed amplicon (16S and 18S) and metagenomic approaches aiming to reveal the seasonal patterns of microbial communities, bacterial-eukaryotic interactions, whole metabolic potential, and their coupling mechanisms with carbon (C), nitrogen (N), and sulfur (S) cycling in marine ranching sediments. Additionally, the characterization and diversity of viral communities in different seasons were explored in marine ranching sediments.

RESULTS

The current study demonstrated that seasonal variations dramatically affected the diversity of microbial communities in marine ranching sediments and the bacterial-eukaryotic interkingdom co-occurrence networks. Metabolic reconstruction of the 113 medium to high-quality metagenome-assembled genomes (MAGs) was conducted, and a total of 8 MAGs involved in key metabolic genes and pathways (methane oxidation - denitrification - S oxidation), suggesting a possible coupling effect between the C, N, and S cycles. In total, 338 viral operational taxonomic units (vOTUs) were identified, all possessing specific ecological characteristics in different seasons and primarily belonging to Caudoviricetes, revealing their widespread distribution and variety in marine sediment ecosystems. In addition, predicted virus-host linkages showed that high host specificity was observed, with few viruses associated with specific hosts.

CONCLUSIONS

This finding deepens our knowledge of element cycling and viral diversity in fisheries enrichment ecosystems, providing insights into microbial-virus interactions in marine sediments and their effects on biogeochemical cycling. These findings have potential applications in marine ranching management and ecological conservation. Video Abstract.

Inhibition of denitrification and enhancement of microbial interactions in the AGS system by high concentrations of quinoline

Quinoline represents a highly toxic and structurally stable nitrogen-containing heterocyclic compound in coking wastewater, posing a potential threat to human beings and the ecological environment. In this study, we investigated the impact of gradually elevating quinoline concentration on pollutant removal efficiency, sludge characteristics, microbial community and their interactions in the aerobic granular sludge (AGS) system. The results demonstrated that AGS was capable of effectively degrading quinoline, with a final removal rate of 90 mg/L quinoline reaching 98.54 ± 0.28%. Notably, the denitrification process was significantly impeded in the presence of 90 mg/L quinoline, with the Phase D effluent displaying a notably high NO3--N concentration of 37.09 ± 21.81 mg/L, primarily attributed to the reduced abundance of norank_f_A4b bacteria. As the quinoline concentration increased, the sludge particle size diminished from 3.46 to 2.60 mm, while the settling performance deteriorated significantly, escalating from 31.29 ± 1.63 mL/g to 62.32 ± 2.87 mL/g. Meanwhile, the protein (PN) content in EPS gradually increased (from 19.87 ± 0.88 mg/g MLVSS to 51.22 ± 3.21 mg/g MLVSS), while the polysaccharide (PS) content fluctuated. Quinoline profoundly modified microbial community composition and structure, with deterministic processes dominating community assembly. Network analysis indicated intensified and complex microbial interactions at 90 mg/L quinoline, characterized by significantly higher positive correlations. In addition, rare taxa (RT) dominated the network nodes, with 74 of 93 key species belonging to RT, highlighting their pivotal roles in sustaining system functions and strengthening microbial connections. This study provides new insights into the effects of quinoline on microbial community structure and interactions in AGS system.

Honeybee gut bacterial strain improved survival and gut microbiota homeostasis in Apis mellifera exposed in vivo to clothianidin

Pesticides are causing honeybee mortality worldwide. Research carried out on honeybees indicates that application of pesticides has a significant impact on the core gut community, which ultimately leads to an increase in the growth of harmful pathogens. Disturbances caused by pesticides also affect the way bacterial members interact, which results in gut microbial dysbiosis. Administration of beneficial microbes has been previously demonstrated to be effective in treating or preventing disease in honeybees. The objective of this study was to measure under in vivo conditions the ability of two bacterial strains (the Enterobacter sp. and Pantoea sp.) isolated from honeybee gut to improve survival and mitigate gut microbiota dysbiosis in honeybees exposed to a sublethal clothianidin dose (0.1 ppb). Both gut bacterial strains were selected for their ability to degrade clothianidin in vitro regardless of their host-microbe interaction characteristics (e.g., beneficial, neutral, or harmful). To this end, we conducted cage trials on 4- to 6-day-old newly emerging honeybees. During microbial administration, we jointly monitored the taxonomic distribution and activity level of bacterial symbionts quantifying 16S rRNA transcripts. First, curative administration of the Pantoea sp. strain significantly improved the survival of clothianidin-exposed honeybees compared to sugar control bees (i.e., supplemented with sugar [1:1]). Second, curative administration of the Enterobacter sp. strain significantly mitigated the clothianidin-induced dysbiosis observed in the midgut structural network, but without improving survival.

IMPORTANCE

The present work suggests that administration of bacterial strains isolated from honeybee gut may promote recovery of gut microbiota homeostasis after prolonged clothianidin exposure, while improving survival. This study highlights that gut bacterial strains hold promise for developing efficient microbial formulations to mitigate environmental pesticide exposure in honeybee colonies.

Structure matters: Assessing the statistical significance of network topologies

Network analysis has found widespread utility in many research areas. However, assessing the statistical significance of observed relationships within networks remains a complex challenge. Traditional node permutation tests are often insufficient in capturing the effect of changing network topology by creating reliable null distributions. We propose two randomization alternatives to address this gap: random rewiring and controlled rewiring. These methods incorporate changes in the network topology through edge swaps. However, controlled rewiring allows for more nuanced alterations of the original network than random rewiring. In this sense, this paper introduces a novel evaluation tool, the Expanded Quadratic Assignment Procedure (EQAP), designed to calculate a specific p-value and interpret statistical tests with enhanced precision. The combination of EQAP and controlled rewiring provides a robust network comparison and statistical analysis framework. The methodology is exemplified through two real-world examples: the analysis of an organizational network structure, illustrated by the Enron-Email dataset, and a social network case, represented by the UK Faculty friendship network. The utility of these statistical tests is underscored by their capacity to safeguard researchers against Type I errors when exploring network metrics dependent on intricate topologies.

Changes in the structure of spontaneous speech predict the disruption of hierarchical brain organization in first-episode psychosis

Psychosis implicates changes across a broad range of cognitive functions. These functions are cortically organized in the form of a hierarchy ranging from primary sensorimotor (unimodal) to higher-order association cortices, which involve functions such as language (transmodal). Language has long been documented as undergoing structural changes in psychosis. We hypothesized that these changes as revealed in spontaneous speech patterns may act as readouts of alterations in the configuration of this unimodal-to-transmodal axis of cortical organization in psychosis. Results from 29 patients with first-episodic psychosis (FEP) and 29 controls scanned with 7 T resting-state fMRI confirmed a compression of the cortical hierarchy in FEP, which affected metrics of the hierarchical distance between the sensorimotor and default mode networks, and of the hierarchical organization within the semantic network. These organizational changes were predicted by graphs representing semantic and syntactic associations between meaningful units in speech produced during picture descriptions. These findings unite psychosis, language, and the cortical hierarchy in a single conceptual scheme, which helps to situate language within the neurocognition of psychosis and opens the clinical prospect for mental dysfunction to become computationally measurable in spontaneous speech.

