Therapeutic Alliance in Family-Based Treatment of Anorexia Nervosa: In-Person Versus Telehealth.

OBJECTIVE: The therapeutic alliance is broadly linked with positive outcomes. However, nearly all research in this area involves in-person therapy, whereas teletherapy has grown increasing common since the COVID-19 pandemic. There is now a pressing need to establish whether the nature and importance of the therapeutic alliance is impacted by teletherapy. This study examined therapeutic alliance in families of youth with anorexia nervosa who were participating in a randomized controlled trial that transitioned from in-person to telehealth visits during the COVID-19 pandemic. METHOD: We analysed data from 53 adolescents and their parents (20 began in-person, 33 began with telehealth). Both parents, youth and therapist completed the Working Alliance Inventory-Short Revised after 4 weeks of treatment. RESULTS: We found no significant differences across telehealth and in-person treatment for paternal or therapist reported data. However, both adolescents and mothers reported higher bond and goal-related alliance for in-person sessions compared to telehealth. CONCLUSIONS: Findings regarding alliance across telehealth and in-person sessions were mixed, with some preference among mothers and youth for in-person treatment. Future studies should determine whether possible adaptations can improve working alliance during family-based treatment for anorexia nervosa via telehealth.

White matter connectivity and social functioning in survivors of pediatric brain tumor.

OBJECTIVE: Survivors of pediatric brain tumors (SPBT) are at risk for social deficits, fewer friendships, and poor peer relations. SPBT also experience reduced brain connectivity via microstructural disruptions to white matter from neurological insults. Research with other populations implicates white matter connectivity as a key contributor to poor social functioning. This case-controlled diffusion-weighted imaging study evaluated structural connectivity in SPBT and typically developing controls (TDC) and associations between metrics of connectivity and social functioning. METHODS: Diffusion weighted-imaging results from 19 SPBT and 19 TDC were analyzed using probabilistic white matter tractography. Survivors were at least 5 years post-diagnosis and 2 years off treatment. Graph theory statistics measured group differences across several connectivity metrics, including average strength, global efficiency, assortativity, clustering coefficient, modularity, and betweenness centrality. Analyses also evaluated the effects of neurological risk on connectivity among SPBT. Correlational analyses evaluated associations between connectivity and indices of social behavior. RESULTS: SPBT demonstrated reduced global connectivity compared to TDC. Several medical factors (e.g., chemotherapy, recurrence, multimodal therapy) were related to decreased connectivity across metrics of integration (e.g., average strength, global efficiency) in SPBT. Connectivity metrics were related to peer relationship quality and social challenges in the SPBT group and to social challenges in the total sample. CONCLUSIONS: Microstructural white matter connectivity is diminished in SPBT and related to neurological risk and peer relationship quality. Additional neuroimaging research is needed to evaluate associations between brain connectivity metrics and social functioning in SPBT.

Ethical considerations for integrating multimodal computer perception and neurotechnology.

Background: Artificial intelligence (AI)-based computer perception technologies (e.g., digital phenotyping and affective computing) promise to transform clinical approaches to personalized care in psychiatry and beyond by offering more objective measures of emotional states and behavior, enabling precision treatment, diagnosis, and symptom monitoring. At the same time, passive and continuous nature by which they often collect data from patients in non-clinical settings raises ethical issues related to privacy and self-determination. Little is known about how such concerns may be exacerbated by the integration of neural data, as parallel advances in computer perception, AI, and neurotechnology enable new insights into subjective states. Here, we present findings from a multi-site NCATS-funded study of ethical considerations for translating computer perception into clinical care and contextualize them within the neuroethics and neurorights literatures. Methods: We conducted qualitative interviews with patients (n = 20), caregivers (n = 20), clinicians (n = 12), developers (n = 12), and clinician developers (n = 2) regarding their perspective toward using PC in clinical care. Transcripts were analyzed in MAXQDA using Thematic Content Analysis. Results: Stakeholder groups voiced concerns related to (1) perceived invasiveness of passive and continuous data collection in private settings; (2) data protection and security and the potential for negative downstream/future impacts on patients of unintended disclosure; and (3) ethical issues related to patients' limited versus hyper awareness of passive and continuous data collection and monitoring. Clinicians and developers highlighted that these concerns may be exacerbated by the integration of neural data with other computer perception data. Discussion: Our findings suggest that the integration of neurotechnologies with existing computer perception technologies raises novel concerns around dignity-related and other harms (e.g., stigma, discrimination) that stem from data security threats and the growing potential for reidentification of sensitive data. Further, our findings suggest that patients' awareness and preoccupation with feeling monitored via computer sensors ranges from hypo- to hyper-awareness, with either extreme accompanied by ethical concerns (consent vs. anxiety and preoccupation). These results highlight the need for systematic research into how best to implement these technologies into clinical care in ways that reduce disruption, maximize patient benefits, and mitigate long-term risks associated with the passive collection of sensitive emotional, behavioral and neural data.

