Examining overlap and homogeneity in ASD, ADHD, and OCD: a data-driven, diagnosis-agnostic approach

Gender diversity is correlated with dimensional neurodivergent traits but not categorical neurodevelopmental diagnoses in children

BACKGROUND

Gender clinic and single-item questionnaire-based data report increased co-occurrence of gender diversity and neurodevelopmental conditions. The nuances of these associations are under-studied. We used a transdiagnostic approach, combining categorical and dimensional characterization of neurodiversity, to further the understanding of its associations with gender diversity in identity and expression in children.

METHODS

Data from 291 children (Autism N = 104, ADHD N = 104, Autism + ADHD N = 17, neurotypical N = 66) aged 4-12 years enrolled in the Province of Ontario Neurodevelopmental Network were analyzed. Gender diversity was measured multi-dimensionally using a well-validated parent-report instrument, the Gender Identity Questionnaire for Children (GIQC). We used gamma regression models to determine the significant correlates of gender diversity among age, puberty, sex-assigned-at-birth, categorical neurodevelopmental diagnoses, and dimensional neurodivergent traits (using the Social Communication Questionnaire and the Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Rating Scales). Internalizing and externalizing problems were included as covariates.

RESULTS

Neither a categorical diagnosis of autism nor ADHD significantly correlated with current GIQC-derived scores. Instead, higher early-childhood dimensional autistic social-communication traits correlated with higher current overall gender incongruence (as defined by GIQC-14 score). This correlation was potentially moderated by sex-assigned-at-birth: greater early-childhood autistic social-communication traits were associated with higher current overall gender incongruence in assigned-males-at-birth, but not assigned-females-at-birth. For fine-grained gender diversity domains, greater autistic restricted-repetitive behavior traits were associated with greater diversity in gender identity across sexes-assigned-at-birth; greater autistic social-communication traits were associated with lower stereotypical male expression across sexes-assigned-at-birth.

CONCLUSIONS

Dimensional autistic traits, rather than ADHD traits or categorical neurodevelopmental diagnoses, were associated with gender diversity domains across neurodivergent and neurotypical children. The association between early-childhood autistic social-communication traits and overall current gender diversity was most evident in assigned-males-at-birth. Nuanced interrelationships between neurodivergence and gender diversity should be better understood to clarify developmental links and to offer tailored support for neurodivergent and gender-diverse populations.

Repetitive Behaviors in Autism and Obsessive-Compulsive Disorder: A Systematic Review

PURPOSE

Obsessive-compulsive disorder (OCD) and autism are characterized by the presence of repetitive behaviors. Differentiating between repetitive behaviors attributable to a diagnosis of autism, and those attributable to OCD, poses challenges for differential and co-occurring diagnosis. Differentiation is important to inform appropriate supports and interventions for phenotypically similar but functionally distinct behaviors. In this systematic review, the quantitative literature was examined to explore the similarities and differences in repetitive behaviors (including restricted and repetitive behaviors and interests, and obsessive-compulsive behaviors) in autistic individuals and those with OCD, and those with co-occurring diagnoses, in terms of: (1) expression, (2) content, and (3) associated factors.

METHODS

Thirty-one studies were identified that compared repetitive behaviors in autistic individuals, individuals with OCD, or individuals with both diagnoses.

RESULTS

The results suggest considerable overlap in the intensity and content of repetitive behaviors between groups. The findings of this review highlight that research aimed specifically at understanding similarities and differences in repetitive behaviors between autistic individuals and individuals with OCD is limited and frequently only compare at total score or composite measure levels.

CONCLUSION

Further research into differences in the presentation of repetitive behaviors at a subscale and item level is required to inform clearer differentiation of specific behaviors in autism versus OCD. Understanding and more accurately differentiating is essential for efficient diagnosis, effective treatment, and better outcomes.

Behaviour-correlated profiles of cerebellar-cerebral functional connectivity observed in independent neurodevelopmental disorder cohorts

The cerebellum, through its connectivity with the cerebral cortex, plays an integral role in regulating cognitive and affective processes, and its dysregulation can result in neurodevelopmental disorder (NDD)-related behavioural deficits. Identifying cerebellar-cerebral functional connectivity (FC) profiles in children with NDDs can provide insight into common connectivity profiles and their correlation to NDD-related behaviours. 479 participants from the Province of Ontario Neurodevelopmental Disorders (POND) network (typically developing = 93, Autism Spectrum Disorder = 172, Attention Deficit/Hyperactivity Disorder = 161, Obsessive-Compulsive Disorder = 53, mean age = 12.2) underwent resting-state functional magnetic resonance imaging and behaviour testing (Social Communication Questionnaire, Toronto Obsessive-Compulsive Scale, and Child Behaviour Checklist - Attentional Problems Subscale). FC components maximally correlated to behaviour were identified using canonical correlation analysis. Results were then validated by repeating the investigation in 556 participants from an independent NDD cohort provided from a separate consortium (Healthy Brain Network (HBN)). Replication of canonical components was quantified by correlating the feature vectors between the two cohorts. The two cerebellar-cerebral FC components that replicated to the greatest extent were correlated to, respectively, obsessive-compulsive behaviour (behaviour feature vectors, rPOND-HBN = -0.97; FC feature vectors, rPOND-HBN = -0.68) and social communication deficit contrasted against attention deficit behaviour (behaviour feature vectors, rPOND-HBN = -0.99; FC feature vectors, rPOND-HBN = -0.78). The statistically stable (|z| > 1.96) features of the FC feature vectors, measured via bootstrap re-sampling, predominantly comprised of correlations between cerebellar attentional and control network regions and cerebral attentional, default mode, and control network regions. In both cohorts, spectral clustering on FC loading values resulted in subject clusters mixed across diagnostic categories, but no cluster was significantly enriched for any given diagnosis as measured via chi-squared test (p > 0.05). Overall, two behaviour-correlated components of cerebellar-cerebral functional connectivity were observed in two independent cohorts. This suggests the existence of generalizable cerebellar network differences that span across NDD diagnostic boundaries.

Predictors of health-related quality of life for children with neurodevelopmental conditions

Neurodevelopmental conditions can be associated with decreased health-related quality of life; however, the predictors of these outcomes remain largely unknown. We characterized the predictors of health-related quality of life (HRQoL) in a sample of neurodiverse children and youth. We used a cross-sectional subsample from the Province of Ontario Neurodevelopmental Disorders Network (POND) consisting of those children and young people in the POND dataset with complete study data (total n = 615; 31% female; age: 11.28 years ± 2.84 years). Using a structural equation model, we investigated the effects of demographics (age, sex, socioeconomic status), core features (Social Communication Questionnaire, Toronto Obsessive Compulsive Scale, Strengths and Weaknesses of attention deficit/hyperactivity disorder (ADHD)-symptoms and Normal Behavior), co-occurring symptoms (Child Behaviour Checklist), and adaptive functioning (Adaptive Behaviour Assessment System) on HRQoL (KINDL). A total of 615 participants had complete data for this study (autism = 135, ADHD = 273, subthreshold ADHD = 7, obsessive-compulsive disorder (OCD) = 38, sub-threshold OCD = 1, neurotypical = 161). Of these participants, 190 (31%) identified as female, and 425 (69%) identified as male. The mean age was 11.28 years ± 2.84 years. Health-related quality of life was negatively associated with co-occurring symptoms (B = - 0.6, SE = 0.20, CI (- 0.95, - 0.19), p = 0.004)) and age (B = - 0.1, SE = 0.04, CI (- 0.19, - 0.01), p = 0.037). Fewer co-occurring symptoms were associated with higher socioeconomic status (B = - 0.5, SE = - 0.05, CI (- 0.58, - 0.37), p < 0.001). This study used a cross-sectional design. Given that one's experiences, needs, supports, and environment and thus HrQoL may change significantly over the lifespan and a longitudinal analysis of predictors is needed to capture these changes. Future studies with more diverse participant groups are needed. These results demonstrate the importance of behavioural and sociodemographic characteristics on health-related quality of life across neurodevelopmental conditions.

The role of stimulant washout status in functional connectivity of default mode and fronto-parietal networks in children with neurodevelopmental conditions

BACKGROUND

Stimulant medication is the primary pharmacological treatment for attention dysregulation and is commonly prescribed for children with Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism. Neuroimaging studies of these groups commonly use a 24-48-hour washout period to mediate the effects of stimulant medication on functional connectivity (FC) metrics. However, the impact of washout on functional connectivity has received limited study.

