Tracking reading skills and reading-related skills in dyslexia before (age 5) and after (ages 10-17) diagnosis.

This study had three goals: to examine the stability of deficits in the phonological and lexical routes in dyslexia (group study), to determine the prevalence of dyslexia profiles (multiple-case study), and to identify the prediction of phonemic segmentation and discrimination skills before reading acquisition on future reading level. Among a group of 373 non-readers seen at age 5, 38 students were subsequently diagnosed as either consistent dyslexic readers (18 DYS) or consistent typical readers (20 TR). Their phonological and lexical reading skills were assessed at ages 10 and 17 and their phonemic segmentation and discrimination skills at age 5. In comparison with TR of the same chronological age (CA-TR), individuals with dyslexia demonstrated an impairment of the two reading routes, especially of the phonological reading route. In the comparison with younger TR (age 10) of the same reading level (RL-TR), only a deficit of the phonological route is observed. In the multiple-case study, the comparisons with CA-TR showed a prevalence of mixed profiles and very few dissociated profiles, whereas the comparison with RL-TR resulted mostly in two profiles depending on the measure: a phonological profile when accuracy was used and a delayed profile when speed was used. In addition, the correlations between early phonemic segmentation and discrimination skills (age 5) and later reading skills (age 17) were significant, and in the group of individuals with dyslexia, early phonemic segmentation skills significantly predicted these later reading skills. Phonological reading deficits are persistent and mainly caused by early phonemic impairments.

The assignment and distribution of the dyslexia label: Using the UK Millennium Cohort Study to investigate the socio-demographic predictors of the dyslexia label in England and Wales.

The prevalence of dyslexia identification has increased significantly over the last two decades. Yet there is debate over whether there are distinct biological and cognitive differences between those with literacy difficulties and the subgroup of people identified as dyslexic. This is the first paper that provides evidence for this ongoing debate by investigating the socio-demographic factors, outside biology and cognition, that predict whether a child is identified as dyslexic in the UK. Using secondary data from the UK's Millennium Cohort Study, this paper examines the socio-demographic factors that predict whether a child's teacher identifies them as dyslexic at age 11. Gender, season of birth, socio-economic class and parental income are found to be significant predictors of the dyslexia label. Therefore, factors seemingly unrelated to the clinical aspects of dyslexia influence whether a child is identified as dyslexic in England and Wales. This suggests that label may not be evenly distributed across a population; furthermore, it may also indicate that resources for support may not be fairly allocated. The findings further support the argument that a 'dyslexic sub-group' within poor readers is created due to the impact of environmental factors. The results from this national-scale study thus questions the reliability, validity and moral integrity of the allocation of the dyslexia label across current education systems in the UK.

Geração de dados sintéticos para classificação de disléxicos por meio de aprendizado de máquina / Synthetic data generation for classification of dyslexics by machine learning / Generación de datos sintéticos para clasificación de disléxicos mediante aprendizaje automático

Objetivo: Este estudo pretende aplicar a técnica de geração de dados sintéticos com auxílio de técnicas de limpeza de dados para a classificação de disléxicos e não - disléxicos. Método: Os outliers foram selecionados por especialista. Foi feito uma geração sintética de dados. para cada um de cinco algoritmos foram selecionados características com busca exaustiva. Cada algoritmo foi executado com as características selecionadas e então suas curvas de calibração foram comparadas. Resultados: A regressão logística se destacou como o melhor algoritmo, apresentando o resultado de 99% de acurácia e área sob a curva ROC de 0,999, além de ter obtido a melhor curva de calibração Conclusão: O uso da geração sintética de dados e seleção de características foram capazes de fazer todos os algoritmos avaliados obterem ótimos resultados na classificação de disléxicos e não disléxicos. A regressão logística foi selecionado como melhor algoritmo para classificação de disléxicos.

Objective: This study aims to apply the synthetic data generation technique with the aid of data cleaning techniques for the classification of dyslexics and non - dyslexics. Method: Outliers were selected by specialist. Synthetic of data Generated. For each of five algorithms, characteristics were selected with exhaustive search. Each algorithm was executed with the selected characteristics and then their calibration curves were compared. Results: Logistic regression presented the best results with 99% accuracy and area under the ROC curve of 0.999, besides obtaining the best calibration curve. Conclusion: The use of synthetic data generation and feature selection were able to make all algorithms achieve excellent results in the classification of dyslexic and non - dyslexic. Logistic regression was selected as the best algorithm for dyslexic classification.