Network statistics of the whole-brain connectome of Drosophila

Brains comprise complex networks of neurons and connections, similar to the nodes and edges of artificial networks. Network analysis applied to the wiring diagrams of brains can offer insights into how they support computations and regulate the flow of information underlying perception and behaviour. The completion of the first whole-brain connectome of an adult fly, containing over 130,000 neurons and millions of synaptic connections1-3, offers an opportunity to analyse the statistical properties and topological features of a complete brain. Here we computed the prevalence of two- and three-node motifs, examined their strengths, related this information to both neurotransmitter composition and cell type annotations4,5, and compared these metrics with wiring diagrams of other animals. We found that the network of the fly brain displays rich-club organization, with a large population (30% of the connectome) of highly connected neurons. We identified subsets of rich-club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex ( https://codex.flywire.ai ) and should serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

Brain network changes after the first seizure: an insight into medication response?

After a first epileptic seizure, anti-seizure medications (ASMs) can change the likelihood of having a further event. This prospective study aimed to quantify brain network changes associated with taking ASM monotherapy. We applied graph theoretical network analysis to longitudinal resting-state functional MRI (fMRI) data from 28 participants who had recently experienced their first seizure. Participants were imaged before and during long-term ASM therapy, with a mean inter-scan interval of 6.9 months. After commencing ASM, we observed an increase in the clustering coefficient and a decrease in network path length. Brain changes after ASM treatment were most prominent in the superior frontoparietal and inferior fronto-temporal regions. Participants with recurrent seizures display the most pronounced network changes after ASM treatment. This study shows changes in brain network function after ASM administration, particularly in participants with recurrent seizures. Larger studies that ideally include control cohorts are required to understand further the connection between ASM-related brain network changes and longer-term seizure status.

Hierarchical communities in the larval Drosophila connectome: Links to cellular annotations and network topology

One of the longstanding aims of network neuroscience is to link a connectome's topological properties-i.e., features defined from connectivity alone-with an organism's neurobiology. One approach for doing so is to compare connectome properties with annotational maps. This type of analysis is popular at the meso-/macroscale, but is less common at the nano-scale, owing to a paucity of neuron-level connectome data. However, recent methodological advances have made possible the reconstruction of whole-brain connectomes at single-neuron resolution for a select set of organisms. These include the fruit fly, Drosophila melanogaster, and its developing larvae. In addition to fine-scale descriptions of connectivity, these datasets are accompanied by rich annotations. Here, we use a variant of the stochastic blockmodel to detect multilevel communities in the larval Drosophila connectome. We find that communities partition neurons based on function and cell type and that most interact assortatively, reflecting the principle of functional segregation. However, a small number of communities interact nonassortatively, forming form a "rich-club" of interneurons that receive sensory/ascending inputs and deliver outputs along descending pathways. Next, we investigate the role of community structure in shaping communication patterns. We find that polysynaptic signaling follows specific trajectories across modular hierarchies, with interneurons playing a key role in mediating communication routes between modules and hierarchical scales. Our work suggests a relationship between system-level architecture and the biological function and classification of individual neurons. We envision our study as an important step toward bridging the gap between complex systems and neurobiological lines of investigation in brain sciences.

Social network analysis of regional transport carbon emissions in China: Based on motif analysis and exponential random graph model

Given the significant impact of transportation-related carbon emissions on air quality and climate change, understanding the regional dynamics of these emissions is crucial. Despite numerous studies on carbon emissions, there is a lack of comprehensive analysis of China's interprovincial transport carbon emission correlation network. Based on China's provincial data from 2007 to 2021, we analyzed the network's basic structural characteristics and categorized it into four significant plates to investigate their interactions. Subsequently, motif analysis is employed to examine the micro-correlation patterns within the network, and the Exponential random graph model (ERGM) is utilized to analyze the network's formation mechanism. Findings reveal that: (1) Provinces with high correlation intensity are predominantly concentrated in the eastern region, such as Shanghai and Beijing. Additionally, provinces in the eastern region assume a central role in the transport carbon emission correlation network, mainly receiving carbon emissions from other provinces. In contrast, the western region primarily emits carbon emissions to other provinces, continuously converging towards the center. (2) The network is segmented into net beneficiary plate, net overflow plate, bidirectional spillover plate, and broker plate, with distinct roles and influences across different years. (3) Bidirectional correlation motif structures emerge as primary influencers within the network, although specific structures impede interregional communication and collaborative emission reduction. (4) Internal network's structural variables, such as mutuality, cyclic triple, and geometrically weighted edgewise shared partner, along with influencing factors including government intervention, urbanization rate, openness, fiscal expenditure on transport, and province adjacency significantly impact the formation of the transport carbon emission correlation network. The above transportation network research provides a theoretical basis for the country to promote low-carbon transportation and improve air quality, and also has important guiding significance for the cross-regional collaborative emission reduction work of provinces.

Morphological Brain Networks of White Matter: Mapping, Evaluation, Characterization, and Application

Although white matter (WM) accounts for nearly half of adult brain, its wiring diagram is largely unknown. Here, an approach is developed to construct WM networks by estimating interregional morphological similarity based on structural magnetic resonance imaging. It is found that morphological WM networks showed nontrivial topology, presented good-to-excellent test-retest reliability, accounted for phenotypic interindividual differences in cognition, and are under genetic control. Through integration with multimodal and multiscale data, it is further showed that morphological WM networks are able to predict the patterns of hamodynamic coherence, metabolic synchronization, gene co-expression, and chemoarchitectonic covariance, and associated with structural connectivity. Moreover, the prediction followed WM functional connectomic hierarchy for the hamodynamic coherence, is related to genes enriched in the forebrain neuron development and differentiation for the gene co-expression, and is associated with serotonergic system-related receptors and transporters for the chemoarchitectonic covariance. Finally, applying this approach to multiple sclerosis and neuromyelitis optica spectrum disorders, it is found that both diseases exhibited morphological dysconnectivity, which are correlated with clinical variables of patients and are able to diagnose and differentiate the diseases. Altogether, these findings indicate that morphological WM networks provide a reliable and biologically meaningful means to explore WM architecture in health and disease.

Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies

The mechanisms linking the brain's network structure to cognitively relevant activation patterns remain largely unknown. Here, by leveraging principles of network control, we show how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic database. Specifically, we systematically integrated large-scale multimodal neuroimaging data from functional magnetic resonance imaging, diffusion tractography, cortical morphometry and positron emission tomography to simulate how anatomically guided transitions between cognitive states can be reshaped by neurotransmitter engagement or by changes in cortical thickness. Our model incorporates neurotransmitter-receptor density maps (18 receptors and transporters) and maps of cortical thickness pertaining to a wide range of mental health, neurodegenerative, psychiatric and neurodevelopmental diagnostic categories (17,000 patients and 22,000 controls). The results provide a comprehensive look-up table charting how brain network organization and chemoarchitecture interact to manifest different cognitive topographies, and establish a principled foundation for the systematic identification of ways to promote selective transitions between cognitive topographies.

Functional brain network properties correlate with individual risk tolerance in young adults

Background

Individuals differ substantially in their degree of acceptance of risks, referred to as risk tolerance, and these differences are associated with real-life outcomes such as risky health-related behaviors. While previous studies have identified brain regions that are functionally associated with individual risk tolerance, little is known about the relationship between individual risk tolerance and whole-brain functional organization.

Methods

This study investigated whether the topological properties of individual functional brain networks in healthy young adults (n = 67) are associated with individual risk tolerance using resting-state fMRI data in conjunction with a graph theoretical analysis approach.

Results

The analysis revealed that individual risk tolerance was positively associated with global topological properties, including the normalized clustering coefficient and small-worldness, which represent the degree of information segregation and the balance between information segregation and integration in a network, respectively. Additionally, individuals with higher risk tolerance exhibited greater centrality in the ventromedial prefrontal cortex (vmPFC), which is associated with the subjective value of the available options.

Conclusion

These results extend our understanding of how individual differences in risk tolerance, especially in young adults, are associated with functional brain organization, particularly regarding the balance between segregation and integration in functional networks, and highlight the important role of the connections between the vmPFC and the rest of the brain in the functional networks in relation to risk tolerance.

Exploring seasonal variations, assembly dynamics, and relationships of bacterial communities in different habitats of marine ranching

Offshore coastal marine ranching ecosystems provide habitat for diverse and active bacterial communities. In this study, 16S rRNA gene sequencing and multiple bioinformatics methods were applied to investigate assembly dynamics and relationships in different habitats. The higher number of edges in the water network, more balanced ratio of positive and negative links, and more keystone species included in the co-occurrence network of water. Stochastic processes dominated in shaping gut and sediment community assembly (R2 < 0.5), while water bacterial community assembly were dominated by deterministic processes (R2 > 0.5). Dissimilarity-overlap curve model indicated that the communities in different habitats have general dynamics and interspecific interaction (P < 0.001). Bacterial source-tracking analysis revealed that the gut was more similar to the sediment than the water bacterial communities. In summary, this study provides basic data for the ecological study of marine ranching through the study of bacterial community dynamics.

Cortical Morphological Networks Differ Between Gyri and Sulci

This study explored how the human cortical folding pattern composed of convex gyri and concave sulci affected single-subject morphological brain networks, which are becoming an important method for studying the human brain connectome. We found that gyri-gyri networks exhibited higher morphological similarity, lower small-world parameters, and lower long-term test-retest reliability than sulci-sulci networks for cortical thickness- and gyrification index-based networks, while opposite patterns were observed for fractal dimension-based networks. Further behavioral association analysis revealed that gyri-gyri networks and connections between gyral and sulcal regions significantly explained inter-individual variance in Cognition and Motor domains for fractal dimension- and sulcal depth-based networks. Finally, the clinical application showed that only sulci-sulci networks exhibited morphological similarity reductions in major depressive disorder for cortical thickness-, fractal dimension-, and gyrification index-based networks. Taken together, these findings provide novel insights into the constraint of the cortical folding pattern to the network organization of the human brain.

Stimulus type shapes the topology of cellular functional networks in mouse visual cortex

On the timescale of sensory processing, neuronal networks have relatively fixed anatomical connectivity, while functional interactions between neurons can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed single-cell resolution electrophysiological data from the Allen Institute, with simultaneous recordings of stimulus-evoked activity from neurons across 6 different regions of mouse visual cortex. Comparing the functional connectivity patterns during different stimulus types, we made several nontrivial observations: (1) while the frequencies of different functional motifs were preserved across stimuli, the identities of the neurons within those motifs changed; (2) the degree to which functional modules are contained within a single brain region increases with stimulus complexity. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.

Finding multifaceted communities in multiplex networks

Identifying communities in multilayer networks is crucial for understanding the structural dynamics of complex systems. Traditional community detection algorithms often overlook the presence of overlapping edges within communities, despite the potential significance of such relationships. In this work, we introduce a novel modularity measure designed to uncover communities where nodes share specific multiple facets of connectivity. Our approach leverages a null network, an empirical layer of the multiplex network, not a random network, that can be one of the network layers or a complement graph of that, depending on the objective. By analyzing real-world social networks, we validate the effectiveness of our method in identifying meaningful communities with overlapping edges. The proposed approach offers valuable insights into the structural dynamics of multiplex systems, shedding light on nodes that share similar multifaceted connections.

Salt stimulates sulfide-driven autotrophic denitrification: Microbial network and metagenomics analyses

Sulfur autotrophic denitrification (SADN) is a promising biological wastewater treatment technology for nitrogen removal, and its performance highly relies on the collective activities of the microbial community. However, the effect of salt (a prevailing characteristic of some nitrogen-containing industrial wastewaters) on the microbial community of SADN is still unclear. In this study, the response of the sulfide-SADN process to different salinities (i.e., 1.5 % salinity, 0.5 % salinity, and without salinity) as well as the involved microbial mechanisms were investigated by molecular ecological network and metagenomics analyses. Results showed that the satisfactory nitrogen removal efficiency (>97 %) was achieved in the sulfide-SADN process (S/N molar ratio of 0.88) with 1.5 % salinity. In salinity scenarios, the genus Thiobacillus significantly proliferated and was detected as the dominant sulfur-oxidizing bacteria in the sulfide-SADN system, occupying a relative abundance of 29.4 %. Network analysis further elucidated that 1.5 % salinity had enabled the microbial community to form a more densely clustered network, which intensified the interactions between microorganisms and effectively improved the nitrogen removal performance of the sulfide-SADN. Metagenomics sequencing revealed that the abundance of functional genes encoding for key enzymes involved in SADN, dissimilatory nitrate reduction to ammonium, and nitrification was up-regulated in the 1.5 % salinity scenario compared to that without salinity, stimulating the occurrence of multiple nitrogen transformation pathways. These multi-paths contributed to a robust SADN process (i.e., nitrogen removal efficiency >97 %, effluent nitrogen <2.5 mg N/L). This study deepens our understanding of the effect of salt on the SADN system at the community and functional level, and favors to advance the application of this sustainable bioprocess in saline wastewater treatment.

GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity

The interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major histocompatibility complex molecules (MHC) is fundamental to the immune response. Accurate prediction of TCR-epitope interactions is crucial for advancing the understanding of various diseases and their prevention and treatment. Existing methods primarily rely on sequence-based approaches, overlooking the inherent topology structure of TCR-epitope interaction networks. In this study, we present $GTE$, a novel heterogeneous Graph neural network model based on inductive learning to capture the topological structure between TCRs and Epitopes. Furthermore, we address the challenge of constructing negative samples within the graph by proposing a dynamic edge update strategy, enhancing model learning with the nonbinding TCR-epitope pairs. Additionally, to overcome data imbalance, we adapt the Deep AUC Maximization strategy to the graph domain. Extensive experiments are conducted on four public datasets to demonstrate the superiority of exploring underlying topological structures in predicting TCR-epitope interactions, illustrating the benefits of delving into complex molecular networks. The implementation code and data are available at https://github.com/uta-smile/GTE.

Disrupted single-subject gray matter networks are associated with cognitive decline and cortical atrophy in Alzheimer's disease

Background

Research has shown disrupted structural network measures related to cognitive decline and future cortical atrophy during the progression of Alzheimer's disease (AD). However, evidence regarding the individual variability of gray matter network measures and the associations with concurrent cognitive decline and cortical atrophy related to AD is still sparse.

Objective

To investigate whether alterations in single-subject gray matter networks are related to concurrent cognitive decline and cortical gray matter atrophy during AD progression.

Methods

We analyzed structural MRI data from 185 cognitively normal (CN), 150 mild cognitive impairment (MCI), and 153 AD participants, and calculated the global network metrics of gray matter networks for each participant. We examined the alterations of single-subject gray matter networks in patients with MCI and AD, and investigated the associations of network metrics with concurrent cognitive decline and cortical gray matter atrophy.

Results

The small-world properties including gamma, lambda, and sigma had lower values in the MCI and AD groups than the CN group. AD patients had reduced degree, clustering coefficient, and path length than the CN and MCI groups. We observed significant associations of cognitive ability with degree in the CN group, with gamma and sigma in the MCI group, and with degree, connectivity density, clustering coefficient, and path length in the AD group. There were significant correlation patterns between sigma values and cortical gray matter volume in the CN, MCI, and AD groups.

Conclusion

These findings suggest the individual variability of gray matter network metrics may be valuable to track concurrent cognitive decline and cortical atrophy during AD progression. This may contribute to a better understanding of cognitive decline and brain morphological alterations related to AD.

Quantifying synergy and redundancy between networks

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

Profiling functional networks identify activation of corticostriatal connectivity in ET patients after MRgFUS thalamotomy

BACKGROUND

MR-guided focused ultrasound (MRgFUS) thalamotomy is a novel and effective treatment for medication-refractory tremor in essential tremor (ET), but how the brain responds to this deliberate lesion is not clear.

OBJECTIVE

The current study aimed to evaluate the immediate and longitudinal alterations of functional networks after MRgFUS thalamotomy.

METHODS

We retrospectively obtained preoperative and postoperative 30-day, 90-day, and 180-day data of 31 ET patients subjected with MRgFUS thalamotomy from 2018 to 2020. Their archived resting-state functional MRI data were used for functional network comparison as well as graph-theory metrics analysis. Both partial least squares (PLS) regression and linear regression were conducted to associate functional features to tremor symptoms.

RESULTS

MRgFUS thalamotomy dramatically abolished tremors, while global functional network only sustained immediate fluctuation within one week after the surgery. Network-based statistics have identified a long-term enhanced corticostriatal subnetwork by comparison between 180-day and preoperative data (P = 0.019). Within this subnetwork, network degree, global efficiency and transitivity were significantly recovered in ET patients right after MRgFUS thalamotomy compared to the pre-operative timepoint (P < 0.05), as well as hemisphere lateralization (P < 0.001). The PLS main component significantly accounted for 33.68 % and 34.16 % of the total variances of hand tremor score and clinical rating scale for tremor (CRST)-total score (P = 0.037 and 0.027). Network transitivity of this subnetwork could serve as a reliable biomarker for hand tremor score control prediction at 180-day after the surgery (β = 2.94, P = 0.03).

CONCLUSION

MRgFUS thalamotomy promoted corticostriatal connectivity activation correlated with tremor improvement in ET patient after MRgFUS thalamotomy.

MEA-NAP compares microscale functional connectivity, topology, and network dynamics in organoid or monolayer neuronal cultures

Microelectrode array (MEA) recordings are commonly used to compare firing and burst rates in neuronal cultures. MEA recordings can also reveal microscale functional connectivity, topology, and network dynamics-patterns seen in brain networks across spatial scales. Network topology is frequently characterized in neuroimaging with graph theoretical metrics. However, few computational tools exist for analyzing microscale functional brain networks from MEA recordings. Here, we present a MATLAB MEA network analysis pipeline (MEA-NAP) for raw voltage time-series acquired from single- or multi-well MEAs. Applications to 3D human cerebral organoids or 2D human-derived or murine cultures reveal differences in network development, including topology, node cartography, and dimensionality. MEA-NAP incorporates multi-unit template-based spike detection, probabilistic thresholding for determining significant functional connections, and normalization techniques for comparing networks. MEA-NAP can identify network-level effects of pharmacologic perturbation and/or disease-causing mutations and, thus, can provide a translational platform for revealing mechanistic insights and screening new therapeutic approaches.

Interplay between tie strength and neighbourhood topology in complex networks

Granovetter's weak ties theory is a very important sociological theory according to which a correlation between edge weight and the network's topology should exist. More specifically, the neighbourhood overlap of two nodes connected by an edge should be positively correlated with edge weight (tie strength). However, some real social networks exhibit a negative correlation-the most prominent example is the scientific collaboration network, for which overlap decreases with edge weight. It has been demonstrated that the aforementioned inconsistency with Granovetter's theory can be alleviated in the scientific collaboration network through the use of asymmetric measures. In this paper, we explain that while asymmetric measures are often necessary to describe complex networks and to confirm Granovetter's theory, their interpretation is not simple, and there are pitfalls that one must be wary of. The definitions of asymmetric weights and overlaps introduce structural correlations that must be filtered out. We show that correlation profiles can be used to overcome this problem. Using this technique, not only do we confirm Granovetter's theory in various real and artificial social networks, but we also show that Granovetter-like weight-topology correlations are present in other complex networks (e.g. metabolic and neural networks). Our results suggest that Granovetter's theory is a sociological manifestation of more general principles governing various types of complex networks.