METHODS

We used fMRI data from participants with diagnosis of Autism and ADHD (and an off stimulant control) from the Adolescent Brain and Cognitive Development (ABCD) and Autism Brain Imaging Data Exchange (ABIDE) databases to explore the effect of simulant washout on FC. Connectivity within and between the default mode (DMN) and fronto-parietal networks (FPN) was examined, as these networks have previously been implicated in attention dysregulation and associated with stimulant medication usage. For each diagnostic group, we assessed effects in interconnectivity between DMN and FPN, intraconnectivity within DMN, and intraconnectivity within FPN.

RESULTS

We found no significant effect of medication status in intra- and inter-connectivity of the DMN and the FPN in either diagnostic group.

IMPLICATIONS

Our findings suggest that more information is needed about the effect of stimulant medication, and washout, on the FC of attention networks in clinical populations.

Predictors of Health-Related Quality of Life in Neurodivergent Children: A Systematic Review

Health-related Quality of Life (HRQoL) is a multi-faceted construct influenced by a myriad of environmental, demographic, and individual characteristics. Our understanding of these influencers remains highly limited in neurodevelopmental conditions. Existing research in this area is sparse, highly siloed by diagnosis labels, and focused on symptoms. This review synthesized the evidence in this area using a multi-dimensional model of HRQoL and trans-diagnostically across neurodevelopmental conditions. The systematic review, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Checklist, was completed in June 2023 using Medline, PsycInfo, Embase, PubMed, and Cochrane Library. Our search revealed 78 studies that examined predictors of HRQoL in neurodevelopmental conditions. The majority of these studies focused on autism and ADHD with a paucity of literature in other conditions. Cross-diagnosis investigations were limited despite the fact that many of the examined predictors transcend diagnostic boundaries. Significant gaps were revealed in domains of biology/physiology, functioning, health perceptions, and environmental factors. Very preliminary evidence suggested potentially shared predictors of HRQoL across conditions including positive associations between HRQoL and adaptive functioning, male sex/gender, positive self-perception, physical activity, resources, and positive family context, and negative associations with diagnostic features and mental health symptoms. Studies of transdiagnostic predictors across neurodevelopmental conditions are critically needed to enable care models that address shared needs of neurodivergent individuals beyond diagnostic boundaries. Further understanding of HRQoL from the perspective of neurodivergent communities is a critical area of future work.

Understanding divergence: Placing developmental neuroscience in its dynamic context

Neurodevelopment is not merely a process of brain maturation, but an adaptation to constraints unique to each individual and to the environments we co-create. However, our theoretical and methodological toolkits often ignore this reality. There is growing awareness that a shift is needed that allows us to study divergence of brain and behaviour across conventional categorical boundaries. However, we argue that in future our study of divergence must also incorporate the developmental dynamics that capture the emergence of those neurodevelopmental differences. This crucial step will require adjustments in study design and methodology. If our ultimate aim is to incorporate the developmental dynamics that capture how, and ultimately when, divergence takes place then we will need an analytic toolkit equal to these ambitions. We argue that the over reliance on group averages has been a conceptual dead-end with regard to the neurodevelopmental differences. This is in part because any individual differences and developmental dynamics are inevitably lost within the group average. Instead, analytic approaches which are themselves new, or simply newly applied within this context, may allow us to shift our theoretical and methodological frameworks from groups to individuals. Likewise, methods capable of modelling complex dynamic systems may allow us to understand the emergent dynamics only possible at the level of an interacting neural system.

Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample

Major Depressive Disorder (MDD) is a heterogeneous disorder, resulting in challenges with early detection. However, changes in sleep and movement patterns may help improve detection. Thus, this study aimed to explore the utility of wrist-worn actigraphy data in combination with machine learning (ML) and deep learning techniques to detect MDD using a commonly used screening method: Patient Health Questionnaire-9 (PHQ-9). Participants (N = 8,378; MDD Screening = 766 participants) completed the and wore Actigraph GT3X+ for one week as part of the National Health and Nutrition Examination Survey (NHANES). Leveraging minute-level, actigraphy data, we evaluated the efficacy of two commonly used ML approaches and identified actigraphy-derived biomarkers indicative of MDD. We employed two ML modeling strategies: (1) a traditional ML approach with theory-driven feature derivation, and (2) a deep learning Convolutional Neural Network (CNN) approach, coupled with gramian angular field transformation. Findings revealed movement-related features to be the most influential in the traditional ML approach and nighttime movement to be the most influential in the CNN approach for detecting MDD. Using a large, nationally-representative sample, this study highlights the potential of using passively-collected, actigraphy data for understanding MDD to better improve diagnosing and treating MDD.

Modelling the overlap and divergence of autistic and schizotypal traits on hippocampal subfield volumes and regional cerebral blood flow

Psychiatric disorders show high co-morbidity, including co-morbid expressions of subclinical psychopathology across multiple disease spectra. Given the limitations of classical case-control designs in elucidating this overlap, new approaches are needed to identify biological underpinnings of spectra and their interaction. We assessed autistic-like traits (using the Autism Quotient, AQ) and schizotypy - as models of subclinical expressions of disease phenotypes and examined their association with volumes and regional cerebral blood flow (rCBF) of anterior, mid- and posterior hippocampus segments from  structural MRI scans in 318 and arterial spin labelling (ASL) in 346 nonclinical subjects, which overlapped with the structural imaging sample (N = 298). We demonstrate significant interactive effects of positive schizotypy and AQ social skills as well as of positive schizotypy and AQ imagination on hippocampal subfield volume variation. Moreover, we show that AQ attention switching modulated hippocampal head rCBF, while positive schizotypy by AQ attention to detail interactions modulated hippocampal tail rCBF. In addition, we show significant correlation of hippocampal volume and rCBF in both region-of-interest and voxel-wise analyses, which were robust after removal of variance related to schizotypy and autistic traits. These findings provide empirical evidence for both the modulation of hippocampal subfield structure and function through subclinical traits, and in particular how only the interaction of phenotype facets leads to significant reductions or variations in these parameters. This makes a case for considering the synergistic impact of different (subclinical) disease spectra on transdiagnostic biological parameters in psychiatry.

Transdiagnostic Patterns of Sensory Processing in Autism and ADHD

Sensory processing abilities are highly variable within and across people diagnosed with autism and attention-deficit/hyperactivity disorder (ADHD). This study examined the transdiagnostic nature of sensory processing abilities, and their association with features of autism and ADHD, in a large sample of autistic people (n = 495) and people with ADHD (n = 461). Five similar data-driven sensory phenotypes characterized sensory processing abilities, and showed similar patterns of association with features of autism and ADHD, across both diagnostic groups. These results demonstrate the transdiagnostic nature of sensory processing abilities, while contributing to a growing body of literature that suggests the autism and ADHD diagnostic labels have poor explanatory power.

Fifty years of executive control research in attention-deficit/hyperactivity disorder:What we have learned and still need to know

For 50 years, attention-deficit/hyperactivity disorder (ADHD) has been considered a disorder of executive control (EC), the higher-order, cognitive skills that support self-regulation, goal attainment and what we generally call "attention." This review surveys our current understanding of the nature of EC as it pertains to ADHD and considers the evidence in support of eight hypotheses that can be derived from the EC theory of ADHD. This paper provides a resource for practitioners to aid in clinical decision-making. To support theory building, I draw a parallel between the EC theory of ADHD and the common gene-common variant model of complex traits such as ADHD. The conclusion offers strategies for advancing collaborative research.

Testing the triple network model of psychopathology in a transdiagnostic neurodevelopmental cohort

AIM

The triple network model of psychopathology posits that altered connectivity between the Salience (SN), Central Executive (CEN), and Default Mode Networks (DMN) may underlie neurodevelopmental conditions. However, this has yet to be tested in a transdiagnostic sample of young people.

METHOD

We investigated this in 175 children (60 girls) that represent a heterogeneous population who are experiencing neurodevelopmental difficulties in cognition and behavior, and 60 comparison children (33 girls). Hyperactivity/impulsivity and inattention were assessed by parent-report. Resting-state functional Magnetic Resonance Imaging data were acquired and functional connectivity was calculated between independent network components and regions of interest. We then examined whether connectivity between the SN, CEN and DMN was dimensionally related to hyperactivity/impulsivity and inattention, whilst controlling for age, gender, and motion.

RESULTS

Hyperactivity/impulsivity was associated with increased functional connectivity between the SN, CEN, and DMN in at-risk children, whereas it was associated with decreased functional connectivity between the CEN and DMN in comparison children. These effects replicated in an adult parcellation of brain function and when using increasingly stringent exclusion criteria for in-scanner motion.

CONCLUSION

Triple network connectivity characterizes transdiagnostic neurodevelopmental difficulties with hyperactivity/impulsivity. We suggest that this may arise from delayed network segregation, difficulties sustaining CEN activity to regulate behavior, and/or a heightened developmental mismatch between neural systems implicated in cognitive control relative to those implicated in reward/affect processing.

