The neural correlates of working memory training in typically developing children

Working memory training improves children's cognitive performance on untrained tasks; however, little is known about the underlying neural mechanisms. This was investigated in 32 typically developing children aged 10–14 years (19 girls and 13 boys) using a randomized controlled design and multi-modal magnetic resonance imaging (Devon, UK; 2015–2016). Training improved working memory performance and increased intrinsic functional connectivity between the bilateral intraparietal sulci. Furthermore, improvements in working memory were associated with greater recruitment of the left middle frontal gyrus on a complex span task. Repeated engagement of fronto-parietal regions during training may increase their activity and functional connectivity over time, affording greater working memory performance. The plausibility of generalizable cognitive benefits from a neurobiological perspective and implications for neurodevelopmental theory are discussed.     

Connecting inside out: Development of the social brain in infants and toddlers with a focus on myelination as a marker of brain maturation

Early childhood is a sensitive period for learning and social skill development. The maturation of cerebral regions underlying social processing lays the foundation for later social-emotional competence. This study explored myelin changes in social brain regions and their association with changes in parent-rated social-emotional development in a cohort of 129 children (64 females, 0–36 months, 77 White). Results reveal a steep increase in myelination throughout the social brain in the first 3 years of life that is significantly associated with social-emotional development scores. These findings add knowledge to the emerging picture of social brain development by describing neural underpinnings of human social behavior. They can contribute to identifying age-/stage-appropriate early life factors in this developmental domain.     

Does modality matter? A latent profile and transition analysis of sedentary behaviours among school-aged youth: The UP&DOWN study

ABSTRACT

The aims of this study were to identify profiles of sedentary behaviour (SB) patterns, based on leisure-time self-reported SB modalities (screen, educative, social, and relaxing) and to evaluate changes in these profiles over 2 years among Spanish youth aged 8–18 years. Latent profile analysis (LPA), a data-driven analytic approach, was used to identify groups of boys and girls (n = 1553; 48% girls; mean±SD age: 12.56 ± 2.49 y) with distinct SB profiles using the SB modalities (time/d) as input variables. Latent transition analysis, an extension of LPA that uses longitudinal data, was used to analyse 2-year changes in these profiles. At baseline, four and three SB profiles were found among boys (labelled: screen, educative, social, and relaxing) and girls (labelled: screen/social, educative, and relaxing), respectively. Overall, more girls (range: 48%-67%) had the same profile over time, than boys (40%-52%). Participants with a screen or relaxing SB profile at baseline were more likely to have an educative profile after 2 years. Youth with a social and an educative SB profile at baseline were more likely to transition to profiles characterized by higher screen and social SB, respectively. Using a novel and person-centered approach, this study identified gender-specific SB profiles that were moderately stable over time.     

Digital Module 16: Longitudinal Data Analysis

In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module begins with a nontechnical overview of longitudinal data analyses drawing distinctions with cross-sectional analyses in terms of research questions to be addressed. Nuances of longitudinal designs, timing of measurements, and the real possibility of missing data are then discussed. The three interconnected components of the LME model—(1) a model for individual and mean response profiles, (2) a model to characterize the covariation among the time-specific residuals, and (3) a set of models that summarize the extent that individual coefficients vary—are discussed in the context of the set of activities comprising an analysis. Finally, they demonstrate how to estimate the linear mixed effects model within an open-source environment (R). The digital module contains sample R code, diagnostic quiz questions, hands-on activities in R, curated resources, and a glossary.     

Analyzing Food Science Research for Publishing Characteristics as a Model for Understanding the Needs of Researchers

Abstract

This paper provides a methodology to study the characteristics of the research output from a university department. The faculty publications and their cited references over a 10-year period were used as the basis for this study to identify their publishing patterns and the types of material they are publishing; a core set of journals and other resources they are publishing in and citing over this period; the characteristics of the journals in which they publish and cite; the degree of openness of their publications and their citation advantage; and the age of resources that are referenced.