Cortical tracking of speech in noise accounts for reading strategies in children

Abstract

Humans' propensity to acquire literacy relates to several factors, including the ability to understand speech in noise (SiN). Still, the nature of the relation between reading and SiN perception abilities remains poorly understood. Here, we dissect the interplay between (1) reading abilities, (2) classical behavioral predictors of reading (phonological awareness, phonological memory, and rapid automatized naming), and (3) electrophysiological markers of SiN perception in 99 elementary school children (26 with dyslexia). We demonstrate that, in typical readers, cortical representation of the phrasal content of SiN relates to the degree of development of the lexical (but not sublexical) reading strategy. In contrast, classical behavioral predictors of reading abilities and the ability to benefit from visual speech to represent the syllabic content of SiN account for global reading performance (i.e., speed and accuracy of lexical and sublexical reading). In individuals with dyslexia, we found preserved integration of visual speech information to optimize processing of syntactic information but not to sustain acoustic/phonemic processing. Finally, within children with dyslexia, measures of cortical representation of the phrasal content of SiN were negatively related to reading speed and positively related to the compromise between reading precision and reading speed, potentially owing to compensatory attentional mechanisms. These results clarify the nature of the relation between SiN perception and reading abilities in typical child readers and children with dyslexia and identify novel electrophysiological markers of emergent literacy.

The role of reading experience in atypical cortical tracking of speech and speech-in-noise in dyslexia

“…Table 1 shows that all groups had similar IQ and socio-economic status, that dyslexic readers compared with age-matched controls had about the same age and lower reading scores, and that dyslexic readers compared with reading-level-matched controls were older and had similar reading scores. A previous analysis of the reading scores also revealed that our dyslexic readers had a rather homogenous reading profile, characterized by similar reading difficulties in the two reading pathways (Destoky et al 2020).…”

“…Finally, concerning the severity of the reading deficit of the included children with dyslexia, the phonological deficit is more commonly seen in severe than mild dyslexia (Saksida et al, 2016). Also along this line, reading abilities correlate with some aspects of CTS in noisy conditions (Destoky et al, 2020). Yet, none of the previous studies assessing CTS in dyslexia (Di Liberto et al, 2018a;Lizarazu et al, 2021a;Molinaro et al, 2016a;Power et al, 2016a) considered the possibility that CTS is altered only in the most severe form of dyslexia.…”

“…Seventy-eight children enrolled in elementary school were included in this study: 26 children with a formal diagnosis of dyslexia (Dys; mean ± SD age, 10.2 ± 1.1 years; 17 females), 26 typical readers matched for age (Ctrl-Age; 10.0 ± 1.0 years; 13 females), and 26 younger children matched for reading level (Ctrl-Read; 7.8 ± 0.6; 11 females). A previous study of our group has already reported on this sample, and on the outcome of the comprehensive neuropsychological evaluation they underwent (Destoky et al, 2020).…”

“…As a preliminary step to test our working hypothesis that CTS is modulated by the severity of the reading deficit in dyslexia, we partitioned the Dys group into two subgroups maximally differing on their reading abilities. To do so, the 5 reading scores (see Table 1) were first corrected for age, time spent at school and IQ as done in our previous study (Destoky et al, 2020), and further standardized. We then used the k-means clustering algorithm implemented in MATLAB to identify the 2 subgroups.…”

“…Most importantly, they did not differ significantly on their age (t24 = 0.86, p = 0.40) or socio-economic level (t23 = -0.13, p = 0.90). Figure 1 illustrates the time-course of the video stimuli, which were exactly the same as in a previous study from our group (for more details, see Destoky et al 2020). Video stimuli were derived from 12 audiovisual recordings of four native French speaking narrators (two females, three recordings per narrator) telling a story for approximately 6 min (mean ± SD, 6.0 ± 0.8 min).…”