Social impairment in survivors of pediatric brain tumors via reduced social attention and emotion-specific facial expression recognition.

BACKGROUND/OBJECTIVES: Survivors of pediatric brain tumors (SPBT) experience significant social challenges, including fewer friends and greater isolation than peers. Difficulties in face processing and visual social attention have been implicated in these outcomes. This study evaluated facial expression recognition (FER), social attention, and their associations with social impairments in SPBT. METHODS: SPBT (N = 54; ages 7-16) at least 2 years post treatment completed a measure of FER, while parents completed measures of social impairment. A subset (N = 30) completed a social attention assessment that recorded eye gaze patterns while watching videos depicting pairs of children engaged in joint play. Social Prioritization scores were calculated, with higher scores indicating more face looking. Correlations and regression analyses evaluated associations between variables, while a path analysis modeling tool (PROCESS) evaluated the indirect effects of Social Prioritization on social impairments through emotion-specific FER. RESULTS: Poorer recognition of angry and sad facial expressions was significantly correlated with greater social impairment. Social Prioritization was positively correlated with angry FER but no other emotions. Social Prioritization had significant indirect effects on social impairments through angry FER. CONCLUSION: Findings suggest interventions aimed at improving recognition of specific emotions may mitigate social impairments in SPBT. Further, reduced social attention (i.e., diminished face looking) could be a factor in reduced face processing ability, which may result in social impairments. Longitudinal research is needed to elucidate temporal associations between social attention, face processing, and social impairments.

Comparison of Human Experts and AI in Predicting Autism from Facial Behavior.

Advances in computational behavior analysis via artificial intelligence (AI) promise to improve mental healthcare services by providing clinicians with tools to assist diagnosis or measurement of treatment outcomes. This potential has spurred an increasing number of studies in which automated pipelines predict diagnoses of mental health conditions. However, a fundamental question remains unanswered: How do the predictions of the AI algorithms correspond and compare with the predictions of humans? This is a critical question if AI technology is to be used as an assistive tool, because the utility of an AI algorithm would be negligible if it provides little information beyond what clinicians can readily infer. In this paper, we compare the performance of 19 human raters (8 autism experts and 11 non-experts) and that of an AI algorithm in terms of predicting autism diagnosis from short (3-minute) videos of N = 42 participants in a naturalistic conversation. Results show that the AI algorithm achieves an average accuracy of 80.5%, which is comparable to that of clinicians with expertise in autism (83.1%) and clinical research staff without specialized expertise (78.3%). Critically, diagnoses that were inaccurately predicted by most humans (experts and non-experts, alike) were typically correctly predicted by AI. Our results highlight the potential of AI as an assistive tool that can augment clinician diagnostic decision-making.

Creation of a versatile automated two-step purification system with increased throughput capacity for preclinical mAb material generation.