Probing the overarching continuum theory: data-driven phenotypic clustering of children with ASD or ADHD

The clinical validity of the distinction between ADHD and ASD is a longstanding discussion. Recent advances in the realm of data-driven analytic techniques now enable us to formally investigate theories aiming to explain the frequent co-occurrence of these neurodevelopmental conditions. In this study, we probe different theoretical positions by means of a pre-registered integrative approach of novel classification, subgrouping, and taxometric techniques in a representative sample (N = 434), and replicate the results in an independent sample (N = 219) of children (ADHD, ASD, and typically developing) aged 7-14 years. First, Random Forest Classification could predict diagnostic groups based on questionnaire data with limited accuracy-suggesting some remaining overlap in behavioral symptoms between them. Second, community detection identified four distinct groups, but none of them showed a symptom profile clearly related to either ADHD or ASD in neither the original sample nor the replication sample. Third, taxometric analyses showed evidence for a categorical distinction between ASD and typically developing children, a dimensional characterization of the difference between ADHD and typically developing children, and mixed results for the distinction between the diagnostic groups. We present a novel framework of cutting-edge statistical techniques which represent recent advances in both the models and the data used for research in psychiatric nosology. Our results suggest that ASD and ADHD cannot be unambiguously characterized as either two separate clinical entities or opposite ends of a spectrum, and highlight the need to study ADHD and ASD traits in tandem.

Single-label machine learning classification revealed some hidden but inter-related causes of five psychotic disorder diseases

Psychotic disorder diseases (PDD) or mental illnesses are group of illnesses that affect the minds and impair the cognitive ability, retard emotional ability and obstruct the process of communication and relationship with others and are characterized by delusions, hallucinations and disoriented or disordered pattern of thinking. Prognosis of PDD is not sufficient because of the nature of the diseases and as such adequate form of diagnosis is required to detect, manage and treat the illness. This paper applied the single-label classification (SLC) machine learning approach in mining of electronic health records of people with PDD in Nigeria using eleven independent (demographic) variables and five PDD as target variables. The five PDDs are Insomnia, Schizophrenia, Minimal Brain dysfunction (MBD), which is also known as Attention-Deficit/Hyperactivity Disorder (ADHD), Vascular Dementia (VD) and Bipolar Disorder (BD). The aim of using SLC is that it would be easier to detect some PDDs that are related to each other without the loss of information, which is a plus over multi-label classification (MLC). ReliefF algorithm was used at each experiment to precipitate the order of importance of the independent variables and redundant variables were excluded from the analysis. The order of the variables in feature selection was matched with feature importance after the classifications and quantified using the Spearman rank correlation coefficient. The data was divided into: 70% for training and 30% for testing. Four new performance metrics adapted from the root mean square (RMSE) were proposed and used to measure the differences between the performance results of the 10 Machine learning models in terms of the training and testing and secondly, feature and without feature selection. The new metrics are close to zero which is an indication that the use of feature selection and cross validation may not greatly affects the accuracy of the SLC. When the PDDs are included as predictors for classifying others, there was a tremendous improvement as revealed by the four new metrics for classification accuracy (CA), precision and recall. Analysis of variance showed the four different metrics differs significantly for classification accuracy (CA) and precision. However, there were no significant difference between the CA and precision when the duo are compared together across the four evaluation metrics at p value less than 0.05.

Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder in children and has a high chance of persisting in adulthood. The development of individualized, efficient, and reliable treatment strategies is limited by the lack of understanding of the underlying neural mechanisms. Diverging and inconsistent findings from existing studies suggest that ADHD may be simultaneously associated with multivariate factors across cognitive, genetic, and biological domains. Machine learning algorithms are more capable of detecting complex interactions between multiple variables than conventional statistical methods. Here we present a narrative review of the existing machine learning studies that have contributed to understanding mechanisms underlying ADHD with a focus on behavioral and neurocognitive problems, neurobiological measures including genetic data, structural magnetic resonance imaging (MRI), task-based and resting-state functional MRI (fMRI), electroencephalogram, and functional near-infrared spectroscopy, and prevention and treatment strategies. Implications of machine learning models in ADHD research are discussed. Although increasing evidence suggests that machine learning has potential in studying ADHD, extra precautions are still required when designing machine learning strategies considering the limitations of interpretability and generalization.

Structural neuroimaging phenotypes and associated molecular and genomic underpinnings in autism: a review

Autism has been associated with differences in the developmental trajectories of multiple neuroanatomical features, including cortical thickness, surface area, cortical volume, measures of gyrification, and the gray-white matter tissue contrast. These neuroimaging features have been proposed as intermediate phenotypes on the gradient from genomic variation to behavioral symptoms. Hence, examining what these proxy markers represent, i.e., disentangling their associated molecular and genomic underpinnings, could provide crucial insights into the etiology and pathophysiology of autism. In line with this, an increasing number of studies are exploring the association between neuroanatomical, cellular/molecular, and (epi)genetic variation in autism, both indirectly and directly in vivo and across age. In this review, we aim to summarize the existing literature in autism (and neurotypicals) to chart a putative pathway from (i) imaging-derived neuroanatomical cortical phenotypes to (ii) underlying (neuropathological) biological processes, and (iii) associated genomic variation.

Social competence as a predictor of adult outcomes in autism spectrum disorder

There is a wealth of literature characterizing social difficulties in autism spectrum disorder (ASD). However, little work has replicated longitudinal findings from typical development that adolescent social competence predicts positive adult outcomes in ASD. The current study examined social competence trajectories from 2 to 26 and the utility of three social competence measures collected in adolescence in predicting work, residential status, friendship, and romantic outcomes in a longitudinal cohort (n = 253) of ASD. Using group-based trajectory modeling, we identified two patterns of social competence development: a low trajectory characterized by slow linear gains throughout childhood and plateauing in adulthood, and a high trajectory characterized by steeper linear gains in childhood followed by decline in adulthood. Regression models indicated one social competence measure, Vineland Social-AE scores, significantly predicted employment, residential status, and friendships in adulthood. One other social competence measure, SSQ total scores, also significantly predicted friendship in adulthood. Only nonverbal IQ at 9 predicted the likelihood of having ever had a romantic relationship. These findings highlight the role of social competence in both atypical and typical development and suggest the social impairments associated with ASD do not necessarily impact all realms of social functioning equally.

The Association Between Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Symptoms in Medical Students

ABSTRACT

Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are the two most prevalent neurodevelopmental disorders affecting communication and behavior. The co-occurrence of these conditions is probable and can contribute to several challenges in adaptive functioning and academic achievement.In this cross-sectional study, 168 Iranian medical students (107 female, 61 male) studying at Tehran University of Medical Sciences in 2021 were enrolled. We administered the Ritvo Autism Asperger Diagnostic Scale-Revised (RAADS-R) and Adult ADHD Self-Report Scale (ASRS) questionnaires online to assess different symptoms of ASD and ADHD in our sample. In this study, the RAADS-R was translated into Persian and validated for the first time in Iran.The correlation tests demonstrated a significant association between the total score and different subscales' scores of the RAADS-R and the total score and the two subscales' scores of the ASRS questionnaire (p < 0.001, 0.27 < Spearman correlation coefficient < 0.51). This study also illustrated a high prevalence of ASD and ADHD symptoms among the participants. Moreover, male respondents had a significantly higher prevalence of ASD symptoms (57.3% in males vs. 28.03% in females, p < 0.001).This study indicated that the distinct impairments in behavior and cognition attributed to ASD and ADHD could be common manifestations in medical students. Given that the co-occurrence of these disorders may lead to significant challenges in their professional life, the early diagnosis and subsequent support for medical students with co-occurring expressions of ASD and ADHD could be extremely helpful, as it could indirectly improve the medical services provided to patients by future physicians, leading to an improvement in public health.

Cerebellar gamma-aminobutyric acid: Investigation of group effects in neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) including autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD) are thought to arise in part from the disruption in the excitatory/inhibitory balance of gamma-aminobutyric acid (GABA) and glutamate in the brain. Recent evidence has shown the involvement of the cerebellum in cognition and affect regulation, and cerebellar atypical function or damage is reported frequently in NDDs. Magnetic resonance spectroscopy studies have reported decreases in GABA in cortical brain areas in the NDDs, however, GABA levels in the cerebellum have not been examined. To determine possible group effects, we used a MEGA-PRESS acquisition to investigate GABA+ levels in a cerebellar voxel in 343 individuals (aged 2.5-22 years) with ASD, ADHD, OCD and controls. Using a mixed effects model, we found no significant differences between groups in GABA+ concentration. Our findings suggest that cerebellar GABA+ levels do not differentiate NDD groups.