Disrupted morphological brain network organization in subjective cognitive decline and mild cognitive impairment

We aim to investigate the alterations in gray matter for subjective cognitive decline (SCD) and mild cognitive impairment (MCI) from the perspective of the human connectome. High-resolution T1-weighted images were acquired from 54 patients with SCD, 95 patients with MCI, and 65 healthy controls (HC). Morphological brain networks (MBN) were constructed using similarities in the distribution of gray matter volumes between regions. The strength of morphological connections and topographic metrics derived from the graph-theoretical analysis were compared. Furthermore, we assessed the relationship between the observed morphological abnormalities and disease severity. According to the results, we found a significantly decreased morphological connection between the somatomotor network and ventral attention network in SCD compared to HC and MCI compared to SCD. The graph-theoretic analysis illustrated disruptions in the whole network organization, where the normalized shortest path increased and the global efficiency (Eg) decreased in MCI compared to SCD. In addition, Montreal Cognitive Assessment scores of SCD patients had a significantly negative correlation with Eg. The primary limitations of the present study include the cross-sectional design, no enrolled AD patients, no assessment of amyloidosis, and the need for more comprehensive neuropsychological tests. Our findings indicate the abnormalities of morphological networks at early stages in the AD continuum, which could be interpreted as compensatory changes to retain a normal level of cognitive function. The present study could provide new insight into the mechanism of AD.

Preponderance of generalized chain functions in reconstructed Boolean models of biological networks

Boolean networks (BNs) have been extensively used to model gene regulatory networks (GRNs). The dynamics of BNs depend on the network architecture and regulatory logic rules (Boolean functions (BFs)) associated with nodes. Nested canalyzing functions (NCFs) have been shown to be enriched among the BFs in the large-scale studies of reconstructed Boolean models. The central question we address here is whether that enrichment is due to certain sub-types of NCFs. We build on one sub-type of NCFs, the chain functions (or chain-0 functions) proposed by Gat-Viks and Shamir. First, we propose two other sub-types of NCFs, namely, the class of chain-1 functions and generalized chain functions, the union of the chain-0 and chain-1 types. Next, we find that the fraction of NCFs that are chain-0 (also holds for chain-1) functions decreases exponentially with the number of inputs. We provide analytical treatment for this and other observations on BFs. Then, by analyzing three different datasets of reconstructed Boolean models we find that generalized chain functions are significantly enriched within the NCFs. Lastly we illustrate that upon imposing the constraints of generalized chain functions on three different GRNs we are able to obtain biologically viable Boolean models.

An organism-wide atlas of hormonal signaling based on the mouse lemur single-cell transcriptome

Hormones mediate long-range cell communication and play vital roles in physiology, metabolism, and health. Traditionally, endocrinologists have focused on one hormone or organ system at a time. Yet, hormone signaling by its very nature connects cells of different organs and involves crosstalk of different hormones. Here, we leverage the organism-wide single cell transcriptional atlas of a non-human primate, the mouse lemur (Microcebus murinus), to systematically map source and target cells for 84 classes of hormones. This work uncovers previously-uncharacterized sites of hormone regulation, and shows that the hormonal signaling network is densely connected, decentralized, and rich in feedback loops. Evolutionary comparisons of hormonal genes and their expression patterns show that mouse lemur better models human hormonal signaling than mouse, at both the genomic and transcriptomic levels, and reveal primate-specific rewiring of hormone-producing/target cells. This work complements the scale and resolution of classical endocrine studies and sheds light on primate hormone regulation.

Disrupted properties of functional brain networks in major depressive disorder during emotional face recognition: an EEG study via graph theory analysis

Previous neuroimaging studies have revealed abnormal brain networks in patients with major depressive disorder (MDD) in emotional processing. While any cognitive task consists of a series of stages, little is yet known about the topology of functional brain networks in MDD for these stages during emotional face recognition. To address this problem, electroencephalography (EEG)-based functional brain networks of MDD patients at different stages of facial information processing were investigated in this study. First, EEG signals were collected from 16 patients with MDD and 18 age-, gender-, and education-matched normal subjects when performing an emotional face recognition task. Second, the global field power (GFP) method was employed to divide group-averaged event-related potentials into different stages. Third, using the phase transfer entropy (PTE) approach, the brain networks of MDD patients and normal individuals were constructed for each stage in negative and positive face processing, respectively. Finally, we compared the topological properties of brain networks of each stage between the two groups using graph theory approaches. The results showed that the analyzed three stages of emotional face processing corresponded to specific neurophysiological phases, namely, visual perception, face recognition, and emotional decision-making. It was also demonstrated that depressed patients showed abnormally decreased characteristic path length at the visual perception stage of negative face recognition and normalized characteristic path length in the stage of emotional decision-making during positive face processing compared to healthy subjects. Furthermore, while both the MDD and normal groups' brain networks were found to exhibit small-world network characteristics, the brain network of patients with depression tended to be randomized. Moreover, for patients with MDD, the centro-parietal region may lose its status as a hub in the process of facial expression identification. Together, our findings suggested that altered emotional function in MDD patients might be associated with disruptions in the topological organization of functional brain networks during emotional face recognition, which further deepened our understanding of the emotion processing dysfunction underlying MDD.

Protein-protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis

BACKGROUND

It is valuable to analyze the genome-wide association studies (GWAS) data for a complex disease phenotype in the context of the protein-protein interaction (PPI) network, as the related pathophysiology results from the function of interacting polyprotein pathways. The analysis may include the design and curation of a phenotype-specific GWAS meta-database incorporating genotypic and eQTL data linking to PPI and other biological datasets, and the development of systematic workflows for PPI network-based data integration toward protein and pathway prioritization. Here, we pursued this analysis for blood pressure (BP) regulation.

METHODS

The relational scheme of the implemented in Microsoft SQL Server BP-GWAS meta-database enabled the combined storage of: GWAS data and attributes mined from GWAS Catalog and the literature, Ensembl-defined SNP-transcript associations, and GTEx eQTL data. The BP-protein interactome was reconstructed from the PICKLE PPI meta-database, extending the GWAS-deduced network with the shortest paths connecting all GWAS-proteins into one component. The shortest-path intermediates were considered as BP-related. For protein prioritization, we combined a new integrated GWAS-based scoring scheme with two network-based criteria: one considering the protein role in the reconstructed by shortest-path (RbSP) interactome and one novel promoting the common neighbors of GWAS-prioritized proteins. Prioritized proteins were ranked by the number of satisfied criteria.

RESULTS

The meta-database includes 6687 variants linked with 1167 BP-associated protein-coding genes. The GWAS-deduced PPI network includes 1065 proteins, with 672 forming a connected component. The RbSP interactome contains 1443 additional, network-deduced proteins and indicated that essentially all BP-GWAS proteins are at most second neighbors. The prioritized BP-protein set was derived from the union of the most BP-significant by any of the GWAS-based or the network-based criteria. It included 335 proteins, with ~ 2/3 deduced from the BP PPI network extension and 126 prioritized by at least two criteria. ESR1 was the only protein satisfying all three criteria, followed in the top-10 by INSR, PTN11, CDK6, CSK, NOS3, SH2B3, ATP2B1, FES and FINC, satisfying two. Pathway analysis of the RbSP interactome revealed numerous bioprocesses, which are indeed functionally supported as BP-associated, extending our understanding about BP regulation.

