Toward a more comprehensive understanding of network centrality disruption in amnestic mild cognitive impairment: a MEG multilayer approach.

BACKGROUND: Alzheimer's Disease (AD) is the most common form of dementia. Its early stage, amnestic Mild Cognitive Impairment (aMCI), is characterized by disrupted information flow in the brain. Previous studies have yielded inconsistent results when using electrophysiological techniques to investigate functional connectivity changes in AD, and a contributing factor may be the study of brain activity divided into frequencies. METHODS: Our study aimed to address this issue by employing a cross-frequency approach to compare the functional networks of 172 healthy subjects and 105 aMCI patients. Using magnetoencephalography, we constructed source-based multilayer graphs considering both intra- and inter-frequency functional connectivity. We then assessed changes in network organization through three centrality measures, and combined them into a unified centrality score to provide a comprehensive assessment of centrality disruption in aMCI. RESULTS: The results revealed a noteworthy shift in centrality distribution in aMCI patients, both in terms of spatial distribution and frequency. Posterior brain regions decrease synchrony between their high-frequency oscillations and other regions' activity across all frequencies, while anterior regions increase synchrony between their low-frequency oscillations and other regions' activity across all frequencies. Thus, posterior regions reduce their relative importance in favor of anterior regions. CONCLUSIONS: Our findings provide valuable insights into the intricate changes that occur in functional brain networks during the early stages of AD, demonstrating that considering the interplays between different frequency bands enhances our understanding of AD network dynamics and setting a precedent for the study of functional networks using a multilayer approach.

Local and regional processes drive distance decay in structure in a spatial multilayer plant-pollinator network.

Understanding spatial variation in species distribution and community structure is at the core of community ecology. Nevertheless, the effect of distance on metacommunity structure remains little studied. We examine how plant-pollinator community structure changes across geographical distances at a regional scale and disentangle its underlying local and regional processes. We use a multilayer network to represent linked plant-pollinator communities as a metacommunity in the Canary Islands. We used modularity (i.e. the extent to which the community is partitioned into groups of densely interacting species) to quantify distance decay in structure across space. In multilayer modularity, the same species can belong to different modules in different communities, and modules can span communities. This enabled quantifying how similarity in module composition varied with distance between islands. We developed three null models, each controlling for a separate component of the multilayer network, to disentangle the role of species turnover, interaction rewiring and local factors in driving distance decay in structure. We found a pattern of distance decay in structure, indicating that islands tended to share fewer modules with increasing distance. Species turnover (but not interaction rewiring) was the primary regional process triggering distance decay in structure. Local interaction structure also played an essential role in determining the structure similarity of communities at a regional scale. Therefore, local factors that determine species interactions occurring at a local scale drive distance decay in structure at a regional scale. Our work highlights the interplay between local and regional processes underlying community structure. The methodology, and specifically the null models, we developed provides a general framework for linking communities in space and testing different hypotheses regarding the factors generating spatial structure.

Exploring animal behaviour multilayer networks in immersive environments - a conceptual framework.

Animal behaviour is often modelled as networks, where, for example, the nodes are individuals of a group and the edges represent behaviour within this group. Different types of behaviours or behavioural categories are then modelled as different yet connected networks which form a multilayer network. Recent developments show the potential and benefit of multilayer networks for animal behaviour research as well as the potential benefit of stereoscopic 3D immersive environments for the interactive visualisation, exploration and analysis of animal behaviour multilayer networks. However, so far animal behaviour research is mainly supported by libraries or software on 2D desktops. Here, we explore the domain-specific requirements for (stereoscopic) 3D environments. Based on those requirements, we provide a proof of concept to visualise, explore and analyse animal behaviour multilayer networks in immersive environments.

A generalized eigenvector centrality for multilayer networks with inter-layer constraints on adjacent node importance.

We present a novel approach for computing a variant of eigenvector centrality for multilayer networks with inter-layer constraints on node importance. Specifically, we consider a multilayer network defined by multiple edge-weighted, potentially directed, graphs over the same set of nodes with each graph representing one layer of the network and no inter-layer edges. As in the standard eigenvector centrality construction, the importance of each node in a given layer is based on the weighted sum of the importance of adjacent nodes in that same layer. Unlike standard eigenvector centrality, we assume that the adjacency relationship and the importance of adjacent nodes may be based on distinct layers. Importantly, this type of centrality constraint is only partially supported by existing frameworks for multilayer eigenvector centrality that use edges between nodes in different layers to capture inter-layer dependencies. For our model, constrained, layer-specific eigenvector centrality values are defined by a system of independent eigenvalue problems and dependent pseudo-eigenvalue problems, whose solution can be efficiently realized using an interleaved power iteration algorithm. We refer to this model, and the associated algorithm, as the Constrained Multilayer Centrality (CMLC) method. The characteristics of this approach, and of standard techniques based on inter-layer edges, are demonstrated on both a simple multilayer network and on a range of random graph models. An R package implementing the CMLC method along with example vignettes is available at https://hrfrost.host.dartmouth.edu/CMLC/.

Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements, and future challenges.

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? Here we review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression, and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, including in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.

Unravelling the multisensory learning advantage: Different patterns of within and across frequency-specific interactions drive uni- and multisensory neuroplasticity.

In the field of learning theory and practice, the superior efficacy of multisensory learning over uni-sensory is well-accepted. However, the underlying neural mechanisms at the macro-level of the human brain remain largely unexplored. This study addresses this gap by providing novel empirical evidence and a theoretical framework for understanding the superiority of multisensory learning. Through a cognitive, behavioral, and electroencephalographic assessment of carefully controlled uni-sensory and multisensory training interventions, our study uncovers a fundamental distinction in their neuroplastic patterns. A multilayered network analysis of pre- and post- training EEG data allowed us to model connectivity within and across different frequency bands at the cortical level. Pre-training EEG analysis unveils a complex network of distributed sources communicating through cross-frequency coupling, while comparison of pre- and post-training EEG data demonstrates significant differences in the reorganizational patterns of uni-sensory and multisensory learning. Uni-sensory training primarily modifies cross-frequency coupling between lower and higher frequencies, whereas multisensory training induces changes within the beta band in a more focused network, implying the development of a unified representation of audiovisual stimuli. In combination with behavioural and cognitive findings this suggests that, multisensory learning benefits from an automatic top-down transfer of training, while uni-sensory training relies mainly on limited bottom-up generalization. Our findings offer a compelling theoretical framework for understanding the advantage of multisensory learning.

The spatial distribution of coupling between tau and neurodegeneration in amyloid-ß positive mild cognitive impairment.

Synergies between amyloid-ß (Aß), tau, and neurodegeneration persist along the Alzheimer's disease (AD) continuum. This study aimed to evaluate the extent of spatial coupling between tau and neurodegeneration (atrophy) and its relation to Aß positivity in mild cognitive impairment (MCI). Data from 409 participants were included (95 cognitively normal controls, 158 Aß positive (Aß+) MCI, and 156 Aß negative (Aß-) MCI). Florbetapir PET, Flortaucipir PET, and structural MRI were used as biomarkers for Aß, tau and atrophy, respectively. Individual correlation matrices for tau load and atrophy were used to layer a multilayer network, with separate layers for tau and atrophy. A measure of coupling between corresponding regions of interest (ROIs) in the tau and atrophy layers was computed, as a function of Aß positivity. Fewer than 25% of the ROIs across the brain showed heightened coupling between tau and atrophy in Aß+ , relative to Aß- MCI. Coupling strengths in the right rostral middle frontal and right paracentral gyri, in particular, mediated the association between Aß burden and cognition in this sample.

Multilayer analysis of dynamic network reconfiguration in pediatric posttraumatic stress disorder.

Neuroimage studies have reported functional connectome abnormalities in posttraumatic stress disorder (PTSD), especially in adults. However, these studies often treated the brain as a static network, and time-variance of connectome topology in pediatric posttraumatic stress disorder remain unclear. To explore case-control differences in dynamic connectome topology, resting-state functional magnetic resonance imaging data were acquired from 24 treatment-naïve non-comorbid pediatric posttraumatic stress disorder patients and 24 demographically matched trauma-exposed non-posttraumatic stress disorder controls. A graph-theoretic analysis was applied to construct time-varying modular structure of whole-brain networks by maximizing the multilayer modularity. Network switching rate at the global, subnetwork, and nodal levels were calculated and compared between posttraumatic stress disorder and trauma-exposed non-posttraumatic stress disorder groups, and their associations with posttraumatic stress disorder symptom severity and sex interactions were explored. At the global level, individuals with posttraumatic stress disorder exhibited significantly lower network switching rates compared to trauma-exposed non-posttraumatic stress disorder controls. This difference was mainly involved in default-mode and dorsal attention subnetworks, as well as in inferior temporal and parietal brain nodes. Posttraumatic stress disorder symptom severity was negatively correlated with switching rate in the global network and default mode network. No significant differences were observed in the interaction between diagnosis and sex/age. Pediatric posttraumatic stress disorder is associated with dynamic reconfiguration of brain networks, which may provide insights into the biological basis of this disorder.

Characterizing the role of human behavior in the effectiveness of contact-tracing applications.

