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This large cross-sectional study examined the impact of COVID-19 emergency measures on child/adolescent mental health for children/adolescents with and without pre-existing psychiatric diagnoses. Using adapted measures from the CRISIS questionnaire, parents of children aged 6–18 ( N  = 1013; 56% male; 62% pre-existing psychiatric diagnosis) and self-reporting children/adolescents aged 10–18 ( N  = 385) indicated changes in mental health across six domains: depression, anxiety, irritability, attention, hyperactivity, and obsessions/compulsions. Changes in anxiety, irritability, and hyperactivity were calculated for children aged 2–5 years using the Strengths and Difficulties Questionnaire. COVID-19 exposure, compliance with emergency measures, COVID-19 economic concerns, and stress from social isolation were measured with the CRISIS questionnaire. Prevalence of change in mental health status was estimated for each domain; multinomial logistic regression was used to determine variables associated with mental health status change in each domain. Depending on the age group, 67–70% of children/adolescents experienced deterioration in at least one mental health domain; however, 19–31% of children/adolescents experienced improvement in at least one domain. Children/adolescents without and with psychiatric diagnoses tended to experience deterioration during the first wave of COVID-19. Rates of deterioration were higher in those with a pre-exiting diagnosis. The rate of deterioration was variable across different age groups and pre-existing psychiatric diagnostic groups: depression 37–56%, anxiety 31–50%, irritability 40–66%, attention 40–56%, hyperactivity 23–56%, obsessions/compulsions 13–30%. Greater stress from social isolation was associated with deterioration in all mental health domains (all ORs 11.12–55.24). The impact of pre-existing psychiatric diagnosis was heterogenous, associated with deterioration in depression, irritability, hyperactivity, obsession/compulsions for some children (ORs 1.96–2.23) but also with improvement in depression, anxiety, and irritability for other children (ORs 2.13–3.12). Economic concerns were associated with improvement in anxiety, attention, and obsessions/compulsions (ORs 3.97–5.57). Children/adolescents with and without pre-existing psychiatric diagnoses reported deterioration. Deterioration was associated with increased stress from social isolation. Enhancing social interactions for children/adolescents will be an important mitigation strategy for current and future COVID-19 waves.

The validity of diagnostic labels of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive compulsive disorder (OCD) is an open question given the mounting evidence that these categories may not correspond to conditions with distinct etiologies, biologies, or phenotypes. The objective of this study was to determine the agreement between existing diagnostic labels and groups discovered based on a data-driven, diagnosis-agnostic approach integrating cortical neuroanatomy and core-domain phenotype features. A machine learning pipeline, called bagged-multiview clustering, was designed to discover homogeneous subgroups by integrating cortical thickness data and measures of core-domain phenotypic features of ASD, ADHD, and OCD. This study was conducted using data from the Province of Ontario Neurodevelopmental Disorders (POND) Network, a multi-center study in Ontario, Canada. Participants (n = 226) included children between the ages of 6 and 18 with a diagnosis of ASD (n = 112, median [IQR] age = 11.7[4.8], 21% female), ADHD (n = 58, median [IQR] age = 10.2[3.3], 14% female), or OCD (n = 34, median [IQR] age = 12.1[4.2], 38% female), as well as typically developing controls (n = 22, median [IQR] age = 11.0[3.8], 55% female). The diagnosis-agnostic groups were significantly different than each other in phenotypic characteristics (SCQ: χ2(9) = 111.21, p < 0.0001; SWAN: χ2(9) = 142.44, p < 0.0001) as well as cortical thickness in 75 regions of the brain. The analyses revealed disagreement between existing diagnostic labels and the diagnosis-agnostic homogeneous groups (normalized mutual information < 0.20). Our results did not support the validity of existing diagnostic labels of ASD, ADHD, and OCD as distinct entities with respect to phenotype and cortical morphology.

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.

Restricted interests and repetitive behaviors (RRBs) are core symptoms of autism spectrum disorder (ASD), and commonly occur in attention-deficit/hyperactivity disorder (ADHD). Little is known about how RRBs manifest in ADHD. We quantified and compared factor structures of RRBs in children with ASD (n = 634) or ADHD (n = 448), and related factors to sex and IQ. A four-factor solution emerged, including Stereotypy, Self-Injury, Compulsions, and Ritualistic/Sameness. Factor structures were equivalent across diagnoses, though symptoms were more severe in ASD. IQ negatively correlated with Stereotypy, Self-Injury, and Compulsions in ASD, and negatively correlated with Compulsions and Ritualistic/Sameness behaviors in ADHD. In ASD only, females exhibited higher Self-Injury. Thus, patterns of RRBs are preserved across ASD and ADHD, but severity and relationship with IQ differed.

Autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD) have been associated with difficulties recognizing and responding to social cues. Neuroimaging studies have begun to map the social brain; however, the specific neural substrates contributing to social deficits in neurodevelopmental disorders remain unclear. Three hundred and twelve children underwent structural magnetic resonance imaging of the brain (controls = 32, OCD = 44, ADHD = 77, ASD = 159; mean age = 11). Their social deficits were quantified on the Social Communication Questionnaire (SCQ) and the Reading the Mind in the Eyes Test (RMET). Multivariable regression models were used to examine the structural neuroimaging correlates of social deficits, with both a region of interest and a whole-brain vertex-wise approach. For the region of interest analysis, social brain regions were grouped into three networks: (1) lateral mentalization (e.g., temporal–parietal junction), (2) frontal cognitive (e.g., orbitofrontal cortex), and (3) subcortical affective (e.g., limbic system) regions. Overall, social communication deficits on the SCQ were associated with thinner cortices in the left lateral regions and the right insula, and decreased volume in the ventral striatum, across diagnostic groups (p = 0.006 to <0.0001). Smaller subcortical volumes were associated with more severe social deficits on the SCQ in ASD and ADHD, and less severe deficits in OCD. On the RMET, larger amygdala/hippocampal volumes were associated with fewer deficits across groups. Overall, patterns of associations were similar in ASD and ADHD, supporting a common underlying biology and the blurring of the diagnostic boundaries between these disorders.

•Overall, there are differences in the participants excluded from four different quality control approaches across three pediatric datasets.•In clinically enriched samples, the greatest correspondence of excluded participants was between automated and visual quality control procedures.•Implementing quality control led to the exclusion of younger participants and those with greater clinical impairments.•Specific QC approach implemented did not lead to measurable differences in clinical or brain metric characteristics. Poor quality T1-weighted brain scans systematically affect the calculation of brain measures. Removing the influence of such scans requires identifying and excluding scans with noise and artefacts through a quality control (QC) procedure. While QC is critical for brain imaging analyses, it is not yet clear whether different QC approaches lead to the exclusion of the same participants. Further, the removal of poor-quality scans may unintentionally introduce a sampling bias by excluding the subset of participants who are younger and/or feature greater clinical impairment. This study had two aims: (1) examine whether different QC approaches applied to T1-weighted scans would exclude the same participants, and (2) examine how exclusion of poor-quality scans impacts specific demographic, clinical and brain measure characteristics between excluded and included participants in three large pediatric neuroimaging samples. We used T1-weighted, resting-state fMRI, demographic and clinical data from the Province of Ontario Neurodevelopmental Disorders network (Aim 1: n = 553, Aim 2: n = 465), the Healthy Brain Network (Aim 1: n = 1051, Aim 2: n = 558), and the Philadelphia Neurodevelopmental Cohort (Aim 1: n = 1087; Aim 2: n = 619). Four different QC approaches were applied to T1-weighted MRI (visual QC, metric QC, automated QC, fMRI-derived QC). We used tetrachoric correlation and inter-rater reliability analyses to examine whether different QC approaches excluded the same participants. We examined differences in age, mental health symptoms, everyday/adaptive functioning, IQ and structural MRI-derived brain indices between participants that were included versus excluded following each QC approach. Dataset-specific findings revealed mixed results with respect to overlap of QC exclusion. However, in POND and HBN, we found a moderate level of overlap between visual and automated QC approaches (rtet=0.52–0.59). Implementation of QC excluded younger participants, and tended to exclude those with lower IQ, and lower everyday/adaptive functioning scores across several approaches in a dataset-specific manner. Across nearly all datasets and QC approaches examined, excluded participants had lower estimates of cortical thickness and subcortical volume, but this effect did not differ by QC approach. The results of this study provide insight into the influence of QC decisions on structural pediatric imaging analyses. While different QC approaches exclude different subsets of participants, the variation of influence of different QC approaches on clinical and brain metrics is minimal in large datasets. Overall, implementation of QC tends to exclude participants who are younger, and those who have more cognitive and functional impairment. Given that automated QC is standardized and can reduce between-study differences, the results of this study support the potential to use automated QC for large pediatric neuroimaging datasets.

Background Although there is high co-occurrence between ASD and ADHD, the nature of this co-occurrence remains unclear. Our study aimed to examine the underlying relationship between ASD and ADHD symptoms in a combined sample of children with a primary clinical diagnosis of ASD or ADHD. Methods Participants included children and youth (aged 3-20 years) with a clinical diagnosis of ASD ( n = 303) or ADHD ( n = 319) for a total of 622 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 behavior (SWAN) questionnaire, a measure of ADHD symptoms. A principal component analysis (PCA) was performed on combined SCQ and SWAN items, followed by a profile analysis comparing normalized component scores between diagnostic groups and gender. Results PCA revealed a four-component solution (inattention, hyperactivity/impulsivity, social-communication, and restricted, repetitive, behaviors, and interests (RRBI)), with no overlap between SCQ and SWAN items in the components. Children with ASD had higher component scores in social-communication and RRBI than children with ADHD, while there was no difference in inattentive and hyperactive/impulsive scores between diagnostic groups. Males had higher scores than females in social-communication, RRBI, and hyperactivity/impulsivity components in each diagnostic group. Limitations We did not formally assess children with ASD for ADHD using our research-criteria for ADHD, and vice versa. High rates of co-occurring ADHD in ASD, for example, may have inflated component scores in inattention and hyperactivity/impulsivity. A disadvantage with using single informant-based reports (i.e., parent-rated questionnaires) is that ASD and ADHD symptoms may be difficult to distinguish by parents, and may be interpreted differently between parents and clinicians. Conclusions ASD and ADHD items loaded on separate components in our sample, suggesting that the measurement structure cannot explain the covariation between the two disorders in clinical samples. High levels of inattention and hyperactivity/impulsivity were seen in both ASD and ADHD in our clinical sample. This supports the need for a dimensional framework that examines neurodevelopmental domains across traditional diagnostic boundaries. Females also had lower component scores across social-communication, RRBI, and hyperactivity/impulsivity than males, suggesting that there may be gender-specific phenotypes related to the two conditions.

