Characterizing the ASD-ADHD phenotype: measurement structure and invariance in a clinical sample

Measuring autism-associated traits in the general population: Factor structure and measurement invariance across sex and diagnosis status of the Social Communication Questionnaire

LAY ABSTRACT

Using questionnaires in research relies on the expectation that they measure the same things across different groups of individuals. If this is not true, then interpretations of results can be misleading when researchers compare responses across different groups of individuals or use in it a group that differs from that in which the questionnaire was developed. For the questionnaire we investigated, the Social Communication Questionnaire (SCQ), we found that parents of boys and girls responded to questionnaire items in largely the same way but that the SCQ measured traits and behaviors slightly differently depending on whether the children had autism. Based on these results, we concluded that researchers using this questionnaire should carefully consider these differences when deciding how to interpret findings. SCQ scores as a reflection of "autism-associated traits" in samples that are mostly or entirely made up of individuals without an autism diagnosis may be misleading and we encourage a more precise interpretation of scores as a broader indication of social-communicative and behavioral traits.

[Formula: see text] Beyond the rubicon: a continuum approach to investigating the impact of ADHD like characteristics on everyday executive function in children with Tourette Syndrome

Across a range of neurodevelopmental conditions, a diagnosis of attention deficit hyperactivity disorder (ADHD) has been shown to be associated with executive function impairment. However, the DSM-V emphasis upon viewing psychological characteristics as existing on a continuous distributed quantitative dimension has enabled the opportunity to consider the influence of sub-diagnostic or sub-referral levels of these psychological characteristics upon cognitive function. This study adopted a continuum approach to the consideration of this ADHD influence and examined the extent to which the difference in parental reported executive functions between children with Tourette syndrome (TS) or typically developing children could be mediated by a concurrent group difference in the possession of sub-referral levels of ADHD-like characteristics. A total of 146 children, 58 with reported TS diagnosis, participated. Parental report measures of ecological executive functioning, the Child Executive Functioning Inventory, and the Vanderbilt ADHD Diagnostic Parent Rating Scale were employed. The analyses with a full sample and a sub-referral sample revealed significant group differences in most of the key measures. In addition, these measures were highly correlated even when controlling for age and gender. A series of mediation analyses indicated that in all models, the ADHD-like measures significantly mediated the group difference in executive function. These results suggest that sub-referrals levels of ADHD-like characteristics continue to contribute to executive challenges in TS. Future intervention research targeting these executive functions should consider the presence of ADHD-like characteristics at sub-referral levels of possession.

Unpacking the overlap between Autism and ADHD in adults: A multi-method approach

The overlap between Autism and Attention-Deficit Hyperactivity Disorder (ADHD) is widely observed in clinical settings, with growing interest in their co-occurrence in neurodiversity research. Until relatively recently, however, concurrent diagnoses of Autism and ADHD were not possible. This has limited the scope for large-scale research on their cross-condition associations, further stymied by a dearth of open science practices in the neurodiversity field. Additionally, almost all previous research linking Autism and ADHD has focused on children and adolescents, despite them being lifelong conditions. Tackling these limitations in previous research, 5504 adults - including a nationally representative sample of the UK (Study 1; n = 504) and a large pre-registered study (Study 2; n = 5000) - completed well-established self-report measures of Autism and ADHD traits. A series of network analyses unpacked the associations between Autism and ADHD at the individual trait level. Low inter-item connectivity was consistently found between conditions, supporting the distinction between Autism and ADHD as separable constructs. Subjective social enjoyment and hyperactivity-impulsivity traits were most condition-specific to Autism and ADHD, respectively. Traits related to attention control showed the greatest Bridge Expected Influence across conditions, revealing a potential transdiagnostic process underlying the overlap between Autism and ADHD. To investigate this further at the cognitive level, participants completed a large, well-powered, and pre-registered study measuring the relative contributions of Autism and ADHD traits to attention control (Study 3; n = 500). We detected age- and sex-related effects, however, attention control did not account for the covariance between Autism and ADHD traits. We situate our findings and discuss future directions in the cognitive science of Autism, ADHD, and neurodiversity, noting how our open datasets may be used in future research.

Sensorimotor Features and Daily Living Skills in Autistic Children With and Without ADHD

Attention-deficit/hyperactivity disorder (ADHD) commonly co-occurs in autistic children. However, additional research is needed to explore the differences in motor skills and sensory features in autistic children with and without ADHD, as well as the impacts of these factors on daily living skills (DLS). This observational study sought to fill this gap with 67 autistic children (6.14-10.84 years-old), 43 of whom had ADHD. Autistic children with ADHD demonstrated higher sensory features and lower motor skills than autistic children without ADHD. In examining autism and ADHD features dimensionally, we found that overall sensory features, seeking, and hyporesponsiveness were driven by both autism and ADHD features, whereas motor skills, enhanced perception, and hyperresponsiveness were driven by only autism features. Additionally, in using these dimensional variables of autism and ADHD features, we found that differences in motor skills, sensory and autism features, but not ADHD features, impact DLS of autistic children, with autism features and motor skills being the strongest individual predictors of DLS. Together, these results demonstrate the uniqueness of motor skills and sensory features in autistic children with and without ADHD, as well as how autism features, sensory features, and motor skills contribute to DLS, emphasizing the importance of a comprehensive understanding of each individual and complexities of human development when supporting autistic children.