Introduction: Although numerous countries relied on contact-tracing (CT) applications as an epidemic control measure against the COVID-19 pandemic, the debate around their effectiveness is still open. Most studies indicate that very high levels of adoption are required to stop disease progression, placing the main interest of policymakers in promoting app adherence. However, other factors of human behavior, like delays in adherence or heterogeneous compliance, are often disregarded. Methods: To characterize the impact of human behavior on the effectiveness of CT apps we propose a multilayer network model reflecting the co-evolution of an epidemic outbreak and the app adoption dynamics over a synthetic population generated from survey data. The model was initialized to produce epidemic outbreaks resembling the first wave of the COVID-19 pandemic and was used to explore the impact of different changes in behavioral features in peak incidence and maximal prevalence. Results: The results corroborate the relevance of the number of users for the effectiveness of CT apps but also highlight the need for early adoption and, at least, moderate levels of compliance, which are factors often not considered by most policymakers. Discussion: The insight obtained was used to identify a bottleneck in the implementation of several apps, such as the Spanish CT app, where we hypothesize that a simplification of the reporting system could result in increased effectiveness through a rise in the levels of compliance.

Multilayer network alignment based on topological assessment via embeddings.

BACKGROUND: Network graphs allow modelling the real world objects in terms of interactions. In a multilayer network, the interactions are distributed over layers (i.e., intralayer and interlayer edges). Network alignment (NA) is a methodology that allows mapping nodes between two or multiple given networks, by preserving topologically similar regions. For instance, NA can be applied to transfer knowledge from one biological species to another. In this paper, we present DANTEml, a software tool for the Pairwise Global NA (PGNA) of multilayer networks, based on topological assessment. It builds its own similarity matrix by processing the node embeddings computed from two multilayer networks of interest, to evaluate their topological similarities. The proposed solution can be used via a user-friendly command line interface, also having a built-in guided mode (step-by-step) for defining input parameters. RESULTS: We investigated the performance of DANTEml based on (i) performance evaluation on synthetic multilayer networks, (ii) statistical assessment of the resulting alignments, and (iii) alignment of real multilayer networks. DANTEml over performed a method that does not consider the distribution of nodes and edges over multiple layers by 1193.62%, and a method for temporal NA by 25.88%; we also performed the statistical assessment, which corroborates the significance of its own node mappings. In addition, we tested the proposed solution by using a real multilayer network in presence of several levels of noise, in accordance with the same outcome pursued for the NA on our dataset of synthetic networks. In this case, the improvement is even more evident: +4008.75% and +111.72%, compared to a method that does not consider the distribution of nodes and edges over multiple layers and a method for temporal NA, respectively. CONCLUSIONS: DANTEml is a software tool for the PGNA of multilayer networks based on topological assessment, that is able to provide effective alignments both on synthetic and real multi layer networks, of which node mappings can be validated statistically. Our experimentation reported a high degree of reliability and effectiveness for the proposed solution.

An Exploratory Application of Multilayer Networks and Pathway Analysis in Pharmacogenomics.

Over the years, network analysis has become a promising strategy for analysing complex system, i.e., systems composed of a large number of interacting elements. In particular, multilayer networks have emerged as a powerful framework for modelling and analysing complex systems with multiple types of interactions. Network analysis can be applied to pharmacogenomics to gain insights into the interactions between genes, drugs, and diseases. By integrating network analysis techniques with pharmacogenomic data, the goal consists of uncovering complex relationships and identifying key genes to use in pathway enrichment analysis to figure out biological pathways involved in drug response and adverse reactions. In this study, we modelled omics, disease, and drug data together through multilayer network representation. Then, we mined the multilayer network with a community detection algorithm to obtain the top communities. After that, we used the identified list of genes from the communities to perform pathway enrichment analysis (PEA) to figure out the biological function affected by the selected genes. The results show that the genes forming the top community have multiple roles through different pathways.

Eco-evolution from deep time to contemporary dynamics: The role of timescales and rate modulators.

Eco-evolutionary dynamics, or eco-evolution for short, are often thought to involve rapid demography (ecology) and equally rapid heritable phenotypic changes (evolution) leading to novel, emergent system behaviours. We argue that this focus on contemporary dynamics is too narrow: Eco-evolution should be extended, first, beyond pure demography to include all environmental dimensions and, second, to include slow eco-evolution which unfolds over thousands or millions of years. This extension allows us to conceptualise biological systems as occupying a two-dimensional time space along axes that capture the speed of ecology and evolution. Using Hutchinson's analogy: Time is the 'theatre' in which ecology and evolution are two interacting 'players'. Eco-evolutionary systems are therefore dynamic: We identify modulators of ecological and evolutionary rates, like temperature or sensitivity to mutation, which can change the speed of ecology and evolution, and hence impact eco-evolution. Environmental change may synchronise the speed of ecology and evolution via these rate modulators, increasing the occurrence of eco-evolution and emergent system behaviours. This represents substantial challenges for prediction, especially in the context of global change. Our perspective attempts to integrate ecology and evolution across disciplines, from gene-regulatory networks to geomorphology and across timescales, from today to deep time.

