Reading intervention and neuroplasticity: A systematic review and meta-analysis of brain changes associated with reading intervention.

Behavioral research supports the efficacy of intervention for reading disability, but the brain mechanisms underlying improvement in reading are not well understood. Here, we review 39 neuroimaging studies of reading intervention to characterize links between reading improvement and changes in the brain. We report evidence of changes in activation, connectivity, and structure within the reading network, and right hemisphere, frontal and sub-cortical regions. Our meta-analysis of changes in brain activation from pre- to post- reading intervention in eight studies did not yield any significant effects. Methodological heterogeneity among studies may contribute to the lack of significant meta-analytic findings. Based on our qualitative synthesis, we propose that brain changes in response to intervention should be considered in terms of interactions among distributed cognitive, linguistic and sensory systems, rather than via a "normalized" vs. "compensatory" dichotomy. Further empirical research is needed to identify effects of moderating factors such as features of intervention programs, neuroimaging tasks, and individual differences among participants.

The impact of computer-assisted technology on literacy acquisition during COVID-19-related school closures: Group-level effects and predictors of individual-level outcomes.

Introduction: The COVID-19 pandemic led to school closure and loss of in-person instruction during the 2019-2020 academic year across the United States, which had a profound impact on the reading development of beginning readers. In this study we tested if a research-informed educational technology (EdTech) program-GraphoLearn-could help alleviate the COVID-19 slide. We also sought to understand the profiles of children who benefitted most from this EdTech program. Methods: We tested participants' (N = 172 K-2 children) early literacy skills using a standardized measure (STAR) before and after playing GraphoLearn, and used the pre to post difference as the dependent variable. We first compared children's STAR actual and expected growth. Then we conducted a multiple regression analysis with data about engagement with GraphoLearn included as predictors. Additional predictors were extracted from GraphoLearn performance at study onset to assess children's letter-sound knowledge, rime awareness, and word recognition. Results: The difference between actual average reading growth and expected growth in a regular school year was not statistically significant. This suggests that children in our sample seem to be gaining reading skills as expected in a regular school year. Our multiple linear regression model (which accounted for R2 = 48% of reading growth) showed that older children, with higher baseline GraphoLearn word recognition, who played more units in a fixed number of days, made significantly more early literacy progress. Discussion: While lacking a control group, our preliminary results suggest that an EdTech program such as GraphoLearn may be a useful reading instructional tool during school shutdowns. In addition, our results suggest that practice with GraphoLearn was more effective and efficient when foundational instruction was already in place.