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Frontiers in Human Neuroscience 2024Slow cortical oscillations play a crucial role in processing the speech amplitude envelope, which is perceived atypically by children with developmental dyslexia. Here...
Slow cortical oscillations play a crucial role in processing the speech amplitude envelope, which is perceived atypically by children with developmental dyslexia. Here we use electroencephalography (EEG) recorded during natural speech listening to identify neural processing patterns involving slow oscillations that may characterize children with dyslexia. In a story listening paradigm, we find that atypical power dynamics and phase-amplitude coupling between delta and theta oscillations characterize dyslexic versus other child control groups (typically-developing controls, other language disorder controls). We further isolate EEG common spatial patterns (CSP) during speech listening across delta and theta oscillations that identify dyslexic children. A linear classifier using four delta-band CSP variables predicted dyslexia status (0.77 AUC). Crucially, these spatial patterns also identified children with dyslexia when applied to EEG measured during a rhythmic syllable processing task. This transfer effect (i.e., the ability to use neural features derived from a story listening task as input features to a classifier based on a rhythmic syllable task) is consistent with a core developmental deficit in neural processing of speech rhythm. The findings are suggestive of distinct atypical neurocognitive speech encoding mechanisms underlying dyslexia, which could be targeted by novel interventions.
PubMed: 38911229
DOI: 10.3389/fnhum.2024.1403677 -
JMIR Public Health and Surveillance Jun 2024Delay in the diagnosis of neurodevelopmental disorders (NDDs) in toddlers and postnatal depression (PND) is a major public health issue. In both cases, early... (Observational Study)
Observational Study
BACKGROUND
Delay in the diagnosis of neurodevelopmental disorders (NDDs) in toddlers and postnatal depression (PND) is a major public health issue. In both cases, early intervention is crucial but too rarely implemented in practice.
OBJECTIVE
Our goal was to determine if a dedicated mobile app can improve screening of 5 NDDs (autism spectrum disorder [ASD], language delay, dyspraxia, dyslexia, and attention-deficit/hyperactivity disorder [ADHD]) and reduce PND incidence.
METHODS
We performed an observational, cross-sectional, data-based study in a population of young parents in France with at least 1 child aged <10 years at the time of inclusion and regularly using Malo, an "all-in-one" multidomain digital health record electronic patient-reported outcome (PRO) app for smartphones. We included the first 50,000 users matching the criteria and agreeing to participate between May 1, 2022, and February 8, 2024. Parents received periodic questionnaires assessing skills in neurodevelopment domains via the app. Mothers accessed a support program to prevent PND and were requested to answer regular PND questionnaires. When any PROs matched predefined criteria, an in-app recommendation was sent to book an appointment with a family physician or pediatrician. The main outcomes were the median age of the infant at the time of notification for possible NDD and the incidence of PND detection after childbirth. One secondary outcome was the relevance of the NDD notification by consultation as assessed by health professionals.
RESULTS
Among 55,618 children median age 4 months (IQR 9), 439 (0.8%) had at least 1 disorder for which consultation was critically necessary. The median ages of notification for probable ASD, language delay, dyspraxia, dyslexia, and ADHD were 32.5 (IQR 12.8), 16 (IQR 13), 36 (IQR 22.5), 80 (IQR 5), and 61 (IQR 15.5) months, respectively. The rate of probable ADHD, ASD, dyslexia, language delay, and dyspraxia in the population of children of the age included between the detection limits of each alert was 1.48%, 0.21%, 1.52%, 0.91%, and 0.37%, respectively. Sensitivity of alert notifications for suspected NDDs as assessed by the physicians was 78.6% and specificity was 98.2%. Among 8243 mothers who completed a PND questionnaire, highly probable PND was detected in 938 (11.4%), corresponding to a reduction of -31% versus our previous study without a support program. Suspected PND was detected a median 96 days (IQR 86) after childbirth. Among 130 users who filled in the satisfaction survey, 99.2% (129/130) found the app easy to use and 70% (91/130) reported that the app improved follow-up of their child. The app was rated 4.8/5 on Apple's App Store.
