Reproducibility of Brain Responses: High for Speech Perception, Low for Reading Difficulties

Neuroscience findings have recently received critique on the lack of replications. To examine the reproducibility of brain indices of speech sound discrimination and their role in dyslexia, a specific reading difficulty, brain event-related potentials using EEG were measured using the same cross-linguistic passive oddball paradigm in about 200 dyslexics and 200 typically reading 8-12-year-old children from four countries with different native languages. Brain responses indexing speech and non-speech sound discrimination were extremely reproducible, supporting the validity and reliability of cognitive neuroscience methods. Significant differences between typical and dyslexic readers were found when examined separately in different country and language samples. However, reading group differences occurred at different time windows and for different stimulus types between the four countries. This finding draws attention to the limited generalizability of atypical brain response findings in children with dyslexia across language environments and raises questions about a common neurobiological factor for dyslexia. Our results thus show the robustness of neuroscience methods in general while highlighting the need for multi-sample studies in the brain research of language disorders

Genome-wide association study reveals new insights into the heritability and genetic correlates of developmental dyslexia

Developmental dyslexia (DD) is a learning disorder affecting the ability to read, with a heritability of 40-60%. A notable part of this heritability remains unexplained, and large genetic studies are warranted to identify new susceptibility genes and clarify the genetic bases of dyslexia. We carried out a genome-wide association study (GWAS) on 2274 dyslexia cases and 6272 controls, testing associations at the single variant, gene, and pathway level, and estimating heritability using single-nucleotide polymorphism (SNP) data. We also calculated polygenic scores (PGSs) based on large-scale GWAS data for different neuropsychiatric disorders and cortical brain measures, educational attainment, and fluid intelligence, testing them for association with dyslexia status in our sample. We observed statistically significant (p <2.8 x 10(-6)) enrichment of associations at the gene level, forLOC388780(20p13; uncharacterized gene), and forVEPH1(3q25), a gene implicated in brain development. We estimated an SNP-based heritability of 20-25% for DD, and observed significant associations of dyslexia risk with PGSs for attention deficit hyperactivity disorder (atp(T) = 0.05 in the training GWAS: OR = 1.23[1.16; 1.30] per standard deviation increase;p = 8 x 10(-13)), bipolar disorder (1.53[1.44; 1.63];p = 1 x 10(-43)), schizophrenia (1.36[1.28; 1.45];p = 4 x 10(-22)), psychiatric cross-disorder susceptibility (1.23[1.16; 1.30];p = 3 x 10(-12)), cortical thickness of the transverse temporal gyrus (0.90[0.86; 0.96];p = 5 x 10(-4)), educational attainment (0.86[0.82; 0.91];p = 2 x 10(-7)), and intelligence (0.72[0.68; 0.76];p = 9 x 10(-29)). This study suggests an important contribution of common genetic variants to dyslexia risk, and novel genomic overlaps with psychiatric conditions like bipolar disorder, schizophrenia, and cross-disorder susceptibility. Moreover, it revealed the presence of shared genetic foundations with a neural correlate previously implicated in dyslexia by neuroimaging evidence.Peer reviewe

Hypothesis-driven genome-wide association studies provide novel insights into genetics of reading disabilities

Peer reviewe

Genome-wide analyses of individual differences in quantitatively assessed reading- and language-related skills in up to 34,000 people

The use of spoken and written language is a fundamental human capacity. Individual differences in reading- and language-related skills are influenced by genetic variation, with twin-based heritability estimates of 30 to 80% depending on the trait. The genetic architecture is complex, heterogeneous, and multifactorial, but investigations of contributions of single-nucleotide polymorphisms (SNPs) were thus far underpowered. We present a multicohort genome-wide association study (GWAS) of five traits assessed individually using psychometric measures (word reading, nonword reading, spelling, phoneme awareness, and nonword repetition) in samples of 13,633 to 33,959 participants aged 5 to 26 y. We identified genome-wide significant association with word reading (rs11208009, P = 1.098 x 10(-8)) at a locus that has not been associated with intelligence or educational attainment. All five reading-/language-related traits showed robust SNP heritability, accounting for 13 to 26% of trait variability. Genomic structural equation modeling revealed a shared genetic factor explaining most of the variation in word/nonword reading, spelling, and phoneme awareness, which only partially overlapped with genetic variation contributing to nonword repetition, intelligence, and educational attainment. A multivariate GWAS of word/nonword reading, spelling, and phoneme awareness maximized power for follow-up investigation. Genetic correlation analysis with neuroimaging traits identified an association with the surface area of the banks of the left superior temporal sulcus, a brain region linked to the processing of spoken and written language. Heritability was enriched for genomic elements regulating gene expression in the fetal brain and in chromosomal regions that are depleted of Neanderthal variants. Together, these results provide avenues for deciphering the biological underpinnings of uniquely human traits.Peer reviewe

