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.     

Early Childhood Parenting Predicts Late Childhood Brain Functional Connectivity During Emotion Perception and Reward Processing

Seventy-nine 3-year olds and their mothers participated in a laboratory-based task to assess maternal hostility. Mothers also reported their behavioral regulation of their child. Seven years later, functional magnetic resonance imaging data were acquired while viewing emotional faces and completing a reward processing task. Maternal hostility predicted more negative amygdala connectivity during exposure to sad relative to neutral faces with frontal and parietal regions as well as more negative left ventral striatal connectivity during monetary gain relative to loss feedback with the right posterior orbital frontal cortex and right inferior frontal gyrus. In contrast, maternal regulation predicted enhanced cingulo-frontal connectivity during monetary gain relative to loss feedback. Results suggest parenting is associated with alterations in emotion and reward processing circuitry 7–8 years later.     

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.     

Teachers' Perceptions of a Learner-Centered Teacher Evaluation System

Educational Assessment, Evaluation and Accountability - Promoting success for all students is the driving force to develop and implement sound teacher evaluation systems. As Stronge suggests,...     

Book reviews

Association of Research Libraries