Clinical and Electrophysiological Differences between Subjects with Dysphonetic Dyslexia and Non-Specific Reading Delay

An Overview on Electrophysiological and Neuroimaging Findings in Dyslexia

Objective: Dyslexia is a prevalent neurodevelopmental condition that is characterized by inaccurate and slow word recognition. This article reviews neural correlates of dyslexia from both electrophysiological and neuroimaging studies. Method : In this brief review, we provide electrophysiological and neuroimaging evidence from electroencephalogram (EEG) and magnetic resonance imaging (MRI) studies in dyslexia to understand functional and structural brain changes in this condition. Results: In both electrophysiological and neuroimaging studies, the most frequently reported functional impairments in dyslexia include aberrant activation of the left hemisphere occipito-temporal cortex (OTC), temporo-parietal cortex (TPC), inferior frontal gyrus (IFG), and cerebellar areas. EEG studies have mostly highlighted the important role of lower frequency bands in dyslexia, especially theta waves. Furthermore, neuroimaging studies have suggested that dyslexia is related to functional and structural impairments in the left hemisphere regions associated with reading and language, including reduced grey matter volume in the left TPC, decreased white matter connectivity between reading networks, and hypo-activation of the left OTC and TPC. In addition, neural evidence from pre-reading children and infants at risk for dyslexia show that there are abnormalities in the dyslexic brain before learning to read begins. Conclusion: Advances in comprehending the neural correlates of dyslexia could bring closer translation from basic to clinical neuroscience and effective rehabilitation for individuals who struggle to read. However, neuroscience still has great potential for clinical translation that requires further research.

k-Means clustering by using the calculated Z-scores from QEEG data of children with dyslexia

Learning the subtype of dyslexia may help shorten the rehabilitation process and focus more on the relevant special education or diet for children with dyslexia. For this purpose, the resting-state eyes-open 2-min QEEG measurement data were collected from 112 children with dyslexia (84 male, 28 female) between 7 and 11 years old for 96 sessions per subject on average. The z-scores are calculated for each band power and each channel, and outliers are eliminated afterward. Using the k-Means clustering method, three different clusters are identified. Cluster 1 (19% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 2 (76% of the cases) has negative z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 3 (5% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers at AF3, F3, FC5, and T7 channels and mostly negative z-scores for other channels. In Cluster 3, there is temporal disruption which is a typical description of dyslexia. In Cluster 1, there is a general brain inflammation as both slow and fast waves are detected in the same channels. In Cluster 2, there is a brain maturation delay and a mild inflammation. After Auto Train Brain training, most of the cases resemble more of Cluster 2, which may mean that inflammation is reduced and brain maturation delay comes up to the surface which might be the result of inflammation. Moreover, Cluster 2 center values at the posterior parts of the brain shift toward the mean values at these channels after 60 sessions. It means, Auto Train Brain training improves the posterior parts of the brain for children with dyslexia, which were the most relevant regions to be strengthened for dyslexia.

EEG correlates of developmental dyslexia: a systematic review

Dyslexia is one of the most studied learning disorders. Despite this, its biological basis and main causes are still not fully understood. Electroencephalography (EEG) could be a powerful tool in identifying the underlying mechanisms, but knowledge of the EEG correlates of developmental dyslexia (DD) remains elusive. We aimed to systematically review the evidence on EEG correlates of DD and establish their quality. In July 2021, we carried out an online search of the PubMed and Scopus databases to identify published articles on EEG correlates in children with dyslexia aged 6 to 12 years without comorbidities. We follow the PRISMA guidelines and assess the quality using the Appraisal Tool questionnaire. Our final analysis included 49 studies (14% high quality, 63% medium, 20% low, and 2% very low). Studies differed greatly in methodology, making a summary of their results challenging. However, some points came to light. Even at rest, children with dyslexia and children in the control group exhibited differences in several EEG measures, particularly in theta and alpha frequencies; these frequencies appear to be associated with learning performance. During reading-related tasks, the differences between dyslexic and control children seem more localized in the left temporoparietal sites. The EEG activity of children with dyslexia and children in the control group differed in many aspects, both at rest and during reading-related tasks. Our data are compatible with neuroimaging studies in the same diagnostic group and expand the literature by offering new insights into functional significance.

Semantic Priming and Its Link to Verbal Comprehension and Working Memory in Children with Learning Disorders

Children with learning disorders (LD children) often have heterogeneous cognitive impairments that affect their ability to learn and use basic academic skills. A proposed cause for this variability has been working memory (WM) capacity. Altered patterns of event-related potentials (ERPs) in these children have also been found in the N400 component associated with semantic priming. However, regarding the semantic priming effect in LD children, no distinction has been made for children with varying WM abilities. This study aims to explore the relationship of WM with the brain's electrophysiological response that underlies semantic priming in LD children that performed a lexical decision task. A total of 40 children (8-10 years old) participated: 28 children with LD and 12 age-matched controls. The ERPs were recorded for each group and analyzed with permutation-based t-tests. The N400 effect was observed only in the control group, and both groups showed a late positive complex (LPC). Permutation-based regression analyses were performed for the results from the LD group using the WISC-IV indices (e.g., Verbal Comprehension and WM) as independent predictors of the ERPs. The Verbal Comprehension Index, but not the WM index, was a significant predictor of the N400 and LPC effects in LD children.