CONCLUSIONS
Algorithm-based early alerts suggesting NDDs were highly specific with good sensitivity as assessed by real-life practitioners. Early detection of 5 NDDs and PNDs was efficient and led to a possible 31% reduction in PND incidence.
TRIAL REGISTRATION
ClinicalTrials.gov NCT06301087; https://www.clinicaltrials.gov/study/NCT06301087.
Topics: Humans; Cross-Sectional Studies; Female; Mobile Applications; Neurodevelopmental Disorders; Early Diagnosis; Male; Child, Preschool; Child; Depression, Postpartum; Infant; France; Adult; Surveys and Questionnaires
PubMed: 38888952
DOI: 10.2196/58565 -
Data in Brief Jun 2024This report presents a dataset of offline handwriting samples among Malaysian schoolchildren with potential dysgraphia. The images contained Malay sentences written by...
This report presents a dataset of offline handwriting samples among Malaysian schoolchildren with potential dysgraphia. The images contained Malay sentences written by primary school students and children under intervention by the Malaysia Dyslexia Association (PDM). Students were expected to copy and write the sentences provided on the paper form that was used to gather data. Students were required to write three sets of sentences. The paper was digitalized by scanning it and converting it into digital form. Furthermore, the images were pre-processed using image processing techniques by converting the images into binary format and interchanging the foreground and background colors. The images were then classified into two categories, namely potential dysgraphia and low potential dysgraphia. The dataset comprised a total of 249 handwriting images, obtained from a sample of 83 participants who were selected in the data collection process, with 114 for potential dysgraphia and 135 for low potential dysgraphia. Both categories of handwriting images were prepared in black and white images.
PubMed: 38868380
DOI: 10.1016/j.dib.2024.110534 -
CoDAS 2024To develop on intervention process to identify children at risk of dyslexia, based on the Response to Intervention model. Specifically, to identify the pattern of...
PURPOSE
To develop on intervention process to identify children at risk of dyslexia, based on the Response to Intervention model. Specifically, to identify the pattern of changes in post-intervention performance in tasks of phonological awareness, working memory, lexical access, reading and writing; and to analyze which cognitive functions had a significant effect on the discriminating students at risk of dyslexia.
METHOD
Sample of 30 participants with Reading and writing difficulties, aged 8-11, from public/private schools, students from 3rd to 5th grade. Participants were submitted to a battery of cognitive-linguistic tests, before and after 12 intervention sessions. To monitor their performance, five reading and writing lists of words and pseudowords were applied. We qualitatively and quantitatively analyzed the differences in pre- and post-intervention performance of each participant; and among participants in the post-assessment, to understand the patterns of dyslexia vs non-dyslexia groups.
RESULTS
There were statistically significant changes in: rapid automatized naming, narrative text comprehension, phonological awareness, rate and typology of hits/misses in reading and writing, and reading speed. Being the last three variables the most sensitive to discriminate the two groups, all with less post-intervention gains for the dyslexia group.
CONCLUSIONS
The intervention focused on the stimulation of phonological skills and explicit and systematic teaching of graphophonemic correspondences contributed positively to the evolution of the group's participants. The intervention response approach favored the identification of children with a profile at risk for dyslexia, as distinct from children with other learning difficulties.
Topics: Humans; Dyslexia; Child; Female; Male; Reading; Language Tests; Writing; Risk Factors; Phonetics; Memory, Short-Term
PubMed: 38865500
DOI: 10.1590/2317-1782/20242023031pt -
Research in Developmental Disabilities Jun 2024Visual search problems are often reported in children with Cerebral Visual Impairment (CVI). To tackle the clinical challenge of objectively differentiating CVI from...
