Peter Tamboer

Specifying Theories of Developmental Dyslexia: A Diffusion Model Analysis of Word Recognition.

The nature of word recognition difficulties in developmental dyslexia is still a topic of controversy. We investigated the contribution of phonological processing deficits and uncertainty to the word recognition difficulties of dyslexic children by mathematical diffusion modeling of visual and auditory lexical decision data. The first study showed that poor visual lexical decision performance of reading disabled children was mainly due to a delay in the evaluation of word characteristics, suggesting impaired phonological processing. The adoption of elevated certainty criteria by the disabled readers suggests that uncertainty contributed to the visual word recognition impairments as well. The second study replicated the outcomes for visual lexical decision with formally diagnosed dyslexic children. In addition, during auditory lexical decision, dyslexics presented with reduced accuracy, which also resulted from delayed evaluation of word characteristics. Since orthographic influences are diminished during auditory lexical decision, this strengthens the phonological processing deficit account. Dyslexic children did not adopt heightened certainty criteria during auditory lexical decision, indicating that uncertainty solely impairs reading and not listening.

Dyslexia and voxel-based morphometry: correlations between five behavioural measures of dyslexia and gray and white matter volumes.

In voxel-based morphometry studies of dyslexia, the relation between causal theories of dyslexia and gray matter (GM) and white matter (WM) volume alterations is still under debate. Some alterations are consistently reported, but others failed to reach significance. We investigated GM alterations in a large sample of Dutch students (37 dyslexics and 57 non-dyslexics) with two analyses: group differences in local GM and total GM and WM volume and correlations between GM and WM volumes and five behavioural measures. We found no significant group differences after corrections for multiple comparisons although total WM volume was lower in the group of dyslexics when age was partialled out. We presented an overview of uncorrected clusters of voxels (p < 0.05, cluster size k > 200) with reduced or increased GM volume. We found four significant correlations between factors of dyslexia representing various behavioural measures and the clusters found in the first analysis. In the whole sample, a factor related to performances in spelling correlated negatively with GM volume in the left posterior cerebellum. Within the group of dyslexics, a factor related to performances in Dutch–English rhyme words correlated positively with GM volume in the left and right caudate nucleus and negatively with increased total WM volume. Most of our findings were in accordance with previous reports. A relatively new finding was the involvement of the caudate nucleus. We confirmed the multiple cognitive nature of dyslexia and suggested that experience greatly influences anatomical alterations depending on various subtypes of dyslexia, especially in a student sample.

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.

Identifying dyslexia in adults: an iterative method using the predictive value of item scores and self-report questions

Methods for identifying dyslexia in adults vary widely between studies. Researchers have to decide how many tests to use, which tests are considered to be the most reliable, and how to determine cut-off scores. The aim of this study was to develop an objective and powerful method for diagnosing dyslexia. We took various methodological measures, most of which are new compared to previous methods. We used a large sample of Dutch first-year psychology students, we considered several options for exclusion and inclusion criteria, we collected as many cognitive tests as possible, we used six independent sources of biographical information for a criterion of dyslexia, we compared the predictive power of discriminant analyses and logistic regression analyses, we used both sum scores and item scores as predictor variables, we used self-report questions as predictor variables, and we retested the reliability of predictions with repeated prediction analyses using an adjusted criterion. We were able to identify 74 dyslexic and 369 non-dyslexic students. For 37 students, various predictions were too inconsistent for a final classification. The most reliable predictions were acquired with item scores and self-report questions. The main conclusion is that it is possible to identify dyslexia with a high reliability, although the exact nature of dyslexia is still unknown. We therefore believe that this study yielded valuable information for future methods of identifying dyslexia in Dutch as well as in other languages, and that this would be beneficial for comparing studies across countries.

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.