Objetivo: Este estudio tiene como objetivo aplicar la técnica de generación de datos sintéticos con la ayuda de técnicas de limpieza de datos para la clasificación de disléxicos y no disléxicos. Método: los valores atípicos fueron seleccionados por especialistas. Se realizó una generación sintética de datos. Para cada uno de los cinco algoritmos, se seleccionaron características con búsqueda exhaustiva. Cada algoritmo se ejecutó con las características seleccionadas y luego se compararon sus curvas de calibración. Resultados: La regresión logística se destacó como el mejor algoritmo, presentando el resultado del 99% de precisión y área bajo la curva ROC de 0.999, además de obtener la mejor curva de calibración. Conclusión: El uso de la generación de datos sintéticos y la selección de Estas características lograron que todos los algoritmos evaluados obtuvieron excelentes resultados en la clasificación de disléxicos y no disléxicos. Se seleccionó la regresión logística como el mejor algoritmo para la clasificación disléxica.

Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism.

Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heterogeneous sources with inconsistent scanner settings. This study presents a method of improving the biological interpretation of dyslexia's neural-biomarkers from MRI datasets sourced from publicly available open databases. The proposed system utilized a modified histogram normalization (MHN) method to improve dyslexia neural-biomarker interpretations by mapping the pixels' intensities of low-quality input neuroimages to range between the low-intensity region of interest (ROIlow) and high-intensity region of interest (ROIhigh) of the high-quality image. This was achieved after initial image smoothing using the Gaussian filter method with an isotropic kernel of size 4mm. The performance of the proposed smoothing and normalization methods was evaluated based on three image post-processing experiments: ROI segmentation, gray matter (GM) tissues volume estimations, and deep learning (DL) classifications using Computational Anatomy Toolbox (CAT12) and pre-trained models in a MATLAB working environment. The three experiments were preceded by some pre-processing tasks such as image resizing, labelling, patching, and non-rigid registration. Our results showed that the best smoothing was achieved at a scale value, σ = 1.25 with a 0.9% increment in the peak-signal-to-noise ratio (PSNR). Results from the three image post-processing experiments confirmed the efficacy of the proposed methods. Evidence emanating from our analysis showed that using the proposed MHN and Gaussian smoothing methods can improve comparability of image features and neural-biomarkers of dyslexia with a statistically significantly high disc similarity coefficient (DSC) index, low mean square error (MSE), and improved tissue volume estimations. After 10 repeated 10-fold cross-validation, the highest accuracy achieved by DL models is 94.7% at a 95% confidence interval (CI) level. Finally, our finding confirmed that the proposed MHN method significantly outperformed the normalization method of the state-of-the-art histogram matching.

Exploring the phonological profiles of children with reading difficulties: A multiple case study.

Studies of group differences have established that the phonological profiles of people with reading difficulties contain both strengths and weaknesses. The current study extends this work by exploring individual differences in phonological ability using a multiple case study approach. A heterogeneous sample of 56 children (M age = 9 years) with reading difficulties completed a battery of tasks measuring literacy, phonological processing, expressive vocabulary and general ability. The phonological tasks included measures of phonological awareness (PA), phonological memory (PM), and rapid naming (RAN). A majority-although not all-of the children had phonological processing impairments. However, there was also substantial variability in the nature of children's phonological difficulties. While multiple impairments encompassing two or more phonological domains were most common, impairments that were specific to PA, PM or RAN also occurred frequently. Even within the domain of PA, where children completed three well-matched tasks, individual children were rarely impaired across all three measures and a number of different profiles were observed. Additional, group-level analyses indicated that PA was a significant predictor of decoding while RAN was a significant predictor of automatic word recognition and comprehension. Findings are discussed with reference to conceptual models of phonological processing and implications for assessment.

The Critical Role of Instructional Response for Identifying Dyslexia and Other Learning Disabilities.

This article addresses the nature of dyslexia and best practices for identification and treatment within the context of multitier systems of support (MTSS). We initially review proposed definitions of dyslexia to identify key commonalities and differences in proposed attributes. We then review empirical evidence for proposed definitional attributes, focusing on key sources of controversy, including the role of IQ, instructional response, as well as issues of etiology and immutability. We argue that current empirical evidence supports a dyslexia classification marked by specific deficits in reading and spelling words combined with inadequate response to evidence-based instruction. We then propose a "hybrid" dyslexia identification process built to gather data relevant to these markers of dyslexia. We argue that this assessment process is best implemented within school-wide MTSS because it leverages data routinely collected in well-implemented MTSS, including documentation of student progress and fidelity of implementation. In contrast with other proposed methods for learning disability (LD) identification, the proposed "hybrid" method demonstrates strong evidence for valid decision-making and directly informs intervention.