Language-related brain areas in childhood epilepsy with centrotemporal spikes studied with MEG

OBJECTIVE

Children with self-limited epilepsy with centrotemporal spikes (SeLECTS) typically indicate cognitive impairment with widespread speech impairment. We explored how epilepsy affects language-related brain areas and areas in their vicinity.

METHODS

Twenty-two children with SeLECTS and declined verbal comprehension (DVC), 21 with SeLECTS and normal verbal comprehension (NVC), and 23 healthy controls (HCs) underwent high-sampling magnetoencephalography recordings. According to a previous study, 24 language-related regions of interest were selected bilaterally, and the relative spectral power was estimated using a minimum norm estimate.

RESULTS

The highest mean power spectral density was observed in the delta band for the DVC group, in the theta band for the NVC group, and in the alpha band for HCs within language-specific brain regions. The distinctions between the DVC and NVC groups in the delta and theta frequency bands were primarily concentrated in the right linguistic brain area.

CONCLUSIONS

Children with SeLECTS may have developmental problems in language-related brain areas, with different developmental levels observed in the DVC, NVC, and HC groups. The DVC group could have inferior speech comprehension due to a more significant number of seizures and more left-sided spike locations.

SIGNIFICANCE

Children having SeLECTS showed impaired brain maturation, leading to associated language impairment.

Improving Functional Connectivity in Developmental Dyslexia through Combined Neurofeedback and Visual Training

This study examined the effects of combined neurofeedback (NF) and visual training (VT) on children with developmental dyslexia (DD). Although NF is the first noninvasive approach to support neurological disorders, the mechanisms of its effects on the brain functional connectivity are still unclear. A key question is whether the functional connectivities of the EEG frequency networks change after the combined NF–VT training of DD children (postD). NF sessions of voluntary α/θ rhythm control were applied in a low-spatial-frequency (LSF) illusion contrast discrimination, which provides feedback with visual cues to improve the brain signals and cognitive abilities in DD children. The measures of connectivity, which are defined by small-world propensity, were sensitive to the properties of the brain electrical oscillations in the quantitative EEG-NF training. In the high-contrast LSF illusion, the z-NF reduced the α/θ scores in the frontal areas, and in the right ventral temporal, occipital–temporal, and middle occipital areas in the postD (vs. the preD) because of their suppression in the local hub θ-network and the altered global characteristics of the functional θ-frequency network. In the low-contrast condition, the z-NF stimulated increases in the α/θ scores, which induced hubs in the left-side α-frequency network of the postD, and changes in the global characteristics of the functional α-frequency network. Because of the anterior, superior, and middle temporal deficits affecting the ventral and occipital–temporal pathways, the z-NF–VT compensated for the more ventral brain regions, mainly in the left hemispheres of the postD group in the low-contrast LSF illusion. Compared to pretraining, the NF–VT increased the segregation of the α, β (low-contrast), and θ networks (high-contrast), as well as the γ2-network integration (both contrasts) after the termination of the training of the children with developmental dyslexia. The remediation compensated more for the dorsal (prefrontal, premotor, occipital–parietal connectivities) dysfunction of the θ network in the developmental dyslexia in the high-contrast LSF illusion. Our findings provide neurobehavioral evidence for the exquisite brain functional plasticity and direct effect of NF–VT on cognitive disabilities in DD children.

Clinical Application of Combined EEG-qEEG Functional Neuroimaging in the Practice of Pediatric Neuroscience: A Personal Perspective

This brief article is an overview of my personal experience over the past almost 10 years of the clinical use of EEG and quantitative EEG (qEEG) functional neuroimaging in a busy pediatric neurology practice. The concomitant use of surface EEG and functional electromagnetic EEG neuroimaging/qEEG in clinical practice provides significant additional clinical and neurophysiologic information. The qEEG is a noninvasive, inexpensive, portable technique with high temporal resolution (milliseconds) and improving spatial resolution (down to 3 mm³) and is an appropriate and validated tool for investigation of abnormal brain dynamics and connectivity of neuronal networks in clinical disorders of the brain. This article describes the daily applicability and utility of this modality in assisting diagnosis and clinical management of patients with a wide variety of presenting symptoms, including headaches, tics, autism spectrum disorder, inattention, sleep dysregulation, anxiety, and depression. The ease of data acquisition and analysis in clinical practices, coupled with skilled interpretation and clinical application, makes this tool one of the most valuable clinical tools to complement a thorough history and examination process.

