Identifying novel data-driven subgroups in congenital heart disease using multi-modal measures of brain structure.

Individuals with congenital heart disease (CHD) have an increased risk of neurodevelopmental impairments. Given the hypothesized complexity linking genomics, atypical brain structure, cardiac diagnoses and their management, and neurodevelopmental outcomes, unsupervised methods may provide unique insight into neurodevelopmental variability in CHD. Using data from the Pediatric Cardiac Genomics Consortium Brain and Genes study, we identified data-driven subgroups of individuals with CHD from measures of brain structure. Using structural magnetic resonance imaging (MRI; N = 93; cortical thickness, cortical volume, and subcortical volume), we identified subgroups that differed primarily on cardiac anatomic lesion and language ability. In contrast, using diffusion MRI (N = 88; white matter connectivity strength), we identified subgroups that were characterized by differences in associations with rare genetic variants and visual-motor function. This work provides insight into the differential impacts of cardiac lesions and genomic variation on brain growth and architecture in patients with CHD, with potentially distinct effects on neurodevelopmental outcomes.

Cross-Diagnosis Structural Correlates of Autistic-Like Social Communication Differences.

Social communication differences are seen in autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD), but the brain mechanisms contributing to these differences remain largely unknown. To address this gap, we used a data-driven and diagnosis-agnostic approach to discover brain correlates of social communication differences in ASD, ADHD, and OCD, and subgroups of individuals who share similar patterns of brain-behavior associations. A machine learning pipeline (regression clustering) was used to discover the pattern of association between structural brain measures (volume, surface area, and cortical thickness) and social communication abilities. Participants (n = 416) included children with a diagnosis of ASD (n = 192, age = 12.0[5.6], 19% female), ADHD (n = 109, age = 11.1[4.1], 18% female), or OCD (n = 50, age = 12.3[4.2], 42% female), and typically developing controls (n = 65, age = 11.6[7.1], 48% female). The analyses revealed (1) associations with social communication abilities in distributed cortical and subcortical networks implicated in social behaviors, language, attention, memory, and executive functions, and (2) three data-driven, diagnosis-agnostic subgroups based on the patterns of association in the above networks. Our results suggest that different brain networks may contribute to social communication differences in subgroups that are not diagnosis-specific.

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.

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.

A review of EEG-based brain-computer interfaces as access pathways for individuals with severe disabilities.

Electroencephalography (EEG) is a non-invasive method for measuring brain activity and is a strong candidate for brain-computer interface (BCI) development. While BCIs can be used as a means of communication for individuals with severe disabilities, the majority of existing studies have reported BCI evaluations by able-bodied individuals. Considering the many differences in body functions and usage scenarios between individuals with disabilities and able-bodied individuals, involvement of the target population in BCI evaluation is necessary. In this review, 39 studies reporting EEG-oriented BCI assessment by individuals with disabilities were identified in the past decade. With respect to participant populations, a need for assessing BCI performance for the pediatric population with severe disabilities was identified as an important future direction. Acquiring a reliable communication pathway during early stages of development is crucial in avoiding learned helplessness in pediatric-onset disabilities. With respect to evaluation, augmenting traditional measures of system performance with those relating to contextual factors was recommended for realizing user-centered designs appropriate for integration in real-life. Considering indicators of user state and developing more effective training paradigms are recommended for future studies of BCI involving individuals with disabilities.

Behavioural reactions to an emotion evoking task in infants at increased likelihood for autism spectrum disorder.

Infants at increased likelihood for autism spectrum disorder (ASD) exhibit more negative affect and avoidance behaviour than typically developing infants, and children with ASD express fear differently than typically developing peers. We examined behavioural reactions to emotion-evoking stimuli in infants at increased familial likelihood for ASD. Participants included 55 increased likelihood (IL) infants (i.e., siblings of children diagnosed with ASD) and 27 typical likelihood (TL) infants (i.e., no family history of ASD). At 18 months, we showed infants two masks that commonly elicit fearful responses in older children and examined potential behavioural differences in approach, avoidance, 'freezing', crying, gaze aversion, and smiling. At 24 months, infants were assessed with the Toddler Module of the Autism Diagnostic Observation Schedule, 2nd edition (ADOS-2). Results of video-based coding showed that (1) IL infants exhibited more intense avoidance behaviour than TL infants in response to masks, and (2) intensity of avoidance and duration of freezing were positively correlated with ADOS-2 symptom severity scores. Findings suggest that differences in response to emotion-eliciting stimuli may predict later ASD symptoms. Such behavioural differences may inform early detection and intervention in ASD.

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.

Quantitative classification of pediatric swallowing through accelerometry.