Skill Profiles of College Students With a History of Developmental Language Disorder and Developmental Dyslexia.

Developmental language disorder (DLD) and developmental dyslexia (DD) are two prevalent subtypes of Specific Learning Disabilities (SLDs; Diagnostic and Statistical Manual of Mental Disorders [5th ed.; DSM-5]). Yet, little information is available regarding the distinct challenges faced by adults with DLD and/or DD in college. The purpose of the present report is to characterize the relative strengths and challenges of college students with a history of DLD and/or DD, as this information is critical for providing appropriate institutional support. We examined the cognitive skill profiles of 352 college students (ages 18-35 years), using standardized and research-validated measures of reading, spoken language, nonverbal cognition, and self-reported childhood diagnostic history. We classified college students as having DLD (n = 50), and/or DD (n = 40), or as typically developed adults (n = 132) according to procedures described for adults with DLD and DD. A structural equation model determined the cognitive, language, and reading measures predicted by the classification group. Adults with DLD demonstrated poor verbal working memory and speeded sentence-level reading. Adults with DD primarily demonstrated deficits in phonology-based skills. These results indicate that adults with DLD and/or DD continue to face similar challenges as they did during childhood, and thus may benefit from differentially targeted accommodations in college.

Naming processes in reading and spelling disorders: An electrophysiological investigation.

OBJECTIVE: Reading fluency deficits characteristic for reading disorders (RD; F81.0) have been shown to be strongly associated with slow naming speed (e.g. in rapid automatized naming tasks). In contrast, children with an isolated spelling disorder in the context of unimpaired reading skills (iSD; F81.1) show naming speed task performances that are similar to typically developing (TD) children. However, the exact nature of the naming speed deficit and its relation to RD and the question whether children with iSD are also on the neurophysiological level similar to TD children is still unresolved. METHODS: The time-course and scalp topography of event-related potentials (ERP) activity recorded during a delayed digit-naming task was investigated in ten-year-old children with RD and iSD compared to a TD group. RESULTS: ERP activity differed between the RD and the TD group at around 300 ms after stimulus presentation (left occipito-temporal P2). In contrast, there were no neurophysiological differences between the TD and the iSD group. The P2 component correlated with behavioural performance on the RAN task. CONCLUSIONS: Slow naming speed in RD might result from a slowed-down access and prolonged processing of the word (lexical) form. SIGNIFICANCE: The study establishes a relation between neurophysiological processes of naming tasks and RD.

Diagnostic implications of the double deficit model for young adolescents with dyslexia.

Considerable support exists for both the phonological core deficit and the naming speed deficit models of dyslexia. The double deficit model proposed that many students with dyslexia might also be impaired in both underlying processes. Employing either performance thresholds (i.e., scores below the 16th or 25th percentile) or k-means clustering as classification methods, the current study investigated whether 154 young adolescents with dyslexia could be categorized into subtypes according to the presence or absence of phonological deficits alone, naming speed deficits alone, or a combination of the two and whether group composition changed depending on classification method. Results support the existence of both single and double deficit groups and confirm that those with both deficits are the most severely impaired across multiple measures. Contrary to previous research, most adolescents were classified as either naming speed only (about a third of the group) or double deficit when defining impairment using performance thresholds to classify groups. This may suggest that although early phonological deficits are amenable to remediation, identification of language symbols fails to become automatized in most individuals with dyslexia and may require more targeted intervention. Classification differences reported in the literature may depend on age and methods employed for classification.

Identification, Classification, and Treatment of Reading and Language Disabilities in Spanish-Speaking EL Students: Introduction to the Special Issue.

This article serves as an introduction to the special issue on Identification, Classification, and Treatment of Reading and Language Disabilities in Spanish-speaking English Learners. The article explains the driving forces behind the need for the special issue, the global nature of linguistic diversity, and provides an overview of the five papers that comprise the special issue.

What Reading Disability? Evidence for Multiple Latent Profiles of Struggling Readers in a Large Russian Sibpair Sample With at Least One Sibling at Risk for Reading Difficulties.