Overcoming vaccine hesitancy by multiplex social network targeting: an analysis of targeting algorithms and implications.

Incorporating social factors into disease prevention and control efforts is an important undertaking of behavioral epidemiology. The interplay between disease transmission and human health behaviors, such as vaccine uptake, results in complex dynamics of biological and social contagions. Maximizing intervention adoptions via network-based targeting algorithms by harnessing the power of social contagion for behavior and attitude changes largely remains a challenge. Here we address this issue by considering a multiplex network setting. Individuals are situated on two layers of networks: the disease transmission network layer and the peer influence network layer. The disease spreads through direct close contacts while vaccine views and uptake behaviors spread interpersonally within a potentially virtual network. The results of our comprehensive simulations show that network-based targeting with pro-vaccine supporters as initial seeds significantly influences vaccine adoption rates and reduces the extent of an epidemic outbreak. Network targeting interventions are much more effective by selecting individuals with a central position in the opinion network as compared to those grouped in a community or connected professionally. Our findings provide insight into network-based interventions to increase vaccine confidence and demand during an ongoing epidemic.

Gene communities in co-expression networks across different tissues.

With the recent availability of tissue-specific gene expression data, e.g., provided by the GTEx Consortium, there is interest in comparing gene co-expression patterns across tissues. One promising approach to this problem is to use a multilayer network analysis framework and perform multilayer community detection. Communities in gene co-expression networks reveal groups of genes similarly expressed across individuals, potentially involved in related biological processes responding to specific environmental stimuli or sharing common regulatory variations. We construct a multilayer network in which each of the four layers is an exocrine gland tissue-specific gene co-expression network. We develop methods for multilayer community detection with correlation matrix input and an appropriate null model. Our correlation matrix input method identifies five groups of genes that are similarly co-expressed in multiple tissues (a community that spans multiple layers, which we call a generalist community) and two groups of genes that are co-expressed in just one tissue (a community that lies primarily within just one layer, which we call a specialist community). We further found gene co-expression communities where the genes physically cluster across the genome significantly more than expected by chance (on chromosomes 1 and 11). This clustering hints at underlying regulatory elements determining similar expression patterns across individuals and cell types. We suggest that KRTAP3-1, KRTAP3-3, and KRTAP3-5 share regulatory elements in skin and pancreas. Furthermore, we find that CELA3A and CELA3B share associated expression quantitative trait loci in the pancreas. The results indicate that our multilayer community detection method for correlation matrix input extracts biologically interesting communities of genes.

Spreading processes with mutations over multilayer networks.

A key scientific challenge during the outbreak of novel infectious diseases is to predict how the course of the epidemic changes under countermeasures that limit interaction in the population. Most epidemiological models do not consider the role of mutations and heterogeneity in the type of contact events. However, pathogens have the capacity to mutate in response to changing environments, especially caused by the increase in population immunity to existing strains, and the emergence of new pathogen strains poses a continued threat to public health. Further, in the light of differing transmission risks in different congregate settings (e.g., schools and offices), different mitigation strategies may need to be adopted to control the spread of infection. We analyze a multilayer multistrain model by simultaneously accounting for i) pathways for mutations in the pathogen leading to the emergence of new pathogen strains, and ii) differing transmission risks in different settings, modeled as network layers. Assuming complete cross-immunity among strains, namely, recovery from any infection prevents infection with any other (an assumption that will need to be relaxed to deal with COVID-19 or influenza), we derive the key epidemiological parameters for the multilayer multistrain framework. We demonstrate that reductions to existing models that discount heterogeneity in either the strain or the network layers may lead to incorrect predictions. Our results highlight that the impact of imposing/lifting mitigation measures concerning different contact network layers (e.g., school closures or work-from-home policies) should be evaluated in connection with their effect on the likelihood of the emergence of new strains.

Expanding network ecology in freshwater ecosystems.