CONCLUSIONS

The implemented workflow could be used for other multifactorial diseases.

Positive associations fuel soil biodiversity and ecological networks worldwide

Microbial interactions are key to maintaining soil biodiversity. However, whether negative or positive associations govern the soil microbial system at a global scale remains virtually unknown, limiting our understanding of how microbes interact to support soil biodiversity and functions. Here, we explored ecological networks among multitrophic soil organisms involving bacteria, protists, fungi, and invertebrates in a global soil survey across 20 regions of the planet and found that positive associations among both pairs and triads of soil taxa governed global soil microbial networks. We further revealed that soil networks with greater levels of positive associations supported larger soil biodiversity and resulted in lower network fragility to withstand potential perturbations of species losses. Our study provides unique evidence of the widespread positive associations between soil organisms and their crucial role in maintaining the multitrophic structure of soil biodiversity worldwide.

Differential Effect of Global Signal Regression Between Awake and Anesthetized Conditions in Mice

Introduction: In resting-state functional magnetic resonance imaging (rs-fMRI) studies, global signal regression (GSR) is a controversial preprocessing strategy. It effectively eliminates global noise driven by motion and respiration but also can introduce artifacts and remove functionally relevant metabolic information. Most preclinical rs-fMRI studies are performed in anesthetized animals, and anesthesia will alter both metabolic and neuronal activity. Methods: In this study, we explored the effect of GSR on rs-fMRI data collected under anesthetized and awake state in mice (n = 12). We measured global signal amplitude, and also functional connectivity (FC), functional connectivity density (FCD) maps, and brain modularity, all commonly used data-driven analysis methods to quantify connectivity patterns. Results: We found that global signal amplitude was similar between the awake and anesthetized states. However, GSR had a different impact on connectivity networks and brain modularity changes between states. We demonstrated that GSR had a more prominent impact on the anesthetized state, with a greater decrease in functional connectivity and increased brain modularity. We classified mice using the change in amplitude of brain modularity coefficient (ΔQ) before and after GSR processing. The results revealed that, when compared with the largest ΔQ group, the smallest ΔQ group had increased FCD in the cortex region in both the awake and anesthetized states. This suggests differences in individual mice may affect how GSR differentially affects awake versus anesthetized functional connectivity. Discussion: This study suggests that, for rs-fMRI studies which compare different physiological states, researchers should use GSR processing with caution.

Network pharmacology study to explore the multiple molecular mechanism of SH003 in the treatment of non-small cell lung cancer

BACKGROUND

Non-small cell lung cancer (NSCLC) is one of the leading causes of human death worldwide. Herbal prescription SH003 has been developed to treat several cancers including NSCLC. Due to the multi-component nature of SH003 with multiple targets and pathways, a network pharmacology study was conducted to analyze its active compounds, potential targets, and pathways for the treatment of NSCLC.

METHODS

We systematically identified oral active compounds within SH003, employing ADME criteria-based screening from TM-MC, OASIS, and TCMSP databases. Concurrently, SH003-related and NSCLC-associated targets were amalgamated from various databases. Overlapping targets were deemed anti-NSCLC entities of SH003. Protein-protein interaction networks were constructed using the STRING database, allowing the identification of pivotal proteins through node centrality measures. Empirical validation was pursued through LC-MS analysis of active compounds. Additionally, in vitro experiments, such as MTT cell viability assays and western blot analyses, were conducted to corroborate network pharmacology findings.

RESULTS

We discerned 20 oral active compounds within SH003 and identified 239 core targets shared between SH003 and NSCLC-related genes. Network analyses spotlighted 79 hub genes, including TP53, JUN, AKT1, STAT3, and MAPK3, crucial in NSCLC treatment. GO and KEGG analyses underscored SH003's multifaceted anti-NSCLC effects from a genetic perspective. Experimental validations verified SH003's impact on NSCLC cell viability and the downregulation of hub genes. LC-MS analysis confirmed the presence of four active compounds, namely hispidulin, luteolin, baicalein, and chrysoeriol, among the eight compounds with a median of > 10 degrees in the herb-compounds-targets network in SH003. Previously unidentified targets like CASP9, MAPK9, and MCL1 were unveiled, supported by existing NSCLC literature, enhancing the pivotal role of empirical validation in network pharmacology.

CONCLUSION

Our study pioneers the harmonization of theoretical predictions with practical validations. Empirical validation illuminates specific SH003 compounds within NSCLC, simultaneously uncovering novel targets for NSCLC treatment. This integrated strategy, accentuating empirical validation, establishes a paradigm for in-depth herbal medicine exploration. Furthermore, our network pharmacology study unveils fresh insights into SH003's multifaceted molecular mechanisms combating NSCLC. Through this approach, we delineate active compounds of SH003 and target pathways, reshaping our understanding of its therapeutic mechanisms in NSCLC treatment.

Connectome-based reservoir computing with the conn2res toolbox

The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial intelligence open fundamentally new opportunities to understand how connection patterns shape computational capacity in biological brain networks. Reservoir computing is a versatile paradigm that uses high-dimensional, nonlinear dynamical systems to perform computations and approximate cognitive functions. Here we present conn2res: an open-source Python toolbox for implementing biological neural networks as artificial neural networks. conn2res is modular, allowing arbitrary network architecture and dynamics to be imposed. The toolbox allows researchers to input connectomes reconstructed using multiple techniques, from tract tracing to noninvasive diffusion imaging, and to impose multiple dynamical systems, from spiking neurons to memristive dynamics. The versatility of the conn2res toolbox allows us to ask new questions at the confluence of neuroscience and artificial intelligence. By reconceptualizing function as computation, conn2res sets the stage for a more mechanistic understanding of structure-function relationships in brain networks.

The drivers of global news spreading patterns

The web radically changed the dissemination of information and the global spread of news. In this study, we aim to reconstruct the connectivity patterns within nations shaping news propagation globally in 2022. We do this by analyzing a dataset of unprecedented size, containing 140 million news articles from 183 countries and related to 37,802 domains in the GDELT database. Unlike previous research, we focus on the sequential mention of events across various countries, thus incorporating a temporal dimension into the analysis of news dissemination networks. Our results show a significant imbalance in online news spreading. We identify news superspreaders forming a tightly interconnected rich club, exerting significant influence on the global news agenda. To further investigate the mechanisms underlying news dissemination and the shaping of global public opinion, we model countries' interactions using a gravity model, incorporating economic, geographical, and cultural factors. Consistent with previous studies, we find that countries' GDP is one of the main drivers to shape the worldwide news agenda.