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, r POND-HBN  = −0.97; FC feature vectors, r POND-HBN  = −0.68) and social communication deficit contrasted against attention deficit behaviour (behaviour feature vectors, r POND-HBN  = −0.99; FC feature vectors, r POND-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.

Background Theory of mind (ToM) is fundamental for social interactions, allowing individuals to appreciate that others have their own mental states. Children and youth with neurodevelopmental conditions (e.g., autism, attention-deficit hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD)) often show differences in ToM abilities compared to their neurotypical (NT) peers. Given the phenotypic heterogeneity and overlap associated with these conditions, this motivates a transdiagnostic investigation of ToM across neurodevelopmental conditions. Methods Five hundred and fifty-five participants (5–22 years; 193 ADHD, 189 autism, 33 OCD, and 140 NT) were recruited via the Province of Ontario Neurodevelopmental Disorders network. To measure ToM, participants completed the Social Attribution Task (SAT), where participants attribute social stories to videos of moving shapes. The Animation Index (ability to attribute social stories to the videos) and Pertinence Index (how pertinent the attributions are) were calculated from the descriptions. Three analyses were performed: (1) a case-control analysis, comparing the SAT indices amongst the diagnostic groups, (2) a univariate dimensional analysis, examining associations with phenotypic variables (e.g., full-scale IQ, verbal IQ, and social communication difficulties), (3) and a multivariate analysis (partial least squares) that identifies a latent space that describes the associations between the SAT and phenotypic measures. Results There were no between-group differences in the Animation Index, but the Pertinence Index was significantly lower in autism compared to the other diagnostic categories. Phenotypic variables (full-scale IQ, verbal IQ, and social communication difficulties) were found to be significantly associated with SAT performance across groups, and explained more variance than the diagnostic categories. In the multivariate analysis, the phenotypic variables contributed more strongly to the identified latent component compared to the diagnostic categories. Limitations The verbal requirement of the SAT limited the inclusion of non-verbal participants, while the overall cognitive demand limited the participation of those with lower IQs. Additionally, our OCD group was significantly smaller than the other groups, which may have limited our ability to detect OCD-specific effects. Conclusions In a large sample, we found that transdiagnostic measures, such as IQ and social communication difficulties, are related to SAT abilities across neurodivergent and neurotypical children and youth and better describe differences in SAT performance compared to the individual diagnostic categories. Although poorer performance on ToM tasks has been classically associated with autism, this study highlights that transdiagnostic, phenotypic variables are a stronger predictor of SAT performance than diagnostic group.

In neurodevelopmental research, within-diagnosis heterogeneity and across-diagnosis overlap necessitate a shift from case-control designs to data-driven clustering approaches. However, our understanding of the replicability of these clustering structures across independent datasets remains limited. Our objective was to examine the replicability of clustering structure in measures of brain morphology in neurodiverse children across two independent datasets, namely the Province of Ontario Neurodevelopmental Disorder (POND) Network and the Healthy Brain Network (HBN). POND and HBN data were collected across various institutions in Ontario, Canada, and New York, United States, respectively. Participants were 5-19 years old and had diagnoses of autism, attention deficit/hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), or were neurotypical. We used measures of cortical volume, surface area, cortical thickness, and subgroup volume from structural MRI data. Principal component analysis (PCA) and clustering were used to examine the replicability of clustering structures across the datasets. Correlations among principle components, measures of clusterability, and alignment between the four brain measures as well as male/female subsets were examined. Brain-behaviour associations were examined using univariate and multivariate approaches. The POND dataset included 747 participants with (autism n = 312, ADHD n = 220, OCD n = 70, neurotypical n = 145). The HBN dataset included 582 participants (autism n = 60, ADHD n = 445, OCD n = 19, neurotypical n = 58). Our results showed significant between-dataset correlations in 82.1% of the principal components derived from brain measures. A two-cluster structure was replicated across datasets, brain measures, and the female/male subsets, however the participant composition of clusters were only aligned between cortical volume and surface area, and cortical thickness and subcortical volume. Regional effect sizes for between-cluster differences were highly correlated across datasets (beta = 0.92+/−0.01, p < 0.0001; adjusted R-squared=0.93). Data-driven clusters did not align with diagnostic labels across datasets. Brain-behaviour associations were only replicated for male subsets and subcortical volume using multivariate analysis. We found evidence of replicability of the clustering structure across two independent datasets; however, caution must be exercised in integrating multiple measures in clustering and interpretation of brain-behaviour associations.