In this study, we performed a latent profile analysis of reading and related skills in a large ( n = 733) sibpair sample of Russian readers at risk for reading difficulties. The analysis suggested the presence of seven latent profiles, of which two were characterized by relatively high performance on measures of spelling and reading comprehension and the remaining five included severely as well as moderately affected readers with deficits in the domains of phonological, orthographic, and morphological processing. The results suggest that the development and manifestation of reading difficulties in Russian is mappable on a complex pattern of interactions between different types and severities of processing deficits. The results point to the psychological reality of multiple different suboptimal patterns of deficits in reading and reading-related skills and support the multifactorial view of the disorder, with intriguing implications for future neurobiological studies.

[A qualitative analysis of spelling mistakes and a systematic supportive learning instruction of spelling disorder]. / Zur qualitativen Rechtschreibfehleranalyse und einer schriftsystematischen lernförderlichen Behandlung der Rechtschreibstörung.

This paper explains how a qualitative analysis of spelling mistakes (Oldenburger Fehleranalyse, Thomé & Thomé, 2014) may be used to select learning materials according to individual needs. The pre-post design with control group serves to evaluate the effects of an intervention that is systematic and learning supportive for pupils with a diagnosed spelling disorder (ages 12 to 14; 6th-8th grade). Therapists of the experimental group were instructed to apply a series of linguistic and psycholinguistic criteria when creating the material for instruction and when carrying out the therapy. Therapists of the control group carried out the intervention without attending to these criteria, although they did have knowledge about the pupil's profile in spelling mistakes. The intervention included 20 sessions. The ANOVA shows improvement for both groups (HSP, May 2012): (F(1, 14) = 15,05, p = .002, η2 = .518). For the experimental group it is stronger, and the difference in achievement gain is significant (F(1, 14) = 4,70, p = .048; η2 = .25). These results support a combination of qualitative analysis and a high qualification for therapists that relates specifically to orthography and its instruction. For some pupils the changes in the qualitative profiles reveal persistent support requirements in phonology or grammar instruction.

[Attention and reading performance in children with ADHD, reading disorder and the combined condition]. / Aufmerksamkeit und Leseleistungen bei Kindern mit ADHS, Lese- und Rechtschreibstörung und der komorbiden Störung.

Attention Deficit Hyperactivity Disorder (ADHD) and Dyslexia co-occur more often than expected by chance. Both disorders can have severe negative impact on children's development. The aim of the present study was to compare attention and reading performance in children with ADHD, dyslexia and the comorbid condition. Ninety-nine German children in 3rd and 4th grade with ADHD (n = 26), dyslexia (n = 22) and the comorbid condition (n = 24) compared to a healthy control group (n = 27) were assessed with a model oriented assessment battery for reading and attention. Additionally, comorbid problems were examined. Children with ADHD were characterized by difficulties in decoding and reading comprehension, while children with dyslexia showed impairments in their attentional performance. Psychometric data revealed that children with dyslexia showed both externalizing and internalizing symptoms, while children with the comorbid condition scored the highest on all psychopathological dimensions. The results suggest, that reading problems in children with ADHD might be an epiphenomenon of the task used dependent on time constraints inherent to the task. Impairments of attentional functions in children with dyslexia emphasize the importance of a sufficient diagnostic procedure for subclinical ADHD symptoms as possible comorbid disorder. Future studies should focus the impact of early treatment of attentional deficits on reading acquisition.

Basic numerical processing, calculation, and working memory in children with dyscalculia and/or ADHD symptoms.

Objective: Deficits in basic numerical skills, calculation, and working memory have been found in children with developmental dyscalculia (DD) as well as children with attention-deficit/hyperactivity disorder (ADHD). This paper investigates cognitive profiles of children with DD and/or ADHD symptoms (AS) in a double dissociation design to obtain a better understanding of the comorbidity of DD and ADHD. Method: Children with DD-only (N = 33), AS-only (N = 16), comorbid DD+AS (N = 20), and typically developing controls (TD, N = 40) were assessed on measures of basic numerical processing, calculation, working memory, processing speed, and neurocognitive measures of attention. Results: Children with DD (DD, DD+AS) showed deficits in all basic numerical skills, calculation, working memory, and sustained attention. Children with AS (AS, DD+AS) displayed more selective difficulties in dot enumeration, subtraction, verbal working memory, and processing speed. Also, they generally performed more poorly in neurocognitive measures of attention, especially alertness. Children with DD+AS mostly showed an additive combination of the deficits associated with DD-only and A_Sonly, except for subtraction tasks, in which they were less impaired than expected. Conclusions: DD and AS appear to be related to largely distinct patterns of cognitive deficits, which are present in combination in children with DD+AS.