BACKGROUND: Dysphagia or swallowing disorder negatively impacts a child's health and development. The gold standard of dysphagia detection is videofluoroscopy which exposes the child to ionizing radiation, and requires specialized clinical expertise and expensive institutionally-based equipment, precluding day-to-day and repeated assessment of fluctuating swallowing function. Swallowing accelerometry is the non-invasive measurement of cervical vibrations during swallowing and may provide a portable and cost-effective bedside alternative. In particular, dual-axis swallowing accelerometry has demonstrated screening potential in older persons with neurogenic dysphagia, but the technique has not been evaluated in the pediatric population. METHODS: In this study, dual-axis accelerometric signals were collected simultaneous to videofluoroscopic records from 29 pediatric participants (age 6.8 ± 4.8 years; 20 males) previously diagnosed with neurogenic dysphagia. Participants swallowed 3-5 sips of barium-coated boluses of different consistencies (normally, from thick puree to thin liquid) by spoon or bottle. Videofluoroscopic records were reviewed retrospectively by a clinical expert to extract swallow timings and ratings. The dual-axis acceleration signals corresponding to each identified swallow were pre-processed, segmented and trimmed prior to feature extraction from time, frequency, time-frequency and information theoretic domains. Feature space dimensionality was reduced via principal components. RESULTS: Using 8-fold cross-validation, 16-17 dimensions and a support vector machine classifier with an RBF kernel, an adjusted accuracy of 89.6% ± 0.9 was achieved for the discrimination between swallows with and with out airway entry. CONCLUSIONS: Our results suggest that dual-axis accelerometry has merit in the non-invasive detection of unsafe swallows in children and deserves further consideration as a pediatric medical device.

Effect of pencil grasp on the speed and legibility of handwriting after a 10-minute copy task in Grade 4 children.

AIM: To investigate the impact of common pencil grasp patterns on the speed and legibility of handwriting after a 10-minute copy task, intended to induce muscle fatigue, in typically developing children and in those non-proficient in handwriting. METHODS: A total of 120 Grade 4 students completed a standardised handwriting assessment before and after a 10-minute copy task. The students indicated the perceived difficulty of the handwriting task at baseline and after 10 minutes. The students also completed a self-report questionnaire regarding their handwriting proficiency upon completion. RESULTS: The majority of the students rated higher effort after the 10-minute copy task than at baseline (rank sum: P = 0.00001). The effort ratings were similar for the different grasp patterns (multiple linear regression: F = 0.37, P = 0.895). For both typically developing children and those with handwriting issues, the legibility of the writing samples decreased after the 10-minute copy task but the speed of writing increased. CONCLUSIONS AND SIGNIFICANCE OF THE STUDY: The quality of the handwriting decreased after the 10-minute copy task; however, there was no difference in the quality or speed scores among the different pencil grasps before and after the copy task. The dynamic tripod pencil grasp did not offer any advantage over the lateral tripod or the dynamic or lateral quadrupod pencil grasps in terms of quality of handwriting after a 10-minute copy task. These four pencil grasp patterns performed equivalently. Our findings question the practice of having students adopt the dynamic tripod pencil grasp.

Examining the effect of a wearable, anxiety detection technology on improving the awareness of anxiety signs in autism spectrum disorder: a pilot randomized controlled trial.

BACKGROUND: Anxiety is prevalent in autism spectrum disorder (ASD) and can negatively impact physical and mental health. Self-awareness of anxiety signs is a key barrier to success of anxiety interventions for many children. METHODS: To address this, we conducted a randomized controlled trial to assess whether the Anxiety Meter, a wearable, real-time anxiety detection technology, can improve awareness of anxiety symptoms and the initiation of relaxation techniques in children with ASD. Twenty-eight children with ASD were trained on the use of the Anxiety Meter and taught a diaphragmatic breathing relaxation technique over three visits. On the fourth visit, participants were randomized to either receive feedback of their anxiety level or no feedback from the Anxiety Meter while completing a stress-eliciting task (public speaking) and asked to engage in deep breathing if anxious. RESULTS: Feedback from the Anxiety Meter was associated with increased likelihood of initiating deep breathing in response to anxiety. LIMITATIONS: Limitations include the small sample size, imbalanced group matching for IQ and sex, and the controlled-laboratory settings which limit the statistical power and generalizability of the results to real-world settings. CONCLUSIONS: Although these results are limited by the relatively small sample size, they support the feasibility of using a wearable device and real-time feedback to improve anxiety symptom awareness. Trial Registration ClinicalTrials.gov Identifier: NCT02160691, registration date: 06/05/2014.

