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. 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. 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. 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.

Albugo spp. are biotrophic parasites that cause white rust in Brassicaceae species, with significant crop losses. The generalist A. candida and the specialist A. laibachii infect Arabidopsis thaliana, and the pathosystem Albugo–Arabidopsis is a model for research in molecular genetics of plant–pathogen interactions. The occurrence of infection by Albugo in wild populations of Arabidopsis and data on the genetics of resistance-susceptibility are compatible with a hypothesis of host–pathogen coevolution. However, the negative impact of Albugo infection on Arabidopsis—a requirement for coevolution—has not been shown under field conditions. To address this question, we analysed the demography and the dynamics of Albugo infection in a wild Arabidopsis population in central Spain and measured plant fitness-related traits. Infection increased mortality by 50%, although lifespan, the fraction of plants that reproduced and seed production were reduced only in plants from the spring cohorts. Despite these negative effects, simulations of demographic dynamics showed that the population growth rate remained unaffected even at unrealistically high infection incidences. The lack of negative effects in autumn–winter cohorts suggests compensatory mechanisms in longer-lived plants. Results support the hypothesis of Albugo–Arabidopsis coevolution.

Pathogen introduction in plant communities can cause serious impacts and biodiversity losses that may take a long time to manage and restore. Effective control of epidemic spreading in the wild is a problem of paramount importance because of its implications in conservation and potential economic losses. Understanding the mechanisms that hinder pathogen propagation is, therefore, crucial. Usual modelization approaches in epidemic spreading are based in compartmentalized models, without keeping track of pathogen concentrations during spreading. In this contribution we present and fully analyze a dynamical model for plant epidemic spreading based on pathogen abundances. The model, which is defined on top of network substrates, is amenable to a deep mathematical analysis in the absence of a limit in the amount of pathogen a plant can tolerate before dying. In the presence of such death threshold, we observe that the fraction of dead plants peaks at intermediate values of network connectivity, and mortality decreases for large average degrees. We discuss the implications of our results as mechanisms to halt infection propagation.

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 using electrophysiological techniques to investigate AD’s functional connectivity changes have yielded inconsistent results; contributing factors may include the specific metric employed and the separate study of brain activity in each frequency band. Our study addresses this by employing a cross-frequency approach to compare functional networks in 172 healthy subjects and 105 aMCI patients. Using magnetoencephalography, we constructed source-based multilayer graphs considering both intra- and inter-band functional connectivity across the five classical frequency bands. We assessed network changes through three centrality measures (strength, eigenvector centrality, and betweenness centrality), and combined them into a unified centrality score for a comprehensive assessment of centrality disruption in aMCI. Results revealed a notable shift in centrality distribution in aMCI patients spatially and across frequencies. Posterior brain regions decrease synchrony between high-frequency oscillations and other regions’ activity across all frequencies, while anterior regions increase synchrony between low-frequency oscillations and other regions’ activity across all bands. Thus, posterior regions reduce their relative importance in favor of anterior regions. This study demonstrates that considering the interplays between different frequency bands enhances understanding of AD network dynamics and sets a precedent for multilayer functional network studies. Our findings provide valuable insights into the intricate changes that occur in functional brain networks during the early stages of AD, offering a framework for developing interventions aimed at modifying the disease trajectory. Significance statement Over 55 million people worldwide suffer from Alzheimer’s Disease. The brain changes causing the symptoms begin around 20 years before their onset, so characterizing these changes in the early stage, mild cognitive impairment, is of utmost importance. Magnetoencephalography and electroencephalography (M/EEG) are two commonly used techniques to assess changes in functional networks; however, the existing studies yield inconsistent results. Our study applies a novel methodology for the analysis of M/EEG data that addresses two factors contributing to this effect: the metric employed to assess network changes and the use of a cross-frequency approach. Results show that posterior regions reduce their relative importance in favor of anterior regions. This study sets a precedent for the functional study of all brain disorders.