Specific, selective or preferential: comments on category specificity in neuroimaging

An important goal of functional neuroimaging has been to localize stimulus-specific processes in the brain. Numerous studies have revealed particular patterns of brain activity in different cortical areas in response to different object categories such as faces, body parts, places, words, letters and so forth. However, quite different patterns of activation have been given a similar interpretation in terms of category or domain specificity. Other characteristics than the response to the target category have sometimes been used to address whether a cortical brain area is functionally specialized for a given stimulus category, such as automatic processing [e.g. Joseph, J., Cerullo, M., Farley, A., Steinmetz, N., Mier, C., 2006. fMRI correlates of cortical specialization and generalization for letter processing. NeuroImage 32, 806–820] or assemblage [Haxby, J.V., Gobbini, M.I., Furey, M.L., Ishai, A., Schouten, J.L., Pietrini, P., 2001. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430]. Here we frame the debate around the notions of category specificity as defined by Fodor [Fodor, J., 1983. The modularity of Mind. MIT Press, Cambridge, MA., Fodor, J., 2001. The mind doesn’t work that way: the scope and limits of computational psychology “A Bradford book” MIT Press, Cambridge, MA] and argue that brain activation patterns consistent with category specificity remain to be demonstrated. We review possible alternatives and lay out the experimental conditions required for a conclusive demonstration of category-specific specialization in brain imaging studies

Feedback during variable training.

<p>Four examples of the histograms that provided visual feedback to the participants in the DIVIDED VARIABLE training group. The dark column represents the performance of alphabetical equation that was obtained under single-task baseline. The light column shows performance that was reached in the dual-task condition. The line represents the level of performance that was expected. (a; b) Examples of an 80% Equation trial where participants were asked to allocate 80% of their attention to the alphanumeric equation task: a) shows a trial where performance was below the expected threshold; (b) shows a trial where participants succeeded to obtain the expected level of performance. (c; d) Example of a 50% Equation trial where participants were asked to allocate 50% of their attention to the alphanumeric equation task: c) shows a trial where performance was below the expected threshold; (d) shows a trial where participants succeeded to obtain the expected level of performance.</p

Activations related to dual-tasking prior to training (pre-training session).

<p>Network of prefrontal activation in dual-task, with more emphasis on equation (80% Equation (80/20)) in <b>A</b> and <b>B</b>; equal division of attention (50% Equation (50/50)) in <b>C</b> and <b>D</b>; and more emphasis on detection (20% Equation (20/80)) in <b>E</b> and <b>F.</b> The threshold for display is P<0.001, uncorrected, 10 voxels. Coloured bar is representative of t scores mentioned in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102710#pone-0102710-t005" target="_blank">Table 5</a>. “L” denotes the left side of the brain, while “R” denotes the right side.</p

Mean age, mean education, and mean score on clinical measures (S.D. in parentheses).

<p>Mean age, mean education, and mean score on clinical measures (S.D. in parentheses).</p

Experimental paradigm.

<p>Schematic representation of the task conditions order in fMRI (A), as well as the alphanumeric equation and visual detection tasks in both single-task (B) and dual-task (C).</p

Activation-related to modulation of attention prior to training (pre-training session).

<p>Subtracting dual-task 80% Equation (80/20) from dual-task 20% Equation (20/80) involves activation in the left superior and medial frontal gyrus (A and B), and left superior temporal and left cingulate gyrus (B). The threshold for display is P<0.001, uncorrected, 10 voxels. Coloured bar is representative of t scores mentioned in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102710#pone-0102710-t005" target="_blank">table 5</a>. “L” denotes the left side of the brain, while “R” denotes the right side.</p