Resolving heterogeneity in schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder through individualized structural covariance network analysis

Disruptions in large-scale brain connectivity are hypothesized to contribute to psychiatric disorders, including schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder. However, high inter-individual variation among patients with psychiatric disorders hinders achievement of unified findings. To this end, we adopted a newly proposed method to resolve heterogeneity of differential structural covariance network in schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder. This method could infer individualized structural covariance aberrance by assessing the deviation from healthy controls. T1-weighted anatomical images of 114 patients with psychiatric disorders (schizophrenia: n = 37; bipolar I disorder: n = 37; attention-deficit/hyperactivity disorder: n = 37) and 110 healthy controls were analyzed to obtain individualized differential structural covariance network. Patients exhibited tremendous heterogeneity in profiles of individualized differential structural covariance network. Despite notable heterogeneity, patients with the same disorder shared altered edges at network level. Moreover, individualized differential structural covariance network uncovered two distinct psychiatric subtypes with opposite differences in structural covariance edges, that were otherwise obscured when patients were merged, compared with healthy controls. These results provide new insights into heterogeneity and have implications for the nosology in psychiatric disorders.

Detecting and dissecting signaling crosstalk via the multilayer network integration of signaling and regulatory interactions

The versatility of cellular response arises from the communication, or crosstalk, of signaling pathways in a complex network of signaling and transcriptional regulatory interactions. Understanding the various mechanisms underlying crosstalk on a global scale requires untargeted computational approaches. We present a network-based statistical approach, MuXTalk, that uses high-dimensional edges called multilinks to model the unique ways in which signaling and regulatory interactions can interface. We demonstrate that the signaling-regulatory interface is located primarily in the intermediary region between signaling pathways where crosstalk occurs, and that multilinks can differentiate between distinct signaling-transcriptional mechanisms. Using statistically over-represented multilinks as proxies of crosstalk, we infer crosstalk among 60 signaling pathways, expanding currently available crosstalk databases by more than five-fold. MuXTalk surpasses existing methods in terms of model performance metrics, identifies additions to manual curation efforts, and pinpoints potential mediators of crosstalk. Moreover, it accommodates the inherent context-dependence of crosstalk, allowing future applications to cell type- and disease-specific crosstalk.

Effect of Magnetic Resonance Image Quality on Structural and Functional Brain Connectivity: The Maastricht Study

In population-based cohort studies, magnetic resonance imaging (MRI) is vital for examining brain structure and function. Advanced MRI techniques, such as diffusion-weighted MRI (dMRI) and resting-state functional MRI (rs-fMRI), provide insights into brain connectivity. However, biases in MRI data acquisition and processing can impact brain connectivity measures and their associations with demographic and clinical variables. This study, conducted with 5110 participants from The Maastricht Study, explored the relationship between brain connectivity and various image quality metrics (e.g., signal-to-noise ratio, head motion, and atlas-template mismatches) that were obtained from dMRI and rs-fMRI scans. Results revealed that in particular increased head motion (R2 up to 0.169, p < 0.001) and reduced signal-to-noise ratio (R2 up to 0.013, p < 0.001) negatively impacted structural and functional brain connectivity, respectively. These image quality metrics significantly affected associations of overall brain connectivity with age (up to -59%), sex (up to -25%), and body mass index (BMI) (up to +14%). Associations with diabetes status, educational level, history of cardiovascular disease, and white matter hyperintensities were generally less affected. This emphasizes the potential confounding effects of image quality in large population-based neuroimaging studies on brain connectivity and underscores the importance of accounting for it.

Bioinspired 3D microprinted cell scaffolds: Integration of graph theory to recapitulate complex network wiring in lymph nodes

Physical networks are ubiquitous in nature, but many of them possess a complex organizational structure that is difficult to recapitulate in artificial systems. This is especially the case in biomedical and tissue engineering, where the microstructural details of 3D cell scaffolds are important. Studies of biological networks-such as fibroblastic reticular cell (FRC) networks-have revealed the crucial role of network topology in a range of biological functions. However, cell scaffolds are rarely analyzed, or designed, using graph theory. To understand how networks affect adhered cells, 3D culture platforms capturing the complex topological properties of biologically relevant networks would be needed. In this work, we took inspiration from the small-world organization (high clustering and low path length) of FRC networks to design cell scaffolds. An algorithmic toolset was created to generate the networks and process them to improve their 3D printability. We employed tools from graph theory to show that the networks were small-world (omega factor, ω = -0.10 ± 0.02; small-world propensity, SWP = 0.74 ± 0.01). 3D microprinting was employed to physicalize networks as scaffolds, which supported the survival of FRCs. This work, therefore, represents a bioinspired, graph theory-driven approach to control the networks of microscale cell niches.

Effect of C/N on the microbial interactions of aerobic granular sludge system

The main focus of this study was to evaluate the operational stability and changes in microbial interactions of aerobic granular sludge (AGS) systems at reduced C/N (16, 8 and 4). The results showed that the removal efficiency of total nitrogen and total phosphorus decreased from 95.99 ± 0.93% and 84.44 ± 0.67% to 48.46 ± 1.92% and 50.93 ± 2.67%, respectively, when C/N was reduced from 16 to 4. The granule settling performance and stability also deteriorated. Molecular ecological network analysis showed that the reduction of the C/N ratio made the overall network as well as the subnetworks of the Proteobacteria and Bacteroidota more complex and tightly connected. Similarly, the subnetworks of two dominant genera (Thiothrix and Defluviicoccus) became more complex as the C/N decreased. Meanwhile, the decreased C/N ratio might promote competition among microbes in these overall networks and subnetworks. In conclusion, reduced C/N added complexity and tightness to microbial linkages within the AGS system, while increased competition between species might have contributed to the deterioration in pollutant removal performance. This study adds a new dimension to our understanding of the effects of C/N on the microbial community of AGS using a molecular ecological network approach.

Aging effects on dual-route speech processing networks during speech perception in noise

Healthy aging leads to complex changes in the functional network of speech processing in a noisy environment. The dual-route neural architecture has been applied to the study of speech processing. Although evidence suggests that senescent increases activity in the brain regions across the dorsal and ventral stream regions to offset reduced periphery, the regulatory mechanism of dual-route functional networks underlying such compensation remains largely unknown. Here, by utilizing functional near-infrared spectroscopy (fNIRS), we investigated the compensatory mechanism of the dual-route functional connectivity, and its relationship with healthy aging by using a speech perception task at varying signal-to-noise ratios (SNR) in healthy individuals (young adults, middle-aged adults, and older adults). Results showed that the speech perception scores showed a significant age-related decrease with the reduction of the SNR. The analysis results of dual-route speech processing networks showed that the functional connection of Wernicke's area and homolog Wernicke's area were age-related increases. Further to clarify the age-related characteristics of the dual-route speech processing networks, graph-theoretical network analysis revealed an age-related increase in the efficiency of the networks, and the age-related differences in nodal characteristics were found both in Wernicke's area and homolog Wernicke's area under noise environment. Thus, Wernicke's area might be a key network hub to maintain efficient information transfer across the speech process network with healthy aging. Moreover, older adults would recruit more resources from the homologous Wernicke's area in a noisy environment. The recruitment of the homolog of Wernicke's area might provide a means of compensation for older adults for decoding speech in an adverse listening environment. Together, our results characterized dual-route speech processing networks at varying noise environments and provided new insight for the compensatory theories of how aging modulates the dual-route speech processing functional networks.