Characterizing Changes in Screen Time During the COVID-19 Pandemic School Closures in Canada and Its Perceived Impact on Children With Autism Spectrum Disorder.

The COVID-19 pandemic has led to an increase in screen time for children and families. Traditionally, screen time has been associated with negative physical and mental health outcomes, and children with autism spectrum disorder (ASD) are at increased risk of these outcomes. The primary objectives of this study were to (1) characterize the change in screen time during COVID-19 school closures for children with ASD, and (2) examine the parent perceived impact of screen time on mental health and quality of life of children and their families. Canadian parents and caregivers of children 19 years of age and younger were eligible to participate in an anonymous, online survey study. This survey was available in English, consisted of 28 questions, took ~10-min to complete, and was available for 6 weeks (May 22 through July 6, 2020). The total sample consisted of 414 responses (ASD: n = 127, mean age = 11.7 ± 4.06 years; community sample: n = 287, mean age = 9.4 ± 4.26 years). Seventy-one respondents were missing responses to our primary question and removed from the analyses (final sample n = 344). Compared to the community sample, the ASD group had a significantly higher screen time use before and during the COVID-19 pandemic school closures [weekdays: difference = 1.14 (SE = 0.18), t = 6.56, p < 0.0001; weekends: difference = 1.41 (SE = 0.20), t = 6.93, p < 0.0001]. Mean total screen time during the pandemic was 6.9 h (95% CI 6.49, 7.21) on weekdays and 6.3 h (95% CI 5.91, 6.63) on weekends for the ASD group, and 5.6 h (95% CI 5.28, 5.92) on weekdays and 5.0 h (95% CI 4.70, 5.34) on weekends for the community sample. There was a significant increase in screen time during the COVID-19 pandemic as compared to before the pandemic period in the ASD group [weekdays: mean difference = 3.8 h (95% CI 3.35-4.25), p < 0.0001; weekends: mean difference = 1.5 h (95% CI 1.17-1.92), p < 0.0001]. Gender was a significant predictor of parent perceived mental health and quality of life, with male gender associated with a higher likelihood of negative impact [quality of life (child/family) OR = 1.8 (95% CI 1.1-2.9), corrected p = 0.040; mental health OR = 1.9 (95% CI 1.1-3.1), corrected p = 0.0028]. Parents' most frequently endorsed emotions toward screen time were guilt, frustration, and worry. Results of this survey study revealed that children with ASD were less likely to benefit from screen time to cope with social isolation, and screen time resulted in significantly more lost time on social interactions than the community sample, which may exacerbate difficulties in social domains. Given the unprecedented circumstances of the COVID-19 pandemic and the novel context of technology use, the findings of this study highlight the need for revision of screen time recommendations to reflect the current needs of children and families.

Physiological measurement of emotion from infancy to preschool: A systematic review and meta-analysis.

INTRODUCTION: Emotion regulation, the ability to regulate emotional responses to environmental stimuli, develops in the first years of life and plays an important role in the development of personality, social competence, and behavior. Substantial literature suggests a relationship between emotion regulation and cardiac physiology; specifically, heart rate changes in response to positive or negative emotion-eliciting stimuli. METHOD: This systematic review and meta-analysis provide an in-depth examination of research that has measured physiological responding during emotional-evoking tasks in children from birth to 4 years of age. RESULTS: The review had three main findings. First, meta-regressions resulted in an age-related decrease in baseline and task-related heart rate (HR) and increases in baseline and task-related respiratory sinus arrhythmia (RSA). Second, meta-analyses suggest task-related increases in HR and decreases in RSA and heart rate variability (HRV), regardless of emotional valence of the task. Third, associations between physiological responding and observed behavioral regulation are not consistently present in children aged 4 and younger. The review also provides a summary of the various methodology used to measure physiological reactions to emotional-evoking tasks, including number of sensors used and placement, various baseline and emotional-evoking tasks used, methods for extracting RSA, as well as percentage of loss and reasons for loss for each study. CONCLUSION: Characterizing the physiological reactivity of typically developing children is important to understanding the role emotional regulation plays in typical and atypical development.

Toward Automatic Anxiety Detection in Autism: A Real-Time Algorithm for Detecting Physiological Arousal in the Presence of Motion.