Identifying Replicable Subgroups in Neurodevelopmental Conditions Using Resting-State Functional Magnetic Resonance Imaging Data

IMPORTANCE

Neurodevelopmental conditions, such as autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD), have highly heterogeneous and overlapping phenotypes and neurobiology. Data-driven approaches are beginning to identify homogeneous transdiagnostic subgroups of children; however, findings have yet to be replicated in independently collected data sets, a necessity for translation into clinical settings.

OBJECTIVE

To identify subgroups of children with and without neurodevelopmental conditions with shared functional brain characteristics using data from 2 large, independent data sets.

DESIGN, SETTING, AND PARTICIPANTS

This case-control study used data from the Province of Ontario Neurodevelopmental (POND) network (study recruitment began June 2012 and is ongoing; data were extracted April 2021) and the Healthy Brain Network (HBN; study recruitment began May 2015 and is ongoing; data were extracted November 2020). POND and HBN data are collected from institutions across Ontario and New York, respectively. Participants who had diagnoses of ASD, ADHD, and OCD or were typically developing (TD); were aged between 5 and 19 years; and successfully completed the resting-state and anatomical neuroimaging protocol were included in the current study.

MAIN OUTCOMES AND MEASURES

The analyses consisted of a data-driven clustering procedure on measures derived from each participant's resting-state functional connectome, performed independently on each data set. Differences between each pair of leaves in the resulting clustering decision trees in the demographic and clinical characteristics were tested.

RESULTS

Overall, 551 children and adolescents were included from each data set. POND included 164 participants with ADHD; 217 with ASD; 60 with OCD; and 110 with TD (median [IQR] age, 11.87 [9.51-14.76] years; 393 [71.2%] male participants; 20 [3.6%] Black, 28 [5.1%] Latino, and 299 [54.2%] White participants) and HBN included 374 participants with ADHD; 66 with ASD; 11 with OCD; and 100 with TD (median [IQR] age, 11.50 [9.22-14.20] years; 390 [70.8%] male participants; 82 [14.9%] Black, 57 [10.3%] Hispanic, and 257 [46.6%] White participants). In both data sets, subgroups with similar biology that differed significantly in intelligence as well as hyperactivity and impulsivity problems were identified, yet these groups showed no consistent alignment with current diagnostic categories. For example, there was a significant difference in Strengths and Weaknesses ADHD Symptoms and Normal Behavior Hyperactivity/Impulsivity subscale (SWAN-HI) between 2 subgroups in the POND data (C and D), with subgroup D having increased hyperactivity and impulsivity traits compared with subgroup C (median [IQR], 2.50 [0.00-7.00] vs 1.00 [0.00-5.00]; U = 1.19 × 104; P = .01; η2 = 0.02). A significant difference in SWAN-HI scores between subgroups g and d in the HBN data was also observed (median [IQR], 1.00 [0.00-4.00] vs 0.00 [0.00-2.00]; corrected P = .02). There were no differences in the proportion of each diagnosis between the subgroups in either data set.

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that homogeneity in the neurobiology of neurodevelopmental conditions transcends diagnostic boundaries and is instead associated with behavioral characteristics. This work takes an important step toward translating neurobiological subgroups into clinical settings by being the first to replicate our findings in independently collected data sets.

CRISIS AFAR: an international collaborative study of the impact of the COVID-19 pandemic on mental health and service access in youth with autism and neurodevelopmental conditions

BACKGROUND

Heterogeneous mental health outcomes during the COVID-19 pandemic are documented in the general population. Such heterogeneity has not been systematically assessed in youth with autism spectrum disorder (ASD) and related neurodevelopmental disorders (NDD). To identify distinct patterns of the pandemic impact and their predictors in ASD/NDD youth, we focused on pandemic-related changes in symptoms and access to services.

METHODS

Using a naturalistic observational design, we assessed parent responses on the Coronavirus Health and Impact Survey Initiative (CRISIS) Adapted For Autism and Related neurodevelopmental conditions (AFAR). Cross-sectional AFAR data were aggregated across 14 European and North American sites yielding a clinically well-characterized sample of N = 1275 individuals with ASD/NDD (age = 11.0 ± 3.6 years; n females = 277). To identify subgroups with differential outcomes, we applied hierarchical clustering across eleven variables measuring changes in symptoms and access to services. Then, random forest classification assessed the importance of socio-demographics, pre-pandemic service rates, clinical severity of ASD-associated symptoms, and COVID-19 pandemic experiences/environments in predicting the outcome subgroups.

RESULTS

Clustering revealed four subgroups. One subgroup-broad symptom worsening only (20%)-included youth with worsening across a range of symptoms but with service disruptions similar to the average of the aggregate sample. The other three subgroups were, relatively, clinically stable but differed in service access: primarily modified services (23%), primarily lost services (6%), and average services/symptom changes (53%). Distinct combinations of a set of pre-pandemic services, pandemic environment (e.g., COVID-19 new cases, restrictions), experiences (e.g., COVID-19 Worries), and age predicted each outcome subgroup.

LIMITATIONS

Notable limitations of the study are its cross-sectional nature and focus on the first six months of the pandemic.

CONCLUSIONS

Concomitantly assessing variation in changes of symptoms and service access during the first phase of the pandemic revealed differential outcome profiles in ASD/NDD youth. Subgroups were characterized by distinct prediction patterns across a set of pre- and pandemic-related experiences/contexts. Results may inform recovery efforts and preparedness in future crises; they also underscore the critical value of international data-sharing and collaborations to address the needs of those most vulnerable in times of crisis.

Transdiagnostic profiles of behaviour and communication relate to academic and socioemotional functioning and neural white matter organisation

BACKGROUND

Behavioural and language difficulties co-occur in multiple neurodevelopmental conditions. Our understanding of these problems has arguably been slowed by an overreliance on study designs that compare diagnostic groups and fail to capture the overlap across different neurodevelopmental disorders and the heterogeneity within them.

METHODS

We recruited a large transdiagnostic cohort of children with complex needs (N = 805) to identify distinct subgroups of children with common profiles of behavioural and language strengths and difficulties. We then investigated whether and how these data-driven groupings could be distinguished from a comparison sample (N = 158) on measures of academic and socioemotional functioning and patterns of global and local white matter connectome organisation. Academic skills were assessed via standardised measures of reading and maths. Socioemotional functioning was captured by the parent-rated version of the Strengths and Difficulties Questionnaire.

RESULTS

We identified three distinct subgroups of children, each with different levels of difficulties in structural language, pragmatic communication, and hot and cool executive functions. All three subgroups struggled with academic and socioemotional skills relative to the comparison sample, potentially representing three alternative but related developmental pathways to difficulties in these areas. The children with the weakest language skills had the most widespread difficulties with learning, whereas those with more pronounced difficulties with hot executive skills experienced the most severe difficulties in the socioemotional domain. Each data-driven subgroup could be distinguished from the comparison sample based on both shared and subgroup-unique patterns of neural white matter organisation. Children with the most pronounced deficits in language, cool executive, or hot executive function were differentiated from the comparison sample by altered connectivity in predominantly thalamocortical, temporal-parietal-occipital, and frontostriatal circuits, respectively.

CONCLUSIONS

These findings advance our understanding of commonly co-morbid behavioural and language problems and their relationship to behavioural outcomes and neurobiological substrates.

Catecholaminergic and cholinergic neuromodulation in autism spectrum disorder: A comparison to attention-deficit hyperactivity disorder

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder characterized by social impairments and restricted, repetitive behaviors. Treatment of ASD is notoriously difficult and might benefit from identification of underlying mechanisms that overlap with those disturbed in other developmental disorders, for which treatment options are more obvious. One example of the latter is attention-deficit hyperactivity disorder (ADHD), given the efficacy of especially stimulants in treatment of ADHD. Deficiencies in catecholaminergic systems [dopamine (DA), norepinephrine (NE)] in ADHD are obvious targets for stimulant treatment. Recent findings suggest that dysfunction in catecholaminergic systems may also be a factor in at least a subgroup of ASD. In this review we scrutinize the evidence for catecholaminergic mechanisms underlying ASD symptoms, and also include in this analysis a third classic ascending arousing system, the acetylcholinergic (ACh) network. We complement this with a comprehensive review of DA-, NE-, and ACh-targeted interventions in ASD, and an exploratory search for potential treatment-response predictors (biomarkers) in ASD, genetically or otherwise. Based on this review and analysis we propose that (1) stimulant treatment may be a viable option for an ASD subcategory, possibly defined by genetic subtyping; (2) cerebellar dysfunction is pronounced for a relatively small ADHD subgroup but much more common in ASD and in both cases may point toward NE- or ACh-directed intervention; (3) deficiency of the cortical salience network is sizable in subgroups of both disorders, and biomarkers such as eye blink rate and pupillometric data may predict the efficacy of targeting this underlying deficiency via DA, NE, or ACh in both ASD and ADHD.

