Exploring Functional Brain Network Modularity in Educational Contexts

The brain's modular functional organization facilitates adaptability. Modularity has been linked with a wide range of cognitive abilities such as intelligence, memory, and learning. However, much of this work has (1) considered modularity while a participant is at rest rather than during tasks conditions and/or (2) relied primarily on lab-based cognitive assessments. Thus, the extent to which modularity can provide information about real-word behavior remains largely unknown. Here, we investigated whether functional modularity during resting-state and task-based fMRI was associated with academic learning (measured by GPA) and ability (measured by PSAT) in a large sample of high school students. Additional questions concerned the extent to which modularity differs between rest and task conditions, and across spatial scales. Results indicated that whole-brain modularity during task conditions was significantly associated with academic learning. In contrast to prior work, no such associations were observed for resting-state modularity. We further showed that differences in modularity between task conditions and resting-state varied across spatial scales. Taken together, the present findings inform how functional brain network modularity - during task conditions and while at rest - relate to a range of cognitive abilities.

Need-Based Transfers Enhance Resilience to Shocks: An Agent-Based Model of a Maasai Risk-Pooling System

Maasai and other Maa-speaking pastoralists in Kenya and Tanzania have a risk-pooling system that they refer to by their word for the umbilical cord (osotua). Gifts from one osotua partner to another are contingent on the recipient’s need and do not create any debt. We refer to such gifts as need-based transfers. Maa-speakers also have a system of debt-based transfers (esile) in which gifts must be repaid. We designed an agent-based model to compare the impacts on herd survival of need-based and debt-based transfers on networks of varying topologies and sizes and with different degrees of temporal correlation of shocks felt by the agents. We found that the use of need-based rather than debt-based transfers, greater network modularity, greater network size, and decreased correlation among shocks were associated with increased rates of survival.

HM-EIICT: Fairness-aware link prediction in complex networks using community information

The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions.

The joint role of coevolutionary selection and network structure in shaping trait matching in mutualisms

Coevolution can sculpt remarkable trait similarity between mutualistic partners. Yet, it remains unclear which network topologies and selection regimes enhance trait matching. To address this, we simulate coevolution in topologically distinct networks under a gradient of mutualistic selection strength. We describe three main insights. First, trait matching is jointly influenced by the strength of mutualistic selection and the structural properties of the network where coevolution is unfolding. Second, the strength of mutualistic selection determines the network descriptors better correlated with higher trait matching. While network modularity enhances trait matching when coevolution is weak, network connectance does so when coevolution is strong. Third, the structural properties of networks outrank those of modules or species in determining the degree of trait matching. Our findings suggest networks can both enhance or constrain trait matching, depending on the strength of mutualistic selection.

Asymmetrical adaptations to increases and decreases in environmental volatility

Humans adapt their learning strategies to changing environments by estimating the volatility of the reinforcement conditions. Here, we examine how volatility affects learning and the underlying functional brain organizations using a probabilistic reward reversal learning task. We found that the order of conditions was critically important; participants adjusted learning rate going from volatile to stable, but not from stable to volatile, environments. Subjective volatility of the environment was encoded in the striatal reward system and its dynamic connections with the prefrontal control system. Flexibility, which captures the dynamic changes of network modularity in the brain, was higher in the environmental transition from volatile to stable than from stable to volatile. These findings suggest that behavioral adaptations and dynamic brain organizations in transitions between stable and volatile environments are asymmetric, providing critical insights into the way that people learn under uncertainty.