Longitudinal changes in brain activation underlying reading fluency

Abstract

Reading fluency—the speed and accuracy of reading connected text—is foundational to educational success. The current longitudinal study investigates the neural correlates of fluency development using a connected‐text paradigm with an individualized presentation rate. Twenty‐six children completed a functional MRI task in 1st/2nd grade (time 1) and again 1–2 years later (time 2). There was a longitudinal increase in activation in the ventral occipito‐temporal (vOT) cortex from time 1 to time 2. This increase was also associated with improvements in reading fluency skills and modulated by individual speed demands. These findings highlight the reciprocal relationship of the vOT region with reading proficiency and its importance for supporting the developmental transition to fluent reading. These results have implications for developing effective interventions to target increased automaticity in reading.

Structural neural connectivity correlates with pre-reading abilities in preschool children

“…Our findings from this large longitudinal sample of young children motivate graph theory structural connectivity as a plausible predictor of later reading outcomes, similar to prior findings using other brain metrics in longitudinal studies of early childhood. Longitudinal studies found increased brain activity correlated with reading fluency in school aged children ( McNorgan et al, 2011 ; Ozernov‐Palchik et al, 2023 ). Additionally, functional connectivity correlates of pre-reading skills (I.e., phonological processing) during the pre-reading period predicted later reading development ( Jasińska et al, 2021 ; Yu et al, 2018 ).…”

Longitudinal trajectories of brain development from infancy to school age and their relationship to literacy development

“…Here, we examined the relationship between longitudinal trajectories of early brain development and acquisition of reading-related (sub)skills in three objectives. First, we generated longitudinal trajectories of early brain structure, including white matter organization, from infancy to school age in regions and tracts previously linked to literacy development (Ben-Shachar et al, 2011; Brem et al, 2010; Dehaene-Lambertz et al, 2018; Di Pietro et al, 2023; Ozernov-Palchik et al, 2023; Reynolds et al, 2019a; Roy et al, 2024; Tang et al, 2024; Wang et al, 2017; Yeatman et al, 2012a; Yu et al, 2018a, 2021; Zuk et al, 2021a), using a novel processing and analysis pipeline appropriate for the early developmental period. Findings showed that longitudinal trajectories were best modeled using logarithmic, compared with linear and quadratic, functions.…”

“…To do this, we leveraged both infant-specific and standard MRI procedures and tools. In preparation for the second and third objectives, which examine literacy development and literacy-related skill acquisition, estimates were extracted from brain areas and tracts related to reading-related skill development in longitudinal studies in older children (Ben-Shachar et al, 2011; Brem et al, 2010; Dehaene-Lambertz et al, 2018; Di Pietro et al, 2023; Ozernov-Palchik et al, 2023; Reynolds et al, 2019a; Roy et al, 2024; Wang et al, 2017; Yeatman et al, 2012a; Yu et al, 2018a) or prospective studies in infants (Tang et al, 2024; Yu et al, 2021; Zuk et al, 2021a). We then compared among several candidate linear and nonlinear mixed effects models, varying according to function and random parameters (e.g., intercepts alone versus intercepts and slopes), to identify the model with the most parsimonious fit (Vijayakumar et al, 2018).…”

“…Accordingly, our third objective was to examine the association of individual differences in early brain development to long-term literacy development. As such, we extracted curve features (e.g., intercepts and slopes) from individual longitudinal trajectories for brain areas and tracts previously shown to relate to reading-related skill development in longitudinal studies in older children (Ben-Shachar et al, 2011; Brem et al, 2010; Dehaene-Lambertz et al, 2018; Di Pietro et al, 2023; Ozernov-Palchik et al, 2023; Reynolds et al, 2019a; Roy et al, 2024; Wang et al, 2017; Yeatman et al, 2012a; Yu et al, 2018a) or prospective studies in infants (Tang et al, 2024; Yu et al, 2021; Zuk et al, 2021a). We then tested these curve features for correlations with two key literacy subskills measured in preschool/early kindergarten: phonological processing and oral language.…”

Unraveling individual differences in learning potential: A dynamic framework for the case of reading development

“…To investigate children’s learning trajectories, we need tailored experimental paradigms, data acquisition approaches, and statistical analysis techniques that enable modeling both group-averaged changes over time and individual differences in these changes. These methodological approaches benefit from the recent increase in the number of longitudinal studies in developmental cognitive neuroscience ( Telzer et al, 2018 ), including those that follow ( Ben-Shachar et al, 2011 , Brem et al, 2010 , Dehaene-Lambertz et al, 2018 , Di Pietro et al, 2023 , Ozernov-Palchik et al, 2023 ) and/or predict ( Bach et al, 2013 , Karipidis et al, 2018 , Maurer et al, 2009 , Wang et al, 2020a , Wang et al, 2020b ) neuro-behavioral changes with reading development. This shift towards longitudinal studies is particularly relevant in the study of reading because cross-sectional age-group comparisons are confounded by children’s highly variable learning rates, due to, for example, age of school entry, IQ, SES, native language and gender, i.e., boys acquire reading more slowly than girls ( Goswami, 2003 ).…”