The ever-increasing speed of biotherapeutic drug discovery has driven the development of automated and high throughput purification capabilities. Typically, purification systems require complex flow paths or third-party components that are not found on a standard fast protein liquid chromatography instrument (FPLC) (e.g., Cytiva's ÄKTA) to enable higher throughput. In early mAb discovery there is often a trade-off between throughput and scale where a high-throughput process requires miniaturized workflows necessitating a sacrifice in the amount of material generated. At the interface of discovery and development, flexible automated systems are required that can perform purifications in a high-throughput manner, while also generating sufficient quantities of preclinical material for biophysical, developability, and preclinical animal studies. In this study we highlight the engineering efforts to generate a highly versatile purification system capable of balancing the purification requirements between throughput, chromatographic versatility, and overall product yields. We incorporated a 150 mL Superloop into an ÄKTA FPLC system to expand our existing purification capabilities. This allowed us to perform a range of automated two-step tandem purifications including primary affinity captures (protein A (ProA)/immobilized metal affinity chromatography (IMAC)/antibody fragment (Fab)) followed by secondary polishing with either size exclusion (SEC) or cation exchange (CEX) chromatography. We also integrated a 96 deep-well plate fraction collector into the ÄKTA FPLC system with purified protein fractions being analyzed by a plate based high performance liquid chromatography instrument (HPLC). This streamlined automated purification workflow allowed us to process up to 14 samples within 24 h, enabling purification of ∼1100 proteins, monoclonal antibodies (mAbs), and mAb related protein scaffolds during a 12-month period. We purified a broad range of cell culture supernatant volumes, between 0.1 and 2 L, with final purification yields up to 2 g. The implementation of this new automated, streamlined protein purification process greatly expanded our sample throughput and purification versatility while also enabling the accelerated production of greater quantities of biotherapeutic candidates for preclinical in vivo animal studies and developability assessment.

Sleep problems in autism: Sex differences in the school-age population.

Clinically significant sleep problems affect up to 86% of the autistic population in school-age. Sleep problems can have negative impacts on child cognition, behavior, and health. However, sex differences in the prevalence and types of sleep problems are not well understood in autism. To evaluate sex differences in sleep problems in the school-age autistic population, we obtained parent-report of sleep problems on the Children's Sleep Habits Questionnaire and conducted direct assessments to establish diagnosis and intellectual ability in 6-12-year-old children (autism n = 250; typical development [TD] n = 114). Almost 85% of autistic females demonstrated sleep problems compared to 65.8% of autistic males, 44.8% of TD females, and 42.4% of TD males; a statistically significant increase for autistic females. Autistic females demonstrated increased bedtime resistance, sleep anxiety, and sleepiness, and decreased sleep duration, but did not differ in sleep onset delay, night wakings, parasomnias, or disordered breathing compared with autistic males. Intellectual ability was not related to increased sleep problems. Higher anxiety scores were associated with more sleep problems for males but not females. In one of the first studies to evaluate sex differences in sleep in the school-age, autistic population, autistic females demonstrated increased sleep problems compared to autistic males, TD females, and TD males. Current autism assessment and intervention practices may benefit from increased attention to sleep problems in autistic school-age females and to anxiety in autistic males to enhance well-being and behavioral and health outcomes.

Predicting Autism from Head Movement Patterns during Naturalistic Social Interactions.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized in part by difficulties in verbal and nonverbal social communication. Evidence indicates that autistic people, compared to neurotypical peers, exhibit differences in head movements, a key form of nonverbal communication. Despite the crucial role of head movements in social communication, research on this nonverbal cue is relatively scarce compared to other forms of nonverbal communication, such as facial expressions and gestures. There is a need for scalable, reliable, and accurate instruments for measuring head movements directly within the context of social interactions. In this study, we used computer vision and machine learning to examine the head movement patterns of neurotypical and autistic individuals during naturalistic, face-to-face conversations, at both the individual (monadic) and interpersonal (dyadic) levels. Our model predicts diagnostic status using dyadic head movement data with an accuracy of 80%, highlighting the value of head movement as a marker of social communication. The monadic data pipeline had lower accuracy (69.2%) compared to the dyadic approach, emphasizing the importance of studying back-and-forth social communication cues within a true social context. The proposed classifier is not intended for diagnostic purposes, and future research should replicate our findings in larger, more representative samples.

Head Movement Patterns during Face-to-Face Conversations Vary with Age.