OBJECTIVE: Anxiety is a significant clinical concern in autism spectrum disorder (ASD) due to its negative impact on physical and psychological health. Treatment of anxiety in ASD remains a challenge due to difficulties with self-awareness and communication of anxiety symptoms. To reduce these barriers to treatment, physiological markers of autonomic arousal, collected through wearable sensors, have been proposed as real-time, objective, and language-free measures of anxiety. A critical limitation of the existing anxiety detection systems is that physiological arousal is not specific to anxiety and can occur with other user states such as physical activity. This can result in false positives, which can hinder the operation of these systems in real-world situations. The objective of this paper was to address this challenge by proposing an approach for real-time detection and mitigation of physical activity effects. METHODS: A novel multiple model Kalman-like filter is proposed to integrate heart rate and accelerometry signals. The filter tracks user heart rate under different motion assumptions and chooses the appropriate model for anxiety detection based on user motion conditions. RESULTS: Evaluation of the algorithm using data from a sample of children with ASD shows a significant reduction in false positives compared to the state-of-the-art, and an overall arousal detection accuracy of 93%. CONCLUSION: The proposed method is able to reduce false detections due to user motion and effectively detect arousal states during movement periods. SIGNIFICANCE: The results add to the growing evidence supporting the feasibility of wearable technologies for anxiety detection and management in naturalistic settings.

Short report: Evaluating the safety and usability of head-mounted virtual reality compared to monitor-displayed video for children with autism spectrum disorder.

LAY ABSTRACT: This study investigated the safety and usability of a virtual reality experience for children with autism spectrum disorder in a laboratory setting. In our study, the negative effects of head-mounted display-virtual reality were similar to monitor-displayed video watching. At the same time, the participants indicated that the head-mounted display-virtual reality experience provided improved realism and sense of presence. This study is a first step in understanding the impact of head-mounted display on children with autism spectrum disorder.

Data-Driven Discovery of Predictors of Virtual Reality Safety and Sense of Presence for Children With Autism Spectrum Disorder: A Pilot Study.

Virtual reality (VR) offers children with autism spectrum disorder (ASD) an inexpensive and motivating medium to learn and practice skills in a personalized, controlled, and safe setting; however, outcomes of VR interventions can vary widely. In particular, there is a need to understand the predictors of VR experience in children with ASD to inform the design of these interventions. To address this gap, a sample of children with ASD (n=35, mean age: 13.0 ± 2.6 years; 10 female) participated in a pilot study involving an immersive VR experience delivered through a head-mounted display. A data-driven approach was used to discover predictors of VR safety and sense of presence among a range of demographic and phenotypic user characteristics. Our results suggest that IQ may be a key predictor of VR sense of presence and that anxiety may modify the association between IQ and sense of presence. In particular, in low-anxiety participants, IQ was linearly related to experienced spatial presence and engagement, whereas, in high-anxiety participants, this association followed a quadratic form. The results of this pilot study, when replicated in larger samples, will inform the design of future studies on VR interventions for children with ASD.

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

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.

Structural neuroimaging correlates of social deficits are similar in autism spectrum disorder and attention-deficit/hyperactivity disorder: analysis from the POND Network.

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.

Clustering of time-evolving scaling dynamics in a complex signal.

Complex time series are widespread in physics and physiology. Multifractal analysis provides a tool to study the scaling dynamics of such time series. However, the temporal evolution of scaling dynamics has been ignored by traditional tools such as the multifractal spectrum. We present scaling maps that add the time dimension to the study of scaling dynamics. This is particularly important in cases in which the dynamics of the underlying processes change in time or in applications that necessitate real-time detection of scaling dynamics. In addition, we present a methodology for automatic clustering of existing scaling regimes in a signal. We demonstrate the methodology on time-evolving correlated and uncorrelated noise and the output of a physiological control system (i.e., cardiac interbeat intervals) in healthy and pathological states.

Specificity of autonomic arousal to anxiety in children with autism spectrum disorder.

Anxiety is one of the most concerning comorbidities in autism spectrum disorder (ASD) due to its high prevalence, negative impact on physical and psychological well-being, and interaction with core deficits of ASD. Current assessment and treatment of anxiety, which rely on the observation of behavior and self-reports, are often ineffective as ASD is associated with deficits in communication and diminished introspective ability. In this light, autonomic nervous system (ANS) activity has been suggested as a marker of physiological arousal associated with anxiety. However, physiological arousal measured by ANS indices also occurs with other cognitive and emotional processes, and it is unclear whether anxiety-related arousal can be differentiated from that related to other cognitive processes. To address this gap, we investigated the use of linear and nonlinear classification techniques for differentiating anxiety-related arousal from arousal due to three cognitive processes (attention, inhibitory control, and social cognition) and physical activity based on electrocardiography signal features. Our results indicate that over 80% classification accuracy can be achieved, suggesting that ANS response can be used as a specific marker of anxiety-related arousal in a subgroup of children with ASD who demonstrate an increase in heart rate in response to anxiogenic stimuli.