Visual search problems are often reported in children with Cerebral Visual Impairment (CVI). To tackle the clinical challenge of objectively differentiating CVI from other neurodevelopmental disorders, we developed a novel test battery. Visual search tasks were coupled with verbal and gaze-based measurements. Two search tasks were performed by children with CVI (n: 22; mean age (SD): 9.63 (.46) years) ADHD (n: 32; mean age (SD): 10.51 (.25) years), dyslexia (n: 28; mean age (SD): 10.29 (.20) years) and neurotypical development (n: 44; mean age (SD): 9.30 (.30) years). Children with CVI had more impaired search performance compared to all other groups, especially in crowded and unstructured displays and even when they had normal visual acuity. In-depth gaze-based analyses revealed that this group searched in overall larger areas and needed more time to recognize a target, particularly after their initial fixation on the target. Our gaze-based approach to visual search offers new insights into the distinct search patterns and behaviours of children with CVI. Their tendency to overlook targets whilst fixating on it, point towards higher-order visual function (HOVF) deficits. The novel method is feasible, valid, and promising for clinical differential-diagnostic evaluation between CVI, ADHD and dyslexia, and for informing individualized training.
PubMed: 38861794
DOI: 10.1016/j.ridd.2024.104767 -
Clinical Neurophysiology : Official... Jun 2024We longitudinally investigated whether infant P1 and N2 ERPs recorded in newborns and at 28 months could predict pre-reading skills at 28 months and 4-5 years.
OBJECTIVE
We longitudinally investigated whether infant P1 and N2 ERPs recorded in newborns and at 28 months could predict pre-reading skills at 28 months and 4-5 years.
METHODS
We recorded ERPs to a pseudoword in newborns and at 28 months in a sample over-represented by infants with familial dyslexia risk. Using multiple linear regression models, we examined P1 and N2 associations with pre-reading skills at 28 months and 4-5 years.
RESULTS
Shorter latencies of the newborn P1 predicted faster serial naming at 28 months. Larger amplitudes and shorter latencies of P1 at 28 months predicted better serial naming abilities and auditory working memory across the pre-reading stage. Right-lateralized P1 and N2 were related to poorer pre-reading skills.
CONCLUSIONS
Infant ERPs, particularly P1, providing information about neural speech encoding abilities, are associated with pre-reading skill development.
SIGNIFICANCE
Infant and early childhood neural speech encoding abilities may work as early predictive markers of reading development and impairment. This study may help to plan early interventions targeting phonological processing to prevent or ameliorate learning deficits.
PubMed: 38852433
DOI: 10.1016/j.clinph.2024.05.016 -
Neurobiology of Language (Cambridge,... 2024Early childhood is a critical period for structural brain development as well as an important window for the identification and remediation of reading difficulties....
Early childhood is a critical period for structural brain development as well as an important window for the identification and remediation of reading difficulties. Recent research supports the implementation of interventions in at-risk populations as early as kindergarten or first grade, yet the neurocognitive mechanisms following such interventions remain understudied. To address this, we investigated cortical structure by means of anatomical MRI before and after a 12-week tablet-based intervention in: (1) at-risk children receiving phonics-based training ( = 29; = 16 complete pre-post datasets), (2) at-risk children engaging with AC training ( = 24; = 15 complete pre-post datasets) and (3) typically developing children ( = 25; = 14 complete pre-post datasets) receiving no intervention. At baseline, we found higher surface area of the right supramarginal gyrus in at-risk children compared to typically developing peers, extending previous evidence that early anatomical differences exist in children who may later develop dyslexia. Our longitudinal analysis revealed significant post-intervention thickening of the left supramarginal gyrus, present exclusively in the intervention group but not the active control or typical control groups. Altogether, this study contributes new knowledge to our understanding of the brain morphology associated with cognitive risk for dyslexia and response to early intervention, which in turn raises new questions on how early anatomy and plasticity may shape the trajectories of long-term literacy development.
PubMed: 38832361
DOI: 10.1162/nol_a_00122 -
JCPP Advances Jun 2024The concept of neurodiversity draws upon scientific research, and lessons from practice and lived experience to suggest new ways of thinking about neurodevelopmental...