Advances in computational behavior analysis have the potential to increase our understanding of behavioral patterns and developmental trajectories in neurotypical individuals, as well as in individuals with mental health conditions marked by motor, social, and emotional difficulties. This study focuses on investigating how head movement patterns during face-to-face conversations vary with age from childhood through adulthood. We rely on computer vision techniques due to their suitability for analysis of social behaviors in naturalistic settings, since video data capture can be unobtrusively embedded within conversations between two social partners. The methods in this work include unsupervised learning for movement pattern clustering, and supervised classification and regression as a function of age. The results demonstrate that 3-minute video recordings of head movements during conversations show patterns that distinguish between participants that are younger vs. older than 12 years with 78% accuracy. Additionally, we extract relevant patterns of head movement upon which the age distinction was determined by our models.

Reduced Fusiform Gyrus Activation During Face Processing in Pediatric Brain Tumor Survivors.

OBJECTIVE: The neural mechanisms contributing to the social problems of pediatric brain tumor survivors (PBTS) are unknown. Face processing is important to social communication, social behavior, and peer acceptance. Research with other populations with social difficulties, namely autism spectrum disorder, suggests atypical brain activation in areas important for face processing. This case-controlled functional magnetic resonance imaging (fMRI) study compared brain activation during face processing in PBTS and typically developing (TD) youth. METHODS: Participants included 36 age-, gender-, and IQ-matched youth (N = 18 per group). PBTS were at least 5 years from diagnosis and 2 years from the completion of tumor therapy. fMRI data were acquired during a face identity task and a control condition. Groups were compared on activation magnitude within the fusiform gyrus for the faces condition compared to the control condition. Correlational analyses evaluated associations between neuroimaging metrics and indices of social behavior for PBTS participants. RESULTS: Both groups demonstrated face-specific activation within the social brain for the faces condition compared to the control condition. PBTS showed significantly decreased activation for faces in the medial portions of the fusiform gyrus bilaterally compared to TD youth, ps ≤ .004. Higher peak activity in the left fusiform gyrus was associated with better socialization (r = .53, p < .05). CONCLUSIONS: This study offers initial evidence of atypical activation in a key face processing area in PBTS. Such atypical activation may underlie some of the social difficulties of PBTS. Social cognitive neuroscience methodologies may elucidate the neurobiological bases for PBTS social behavior.

Evaluating commercially available wireless cardiovascular monitors for measuring and transmitting real-time physiological responses in children with autism.

Commercially available wearable biosensors have the potential to enhance psychophysiology research and digital health technologies for autism by enabling stress or arousal monitoring in naturalistic settings. However, such monitors may not be comfortable for children with autism due to sensory sensitivities. To determine the feasibility of wearable technology in children with autism age 8-12 years, we first selected six consumer-grade wireless cardiovascular monitors and tested them during rest and movement conditions in 23 typically developing adults. Subsequently, the best performing monitors (based on data quality robustness statistics), Polar and Mio Fuse, were evaluated in 32 children with autism and 23 typically developing children during a 2-h session, including rest and mild stress-inducing tasks. Cardiovascular data were recorded simultaneously across monitors using custom software. We administered the Comfort Rating Scales to children. Although the Polar monitor was less comfortable for children with autism than typically developing children, absolute scores demonstrated that, on average, all children found each monitor comfortable. For most children, data from the Mio Fuse (96%-100%) and Polar (83%-96%) passed quality thresholds of data robustness. Moreover, in the stress relative to rest condition, heart rate increased for the Polar, F(1,53) = 135.70, p < 0.001, ηp2  = 0.78, and Mio Fuse, F(1,53) = 71.98, p < 0.001, ηp2  = 0.61, respectively, and heart rate variability decreased for the Polar, F(1,53) = 13.41, p = 0.001, ηp2  = 0.26, and Mio Fuse, F(1,53) = 8.89, p = 0.005, ηp2  = 0.16, respectively. This feasibility study suggests that select consumer-grade wearable cardiovascular monitors can be used with children with autism and may be a promising means for tracking physiological stress or arousal responses in community settings. LAY SUMMARY: Commercially available heart rate trackers have the potential to advance stress research with individuals with autism. Due to sensory sensitivities common in autism, their comfort wearing such trackers is vital to gathering robust and valid data. After assessing six trackers with typically developing adults, we tested the best trackers (based on data quality) in typically developing children and children with autism and found that two of them met criteria for comfort, robustness, and validity.

