The emergence of dyslexia in the developing brain

Kuhl, Ulrike; Neef, Nicole E.; Kraft, Indra; Schaadt, Gesa; Dörr, Liane; Bräuer, Jens; Czepezauer, Ivonne; Müller, Bent; Wilcke, Arndt; Kirsten, Holger; Emmrich, Frank; Boltze, Johannes; Friederici, Angela D.; Skeide, Michael

doi:10.1016/j.neuroimage.2020.116633

Cited by 56 publications

(28 citation statements)

References 88 publications

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“…Looking ahead, most recent advances in the emerging field of neuroimaging genetics have revealed bilateral interplay between genetic profile and neurological function and sometimes even a trilateral interaction between the latter and neuroanatomy. These studies have primarily focused on dyslexia (189)(190)(191)(192), but similar evidence is also emerging for dyscalculia (193)(194)(195) and first evidence has appeared for their comorbidity (132). Although exhaustive review is beyond the scope and space of this paper (for very recent reviews see (193,196), our model and research synthesis is complementary and could be further probed with neuroimaging genetics, specifically, in the pursuit of precise early detection of comorbid reading and math disability well before they emerge in formal educational settings.…”

Section: Discussionmentioning

confidence: 98%

From Schools to Scans: A Neuroeducational Approach to Comorbid Math and Reading Disabilities

Grant

Siegel

2020

Front. Public Health

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We bridge two analogous concepts of comorbidity, dyslexia-dyscalculia and reading-mathematical disabilities, in neuroscience and education, respectively. We assessed the cognitive profiles of 360 individuals (mean age 25.79 ± 13.65) with disability in reading alone (RD group), mathematics alone (MD group) and both (comorbidity: MDRD group), with tests widely used in both psychoeducational and neuropsychological batteries. As expected, the MDRD group exhibited reading deficits like those shown by the RD group. The former group also exhibited deficits in quantitative reasoning like those shown by the MD group. However, other deficits related to verbal working memory and semantic memory were exclusive to the MDRD group. These findings were independent of gender, age, or socioeconomic and demographic factors. Through a systematic exhaustive review of clinical neuroimaging literature, we mapped the resulting cognitive profiles to correspondingly plausible neuroanatomical substrates of dyslexia and dyscalculia. In our resulting "probing" model, the complex set of domain-specific and domain-general impairments shown in the comorbidity of reading and mathematical disabilities are hypothesized as being related to atypical development of the left angular gyrus. The present neuroeducational approach bridges a long-standing transdisciplinary divide and contributes a step further toward improved early prediction, teaching and interventions for children and adults with combined reading and math disabilities.

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Section: Discussionmentioning

confidence: 98%

From Schools to Scans: A Neuroeducational Approach to Comorbid Math and Reading Disabilities

Grant

Siegel

2020

Front. Public Health

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“…Functional and structural changes, such as white matter differences in the left arcuate fasciculus connecting temporoparietal regions with premotor and inferior frontal areas, were detected in children at family risk of developing dyslexia. Such changes have been related to the onset of dyslexia; since they are developed in early childhood, they are detectable before they begin learning to read and write 23 . On the other hand, changes in neural pathway recruitment, identified in imaging examinations, were observed after intervention based on rapid-naming training, with improved oral language and reading fluency 24 .…”

Section: Discussionmentioning

confidence: 99%

Early screening of reading and writing difficulties in the first grade - a pilot study

Fritsch¹,

Silva

Sanchez³

2021

Rev. CEFAC

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Purpose: to investigate the effectiveness of an early identification screening based on the Dynamic Indicators of Basic Early Literacy Skills (DIBELS) test, 6th edition, to early identify first graders at risk of dyslexia. Methods: the sample comprised 34 children assessed at two moments - the screening was conducted while they were in the first grade, while a word dictation was used when they were in the second grade. The data were analyzed with Pearson’s correlation test, linear regression analysis, and ANOVA, with a p-value ≤ 0.05. Results: the results provide evidence that agrees with other studies in that children with signs of risk of dyslexia differ from children with typical development in terms of less phonological skills and that these differences are already present at the beginning of the literacy process. Conclusion: the early screening allows for the identification of the risk factors for dyslexia, making it possible to implement preventive and personalized phonological skills training to make the literacy process easier.

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“…Auditory deficit, otherwise called hearing impairment, manifests when there is impairment in central auditory nervous system responsible for hearing leading to difficulty in sensing, processing, and understanding sound signals. Recent study by Kuhl et al [13] has shown that dyslexia can be predicted through auditory cortex gyrification and abnormal neural connectivity within the speech processing system before a child begins to learn how to read and write. Summarily, these four elements constitute the basis upon which theories of developmental dyslexia are formulated.…”

Section: Introductionmentioning

confidence: 99%

Advance Machine Learning Methods for Dyslexia Biomarker Detection: A Review of Implementation Details and Challenges

et al. 2021

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Dyslexia is a neurological disorder that is characterized by imprecise comprehension of words and generally poor reading performance. It affects a significant population of school-age children, with more occurrences in males, thus, putting them at risk of poor academic performance and low selfesteem for a lifetime. The long-term hope is to have a dyslexia diagnostic method that is informed by neural-biomarkers. In this regard, large numbers of machine learning methods and, more recently, deep learning methods have been implemented across various types of dataset with the above-chance classification accuracy. However, attainment of clinical acceptability of these state-of-the-art methods is bedeviled by certain challenges including lack of biologically-interpretable biomarkers, privacy of dataset and classifiers, hyper-parameter selection/optimization, and overfitting problem among others. This review paper critically analyzes recent machine learning methods for detecting dyslexia and its biomarkers and discusses challenges that require proper attentions from the users of deep learning methods in order to enable them to attain clinically relevance and acceptable level. The review is conducted within the premise of implementation and experimental outcomes for each of the 22 selected articles using the Preferred Reporting Items for Systematic review and Meta-Analyses (PRISMA) protocol, with a view to outlining some critical challenges for achieving high accuracy and reliability of the state-of-the-art machine learning methods. As an evidence-based protocol for reporting in systematic reviews and meta-analyses, PRISMA helps to ensure clarity and transparency of this paper by showing a four-phase flow diagram of the selection process for articles used in this review. It is therefore envisaged that higher classification performance of clinical relevance can be achieved using deep learning models for dyslexia and its biomarkers by addressing identified potential challenges.

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The emergence of dyslexia in the developing brain

Cited by 56 publications

References 88 publications

From Schools to Scans: A Neuroeducational Approach to Comorbid Math and Reading Disabilities

From Schools to Scans: A Neuroeducational Approach to Comorbid Math and Reading Disabilities

Early screening of reading and writing difficulties in the first grade - a pilot study

Advance Machine Learning Methods for Dyslexia Biomarker Detection: A Review of Implementation Details and Challenges

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