How reliable are gray matter disruptions in specific reading disability across multiple countries and languages? insights from a large‐scale voxel‐based morphometry study

Abstract

The neural basis of specific reading disability (SRD) remains only partly understood. A dozen studies have used voxel-based morphometry (VBM) to investigate gray matter volume (GMV) differences between SRD and control children, however, recent meta-analyses suggest that few regions are consistent across studies. We used data collected across three countries (France, Poland, and Germany) with the aim of both increasing sample size (236 SRD and controls) to obtain a clearer picture of group differences, and of further assessing the consistency of the findings across languages. VBM analysis reveals a significant group difference in a single cluster in the left thalamus. Furthermore, we observe correlations between reading accuracy and GMV in the left supramarginal gyrus and in the left cerebellum, in controls only. Most strikingly, we fail to replicate all the group differences in GMV reported in previous studies, despite the superior statistical power. The main limitation of this study is the heterogeneity of the sample drawn from different countries (i.e., speaking languages with varying orthographic transparencies) and selected based on different assessment batteries. Nevertheless, analyses within each country support the conclusions of the cross-linguistic analysis. Explanations for the discrepancy between the present and previous studies may include: (1) the limited suitability of VBM to reveal the subtle brain disruptions underlying SRD; (2) insufficient correction for multiple statistical tests and flexibility in data analysis, and (3) publication bias in favor of positive results. Thus the study echoes widespread concerns about the risk of false-positive results inherent to small-scale VBM studies.

Distinguishing cause from effect – many deficits associated with developmental dyslexia may be a consequence of reduced and suboptimal reading experience

“…A number of cross-sectional MRI studies with adults and children have suggested that functional deficits and structural disruptions of the thalamus are a cause of developmental dyslexia (Diaz, Hintz, Kiebel, & von Kriegstein, 2012;Jednoróg et al, 2015;Livingstone, Rosen, Drislane, & Galaburda, 1991;Preston et al, 2010). Skeide et al (2017) recently combined a controlled longitudinal reading intervention with resting-state fMRI in a sample of 30 illiterate Indian adults.…”

Individual differences in decoding skill, print exposure, and cortical structure in young adults

“…There is some evidence that the structural relation to decoding skill that was observed in children (Jednoróg et al, 2015) may also be present in young adults. In one study, brain morphology was related to group differences in college students (Welcome, Chiarello, Thompson, & Sowell, 2011).…”

“…In a recent example (Jednoróg et al, 2015), two such skills were assessed: rapid automatized naming (RAN) and decoding. The goal was to assess potential differences in grey matter volume (GMV) between two large groups of children, one with developmental dyslexia (n=130), and the other an age-matched control group of unimpaired readers (n=106).…”

“…The approach adopted by Jednoróg and colleagues (2015) is in some respects emblematic of most investigations of grey matter morphology and reading ability. Such studies are often primarily concerned with describing some specific group of interest (e.g., dyslexic or bilingual readers).…”

“…Both assessed reading comprehension, but Goldman and Manis did not examine the right hemisphere, precluding confirmation of differences in hemispheric asymmetry found by Welcome and colleagues. The populations tested differ in each study, with two using convenience samples of college students (age 18-24, Goldman & Manis; age 18-34, Welcome et al), one using pre-collegiate adolescents (age 12-16, Lee et al, 2007), one using children from three different nations (age 8-13, Jednoróg et al, 2015), and one using a wide spectrum of ages (age 7-11, n=9; age 12-17, n=17; age 21-72, n=22; Richardson et al, 2010). These sampling differences are non-trivial for comparing the results of these studies, as there is ample evidence for neurodevelopmental structural changes across these age groups (Giedd et al, 1999; Gogtay et al, 2004; Lu et al, 2009; Paus, 2005; Salat et al, 2004).…”