Research in freshwater ecosystems has always had a strong focus on ecological interactions. The vast majority of studies, however, have investigated trophic interactions and food webs, overlooking a wider suite of non-trophic interactions (e.g. facilitation, competition, symbiosis and parasitism) and the ecological networks they form. Without a complete understanding of all potential interactions, ranging from mutualistic through to antagonistic, we may be missing important ecological processes with consequences for ecosystem assembly, structure and function. Ecological networks can be constructed at different scales, from genes to ecosystems, but also local to global, and as such there is significant opportunity to put them to work in freshwater research. To expand beyond food webs, we need to leverage technological and methodological advances and look to recent research in marine and terrestrial systems-which are far more advanced in terms of detecting, measuring and contextualising ecological interactions. Future studies should look to emerging technologies to aid in merging the wide range of ecological interactions in freshwater ecosystems into networks to advance our understanding and ultimately increase the efficacy of conservation, management, restoration and other applications.

Invasive species modulate the structure and stability of a multilayer mutualistic network.

Species interactions are critical for maintaining community structure and dynamics, but the effects of invasive species on multitrophic networks remain poorly understood. We leveraged an ongoing invasion scenario in Patagonia, Argentina, to explore how non-native ungulates affect multitrophic networks. Ungulates disrupt a hummingbird-mistletoe-marsupial keystone interaction, which alters community composition. We sampled pollination and seed dispersal interactions in intact and invaded sites. We constructed pollination and seed dispersal networks for each site, which we connected via shared plants. We calculated pollination-seed dispersal connectivity, identified clusters of highly connected species, and quantified species' roles in connecting species clusters. To link structural variation to stability, we quantified network tolerance to single random species removal (disturbance propagation) and sequential species removal (robustness) using a stochastic coextinction model. Ungulates reduced the connectivity between pollination and seed dispersal and produced fewer clusters with a skewed size distribution. Moreover, species shifted their structural role, fragmenting the network by reducing the 'bridges' among species clusters. These structural changes altered the dynamics of cascading effects, increasing disturbance propagation and reducing network robustness. Our results highlight invasive species' role in altering community structure and subsequent stability in multitrophic communities.

Sex differences in multilayer functional network topology over the course of aging in 37543 UK Biobank participants.

Aging is a major risk factor for cardiovascular and neurodegenerative disorders, with considerable societal and economic implications. Healthy aging is accompanied by changes in functional connectivity between and within resting-state functional networks, which have been associated with cognitive decline. However, there is no consensus on the impact of sex on these age-related functional trajectories. Here, we show that multilayer measures provide crucial information on the interaction between sex and age on network topology, allowing for better assessment of cognitive, structural, and cardiovascular risk factors that have been shown to differ between men and women, as well as providing additional insights into the genetic influences on changes in functional connectivity that occur during aging. In a large cross-sectional sample of 37,543 individuals from the UK Biobank cohort, we demonstrate that such multilayer measures that capture the relationship between positive and negative connections are more sensitive to sex-related changes in the whole-brain connectivity patterns and their topological architecture throughout aging, when compared to standard connectivity and topological measures. Our findings indicate that multilayer measures contain previously unknown information on the relationship between sex and age, which opens up new avenues for research into functional brain connectivity in aging.

Social network analysis of Twitter interactions: a directed multilayer network approach.

Effective employment of social media for any social influence outcome requires a detailed understanding of the target audience. Social media provides a rich repository of self-reported information that provides insight regarding the sentiments and implied priorities of an online population. Using Social Network Analysis, this research models user interactions on Twitter as a weighted, directed network. Topic modeling through Latent Dirichlet Allocation identifies the topics of discussion in Tweets, which this study uses to induce a directed multilayer network wherein users (in one layer) are connected to the conversations and topics (in a second layer) in which they have participated, with inter-layer connections representing user participation in conversations. Analysis of the resulting network identifies both influential users and highly connected groups of individuals, informing an understanding of group dynamics and individual connectivity. The results demonstrate that the generation of a topically-focused social network to represent conversations yields more robust findings regarding influential users, particularly when analysts collect Tweets from a variety of discussions through more general search queries. Within the analysis, PageRank performed best among four measures used to rank individual influence within this problem context. In contrast, the results of applying both the Greedy Modular Algorithm and the Leiden Algorithm to identify communities were mixed; each method yielded valuable insights, but neither technique was uniformly superior. The demonstrated four-step process is readily replicable, and an interested user can automate the process with relatively low effort or expense.

Influence of Information Blocking on the Spread of Virus in Multilayer Networks.

In this paper, we present the model of the interaction between the spread of disease and the spread of information about the disease in multilayer networks. Next, based on the characteristics of the SARS-CoV-2 virus pandemic, we evaluated the influence of information blocking on the virus spread. Our results show that blocking the spread of information affects the speed at which the epidemic peak appears in our society, and affects the number of infected individuals.