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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Biomusic: An Auditory Interface for Detecting Physiological Indicators of Anxiety in Children.

For children with profound disabilities affecting communication, it can be extremely challenging to identify salient emotions such as anxiety. If left unmanaged, anxiety can lead to hypertension, cardiovascular disease, and other psychological diagnoses. Physiological signals of the autonomic nervous system are indicative of anxiety, but can be difficult to interpret for non-specialist caregivers. This paper evaluates an auditory interface for intuitive detection of anxiety from physiological signals. The interface, called "Biomusic," maps physiological signals to music (i.e., electrodermal activity to melody; skin temperature to musical key; heart rate to drum beat; respiration to a "whooshing" embellishment resembling the sound of an exhalation). The Biomusic interface was tested in two experiments. Biomusic samples were generated from physiological recordings of typically developing children (n = 10) and children with autism spectrum disorders (n = 5) during relaxing and anxiety-provoking conditions. Adult participants (n = 16) were then asked to identify "anxious" or "relaxed" states by listening to the samples. In a classification task with 30 Biomusic samples (1 relaxed state, 1 anxious state per child), classification accuracy, sensitivity, and specificity were 80.8% [standard error (SE) = 2.3], 84.9% (SE = 3.0), and 76.8% (SE = 3.9), respectively. Participants were able to form an early and accurate impression of the anxiety state within 12.1 (SE = 0.7) seconds of hearing the Biomusic with very little training (i.e., < 10 min) and no contextual information. Biomusic holds promise for monitoring, communication, and biofeedback systems for anxiety management.

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.

Atypical sympathetic arousal in children with autism spectrum disorder and its association with anxiety symptomatology.

BACKGROUND: Autism spectrum disorder (ASD) has been associated with autonomic atypicalities, although the nature of these differences remains largely unknown. Moreover, existing literature suggests large variability in autonomic function in ASD, motivating the need to examine the existence of subgroups that exhibit more homogeneous autonomic features. METHODS: Electrodermal activity (EDA), a non-invasive physiological indicator of autonomic activity, was measured in typically developing children (n = 33) and those with ASD (n = 38) as participants performed tasks that elicit anxiety, attention, response inhibition, and social cognition processes. The ASD group was divided into low- (n = 18) and high-anxiety (n = 20) participants, and the groups were compared to mean EDA level and electrodermal reactions frequency (EDR). RESULTS: The ASD group had a significantly blunted mean EDA response to the anxiety tasks (p < 0.004). The EDR response to all tasks, except response inhibition, was also blunted in the ASD group (p < 0.04). For this group, EDR frequency during the anxiety and social cognition tasks was negatively correlated with behavioral scores in the domains that were probed by each task (p < 0.002). The high-anxiety ASD group showed significantly decreased mean EDA compared to both the low-anxiety ASD group (p = 0.02) and the typically developing control group (p = 0.04). The high-anxiety ASD group also had significantly more severe symptoms than the low-anxiety ASD group on domains related to anxiety, attention, rule breaking, aggression, obsessions and compulsions, and depression. CONCLUSIONS: Our results suggest atypical autonomic function in children with ASD, specifically with respect to sympathetic activity. Moreover, anxiety symptomatology defined subgroups with distinct physiological and behavioral profiles. Overall, the results add to the body of literature supporting autonomic dysfunction in ASD and highlight the role of anxiety and autonomic features in explaining the variability in the autism spectrum.

Examining and comparing social perception abilities across childhood-onset neurodevelopmental disorders.

OBJECTIVE: Several neurodevelopmental disorders are associated with social processing deficits. The objective of this study was to compare patterns of social perception abilities across obsessive-compulsive disorder (OCD), attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and control participants. METHOD: A total of 265 children completed the Reading the Mind in the Eyes Test-Child Version (RMET). Parents or caregivers completed established trait/symptom scales. The predicted percentage of accuracy on the RMET was compared across disorders and by item difficulty and item valence (i.e., positive/negative/neutral mental states), then analyzed for associations with trait/symptom scores. RESULTS: The percentage of correct RMET scores varied significantly between diagnostic groups (p < .0001). On pairwise group comparisons controlling for age and sex, children with ADHD and ASD scored lower than the other groups (p < .0001). When IQ was also controlled for in the model, participants with OCD performed better than controls (p < .001), although differences between other groups were less pronounced. Participants with ASD scored lowest on easy items. Those with ASD and ADHD scored significantly lower than other groups on items with positive valence (p < .01). Greater social communication impairment and hyperactivity/impulsivity, but not OCD traits/symptoms, were associated with lower scores on the RMET, irrespective of diagnosis. CONCLUSION: Social perception abilities in neurodevelopmental disorders exist along a continuum. Children with ASD have the greatest deficits, whereas children with OCD may be hypersensitive to social information. Social communication deficits and hyperactive/impulsive traits are associated with impaired social perception abilities; these findings highlight overlapping cognitive and behavioral manifestations across disorders.

A Kalman filtering framework for physiological detection of anxiety-related arousal in children with autism spectrum disorder.

OBJECTIVE: Anxiety is associated with physiological changes that can be noninvasively measured using inexpensive and wearable sensors. These changes provide an objective and language-free measure of arousal associated with anxiety, which can complement treatment programs for clinical populations who have difficulty with introspection, communication, and emotion recognition. This motivates the development of automatic methods for detection of anxiety-related arousal using physiology signals. While several supervised learning methods have been proposed for this purpose, these methods require regular collection and updating of training data and are, therefore, not suitable for clinical populations, where obtaining labelled data may be challenging due to impairments in communication and introspection. In this context, the objective of this paper is to develop an unsupervised and real-time arousal detection algorithm. METHODS: We propose a learning framework based on the Kalman filtering theory for detection of physiological arousal based on cardiac activity. The performance of the system was evaluated on data obtained from a sample of children with autism spectrum disorder. RESULTS: The results indicate that the system can detect anxiety-related arousal in these children with sensitivity and specificity of 99% and 92%, respectively. CONCLUSION AND SIGNIFICANCE: Our results show that the proposed method can detect physiological arousal associated with anxiety with high accuracy, providing support for technical feasibility of augmenting anxiety treatments with automatic detection techniques. This approach can ultimately lead to more effective anxiety treatment for a larger and more diverse population.