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.

Measurement invariance of Attention Deficit/Hyperactivity Disorder symptom criteria as rated by parents and teachers in children and adolescents: A systematic review

This systematic review aimed to establish the extent to which each Attention Deficit/Hyperactivity Disorder (ADHD) symptom criterion is being assessed without being influenced (biased) by factors such as informant, sex/gender, and age. Measurement invariance (MI) testing using confirmatory factor analysis (CFA) is the prime statistical method to ascertain how these factors may affect the measurement and colour the perception or interpretation of symptom criteria. Such effects (non-invariance) can be operationalised in the form of altered association of a symptom criterion with the measured trait (expressed via variations in CFA loadings which represent the weight of each symptom criterion) due to the factor(s) and/or artificially alter the probability of endorsement of a particular symptom criterion (expressed via variations in the CFA threshold(s) representing how mild or severe a given symptom is). Based on a pre-registered protocol (CRD42022276105), we searched PubMed, Global Health, Embase and PsycInfo up to 21-02-23 for studies that included MI assessments on specific ADHD symptom criteria in individuals aged 0-18 years old, using parental and/or teacher report. Self-reports were excluded, given the poor reliability of self-report in ADHD. All included studies met specific COnsensus-based Standards for the selection of health Measurement Instruments (COSMIN) criteria. Results were synthesised in tabular form, grouping results by factors (e.g. informant) from 44 studies retained. Most comparisons indicated both metric (same loadings) and scalar invariance (same thresholds) with regard to informant, gender, age, temporal (repeated assessments) and co-morbidity. Therefore, the available evidence supports the current diagnostic criteria. However, findings could have been improved by systematic reporting of the direction of bias and its effect size. There appears to be a bias towards reporting MI instead of non-invariance. More studies in the literature are needed where the amalgamation of information provided by different informs and the association of specific symptoms with comorbidity are analysed.

Relationship between autonomic arousal and attention orienting in children and adolescents with ADHD, autism and co-occurring ADHD and autism

INTRODUCTION

Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) may be characterized by different profiles of visual attention orienting. However, there are also many inconsistent findings emerging from the literature, probably due to the fact that the potential effect of autonomic arousal (which has been proposed to be dysregulated in these conditions) on oculomotor performance has not been investigated before. Moreover, it is not known how visual attention orienting is affected by the co-occurrence of ADHD and autism in people with a double diagnosis.

METHODS

99 children/adolescents with or without ADHD and/or autism (age 10.79 ± 2.05 years, 65% boys) completed an adapted version of the gap-overlap task (with baseline and overlap trials only). The social salience and modality of stimuli were manipulated between trials. Eye movements and pupil size were recorded. We compared saccadic reaction times (SRTs) between diagnostic groups and investigated if a trial-by-trial association existed between pre-saccadic pupil size and SRTs.

RESULTS

Faster orienting (shorter SRT) was found for baseline compared to overlap trials, faces compared to non-face stimuli and-more evidently in children without ADHD and/or autism-for multi-modal compared to uni-modal stimuli. We also found a linear negative association between pre-saccadic pupil size and SRTs, in autistic participants (without ADHD), and a quadratic association in children with ADHD (without autism), for which SRTs were slower when intra-individual pre-saccadic pupil size was smallest or largest.

CONCLUSION

Our findings are in line with previous literature and indicate a possible effect of dysregulated autonomic arousal on oculomotor mechanisms in autism and ADHD, which should be further investigated in future research studies with larger samples, to reliably investigate possible differences between children with single and dual diagnoses.

Retinal electrophysiology in central nervous system disorders. A review of human and mouse studies

The retina and brain share similar neurochemistry and neurodevelopmental origins, with the retina, often viewed as a "window to the brain." With retinal measures of structure and function becoming easier to obtain in clinical populations there is a growing interest in using retinal findings as potential biomarkers for disorders affecting the central nervous system. Functional retinal biomarkers, such as the electroretinogram, show promise in neurological disorders, despite having limitations imposed by the existence of overlapping genetic markers, clinical traits or the effects of medications that may reduce their specificity in some conditions. This narrative review summarizes the principal functional retinal findings in central nervous system disorders and related mouse models and provides a background to the main excitatory and inhibitory retinal neurotransmitters that have been implicated to explain the visual electrophysiological findings. These changes in retinal neurochemistry may contribute to our understanding of these conditions based on the findings of retinal electrophysiological tests such as the flash, pattern, multifocal electroretinograms, and electro-oculogram. It is likely that future applications of signal analysis and machine learning algorithms will offer new insights into the pathophysiology, classification, and progression of these clinical disorders including autism, attention deficit/hyperactivity disorder, bipolar disorder, schizophrenia, depression, Parkinson's, and Alzheimer's disease. New clinical applications of visual electrophysiology to this field may lead to earlier, more accurate diagnoses and better targeted therapeutic interventions benefiting individual patients and clinicians managing these individuals and their families.