BACKGROUND
The concept of neurodiversity draws upon scientific research, and lessons from practice and lived experience to suggest new ways of thinking about neurodevelopmental conditions. Among the formative observations are that characteristics associated with neurodevelopmental conditions are part of a "broader phenotype" of variation across the whole population, and that there appear to be "transdiagnostic" similarities as well as differences in these characteristics. These observations raise important questions that have implications for understanding diversity in neurodevelopmental conditions and in neurocognitive phenotypes across the whole population.
METHOD
The present work examines broader phenotypes using seven widely used self-report assessments of traits associated with autism, ADHD, dyslexia, Developmental Coordination Disorder/dyspraxia, tic disorders/Tourette's, cortical hyperexcitability associated with subclinical epilepsy, and sensory sensitivities. A representative sample of 995 adults (aged 17-77) in the UK completed self-report measures of neurodiversity, wellbeing, generalized anxiety, and depression, and cognitive abilities (nonverbal intelligence and executive functioning).
RESULTS
We used confirmatory factor analysis to test whether variation and covariation was better characterized (1) by traditional diagnostic labels, or (2) transdiagnostically according to similarities in functions, behaviours, or phenomena. Results indicated that neurodiversity characteristics were best explained using a bifactor model with one general "N" factor and four condition-specific factors.
CONCLUSION
This was the largest examination to date of the factor structure of broader phenotypes relevant to neurodevelopmental conditions. It provides critical benchmark data, and a framework approach for asking systematic questions about the structure of neurocognitive diversities seen in the whole population and in people with one or more diagnoses.
PubMed: 38827989
DOI: 10.1002/jcv2.12219 -
NPJ Science of Learning May 2024Developmental dyslexia (DD) is defined as difficulties in learning to read even with normal intelligence and adequate educational guidance. Deficits in implicit sequence...
Developmental dyslexia (DD) is defined as difficulties in learning to read even with normal intelligence and adequate educational guidance. Deficits in implicit sequence learning (ISL) abilities have been reported in children with DD. We investigated brain plasticity in a group of 17 children with DD, compared with 18 typically developing (TD) children, after two sessions of training on a serial reaction time (SRT) task with a 24-h interval. Our outcome measures for the task were: a sequence-specific implicit learning measure (ISL), entailing implicit recognition and learning of sequential associations; and a general visuomotor skill learning measure (GSL). Gray matter volume (GMV) increased, and white matter volume (WMV) decreased from day 1 to day 2 in cerebellar areas regardless of group. A moderating effect of group was found on the correlation between WMV underlying the left precentral gyrus at day 2 and the change in ISL performance, suggesting the use of different underlying learning mechanisms in DD and TD children during the ISL task. Moreover, DD had larger WMV in the posterior thalamic radiation compared with TD, supporting previous reports of atypical development of this structure in DD. Further studies with larger sample sizes are warranted to validate these results.
PubMed: 38802367
DOI: 10.1038/s41539-024-00250-w -
Brain Sciences Apr 2024Handwriting difficulty is a defining feature of Chinese developmental dyslexia (DD) due to the complex structure and dense information contained within compound...
Handwriting difficulty is a defining feature of Chinese developmental dyslexia (DD) due to the complex structure and dense information contained within compound characters. Despite previous attempts to use deep neural network models to extract handwriting features, the temporal property of writing characters in sequential order during dictation tasks has been neglected. By combining transfer learning of convolutional neural network (CNN) and positional encoding with the temporal-sequential encoding of long short-term memory (LSTM) and attention mechanism, we trained and tested the model with handwriting images of 100,000 Chinese characters from 1064 children in Grades 2-6 (DD = 483; Typically Developing [TD] = 581). Using handwriting features only, the best model reached 83.2% accuracy, 79.2% sensitivity, 86.4% specificity, and 91.2% AUC. With grade information, the best model achieved 85.0% classification accuracy, 83.3% sensitivity, 86.4% specificity, and 89.7% AUC. These findings suggest the potential of utilizing machine learning technology to identify children at risk for dyslexia at an early age.
PubMed: 38790423
DOI: 10.3390/brainsci14050444