The human brain connectome weighted by the myelin content and total intra-axonal cross-sectional area of white matter tracts

A central goal in neuroscience is the development of a comprehensive mapping between structural and functional brain features, which facilitates mechanistic interpretation of brain function. However, the interpretability of structure-function brain models remains limited by a lack of biological detail. Here, we characterize human structural brain networks weighted by multiple white matter microstructural features including total intra-axonal cross-sectional area and myelin content. We report edge-weight-dependent spatial distributions, variance, small-worldness, rich club, hubs, as well as relationships with function, edge length, and myelin. Contrasting networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts, we find opposite relationships with functional connectivity, an edge-length-independent inverse relationship with each other, and the lack of a canonical rich club in myelin-weighted networks. When controlling for edge length, networks weighted by either fractional anisotropy, radial diffusivity, or neurite density show no relationship with whole-brain functional connectivity. We conclude that the co-utilization of structural networks weighted by total intra-axonal cross-sectional area and myelin content could improve our understanding of the mechanisms mediating the structure-function brain relationship.

Static and dynamic fMRI-derived functional connectomes represent largely similar information

Functional connectivity (FC) of blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate), and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.

White matter-based brain network topological properties associated with individual impulsivity

Delay discounting (DD), a parameter derived from the intertemporal choice task, is a representative behavioral indicator of choice impulsivity. Previous research reported not only an association between DD and impulsive control disorders and negative health outcomes but also the neural correlates of DD. However, to date, there are few studies investigating the structural brain network topologies associated with individual differences in DD and whether self-reported measures (BIS-11) of impulsivity associated with DD share the same or distinct neural mechanisms is still unclear. To address these issues, here, we combined graph theoretical analysis with diffusion tensor imaging to investigate the associations between DD and the topological properties of the structural connectivity network and BIS-11 scores. Results revealed that people with a steep DD (greater impatience) had decreased small-worldness (a shift toward weaker small-worldnization) and increased degree centrality in the medial superior prefrontal cortex, associated with subjective value in the task. Though DD was associated with the BIS-11 motor impulsiveness subscale, this subscale was linked to topological properties different from DD; that is, high motor impulsiveness was associated with decreased local efficiency (less segregation) and decreased degree centrality in the precentral gyrus, involved in motor control. These findings provide insights into the systemic brain characteristics underlying individual differences in impulsivity and potential neural markers which could predict susceptibility to impulsive behaviors.

Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture

From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.

Graph theory analysis reveals an assortative pain network vulnerable to attacks

The neural substrate of pain experience has been described as a dense network of connected brain regions. However, the connectivity pattern of these brain regions remains elusive, precluding a deeper understanding of how pain emerges from the structural connectivity. Here, we employ graph theory to systematically characterize the architecture of a comprehensive pain network, including both cortical and subcortical brain areas. This structural brain network consists of 49 nodes denoting pain-related brain areas, linked by edges representing their relative incoming and outgoing axonal projection strengths. Within this network, 63% of brain areas share reciprocal connections, reflecting a dense network. The clustering coefficient, a measurement of the probability that adjacent nodes are connected, indicates that brain areas in the pain network tend to cluster together. Community detection, the process of discovering cohesive groups in complex networks, successfully reveals two known subnetworks that specifically mediate the sensory and affective components of pain, respectively. Assortativity analysis, which evaluates the tendency of nodes to connect with other nodes that have similar features, indicates that the pain network is assortative. Finally, robustness, the resistance of a complex network to failures and perturbations, indicates that the pain network displays a high degree of error tolerance (local failure rarely affects the global information carried by the network) but is vulnerable to attacks (selective removal of hub nodes critically changes network connectivity). Taken together, graph theory analysis unveils an assortative structural pain network in the brain that processes nociceptive information. Furthermore, the vulnerability of this network to attack presents the possibility of alleviating pain by targeting the most connected brain areas in the network.

Land use conversion increases network complexity and stability of soil microbial communities in a temperate grassland

Soils harbor highly diverse microbial communities that are critical to soil health, but agriculture has caused extensive land use conversion resulting in negative effects on critical ecosystem processes. However, the responses and adaptations of microbial communities to land use conversion have not yet been understood. Here, we examined the effects of land conversion for long-term crop use on the network complexity and stability of soil microbial communities over 19 months. Despite reduced microbial biodiversity in comparison with native tallgrass prairie, conventionally tilled (CT) cropland significantly increased network complexity such as connectivity, connectance, average clustering coefficient, relative modularity, and the number of species acting at network hubs and connectors as well as resulted in greater temporal variation of complexity indices. Molecular ecological networks under CT cropland became significantly more robust and less vulnerable, overall increasing network stability. The relationship between network complexity and stability was also substantially strengthened due to land use conversion. Lastly, CT cropland decreased the number of relationships between network structure and environmental properties instead being strongly correlated to management disturbances. These results indicate that agricultural disturbance generally increases the complexity and stability of species "interactions", possibly as a trade-off for biodiversity loss to support ecosystem function when faced with frequent agricultural disturbance.

EEG Based Functional Brain Network Analysis and Classification of Dyslexic Children During Sustained Attention Task

Reading is a complex cognitive skill that involves visual, attention, and linguistic skills. Because attention is one of the most important cognitive skills for reading and learning, the current study intends to examine the functional brain network connectivity implicated during sustained attention in dyslexic children. 15 dyslexic children (mean age 9.83±1.85 years) and 15 non-dyslexic children (mean age 9.91±1.97 years) were selected for this study. The children were asked to perform a visual continuous performance task (VCPT) while their electroencephalogram (EEG) signals were recorded. In dyslexic children, significant variations in task measurements revealed considerable omission and commission errors. During task performance, the dyslexic group with the absence of a small-world network had a lower clustering coefficient, a longer characteristic pathlength, and lower global and local efficiency than the non-dyslexic group (mainly in theta and alpha bands). When classifying data from the dyslexic and non-dyslexic groups, the current study achieved the maximum classification accuracy of 96.7% using a k-nearest neighbor (KNN) classifier. To summarize, our findings revealed indications of poor functional segregation and disturbed information transfer in dyslexic brain networks during a sustained attention task.