The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project design and methodologies: a dimensional approach to understanding neurobiological and genetic aetiology

Where do neurodevelopmental conditions fit in transdiagnostic psychiatric frameworks? Incorporating a new neurodevelopmental spectrum

Features of autism spectrum disorder, attention-deficit/hyperactivity disorder, learning disorders, intellectual disabilities, and communication and motor disorders usually emerge early in life and are associated with atypical neurodevelopment. These "neurodevelopmental conditions" are grouped together in the DSM-5 and ICD-11 to reflect their shared characteristics. Yet, reliance on categorical diagnoses poses significant challenges in both research and clinical settings (e.g., high co-occurrence, arbitrary diagnostic boundaries, high within-disorder heterogeneity). Taking a transdiagnostic dimensional approach provides a useful alternative for addressing these limitations, accounting for shared underpinnings across neurodevelopmental conditions, and characterizing their common co-occurrence and developmental continuity with other psychiatric conditions. Neurodevelopmental features have not been adequately considered in transdiagnostic psychiatric frameworks, although this would have fundamental implications for research and clinical practices. Growing evidence from studies on the structure of neurodevelopmental and other psychiatric conditions indicates that features of neurodevelopmental conditions cluster together, delineating a "neurodevelopmental spectrum" ranging from normative to impairing profiles. Studies on shared genetic underpinnings, overlapping cognitive and neural profiles, and similar developmental course and efficacy of support/treatment strategies indicate the validity of this neurodevelopmental spectrum. Further, characterizing this spectrum alongside other psychiatric dimensions has clinical utility, as it provides a fuller view of an individual's needs and strengths, and greater prognostic utility than diagnostic categories. Based on this compelling body of evidence, we argue that incorporating a new neurodevelopmental spectrum into transdiagnostic frameworks has considerable potential for transforming our understanding, classification, assessment, and clinical practices around neurodevelopmental and other psychiatric conditions.

Age at diagnosis and diagnostic delay across attention-deficit hyperactivity and autism spectrums

BACKGROUND

Despite the known benefits of accurate and timely diagnosis for children with attention-deficit hyperactivity disorder and autism spectrum disorders (autism), for some children this goal is not always achieved. Existing research has explored diagnostic delay for autism and attention-deficit hyperactivity disorder only, and when attention-deficit hyperactivity disorder and autism co-occur, autism has been the focus. No study has directly compared age at diagnosis and diagnostic delay for males and females across attention-deficit hyperactivity disorder, autism and specifically, attention-deficit hyperactivity disorder + autism.

METHODS

Australian caregivers (N = 677) of children with attention-deficit hyperactivity disorder, autism or attention-deficit hyperactivity disorder + autism were recruited via social media (n = 594) and the Monash Autism and ADHD Genetics and Neurodevelopment Project (n = 83). Caregivers reported on their child's diagnostic process. Diagnostic delay was the mean difference between general initial developmental concerns and the child's attention-deficit hyperactivity disorder and autism diagnosis.

RESULTS

Children with autism were significantly younger at autism diagnosis than the attention-deficit hyperactivity disorder + autism group (ηp2 = 0.06), whereas children with attention-deficit hyperactivity disorder were significantly older at attention-deficit hyperactivity disorder diagnosis than the attention-deficit hyperactivity disorder + autism group (ηp2 = 0.01). Delay to attention-deficit hyperactivity disorder and autism diagnosis was significantly longer in the attention-deficit hyperactivity disorder + autism group compared to attention-deficit hyperactivity disorder (ηp2 = 0.02) and autism (η2 = 0.04) only. Delay to autism diagnosis for females with autism (η2 = 0.06) and attention-deficit hyperactivity disorder + autism (η2 = 0.04) was longer compared to males.

CONCLUSIONS

Having attention-deficit hyperactivity disorder + autism and being female were associated with longer delays to diagnosis. The reasons for these delays and possible adverse effects on outcomes require further study.

