The application of Network Science to social systems has introduced new methodologies to analyze classical problems such as the emergence of epidemics, the arousal of cooperation between individuals or the propagation of information along social networks. More recently, the organization of football teams and their performance have been unveiled using metrics coming from Network Science, where a team is considered as a complex network whose nodes (i.e., players) interact with the aim of overcoming the opponent network. Here, we combine the use of different network metrics to extract the particular signature of the F.C. Barcelona coached by Guardiola, which has been considered one of the best teams along football history. We have first compared the network organization of Guardiola’s team with their opponents along one season of the Spanish national league, identifying those metrics with statistically significant differences and relating them with the Guardiola’s game. Next, we have focused on the temporal nature of football passing networks and calculated the evolution of all network properties along a match, instead of considering their average. In this way, we are able to identify those network metrics that enhance the probability of scoring/receiving a goal, showing that not all teams behave in the same way and how the organization Guardiola’s F.C. Barcelona is different from the rest, including its clustering coefficient, shortest-path length, largest eigenvalue of the adjacency matrix, algebraic connectivity and centrality distribution.

Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game

We present one of the first applications of Permutation Entropy (PE) and Statistical Complexity (SC) (measured as the product of PE and Jensen-Shanon Divergence) on Magnetoencephalography (MEG) recordings of 46 subjects suffering from Mild Cognitive Impairment (MCI), 17 individuals diagnosed with Alzheimer’s Disease (AD) and 48 healthy controls. We studied the differences in PE and SC in broadband signals and their decomposition into frequency bands ( $\delta$ , $\theta$ , $\alpha$ and $\beta$ ), considering two modalities: (i) raw time series obtained from the magnetometers and (ii) a reconstruction into cortical sources or regions of interest (ROIs). We conducted our analyses at three levels: (i) at the group level we compared SC in each frequency band and modality between groups; (ii) at the individual level we compared how the [PE, SC] plane differs in each modality; and (iii) at the local level we explored differences in scalp and cortical space. We recovered classical results that considered only broadband signals and found a nontrivial pattern of alterations in each frequency band, showing that SC does not necessarily decrease in AD or MCI.

We investigate the alterations of functional networks of patients suffering from mild cognitive impairment and Alzheimer’s disease (AD) when compared to healthy individuals. Departing from the magnetoencephalographic recordings of these three groups, we construct and analyse the corresponding single layer functional networks at different frequency bands, both at the sensors and the regions of interest (ROI) levels. Different network parameters show statistically significant differences, with global efficiency being the one having the most pronounced differences between groups. Next, we extend the analyses to the frequency-band multilayer networks (MN) of the same dataset. Using the mutual information as a metric to evaluate the coordination between brain regions, we construct the αβ MN and analyse their algebraic connectivity at baseline λ _2−BSL (i.e., the second smallest eigenvalue of the corresponding Laplacian matrices). We report statistically significant differences at the sensor level, despite the fact that these differences are not clearly observed when networks are obtained at the ROIs level (i.e., after a source reconstruction procedure). Next, we modify the weights of the inter-links of the multilayer network to identify the value of the algebraic connectivity λ _2−T leading to a transition where layers can be considered to be fully merged. However, differences between the values of λ _2−T of the three groups are not statistically significant. Finally, we developed nested multinomial logistic regression models (MNR models), with the aim of predicting group labels with the parameters extracted from the MN (λ _2−BSL and λ _2−T ). Using these models, we are able to quantify how age influences the risk of suffering AD and how the algebraic connectivity of frequency-based multilayer functional networks could be used as a biomarker of AD in clinical contexts.

Background: Introduction: Physical activity is essential in preventing and treating age-related chronic diseases and mortality. Retirement is a key period to promote health behaviours, as individuals restructure their routines. Thus, we aimed to identify effective components and behaviour change techniques (BCTs) in interventions promoting physical activity in retirement-age individuals. Methods: We conducted a meta-analysis. Included studies were randomised controlled trials that (p)targeted retirement-age adults (50-70 years), (i)applied BCTs, (c)had any comparator, and (o)promoted physical activity. Screening, full-text review, and data extraction were conducted independently by at least two reviewers. A multilevel random effects model with three effect sizes was fitted, and meta-regressions tested several moderators. Results: 67 studies (N = 12,147) were included. High risk of bias related to larger effects, so these studies were excluded from the main analyses. While individual effects were often non-significant, the overall pooled effect was small but statistically significant. Predictors varied across effect sizes and included action planning, motivational interviewing, and prompts/cues. Email and website delivery were associated with smaller effect sizes. Conclusions: The effectiveness of lifestyle interventions is heterogeneous and presented small effects; implementing action planning, motivational interviewing, and prompts could improve the effectiveness. However, many BCTs that are not frequently used remain unexplored.