A New Self‐Report Inventory of Dyslexia for Students: Criterion and Construct Validity

The validity of a Dutch self-report inventory of dyslexia was ascertained in two samples of students. Six biographical questions, 20 general language statements and 56 specific language statements were based on dyslexia as a multi-dimensional deficit. Dyslexia and non-dyslexia were assessed with two criteria: identification with test results (Sample 1) and classification using biographical information (both samples). Using discriminant analyses, these criteria were predicted with various groups of statements. All together, 11 discriminant functions were used to estimate classification accuracy of the inventory. In Sample 1, 15 statements predicted the test criterion with classification accuracy of 98%, and 18 statements predicted the biographical criterion with classification accuracy of 97%. In Sample 2, 16 statements predicted the biographical criterion with classification accuracy of 94%. Estimations of positive and negative predictive value were 89% and 99%. Items of various discriminant functions were factor analysed to find characteristic difficulties of students with dyslexia, resulting in a five-factor structure in Sample 1 and a four-factor structure in Sample 2. Answer bias was investigated with measures of internal consistency reliability. Less than 20 self-report items are sufficient to accurately classify students with and without dyslexia. This supports the usefulness of self-assessment of dyslexia as a valid alternative to diagnostic test batteries.

Detection of neurodevelopmental diversity in memory clinics—Validation of a self-report measure

BACKGROUND Neurodevelopmental learning and attentional disorders (NLAD) such as dyslexia, dyscalculia and attention deficit hyperactivity disorder (ADHD) affect at least 6% of the adult population or more. They are associated with atypical cognitive patterns in early and adult life. The cognitive patterns of affected individuals in late life have never been described. One main challenge is detecting individuals in clinical settings during which mild cognitive changes could be confounding the clinical presentation. This is a critical research gap because these conditions interact, across the life course, with an individuals risk for dementia. Also, learning disabilities which present in childhood pose persistent cognitive differences in areas involving executive function, reading and math. Clinicians lack tools to detect undiagnosed neurodevelopmental in adults with memory disorders. The majority of patients presenting at memory clinics today come from a generation during which NLAD were not yet clinically recognized. In this study, we hypothesized that a self-report scale can detect NLAD in a memory clinic population. METHODS We developed a self-report, retrospective childhood cognitive questionnaire including key attributes adapted from prior validated measures. 233 participants were included in the primary analysis. RESULTS Confirmatory Factor Analysis resulted in a best-fit model with six labelled factors (Math, Language, Attention, Working Memory, Sequential Processing, and Executive Function) and 15 total question items. The model demonstrated unidimensionality, reliability, convergent validity, discriminant validity, and predictive validity. Using 1.5 standard deviations as the cut-off, subjects were categorized into: Normal (n = 169), Language (n = 10), Math (n = 12), Attention (n = 10) or Other/Mixed (n = 32). CONCLUSION A self-report measure can be a useful tool to elicit childhood cognitive susceptibilities in various domains that could represent NLAD among patients in a memory clinic setting, even in the presence of mild cognitive impairment.

Six factors of adult dyslexia assessed by cognitive tests and self-report questions: Very high predictive validity

The Multiple Diagnostic Digital Dyslexia Test for Adults (MDDDT-A) consists of 12 newly developed tests and self-report questions in the Dutch language. Predictive validity and construct validity were investigated and compared with validity of a standard test battery of dyslexia (STB) in a sample of 154 students. There are three main results. First, various analyses of principal components showed that six or more factors of dyslexia can be distinguished (rapid naming, spelling, reading, short-term memory, confusion, phonology, attention, complexity). All factors are represented by the MDDDT-A. Second, various discriminant analyses showed good predictive validity for both the tests of the MDDDT-A (90%) and the STB (90%). However, predictive validity of the questionnaire was highest (97%). Third, we analysed the best predictors of dyslexia and found that predictive validity is higher when construct validity is high, that is when a set of predictors represents many characteristics of dyslexia. The main conclusion is that a digital test battery can be a reliable screening instrument for dyslexia in students, especially when it is accompanied by self-report questions. A theoretical conclusion is that dyslexia is characterized by at least six cognitive impairments in a complex way. In students, this structure may be modulated by high intelligence and good schooling through various compensation strategies. It is therefore recommended to include assessments of all characteristics of dyslexia to achieve the most reliable diagnoses in different samples and in different countries.