Background: Attention Deficit Hyperactivity Disorder (ADHD) is the most prevalent childhood neurodevelopmental disorder. It shares some genetic risk with Autism Spectrum Disorder (ASD), and the conditions often occur together. Both are potentially associated with abnormal glutamate and GABA neurotransmission, which can be modelled by measuring the synaptic activity in the retina with an electroretinogram (ERG). Reduction of retinal responses in ASD has been reported, but little is known about retinal activity in ADHD. In this study, we compared the light-adapted ERGs of individuals with ADHD, ASD and controls to investigate whether retinal responses differ between these neurodevelopmental conditions.Methods: Full field light-adapted ERGs were recorded from 15 ADHD, 57 ASD (without ADHD) and 59 control participants, aged from 5.4 to 27.3 years old. A Troland protocol was used with a random series of nine flash strengths from −0.367 to 1.204 log photopic cd.s.m⁻². The time-to-peak and amplitude of the a- and b-waves and the parameters of the Photopic Negative Response (PhNR) were compared amongst the three groups of participants, using generalised estimating equations.Results: Statistically significant elevations of the ERG b-wave amplitudes, PhNR responses and faster timings of the b-wave time-to-peak were found in those with ADHD compared with both the control and ASD groups. The greatest elevation in the b-wave amplitudes associated with ADHD were observed at 1.204 log phot cd.s.m⁻²flash strength (p< .0001), at which the b-wave amplitude in ASD was significantly lower than that in the controls. Using this measure, ADHD could be distinguished from ASD with an area under the curve of 0.88.Conclusions: The ERG b-wave amplitude appears to be a distinctive differential feature for both ADHD and ASD, which produced a reversed pattern of b-wave responses. These findings imply imbalances between glutamate and GABA neurotransmission which primarily regulate the b-wave formation. Abnormalities in the b-wave amplitude could provisionally serve as a biomarker for both neurodevelopmental conditions.

Internet usage has seen a stark global rise over the last few decades, particularly among adolescents and young people, who have also been diagnosed increasingly with internet addiction (IA). IA impacts several neural networks that influence an adolescent’s behaviour and development. This article issued a literature review on the resting-state and task-based functional magnetic resonance imaging (fMRI) studies to inspect the consequences of IA on the functional connectivity (FC) in the adolescent brain and its subsequent effects on their behaviour and development. A systematic search was conducted from two databases, PubMed and PsycINFO, to select eligible articles according to the inclusion and exclusion criteria. Eligibility criteria was especially stringent regarding the adolescent age range (10–19) and formal diagnosis of IA. Bias and quality of individual studies were evaluated. The fMRI results from 12 articles demonstrated that the effects of IA were seen throughout multiple neural networks: a mix of increases/decreases in FC in the default mode network; an overall decrease in FC in the executive control network; and no clear increase or decrease in FC within the salience network and reward pathway. The FC changes led to addictive behaviour and tendencies in adolescents. The subsequent behavioural changes are associated with the mechanisms relating to the areas of cognitive control, reward valuation, motor coordination, and the developing adolescent brain. Our results presented the FC alterations in numerous brain regions of adolescents with IA leading to the behavioural and developmental changes. Research on this topic had a low frequency with adolescent samples and were primarily produced in Asian countries. Future research studies of comparing results from Western adolescent samples provide more insight on therapeutic intervention.

Electroretinograms (ERGs) show differences between typically developing populations and those with a diagnosis of autism spectrum disorder (ASD) or attention deficit/hyperactivity disorder (ADHD). In a series of ERGs collected in ASD (n = 77), ADHD (n = 43), ASD + ADHD (n = 21), and control (n = 137) groups, this analysis explores the use of machine learning and feature selection techniques to improve the classification between these clinically defined groups. Standard time domain and signal analysis features were evaluated in different machine learning models. For ASD classification, a balanced accuracy (BA) of 0.87 was achieved for male participants. For ADHD, a BA of 0.84 was achieved for female participants. When a three-group model (ASD, ADHD, and control) the BA was lower, at 0.70, and fell further to 0.53 when all groups were included (ASD, ADHD, ASD + ADHD, and control). The findings support a role for the ERG in establishing a broad two-group classification of ASD or ADHD, but the model’s performance depends upon sex and is limited when multiple classes are included in machine learning modeling.

To ascertain if the photopic negative response of the electroretinogram is different in autism spectrum disorder as a potential clinical marker.Visual function can be atypical in autism spectrum di...

The electroretinogram (ERG) is a clinical test that records the retina’s electrical response to a brief flash of light as a waveform signal. Analysis of the ERG signal offers a promising non-invasive method for studying different neurodevelopmental and neurodegenerative disorders. Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by poor communication, reduced reciprocal social interaction, and restricted and repetitive stereotyped behaviors that should be detected as early as possible to ensure timely and appropriate intervention to support the individual and their family. In this study, we applied gated Multilayer Perceptron (gMLP) for the light-adapted ERG waveform classification as an effective alternative to Transformers. This study presents the first application of gMLP for ASD classification, which employs basic multilayer perceptrons with fewer parameters than Transformers. We compared the performance of different time-series models on an ASD-Control dataset and found that the superiority of gMLP in classification accuracy was the best at 89.7% compared to alternative models and supports the use of gMLP in classification models based on ERG recordings involving case-control comparisons.

Background: Children and young people (CYP) with intellectual and developmental disabilities (IDDs) have significant additional educational needs compared with the general population. In England, the government has established a system of education, health and care plans (EHCPs) to support children with special educational needs and disabilities, but disparities exist between the degree of need and the availability of support. We conducted a prospective UK national cohort study (IMAGINE) of children with rare pathogenic genomic variants, all of which are associated with IDD, to investigate associated neuropsychiatric risk. Subsequently, we obtained information from the UK's National Pupil Database on their educational progress through the state school system. We aimed to identify whether they had received EHCP provision and whether that support was associated with their family's socioeconomic status, region of domicile, ethnicity, sex, primary special educational needs (SEN) type, academic performance and mental health well‐being.Methods: We recruited 2738 CYP from England into the IMAGINE study between 2014 and 2019. The educational histories of the participants (6–28 years old, mean ± standard deviation = 14 ± 4 years, 56% male) were obtained from the Department for Education's National Pupil Database in 2021. Educational data included attainment scores from the Early Year Foundation Stage (<5 years) to key stage 4 (15–16 years). Each family was assigned an index of multiple deprivation (IMD) score based on their home address postcode. Parents or carers rated their child's emotional and behavioural adjustment on the Strengths and Difficulties Questionnaire (SDQ). The association between receiving an EHCP and the child's IMD score, eligibility for free school meals, English region of domicile, ethnicity, sex, primary SEN type, academic attainment and SDQ score was investigated.Results: In this cohort, 78% of participants had received an EHCP. CYP living in the most deprived IMD deciles were substantially less likely to receive EHCP support than those in the least deprived decile, irrespective of their degree of intellectual developmental disability, academic performance or associated mental health problems. There were no sex differences. Children of Asian heritage were more likely to have been granted an EHCP than White children from equivalent IMD deciles. There were striking regional disparities. Participants living in London were significantly more likely to have been awarded an EHCP than participants living anywhere else in England, regardless of their IMD decile; those in the least deprived decile had almost 100% EHCP provision.Conclusions: This study found evidence for nationwide regional inconsistencies in the awarding of EHCP to CYP with significant intellectual impairments of known genetic aetiology. Disparities in funds available to education authorities could be a contributory factor. EHCP support was potentially influenced by how strongly a parent advocates for their child.