Quantitative EEG in Childhood Attention Deficit Hyperactivity Disorder and Learning Disabilities

The clinical use of the quantitative EEG (QEEG) from the pioneering work of John has received a new impetus thanks to new neuroimaging techniques and the possibility of using a number of normative databases both of normal subjects and of subjects with definite pathologies. In this direction, the term personalized medicine is becoming more and more common, a medical procedure that separates patients into different groups based on their predicted response to the quantitative EEG. This has allowed the study of single subjects and to customize health care, with decisions and treatments tailored to each individual patient, as well as improvement of knowledge of the pathophysiological mechanisms of specific diseases. This review article will present the most recent evidence in the field of developmental neuropsychiatric disorders obtained from the application of quantitative EEG both in clinical group studies (attention deficit hyperactivity disorder, developmental dyslexia, oppositional defiant disorder) and in individual case studies not yet published.

Individual Alpha Peak Frequency, an Important Biomarker for Live Z-Score Training Neurofeedback in Adolescents with Learning Disabilities

Learning disabilities (LDs) have an estimated prevalence between 5% and 9% in the pediatric population and are associated with difficulties in reading, arithmetic, and writing. Previous electroencephalography (EEG) research has reported a lag in alpha-band development in specific LD phenotypes, which seems to offer a possible explanation for differences in EEG maturation. In this study, 40 adolescents aged 10–15 years with LDs underwent 10 sessions of Live Z-Score Training Neurofeedback (LZT-NF) Training to improve their cognition and behavior. Based on the individual alpha peak frequency (i-APF) values from the spectrogram, a group with normal i-APF (ni-APF) and a group with low i-APF (li-APF) were compared in a pre-and-post-LZT-NF intervention. There were no statistical differences in age, gender, or the distribution of LDs between the groups. The li-APF group showed a higher theta absolute power in P4 (p = 0.016) at baseline and higher Hi-Beta absolute power in F3 (p = 0.007) post-treatment compared with the ni-APF group. In both groups, extreme waves (absolute Z-score of ≥1.5) were more likely to move toward the normative values, with better results in the ni-APF group. Conversely, the waves within the normal range at baseline were more likely to move out of the range after treatment in the li-APF group. Our results provide evidence of a viable biomarker for identifying optimal responders for the LZT-NF technique based on the i-APF metric reflecting the patient’s neurophysiological individuality.

The Changes of qEEG Approximate Entropy during Test of Variables of Attention as a Predictor of Major Depressive Disorder

Evaluating brain function through biosignals remains challenging. Quantitative electroencephalography (qEEG) outcomes have emerged as a potential intermediate biomarker for diagnostic clarification in psychological disorders. The Test of Variables of Attention (TOVA) was combined with qEEG to evaluate biomarkers such as absolute power, relative power, cordance, and approximate entropy from covariance matrix images to predict major depressive disorder (MDD). EEG data from 18 healthy control and 18 MDD patients were monitored during the resting state and TOVA. TOVA was found to provide aspects for the evaluation of MDD beyond resting electroencephalography. The results showed that the prefrontal qEEG theta cordance of the control and MDD groups were significantly different. For comparison, the changes in qEEG approximate entropy (ApEn) patterns observed during TOVA provided features to distinguish between participants with or without MDD. Moreover, ApEn scores during TOVA were a strong predictor of MDD, and the ApEn scores correlated with the Beck Depression Inventory (BDI) scores. Between-group differences in ApEn were more significant for the testing state than for the resting state. Our results provide further understanding for MDD treatment selection and response prediction during TOVA.

Diagnosis of oculomotor anomalies in children with learning disorders

This systematic review presents the existing scientific evidence for oculomotor anomalies in children with three different types of learning disorders - namely, dyslexia, dyspraxia and attention deficit-hyperactivity disorder (ADHD). This review was registered in the PROSPERO database (registration number: 139317). The QUADAS-2 tool was used to systematically evaluate the quality of the diagnostic tests used in the evaluated studies and to confirm whether the oculomotor alterations observed in the different groups of children with various learning disorders had a consistent diagnostic basis. Using this tool, the design of the articles was well elaborated, although concerns exist regarding the selection of patients and the diagnostic criteria for the binocular conditions. All the studies reviewed conclude that a pattern of oculomotor anomalies exist in the groups of children with these three types of learning disorders compared to healthy children. However, there is a concern regarding the diagnostic methodology, as no clear range of normality for the parameters used to characterise ocular motility was identified and no gold standard or reference test has been defined. In future studies, this range of normality must be developed for different oculomotor skills, and a reference test (possibly video-oculography) for the measurement of these skills must be established.