Does the current state of biomarker discovery in autism reflect the limits of reductionism in precision medicine? Suggestions for an integrative approach that considers dynamic mechanisms between brain, body, and the social environment

Over the past decade, precision medicine has become one of the most influential approaches in biomedical research to improve early detection, diagnosis, and prognosis of clinical conditions and develop mechanism-based therapies tailored to individual characteristics using biomarkers. This perspective article first reviews the origins and concept of precision medicine approaches to autism and summarises recent findings from the first "generation" of biomarker studies. Multi-disciplinary research initiatives created substantially larger, comprehensively characterised cohorts, shifted the focus from group-comparisons to individual variability and subgroups, increased methodological rigour and advanced analytic innovations. However, although several candidate markers with probabilistic value have been identified, separate efforts to divide autism by molecular, brain structural/functional or cognitive markers have not identified a validated diagnostic subgroup. Conversely, studies of specific monogenic subgroups revealed substantial variability in biology and behaviour. The second part discusses both conceptual and methodological factors in these findings. It is argued that the predominant reductionist approach, which seeks to parse complex issues into simpler, more tractable units, let us to neglect the interactions between brain and body, and divorce individuals from their social environment. The third part draws on insights from systems biology, developmental psychology and neurodiversity approaches to outline an integrative approach that considers the dynamic interaction between biological (brain, body) and social mechanisms (stress, stigma) to understanding the origins of autistic features in particular conditions and contexts. This requires 1) closer collaboration with autistic people to increase face validity of concepts and methodologies; (2) development of measures/technologies that enable repeat assessment of social and biological factors in different (naturalistic) conditions and contexts, (3) new analytic methods to study (simulate) these interactions (including emergent properties), and (4) cross-condition designs to understand which mechanisms are transdiagnostic or specific for particular autistic sub-populations. Tailored support may entail both creating more favourable conditions in the social environment and interventions for some autistic people to increase well-being.

Right Superior Frontal Gyrus Cortical Thickness in Pediatric ADHD

OBJECTIVE

We investigated the right Superior Frontal Gyrus (right-SFG) and Anterior Cingulate Cortex (ACC) in children with ADHD and their clinical relevance with Executive Function (EF) and ADHD symptom severity.

METHODS

About 26 children with ADHD and 24 typically developing children (TDC; 7‒16 years) underwent Magnetic Resonance Imaging (MRI) and completed an EF assessment battery.

RESULTS

Significantly thinner right-SFG in the ADHD group was found compared to the TDC group (t (48) = 2.81, p = .007, Cohen's d = 0.84). Linear regression models showed that 12.5% of inattention, 13.6% of hyperactivity, and 9.0% of EF variance was accounted for by the right-SFG thickness.

CONCLUSIONS

Differences in the right-SFG thickness were found in our ADHD group and were associated with parent ratings of inattentive and hyperactive symptoms as well with EF ratings. These results replicate previous findings of thinner right-SFG and are consistent with the delayed cortical maturation theory of ADHD.

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

BACKGROUND

Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) have considerable overlap, supporting the need for a dimensional framework that examines neurodevelopmental domains which cross traditional diagnostic boundaries. In the following study, we use factor analysis to deconstruct the ASD-ADHD phenotype into its underlying phenotypic domains and test for measurement invariance across adaptive functioning, age, gender and ASD/ADHD clinical diagnoses.

METHODS

Participants included children and youth (aged 3-20 years) with a clinical diagnosis of ASD (n = 727) or ADHD (n = 770) for a total of 1,497 participants. Parents of these children completed the Social Communication Questionnaire (SCQ), a measure of autism symptoms, and the Strengths and Weaknesses of ADHD and Normal Behaviour (SWAN) questionnaire, a measure of ADHD symptoms. An exploratory factor analysis (EFA) was performed on combined SCQ and SWAN items. This was followed by a confirmatory factor analysis (CFA) and tests of measurement invariance.

RESULTS

EFA revealed a four-factor solution (inattention, hyperactivity/impulsivity, social-communication, and restricted, repetitive, behaviours and interests (RRBI)) and a CFA confirmed good model fit. This solution also showed good model fit across subgroups of interest.

CONCLUSIONS

Our study shows that a combined ASD-ADHD phenotype is characterized by two latent ASD domains (social communication and RRBIs) and two latent ADHD domains (inattention and hyperactivity/impulsivity). We established measurement invariance of the derived measurement model across adaptive functioning, age, gender and ASD/ADHD diagnoses.

Genomic architecture of autism from comprehensive whole-genome sequence annotation

Fully understanding autism spectrum disorder (ASD) genetics requires whole-genome sequencing (WGS). We present the latest release of the Autism Speaks MSSNG resource, which includes WGS data from 5,100 individuals with ASD and 6,212 non-ASD parents and siblings (total n = 11,312). Examining a wide variety of genetic variants in MSSNG and the Simons Simplex Collection (SSC; n = 9,205), we identified ASD-associated rare variants in 718/5,100 individuals with ASD from MSSNG (14.1%) and 350/2,419 from SSC (14.5%). Considering genomic architecture, 52% were nuclear sequence-level variants, 46% were nuclear structural variants (including copy-number variants, inversions, large insertions, uniparental isodisomies, and tandem repeat expansions), and 2% were mitochondrial variants. Our study provides a guidebook for exploring genotype-phenotype correlations in families who carry ASD-associated rare variants and serves as an entry point to the expanded studies required to dissect the etiology in the ∼85% of the ASD population that remain idiopathic.

Amygdala subnuclei volumes and anxiety behaviors in children and adolescents with autism spectrum disorder, attention deficit hyperactivity disorder, and obsessive-compulsive disorder

Alterations in the structural maturation of the amygdala subnuclei volumes are associated with anxiety behaviors in adults and children with neurodevelopmental and associated disorders. This study investigated the relationship between amygdala subnuclei volumes and anxiety in 233 children and adolescents (mean age = 11.02 years; standard deviation = 3.17) with autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and children with obsessive compulsive disorder (OCD), as well as typically developing (TD) children. Parents completed the Child Behavior Checklist (CBCL), and the children underwent structural MRI at 3 T. FreeSurfer software was used to automatically segment the amygdala subnuclei. A general linear model revealed that children and adolescents with ASD, ADHD, and OCD had higher anxiety scores compared to TD children (p < .001). A subsequent interaction analysis revealed that children with ASD (B = 0.09, p < .0001) and children with OCD (B = 0.1, p < .0001) who had high anxiety had larger right central nuclei volumes compared with TD children. Similar results were obtained for the right anterior amygdaloid area. Amygdala subnuclei volumes may be key to identifying children with neurodevelopmental disorders or those with OCD who are at high risk for anxiety. Findings may inform the development of targeted behavioral interventions to address anxiety behaviors and to assess the downstream effects of such interventions.

Characterizing Creative Thinking and Creative Achievements in Relation to Symptoms of Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder

Previous research on ADHD and ASD has mainly focused on the deficits associated with these conditions, but there is also evidence for strengths. Unfortunately, our understanding of potential strengths in neurodevelopmental conditions is limited. One particular strength, creativity, has been associated with both ADHD and ASD. However, the distinct presentations of both conditions beg the question whether ADHD and ASD associate with the same or different aspects of creativity. Therefore, the current study investigated the links between ADHD and ASD symptoms, creative thinking abilities, and creative achievements. To investigate the spectrum of ADHD and ASD symptoms, self-reported ADHD and ASD symptoms, convergent (Remote Associations Test) and divergent thinking (Alternative Uses Task) and creative achievements (Creative Achievement Questionnaire) were assessed in a self-reportedly healthy sample of adults (n = 470). We performed correlation analysis to investigate the relation between ADHD/ASD symptoms and creativity measures. In a second phase of analysis, data from an adult ADHD case-control study (n = 151) were added to investigate the association between ADHD symptoms and divergent thinking in individuals with and without a diagnosis of ADHD. Our analysis revealed that having more ADHD symptoms in the general population was associated with higher scores on all the outcome measures for divergent thinking (fluency, flexibility, and originality), but not for convergent thinking. Individuals with an ADHD diagnosis in the case-control sample also scored higher on measures of divergent thinking. Combining data of the population based and case-control studies showed that ADHD symptoms predict divergent thinking up to a certain level of symptoms. No significant associations were found between the total number of ASD symptoms and any of the creativity measures. However, explorative analyses showed interesting links between the ASD subdomains of problems with imagination and symptoms that relate to social difficulties. Our findings showed a link between ADHD symptoms and divergent thinking abilities that plateaus in the clinical spectrum of symptoms. For ASD symptoms, no relation was found with creativity measures. Increasing the knowledge about positive phenotypes associated with neurodevelopmental conditions and their symptom dimensions might aid psychoeducation, decrease stigmatization and improve quality of life of individuals living with such conditions.