Machine learning and dyslexia: Classification of individual structural neuro-imaging scans of students with and without dyslexia.

Meta-analytic studies suggest that dyslexia is characterized by subtle and spatially distributed variations in brain anatomy, although many variations failed to be significant after corrections of multiple comparisons. To circumvent issues of significance which are characteristic for conventional analysis techniques, and to provide predictive value, we applied a machine learning technique--support vector machine--to differentiate between subjects with and without dyslexia. In a sample of 22 students with dyslexia (20 women) and 27 students without dyslexia (25 women) (18-21 years), a classification performance of 80% (p < 0.001; d-prime = 1.67) was achieved on the basis of differences in gray matter (sensitivity 82%, specificity 78%). The voxels that were most reliable for classification were found in the left occipital fusiform gyrus (LOFG), in the right occipital fusiform gyrus (ROFG), and in the left inferior parietal lobule (LIPL). Additionally, we found that classification certainty (e.g. the percentage of times a subject was correctly classified) correlated with severity of dyslexia (r = 0.47). Furthermore, various significant correlations were found between the three anatomical regions and behavioural measures of spelling, phonology and whole-word-reading. No correlations were found with behavioural measures of short-term memory and visual/attentional confusion. These data indicate that the LOFG, ROFG and the LIPL are neuro-endophenotype and potentially biomarkers for types of dyslexia related to reading, spelling and phonology. In a second and independent sample of 876 young adults of a general population, the trained classifier of the first sample was tested, resulting in a classification performance of 59% (p = 0.07; d-prime = 0.65). This decline in classification performance resulted from a large percentage of false alarms. This study provided support for the use of machine learning in anatomical brain imaging.

Cognitive and familial risk evidence converged: A data-driven identification of distinct and homogeneous subtypes within the heterogeneous sample of reading disabled children.

The evident degree of heterogeneity observed in reading disabled children has puzzled reading researchers for decades. Recent advances in the genetic underpinnings of reading disability have indicated that the heritable, familial risk for dyslexia is a major risk factor. The present data-driven, classification attempt aims to revisit the possibility of identifying distinct cognitive deficit profiles in a large sample of second to fourth grade reading disabled children. In this sample, we investigated whether genetic and environmental risk factors are able to distinguish between poor reader subtypes. In this profile, we included reading-related measures of phonemic awareness, letter-speech sound processing and rapid naming, known as candidate vulnerability markers associated with dyslexia and familial risk for dyslexia, as well as general cognitive abilities (non-verbal IQ and vocabulary). Clustering was based on a 200 multi-start K-means approach. Results revealed four emerging subtypes of which the first subtype showed no cognitive deficits underlying their poor reading skills (Reading-only impaired poor readers). The other three subtypes shared a core phonological deficit (PA) with a variable and discriminative expression across the other underlying vulnerability markers. More specific, type 2 showed low to poor performance across all reading-related and general cognitive abilities (general poor readers), type 3 showed a specific letter-speech sound mapping deficit next to a PA deficit (PA-LS specific poor readers) and type 4 showed a specific rapid naming deficit complementing their phonological weakness (PA-RAN specific poor readers). The first three poor reader profiles were more characterized by variable environmental risk factor, while the fourth, PA-RAN poor reader subtype showed a significantly strong familial risk for dyslexia. Overall, when we zoom in on the heterogeneous phenomenon of reading disability, unique and distinct cognitive subtypes can be identified, distinguishing between those poor readers more influences by the role of genes and those more influenced by environmental risk factors. Taking into account this diversity of distinct cognitive subtypes, instead of looking at the reading disabled sample as a whole, will help tailor future diagnostic and intervention efforts more specifically to the needs of children with such a specific deficit and risk pattern, as well as providing a more promising way forward for genetic studies of dyslexia.

Prevalence and Profile of Phonological and Surface Subgroups in College Students With a History of Reading Disability.

