Beyond diagnosis: Cross-diagnostic features in canonical resting-state networks in children with neurodevelopmental disorders

Autism Symptom Presentation and Hierarchical Models of Intelligence

BACKGROUND

There is a substantial history studying the relationship between general intelligence and the core symptoms of autism. However, a gap in knowledge is how dimensional autism symptomatology associates with different components of clinically-relevant hierarchical models of intelligence.

METHOD

We examined correlations between autism diagnostic symptom magnitude (Autism Diagnostic Observational Schedule; ADOS) and a hierarchical statistical model of intelligence. One autistic cohort was tested on the fourth edition of Wechsler Intelligence Scale for Children (WISC-IV; N = 131), and another on the fifth edition (WISC-V; N = 83). We anticipated a convergent pattern of results between cohorts.

RESULTS

On WISC-IV, ADOS scores were correlated significantly with g and three out of four intermediate factor scores, which was a broader pattern of correlations than anticipated from the literature. In the WISC-V cohort, only one intermediate factor correlated significantly with the ADOS; correlations with g and the other intermediate factors were less statistically certain. ADOS-factor correlations were larger in the WISC-IV than WISC-V cohort; this difference was significant at the 90% level.

CONCLUSIONS

WISC-IV shows dimensional relationships with ADOS at multiple points in the hierarchical model of intelligence. Moreover, the current results provide evidence that relationship between core autism symptomatology and the construct of general intelligence may depend on how intelligence is measured. Known cohort effects in the relationship between categorical autism diagnosis and general intelligence have previously been attributed to changes in autism diagnostic practices. To our knowledge, this is the first evidence that differing versions of IQ tests may be implicated.

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.

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.

Visuomotor tracking strategies in children: associations with neurodevelopmental symptoms

Children with neurodevelopmental disorders (NDDs) often display motor problems that may impact their daily lives. Studying specific motor characteristics related to spatiotemporal control may inform us about the mechanisms underlying their challenges. Fifty-eight children with varying neurodevelopmental symptoms load (median age: 5.6 years, range: 2.7-12.5 years) performed an interactive tablet-based tracking task. By investigating digit touch errors relative to the target's movement direction, we found that a load of neurodevelopmental symptoms was associated with reduced performance in the tracking of abrupt alternating directions (zigzag) and overshooting the target. In contrast, reduced performance in children without neurodevelopmental symptoms was associated with lagging behind the target. Neurodevelopmental symptom load was also associated with reduced flexibility in correcting for lateral deviations in smooth tracking (spiral). Our findings suggest that neurodevelopmental symptoms are associated with difficulties in motor regulation related to inhibitory control and reduced flexibility, impacting motor control in NDDs.

Systematic comparisons of different quality control approaches applied to three large pediatric neuroimaging datasets

INTRODUCTION

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.

METHODS

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.

RESULTS

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 (r_tet=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.

CONCLUSION

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.

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.

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.

Functional Connectome-Based Predictive Modeling in Autism

Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.

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.

Rhythm and timing as vulnerabilities in neurodevelopmental disorders

Millions of children are impacted by neurodevelopmental disorders (NDDs), which unfold early in life, have varying genetic etiologies and can involve a variety of specific or generalized impairments in social, cognitive and motor functioning requiring potentially lifelong specialized supports. While specific disorders vary in their domain of primary deficit (e.g. autism spectrum disorder (social), attention-deficit/hyperactivity disorder (attention), developmental coordination disorder (motor) and developmental language disorder (language)), comorbidities between NDDs are common. Intriguingly, many NDDs are associated with difficulties in skills related to rhythm, timing and synchrony though specific profiles of rhythm/timing impairments vary across disorders. Impairments in rhythm/timing may instantiate vulnerabilities for a variety of NDDs and may contribute to both the primary symptoms of each disorder as well as the high levels of comorbidities across disorders. Drawing upon genetic, neural, behavioural and interpersonal constructs across disorders, we consider how disrupted rhythm and timing skills early in life may contribute to atypical developmental cascades that involve overlapping symptoms within the context of a disorder's primary deficits. Consideration of the developmental context, as well as common and unique aspects of the phenotypes of different NDDs, will inform experimental designs to test this hypothesis including via potential mechanistic intervention approaches. This article is part of the theme issue 'Synchrony and rhythm interaction: from the brain to behavioural ecology'.

Quantitative and Qualitative Sex Modulations in the Brain Anatomy of Autism

BACKGROUND

Sex-based neurobiological heterogeneity in autism is poorly understood. Research is disproportionately biased to males, leading to an unwarranted presumption that autism neurobiology is the same across sexes. Previous neuroimaging studies using amalgamated multicenter datasets to increase autistic female samples are characterized by large statistical noise.

METHODS

We used a better-powered dataset of 1183 scans of 839 individuals-299 (467 scans) autistic males, 74 (102 scans) autistic females, 240 (334 scans) control males, and 226 (280 scans) control females-to test two whole-brain models of overall/global sex modulations on autism neuroanatomy, by summary measures computed across the brain: the local magnitude model, in which the same brain regions/circuitries are involved across sexes but effect sizes are larger in females, indicating quantitative sex modulation; and spatial dissimilarity model, in which the neuroanatomy differs spatially between sexes, indicating qualitative sex modulation. The male and female autism groups were matched on age, IQ, and autism symptoms. Autism brain features were defined by comparisons with same-sex control individuals.

RESULTS

Across five metrics (cortical thickness, surface area, volume, mean absolute curvature, and subcortical volume), we found no evidence supporting the local magnitude model. We found indicators supporting the spatial dissimilarity model on cortical mean absolute curvature and subcortical volume, but not on other metrics.

CONCLUSIONS

The overall/global autism neuroanatomy in females and males does not simply differ quantitatively in the same brain regions/circuitries. They may differ qualitatively in spatial involvement in cortical curvature and subcortical volume. The neuroanatomy of autism may be partly sex specific. Sex stratification to inform autism preclinical/clinical research is needed to identify sex-informed neurodevelopmental targets.