Modelling the neurodevelopmental pathogenesis in neuropsychiatric disorders. Bioactive kynurenines and their analogues as neuroprotective agents-in celebration of 80th birthday of Professor Peter Riederer

Following introduction of the monoamine oxidase type B inhibitor selegiline for the treatment of Parkinson's disease (PD), discovery of the action mechanism of Alzheimer's disease-modifying agent memantine, the role of iron in PD, and the loss of electron transport chain complex I in PD, and development of the concept of clinical neuroprotection, Peter Riederer launched one of the most challenging research project neurodevelopmental aspects of neuropsychiatric disorders. The neurodevelopmental theory holds that a disruption of normal brain development in utero or during early life underlies the subsequent emergence of neuropsychiatric symptoms during later life. Indeed, the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition and the International Classification of Diseases, 11th Revision categorize autism spectrum disorder and attention deficit hyperactivity disorder in neurodevelopmental disorders (NDDs). More and more evidence, especially from preclinical studies, is revealing that neurodevelopmental pathology is not limited to the diagnostic class above, but also contributes to the development of other psychiatric disorders such as schizophrenia, bipolar disorder, and obsessive-compulsive disorder as well as neurodegenerative diseases such as PD and Huntington's disease. Preclinical animal research is taking a lead in understanding the pathomechanisms of NDDs, searching for novel targets, and developing new neuroprotective agents against NDDs. This narrative review discusses emerging evidence of the neurodevelopmental etiology of neuropsychiatric disorders, recent advances in modelling neurodevelopmental pathogenesis, potential strategies of clinical neuroprotection using novel kynurenine metabolites and analogues, and future research direction for NDDs.

Examining the effect of chronic intranasal oxytocin administration on the neuroanatomy and behavior of three autism-related mouse models

Although initially showing great potential, oxytocin treatment has encountered a translational hurdle in its promise of treating the social deficits of autism. Some debate surrounds the ability of oxytocin to successfully enter the brain, and therefore modify neuroanatomy. Moreover, given the heterogeneous nature of autism, treatment will only amerliorate symptoms in a subset of patients. Therefore, to determine whether oxytocin changes brain circuitry, and whether it does so variably, depending on genotype, we implemented a large randomized, blinded, placebo-controlled, preclinical study on chronic intranasal oxytocin treatment in three different mouse models related to autism with a focus on using neuroanatomical phenotypes to assess and subset treatment response. Intranasal oxytocin (0.6IU) was administered daily, for 28 days, starting at 5 weeks of age to the 16p11.2 deletion, Shank3 (exon 4-9) knockout, and Fmr1 knockout mouse models. Given the sensitivity of structural magnetic resonance imaging (MRI) to the neurological effects of interventions like drugs, along with many other advantages, the mice underwent in vivo longitudinal and high-resolution ex vivo imaging with MRI. The scans included three in vivo T1weighted, 90um isotropic resolution scans and a T2-weighted, 3D fast spin echo with 40um isotropic resolution ex vivo scan to assess the changes in neuroanatomy using established automated image registration and deformation based morphometry approaches in response to oxytocin treatment. The behavior of the mice was assessed in multiple domains, including social behaviours and repetitive behaviours, among others. Treatment effect on the neuroanatomy did not reach significance, although the pattern of trending effects was promising. No significant effect of treatment was found on social behavior in any of the strains, although a significant effect of treatment was found in the Fmr1 mouse, with treatment normalizing a grooming deficit. No other treatment effect on behavior was observed that survived multiple comparisons correction. Overall, chronic treatment with oxytocin had limited effects on the three mouse models related to autism, and no promising pattern of response susceptibility emerged.

Cortico-amygdalar connectivity and externalizing/internalizing behavior in children with neurodevelopmental disorders

Background

Externalizing and internalizing behaviors contribute to clinical impairment in children with neurodevelopmental disorders (NDDs). Although associations between externalizing or internalizing behaviors and cortico-amygdalar connectivity have been found in clinical and non-clinical pediatric samples, no previous study has examined whether similar shared associations are present across children with different NDDs.

Methods

Multi-modal neuroimaging and behavioral data from the Province of Ontario Neurodevelopmental Disorders (POND) Network were used. POND participants aged 6–18 years with a primary diagnosis of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD) or obsessive–compulsive disorder (OCD), as well as typically developing children (TDC) with T1-weighted, resting-state fMRI or diffusion weighted imaging (DWI) and parent-report Child Behavioral Checklist (CBCL) data available, were analyzed (total n = 346). Associations between externalizing or internalizing behavior and cortico-amygdalar structural and functional connectivity indices were examined using linear regressions, controlling for age, gender, and image-modality specific covariates. Behavior-by-diagnosis interaction effects were also examined.

Results

No significant linear associations (or diagnosis-by-behavior interaction effects) were found between CBCL-measured externalizing or internalizing behaviors and any of the connectivity indices examined. Post-hoc bootstrapping analyses indicated stability and reliability of these null results.

Conclusions

The current study provides evidence towards an absence of a shared linear relationship between internalizing or externalizing behaviors and cortico-amygdalar connectivity properties across a transdiagnostic sample of children with different primary NDD diagnoses and TDC. Different methodological approaches, including incorporation of multi-dimensional behavioral data (e.g., task-based fMRI) or clustering approaches may be needed to clarify complex brain-behavior relationships relevant to externalizing/internalizing behaviors in heterogeneous clinical NDD populations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00429-022-02483-0.

Shared and Distinct Patterns of Functional Connectivity to Emotional Faces in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder Children

Impairments in emotional face processing are demonstrated by individuals with neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), which is associated with altered emotion processing networks. Despite accumulating evidence of high rates of diagnostic overlap and shared symptoms between ASD and ADHD, functional connectivity underpinning emotion processing across these two neurodevelopmental disorders, compared to typical developing peers, has rarely been examined. The current study used magnetoencephalography to investigate whole-brain functional connectivity during the presentation of happy and angry faces in 258 children (5–19 years), including ASD, ADHD and typically developing (TD) groups to determine possible differences in emotion processing. Data-driven clustering was also applied to determine whether the patterns of connectivity differed among diagnostic groups. We found reduced functional connectivity in the beta band in ASD compared to TD, and a further reduction in the ADHD group compared to the ASD and the TD groups, across emotions. A group-by-emotion interaction in the gamma frequency band was also observed. Greater connectivity to happy compared to angry faces was found in the ADHD and TD groups, while the opposite pattern was seen in ASD. Data-driven subgrouping identified two distinct subgroups: NDD-dominant and TD-dominant; these subgroups demonstrated emotion- and frequency-specific differences in connectivity. Atypicalities in specific brain networks were strongly correlated with the severity of diagnosis-specific symptoms. Functional connectivity strength in the beta network was negatively correlated with difficulties in attention; in the gamma network, functional connectivity strength to happy faces was positively correlated with adaptive behavioural functioning, but in contrast, negatively correlated to angry faces. Our findings establish atypical frequency- and emotion-specific patterns of functional connectivity between NDD and TD children. Data-driven clustering further highlights a high degree of comorbidity and symptom overlap between the ASD and ADHD children.

Annual Research Review: The transdiagnostic revolution in neurodevelopmental disorders

Practitioners frequently use diagnostic criteria to identify children with neurodevelopmental disorders and to guide intervention decisions. These criteria also provide the organising framework for much of the research focussing on these disorders. Study design, recruitment, analysis and theory are largely built on the assumption that diagnostic criteria reflect an underlying reality. However, there is growing concern that this assumption may not be a valid and that an alternative transdiagnostic approach may better serve our understanding of this large heterogeneous population of young people. This review draws on important developments over the past decade that have set the stage for much-needed breakthroughs in understanding neurodevelopmental disorders. We evaluate contemporary approaches to study design and recruitment, review the use of data-driven methods to characterise cognition, behaviour and neurobiology, and consider what alternative transdiagnostic models could mean for children and families. This review concludes that an overreliance on ill-fitting diagnostic criteria is impeding progress towards identifying the barriers that children encounter, understanding underpinning mechanisms and finding the best route to supporting them.

Annual Research Review: Translational machine learning for child and adolescent psychiatry

Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents.

Using Smartphone App Use and Lagged-Ensemble Machine Learning for the Prediction of Work Fatigue and Boredom

INTRO

As smartphone usage becomes increasingly prevalent in the workplace, the physical and psychological implications of this behavior warrant consideration. Recent research has investigated associations between workplace smartphone use and fatigue and boredom, yet findings are not conclusive.