The purpose of the present study was to identify and characterize surface and phonological subgroups of readers among college students with a prior diagnosis of developmental reading disability (RD). Using a speeded naming task derived from Castles and Coltheart's subtyping study, we identified subgroups of readers from among college students with RD and then compared them on a number of component reading tasks. Most of our adults with RD showed a discrepancy in lexical versus sublexical reading skills. The majority of classified individuals were in the phonological dyslexia group, and this group's performance was worse than that of other groups on a range of reading-related tasks. Specifically, being relatively less skilled at reading nonwords compared to irregular words was associated with deficits in both sublexical and lexical tasks, and with unique deficits compared to the surface dyslexia group not only in an independent measure of phonological coding but also in spelling, rapid automatized naming, and speeded oral reading. The surface dyslexia group was small, and the pattern of results for these readers was not consistent with the predicted profile of a specific deficit in lexical and automatized reading processes. Our surface group did not show reduced skill in lexical mechanisms specifically, nor any unique deficit compared to the phonological group. These results seem more supportive of models of reading that place phonological processing impairments at the core of RD, with all other impairments being clearly subsidiary.

Modeling Polymorphemic Word Recognition: Exploring Differences Among Children With Early-Emerging and Late-Emerging Word Reading Difficulty.

Comprehensive models of derived polymorphemic word recognition skill in developing readers, with an emphasis on children with reading difficulty (RD), have not been developed. The purpose of the present study was to model individual differences in polymorphemic word recognition ability at the item level among 5th-grade children (N = 173) oversampled for children with RD using item-response crossed random-effects models. We distinguish between two subtypes of RD children with word recognition problems, those with early-emerging RD and late-emerging RD. An extensive set of predictors representing item-specific knowledge, child-level characteristics, and word-level characteristics were used to predict item-level variance in polymorphemic word recognition. Results indicate that item-specific root word recognition and word familiarity; child-level RD status, morphological awareness, and orthographic choice; word-level frequency and root word family size; and the interactions between morphological awareness and RD status and root word recognition and root transparency predicted individual differences in polymorphemic word recognition item performance. Results are interpreted within a multisource individual difference model of polymorphemic word recognition skill spanning item-specific, child-level, and word-level knowledge.

Five Describing Factors of Dyslexia.

Two subtypes of dyslexia (phonological, visual) have been under debate in various studies. However, the number of symptoms of dyslexia described in the literature exceeds the number of subtypes, and underlying relations remain unclear. We investigated underlying cognitive features of dyslexia with exploratory and confirmatory factor analyses. A sample of 446 students (63 with dyslexia) completed a large test battery and a large questionnaire. Five factors were found in both the test battery and the questionnaire. These 10 factors loaded on 5 latent factors (spelling, phonology, short-term memory, rhyme/confusion, and whole-word processing/complexity), which explained 60% of total variance. Three analyses supported the validity of these factors. A confirmatory factor analysis fit with a solution of five factors (RMSEA = .03). Those with dyslexia differed from those without dyslexia on all factors. A combination of five factors provided reliable predictions of dyslexia and nondyslexia (accuracy >90%). We also looked for factorial deficits on an individual level to construct subtypes of dyslexia, but found varying profiles. We concluded that a multiple cognitive deficit model of dyslexia is supported, whereas the existence of subtypes remains unclear. We discussed the results in relation to advanced compensation strategies of students, measures of intelligence, and various correlations within groups of those with and without dyslexia.

WISC-IV Intellectual Profiles in Italian Children With Specific Learning Disorder and Related Impairments in Reading, Written Expression, and Mathematics.

The fifth edition of theDiagnostic and Statistical Manual of Mental Disordersgrouped specific learning disabilities in the single diagnostic category of specific learning disorder (SLD), with specifiers for impairments in reading, written expression, and mathematics. This study aimed at investigating the intellectual profile, assessed with the fourth edition of theWechsler Intelligence Scale for Children(WISC-IV), of 172 children with a diagnosis of SLD, compared to 74 clinical referral controls. WISC-IV intellectual functioning in children with SLD was characterized by a significant discrepancy between general ability and cognitive proficiency (General Ability Index [GAI] > Cognitive Proficiency Index [CPI]), and worse performances on the Similarities, Digit Span, Letter-Number Sequencing, and Coding subtests, supporting models of multiple cognitive deficits at the basis of neurodevelopmental disorders as SLD. GAI was the best and more conservative measure provided by the WISC-IV to identify intellectual functioning in children with SLD, and the intellectual discrepancy between GAI and CPI could be considered a "cognitive sign" for the presence of SLD in a single diagnostic category. Cognitive deficits differed in subtypes of impairment (reading, written expression, and mathematics), supporting their distinction for empirical, educational, and rehabilitative purposes. These findings need further replication in larger samples and in comparison to typically developing children.