Behavioural and neurodevelopmental characteristics of SYNGAP1.

BACKGROUND: SYNGAP1 variants are associated with varying degrees of intellectual disability (ID), developmental delay (DD), epilepsy, autism, and behavioural difficulties. These features may also be observed in other monogenic conditions. There is a need to systematically compare the characteristics of SYNGAP1 with other monogenic causes of ID and DD to identify features unique to the SYNAGP1 phenotype. We aimed to contrast the neurodevelopmental and behavioural phenotype of children with SYNGAP1-related ID (SYNGAP1-ID) to children with other monogenic conditions and a matched degree of ID. METHODS: Participants were identified from the IMAGINE-ID study, a UK-based, national cohort study of neuropsychiatric risk in children with ID of known genetic origin. Thirteen children with SYNGAP1 variants (age 4-16 years; 85% female) were matched (2:1) with 26 controls with other monogenic causes of ID for chronological and mental age, sex, socio-economic deprivation, adaptive behaviour, and physical health difficulties. Caregivers completed the Development and Wellbeing Assessment (DAWBA) and physical health questionnaires. RESULTS: Our results demonstrate that seizures affected children with SYNGAP1-ID (84.6%) more frequently than the ID-comparison group (7.6%; p = < 0.001). Fine-motor development was disproportionally impaired in SYNGAP1-ID, with 92.3% of children experiencing difficulties compared to 50% of ID-comparisons(p = 0.03). Gross motor and social development did not differ between the two groups. Children with SYNGAP1-ID were more likely to be non-verbal (61.5%) than ID-comparisons (23.1%; p = 0.01). Those children able to speak, spoke their first words at the same age as the ID-comparison group (mean = 3.25 years), yet achieved lower language competency (p = 0.04). Children with SYNGAP1-ID compared to the ID-comparison group were not more likely to meet criteria for autism (SYNGAP1-ID = 46.2%; ID-comparison = 30.7%; p = .35), attention-deficit hyperactivity disorder (15.4%;15.4%; p = 1), generalised anxiety (7.7%;15.4%; p = .49) or oppositional defiant disorder (7.7%;0%; p = .15). CONCLUSION: For the first time, we demonstrate that SYNGAP1-ID is associated with fine motor and language difficulties beyond those experienced by children with other genetic causes of DD and ID. Targeted occupational and speech and language therapies should be incorporated early into SYNGAP1-ID management.

Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths.

PURPOSE: Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are conditions that similarly alter cognitive functioning ability and challenge the social interaction, attention, and communication skills of affected individuals. Yet these are distinct neurological conditions that can exhibit diverse characteristics which require different management strategies. It is desirable to develop tools to assist with early distinction so that appropriate early interventions and support may be tailored to an individual's specific requirements. The current diagnostic procedures for ASD and ADHD require a multidisciplinary approach and can be lengthy. This study investigated the potential of electroretinogram (ERG), an eye test measuring retinal responses to light, for rapid screening of ASD and ADHD. METHODS: Previous studies identified differences in ERG amplitude between ASD and ADHD, but this study explored time-frequency analysis (TFS) to capture dynamic changes in the signal. ERG data from 286 subjects (146 control, 94 ASD, 46 ADHD) was analyzed using two TFS techniques. RESULTS: Key features were selected, and machine learning models were trained to classify individuals based on their ERG response. The best model achieved 70% overall accuracy in distinguishing control, ASD, and ADHD groups. CONCLUSION: The ERG to the stronger flash strength provided better separation and the high frequency dynamics (80-300 Hz) were more informative features than lower frequency components. To further improve classification a greater number of different flash strengths may be required along with a discrimination comparison to participants who meet both ASD and ADHD classifications and carry both diagnoses.