METHODS

To build off recent efforts, we applied an ensemble machine learning model on a previously published dataset of N = 83 graduate students in the Netherlands to predict work boredom and fatigue from passively collected smartphone app use information. Using time-based feature engineering and lagged variations of the data to train, validate, and test idiographic models, we evaluated the efficacy of a lagged-ensemble predictive paradigm on sparse temporal data. Moreover, we probed the relative importance of both derived app use variables and lags within this predictive framework.

RESULTS

The ability to predict fatigue and boredom trajectories from app use information was heterogeneous and highly person-specific. Idiographic modeling reflected moderate to high correlative capacity (r > 0.4) in 47% of participants for fatigue and 24% for boredom, with better overall performance in the fatigue prediction task. App use relating to duration, communication, and patterns of use frequency were among the most important features driving predictions across lags, with longer lags contributing more heavily to final ensemble predictions compared with shorter ones.

CONCLUSION

A lag- specific ensemble predictive paradigm is a promising approach to leveraging high-dimensional app use behavioral data for the prediction of work fatigue and boredom. Future research will benefit from evaluating associations on densely collected data across longer time scales.

How to support adults with anorexia nervosa and autism: Qualitative study of clinical pathway case series

INTRODUCTION

Previous research has explored the overlapping presentation between autism and eating disorders (ED). This study aims to summarize the clinical challenges associated with co-occurring autism and anorexia nervosa (AN) based on clinicians' case notes and minutes from case discussions, to understand how to better support people with the comorbidity.

METHOD

Thematic analysis was conducted on de-identified notes on 20 cases with AN and autistic characteristics and minutes from case discussions. Themes relevant to clinical challenges in supporting those with the comorbidity were identified, and a thematic map was produced to visually represent the results.

RESULTS

The key challenges faced by clinicians when treating patients with AN and autism included: communication difficulties, maintaining boundaries, autism screening, presence of other comorbidities, sensory difficulties, atypical presentation of eating difficulties, cognitive rigidity, and emotional difficulties. Adaptations to resolve some of these difficulties included exposure-based food experiments, keeping a record of patients' self-reported communication preferences, individual-level modification of communication style, and providing tools for patients to identify emotions.

CONCLUSIONS AND IMPLICATIONS

Further exploration to establish the effectiveness of the adaptations is warranted. Furthermore, tools for differentiating between ED, autism and other comorbidities are needed to help clinicians clarify the cause of a presenting symptom, and help them to best support and maintain boundaries with patients.

Therapeutic applications of transcutaneous auricular vagus nerve stimulation with potential for application in neurodevelopmental or other pediatric disorders

Non-invasive transcutaneous auricular vagus nerve stimulation (taVNS) as a newly developed technique involves stimulating the cutaneous receptive field formed by the auricular branch of the vagus nerve in the outer ear, with resulting activation of vagal connections to central and peripheral nervous systems. Increasing evidence indicates that maladaptive neural plasticity may underlie the pathology of several pediatric neurodevelopmental and psychiatric disorders, such as autism spectrum disorder, attention deficit hyperactivity disorder, disruptive behavioral disorder and stress-related disorder. Vagal stimulation may therefore provide a useful intervention for treating maladaptive neural plasticity. In the current review we summarize the current literature primarily on therapeutic use in adults and discuss the prospects of applying taVNS as a therapeutic intervention in specific pediatric neurodevelopmental and other psychiatric disorders. Furthermore, we also briefly discuss factors that would help optimize taVNS protocols in future clinical applications. We conclude from these initial findings that taVNS may be a promising alternative treatment for pediatric disorders which do not respond to other interventions.

Latent Structure of Combined Autistic and ADHD Symptoms in Clinical and General Population Samples: A Scoping Review

Background: Many phenotypic studies have estimated the degree of comorbidity between Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), but few have examined the latent, or unobserved, structure of combined ASD and ADHD symptoms. This is an important perquisite toward better understanding the overlap between ASD and ADHD. Methods: We conducted a scoping review of studies that examined the factor or latent class structure of ASD and ADHD symptoms within the same clinical or general population sample. Results: Eight studies met final inclusion criteria. Four factor analysis studies found that ASD and ADHD domains loaded separately and one found that some ASD and ADHD domains loaded together. In the three latent class studies, there were evidence of profiles with high levels of co-occurring ASD and ADHD symptoms. Conclusions: Our scoping review provides some evidence of phenotypic overlap between ASD and ADHD at the latent, or unobserved, level, particularly when using a "person-centered" (latent class analysis) vs. a "variable-centered" (factor analysis) approach.

Testing the Stability and Validity of an Executive Dysfunction Classification Using Task-Based Assessment in Children and Adolescents

OBJECTIVE

It is unclear if pediatric executive dysfunction assessed only with cognitive tasks predicts clinically relevant outcomes independently of psychiatric diagnoses. This study tested the stability and validity of a task-based classification of executive function.

METHOD

A total of 2,207 individuals (6-17 years old) from the Brazilian High-Risk Cohort Study participated in this study (1,930 at baseline, 1,532 at follow-up). Executive function was measured using tests of working memory and inhibitory control. Dichotomized age- and sex-standardized performances were used as input in latent class analysis and receiver operating curves to create an executive dysfunction classification (EDC). The study tested EDC's stability over time, association with symptoms, functional impairment, a polymorphism in the CADM2 gene, polygenic risk scores (PRS), and brain structure. Analyses covaried for age, sex, social class, IQ, and psychiatric diagnoses.

RESULTS

EDC at baseline predicted itself at follow-up (odds ratio [OR] = 5.11; 95% CI 3.41-7.64). Participants in the EDC reported symptoms spanning several domains of psychopathology and exhibited impairment in multiple settings, including more adverse school events (OR = 2.530; 95% CI 1.838-3.483). Children in the EDC presented higher attention-deficit/hyperactivity disorder and lower educational attainment PRS at baseline; higher schizophrenia PRS at follow-up; and lower chances of presenting a polymorphism in a gene previously linked to high performance in executive function (CADM2 gene). They also exhibited smaller intracranial volumes and smaller bilateral cortical surface areas in several brain regions.

CONCLUSION

Task-based executive dysfunction is associated with several validators, independently of psychiatric diagnoses and intelligence. Further refinement of task-based assessments might generate clinically useful tools.

A transdiagnostic data-driven study of children's behaviour and the functional connectome

Behavioural difficulties are seen as hallmarks of many neurodevelopmental conditions. Differences in functional brain organisation have been observed in these conditions, but little is known about how they are related to a child's profile of behavioural difficulties. We investigated whether behavioural difficulties are associated with how the brain is functionally organised in an intentionally heterogeneous and transdiagnostic sample of 957 children aged 5-15. We used consensus community detection to derive data-driven profiles of behavioural difficulties and constructed functional connectomes from a subset of 238 children with resting-state functional Magnetic Resonance Imaging (fMRI) data. We identified three distinct profiles of behaviour that were characterised by principal difficulties with hot executive function, cool executive function, and learning. Global organisation of the functional connectome did not differ between the groups, but multivariate patterns of connectivity at the level of Intrinsic Connectivity Networks (ICNs), nodes, and hubs significantly predicted group membership in held-out data. Fronto-parietal connector hubs were under-connected in all groups relative to a comparison sample and children with hot vs cool executive function difficulties were distinguished by connectivity in ICNs associated with cognitive control, emotion processing, and social cognition. This demonstrates both general and specific neurodevelopmental risk factors in the functional connectome.

Brain mapping across 16 autism mouse models reveals a spectrum of functional connectivity subtypes

Autism Spectrum Disorder (ASD) is characterized by substantial, yet highly heterogeneous abnormalities in functional brain connectivity. However, the origin and significance of this phenomenon remain unclear. To unravel ASD connectopathy and relate it to underlying etiological heterogeneity, we carried out a bi-center cross-etiological investigation of fMRI-based connectivity in the mouse, in which specific ASD-relevant mutations can be isolated and modeled minimizing environmental contributions. By performing brain-wide connectivity mapping across 16 mouse mutants, we show that different ASD-associated etiologies cause a broad spectrum of connectional abnormalities in which diverse, often diverging, connectivity signatures are recognizable. Despite this heterogeneity, the identified connectivity alterations could be classified into four subtypes characterized by discrete signatures of network dysfunction. Our findings show that etiological variability is a key determinant of connectivity heterogeneity in ASD, hence reconciling conflicting findings in clinical populations. The identification of etiologically-relevant connectivity subtypes could improve diagnostic label accuracy in the non-syndromic ASD population and paves the way for personalized treatment approaches.