Management of sleep disorders in autism spectrum disorder with co-occurring attention-deficit hyperactivity disorder: update for clinicians

AIMS

To update and examine available literature germane to the recognition, assessment and treatment of comorbid autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD) and sleep disruption, with a predominant focus on children, adolescents and emerging adults.

BACKGROUND

Considerable overlaps exist among ASD, ADHD and sleep disruption. Literature and guidance for clinicians, administrators, policy makers and families have been limited, as such deliberations were rarely considered until 2013.

METHOD

This narrative review of the literature addressing sleep disruption issues among those with ASD, ADHD and comorbid ASD and ADHD involved searching multiple databases and use of reverse citations up to the end of September 2022. Emphasis is placed on secondary sources and relevant data for clinical practice.

RESULTS

Complex clinical presentations of ASD/ADHD/sleep disruption are frequently encountered in clinical practice. Prior to 2013, prevalence, clinical presentation, pathophysiology, prognosis, other sleep-related factors and interventions were determined separately for each disorder, often with overlapping objective and subjective methods employed in the process. High percentages of ADHD and ASD patients have both disorders and sleep disruption. Here, the extant literature is integrated to provide a multidimensional understanding of the relevant issues and insights, allowing enhanced awareness and better care of this complex clinical population. Database limitations are considered.

CONCLUSIONS

Assessment of ASD symptomatology in youth with ADHD, and the reverse, in cases with disrupted sleep is critical to address the special challenges for case formulation and treatment. Evidence-based approaches to treatment planning and multi-treatment modalities should consider combining psychosocial and biological interventions to address the complexities of each case.

Machine Learning Differentiation of Autism Spectrum Sub-Classifications

PURPOSE

Disorders on the autism spectrum have characteristics that can manifest as difficulties with communication, executive functioning, daily living, and more. These challenges can be mitigated with early identification. However, diagnostic criteria has changed from DSM-IV to DSM-5, which can make diagnosing a disorder on the autism spectrum complex. We evaluated machine learning to classify individuals as having one of three disorders of the autism spectrum under DSM-IV, or as non-spectrum.

METHODS

We employed machine learning to analyze retrospective data from 38,560 individuals. Inputs encompassed clinical, demographic, and assessment data.

RESULTS

The algorithm achieved AUROCs ranging from 0.863 to 0.980. The model correctly classified 80.5% individuals; 12.6% of individuals from this dataset were misclassified with another disorder on the autism spectrum.

CONCLUSION

Machine learning can classify individuals as having a disorder on the autism spectrum or as non-spectrum using minimal data inputs.

Everyday executive functioning in pediatric obsessive-compulsive disorder: diagnostic specificity, clinical correlations, and outcome

BACKGROUND

Obsessive-compulsive disorder (OCD) typically onsets during childhood or adolescence and difficulties with executive functioning (EF) may be involved in its onset and maintenance. Yet, few studies have examined everyday EF difficulties in youth with OCD and no study has compared EF in youth with OCD to EF in youth with anxiety disorders, leaving the diagnostic specificity of EF unclear.

METHODS

In this study, parents of treatment-seeking children and adolescents with OCD (n = 96, Mage = 13.3, SD = 2.7, 59% girls) or anxiety disorders (n = 67, Mage = 14.0, SD = 2.6, 78% girls) reported on their children's everyday EF using the Behavior Rating Inventory of Executive Function (BRIEF) measure.

RESULTS

Compared to community youth, the two clinical groups showed moderately elevated EF deficits but did not differ significantly from each other. EF deficits were not associated with the major symptom dimensions of OCD, age of OCD symptom onset, duration of OCD symptoms, and OCD severity, and did not predict treatment outcome in OCD.

CONCLUSIONS

Compared to peers, youth with OCD show moderate difficulties with EF, but very similar difficulties are seen in youth with anxiety disorders, and it is unclear whether these difficulties are of clinical relevance. Among youth with OCD, EF difficulties were not differentially associated with the major symptom dimensions of OCD, which is inconsistent with findings from adults. Difficulties with EF did not predict treatment outcome, indicating that integrating EF modules into OCD treatment may be of limited value, although EF may be important for treatment planning in individual cases.

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.