Autistic Traits and Attention-Deficit Hyperactivity Disorder Symptoms Associated With Greater Pain Interference and Depression, and Reduced Health-Related Quality of Life in Children With Chronic Pain

Previous research indicates elevated levels of clinically significant traits and symptoms of autism spectrum disorder and attention-deficit hyperactivity disorder (ADHD) in children with chronic pain, but associations with functioning and depression are yet unclear. The current study examined the relationships of autistic traits and ADHD symptoms with pain interference, depression, and health-related quality of life, as well as the mediating roles of insomnia and psychological inflexibility, in children with chronic pain (n = 146, 8–17 years, 102 girls) presenting at a tertiary pain clinic. Children completed measures of pain intensity, depression, pain interference, health-related quality of life, insomnia, and psychological inflexibility. Parents (n = 146, 111 mothers) completed measures to assess autistic traits and ADHD symptoms in their children. Children with clinically significant autistic traits and ADHD symptoms presented with significantly higher levels of depressive symptoms and pain interference, and significantly lower health-related quality of life, than did the other children. Autistic traits and ADHD symptoms contributed significantly to the prediction of pain interference and depressive symptoms, as well as health-related quality of life. Psychological inflexibility mediated the relationships between ADHD symptoms and autistic traits on the one hand and depression, pain interference, and health-related quality of life on the other, while insomnia mediated the relationships between ADHD symptoms and depression, pain interference, and health-related quality of life. All analyses were adjusted for demographics and pain intensity. Results suggest the utility of screening for neurodevelopmental disorders in children with chronic pain. Furthermore, the findings may indicate insomnia and skills related to psychological flexibility as potential treatment targets in interventions aiming at improving functioning and health-related quality of life in children with chronic pain and co-occurring symptoms of neurodevelopmental disorders.

Association Between Exposure of Children to General Anesthesia and Autism Spectrum Disorder

This study tested the hypothesis that exposure of children prior to their third birthday to procedures requiring general anesthesia is associated with an increased incidence of autism spectrum disorder (ASD) in later life. This study employed a nested, 1:2 matched-case control study design using ASD cases identified in a population-based birth cohort of children born in Olmsted County, MN from 1976 to 2000. Matching variables included sex, date of birth, and mother's age in conditional logistic regression including 499 ASD cases and 998 controls. After adjusting for birth weight and health status, there was no significant association between exposure and ASD (OR 1.27 [95% CI 0.92-1.76]), indicating that general anesthesia is not associated with an increased risk of ASD.

Cognitive Dimensions of Learning in Children With Problems in Attention, Learning, and Memory

A data-driven, transdiagnostic approach was used to identify the cognitive dimensions linked with learning in a mixed group of 805 children aged 5 to 18 years recognised as having problems in attention, learning and memory by a health or education practitioner. Assessments included phonological processing, information processing speed, short-term and working memory, and executive functions, and attainments in word reading, spelling, and maths. Data reduction methods identified three dimensions of phonological processing, processing speed and executive function for the sample as a whole. This model was comparable for children with and without ADHD. The severity of learning difficulties in literacy was linked with phonological processing skills, and in maths with executive control. Associations between cognition and learning were similar across younger and older children and individuals with and without ADHD, although stronger links between learning-related problems and both executive skills and processing speed were observed in children with ADHD. The results establish clear domain-specific cognitive pathways to learning that distinguish individuals in the heterogeneous population of children struggling to learn.

Cortical Gyrification Morphology in ASD and ADHD: Implication for Further Similarities or Disorder-Specific Features?

Shared etiological pathways are suggested in ASD and ADHD given high rates of comorbidity, phenotypic overlap and shared genetic susceptibility. Given the peak of cortical gyrification expansion and emergence of ASD and ADHD symptomology in early development, we investigated gyrification morphology in 539 children and adolescents (6–17 years of age) with ASD (n=197) and ADHD (n=96) compared to typically developing controls (n=246) using the local Gyrification Index (lGI) to provide insight into contributing etiopathological factors in these two disorders. We also examined IQ effects and functional implications of gyrification by exploring the relation between lGI and ASD and ADHD symptomatology beyond diagnosis. General Linear Models yielded no group differences in lGI, and across groups, we identified an age-related decrease of lGI and greater lGI in females compared to males. No diagnosis-by-age interactions were found. Accounting for IQ variability in the model (n=484) yielded similar results. No significant associations were found between lGI and social communication deficits, repetitive and restricted behaviours, inattention or adaptive functioning. By examining both disorders and controls using shared methodology, we found no evidence of atypicality in gyrification as measured by the lGI in these conditions.

Behavioural and cognitive behavioural therapy for obsessive compulsive disorder (OCD) in individuals with autism spectrum disorder (ASD)

BACKGROUND

Autistic spectrum disorder (ASD) is an increasingly recognised neurodevelopmental condition; that is, a neurologically-based condition which interferes with the acquisition, retention or application of specific skills. ASD is characterised by challenges with socialisation and communication, and by stereotyped and repetitive behaviours. A stereotyped behaviour is one which is repeated over and over again and which seems not to have any useful function. ASD often co-occurs with mental health disorders, including obsessive compulsive disorder (OCD). People with ASD may show certain cognitive differences (i.e. differences in ways of thinking) which influence their response to therapies. Thus, there is a need for evidence-based guidelines to treat mental health issues in this group. OCD, a common condition characterised by repeated obsessional thoughts and compulsive acts, occurs with greater frequency in persons with ASD than in the general population. Genetic, anatomic, neurobiological and psychological factors have been proposed to explain this co-occurrence. However, care should be taken to distinguish stereotyped and repetitive behaviours characteristic of ASD from obsessive compulsive acts in OCD. Cognitive behavioural therapy (CBT) is the recommended treatment for OCD, but studies have suggested that this treatment may be less effective in those with OCD co-occurring with ASD. Hence, modifications to CBT treatment may be helpful when treating OCD co-occurring with ASD to optimise outcomes.

OBJECTIVES

To assess the effectiveness of behavioural and cognitive behavioural therapy for obsessive compulsive disorder (OCD) in children and adults with autism spectrum disorder (ASD).

SEARCH METHODS

We searched for studies in the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, PsycINFO, five other bibliographic databases, international trial registries and other sources of grey literature (to 24 August 2020). We checked the reference lists of included studies and relevant systematic reviews to identify additional studies missed from the original electronic searches. We contacted subject experts for further information when needed.

SELECTION CRITERIA

We included randomised controlled trials (RCTs), cross-over, cluster- and quasi-randomised controlled trials involving both adults and children with diagnoses of OCD and ASD. We included studies of participants with co-occurring conditions (i.e. those experiencing other mental illnesses or neurodevelopmental conditions at the same time), but we did not include individuals who had a co-occurring global learning difficulty. Treatment could be in any setting or format and include behavioural therapy (BT) and cognitive behavioural therapy (CBT), which may have been adapted for those with ASD. Comparator interventions included no treatment, waiting list, attention placebo (where the control group receives non-specific aspects of therapy, but not the active ingredient) and treatment as usual (TAU, where the control group receives the usual treatment, according to accepted standards).

DATA COLLECTION AND ANALYSIS

Three review authors independently screened studies for inclusion. The authors extracted relevant data from the one eligible study, assessed the risk of bias and certainty of evidence (GRADE). Outcomes of interest were changes in OCD symptoms and treatment completion (primary outcome), and severity of depressive symptoms, anxiety symptoms and behavioural difficulties, as well as degree of family accommodation (secondary outcomes). We did not conduct meta-analyses as only one study met the selection criteria.

MAIN RESULTS

We included only one RCT of 46 participants in our analysis. This study compared CBT for OCD in persons with high-functioning ASD with a control group who received anxiety management only. There were no differences in rates of treatment completion between the CBT (87%) and anxiety management (87%) groups (risk ratio (RR) 1.00, 95% confidence interval (CI) 0.80 to 1.25; low-certainty evidence). Behavioural difficulties were not included as an outcome measure in the study. This study showed that there may be a benefit at the end of treatment favouring CBT compared with anxiety management in OCD symptoms (mean difference (MD) -3.00, 95% CI -8.02 to 2.02), depression symptoms (MD -1.80, 95% CI -11.50 to 7.90), anxiety symptoms (MD -3.20, 95% CI -11.38 to 4.98), and quality of life (MD 5.20, 95% CI -1.41 to 11.81), but the evidence was of low certainty.  AUTHORS' CONCLUSIONS: Evidence is limited regarding the efficacy of CBT for treatment of OCD in ASD. There is much scope for future study, not only examining the efficacy of CBT for OCD in ASD, but also the particular ways that OCD manifests in and affects people with ASD and the role of the family in treatment response.