Dissociating regional gray matter density and gray matter volume in autism spectrum condition.

BACKGROUND: Despite decades of research, there is continued uncertainty regarding whether autism is associated with a specific profile of gray matter (GM) structure. This inconsistency may stem from the widespread use of voxel-based morphometry (VBM) methods that combine indices of GM density and GM volume. If GM density or volume, but not both, prove different in autism, the traditional VBM approach of combining the two indices may obscure the difference. The present study measures GM density and volume separately to examine whether autism is associated with alterations in GM volume, density, or both. METHODS: Differences in MRI-based GM density and volume were examined in 6-25 year-olds with a diagnosis of autism spectrum disorder (n = 213, 80.8% male, IQ 47-154) and a typically developing (TD) sample (n = 190, 71.6% male, IQ 67-155). High-resolution T1-weighted anatomical images were collected on a single MRI scanner. Regional density and volume were estimated via whole-brain parcellation comprised of 1625 parcels. Parcel-wise analyses were conducted using generalized additive models while controlling the false discovery rate (FDR, q < 0.05). Volume differences in the 68-region Desikan-Killiany atlas derived from Freesurfer were also examined, to establish the generalizability of findings across methods. RESULTS: No density differences were observed between the autistic and TD groups, either in individual parcels or whole brain mean density. Increased volume was observed in autism compared to the TD group when controlling for Age, Sex, and IQ, both at the level of the whole brain (total volume) and in 45 parcels (2.8% of total parcels). Parcels with greater volume included inferior, middle, and superior temporal gyrus, inferior and superior frontal gyrus, precuneus, and fusiform gyrus. Converging evidence from a standard Freesurfer pipeline also identified greater volume in a number of overlapping regions. LIMITATIONS: The method for determining "density" is not a direct measure of neuronal density, and this study cannot reveal underlying cellular differences. While this study represents possibly the largest single-site sample of its kind, it is underpowered to detect very small differences. CONCLUSIONS: These results provide compelling evidence that autism is associated with regional GM volumetric differences, which are more prominent than density differences. This underscores the importance of examining volume and density separately, and suggests that direct measures of volume (e.g. region-of-interest or tensor-based morphometry approaches) may be more sensitive to autism-relevant differences in neuroanatomy than concentration/density-based approaches.

Decreased Neural Connectivity in the Default Mode Network Among Youth and Young Adults With Chronic Kidney Disease.

An increasing amount of literature has indicated that chronic kidney disease (CKD) is associated with cognitive deficits that increase with worsening disease severity. Although abnormalities in brain structure have been widely documented, few studies to date have examined the functioning of brain areas associated with the specific cognitive domains affected by CKD (namely, attention and executive functions). Furthermore, few studies have examined functional connectivity among CKD youth who are relatively early in the course of the disease. The present study used functional magnetic resonance imaging to examine the resting state connectivity in 67 youth with CKD (mean age, 17 y) and 58 age-matched healthy controls. Using seed-based multiple regression, decreased connectivity was observed within the anterior cingulate portion of the default mode network. In addition, decreased connectivity within the dorsolateral prefrontal cortex, paracingulate gyrus, and frontal pole were correlated significantly with disease severity. These data indicate that connectivity deficits in circuits implementing attentional processes may represent an early marker for cognitive decline in CKD.

Face Processing and Social Functioning in Pediatric Brain Tumor Survivors.

OBJECTIVE: Pediatric brain tumor survivors (PBTS) experience deficits in social functioning. Facial expression and identity recognition are key components of social information processing and are widely studied as an index of social difficulties in youth with autism spectrum disorder (ASD) and other neurodevelopmental conditions. This study evaluated facial expression and identity recognition among PBTS, youth with ASD, and typically developing (TD) youth, and the associations between these face processing skills and social impairments. METHODS: PBTS (N = 54; ages 7-16) who completed treatment at least 2 years prior were matched with TD (N = 43) youth and youth with ASD (N = 55) based on sex and IQ. Parents completed a measure of social impairments and youth completed a measure of facial expression and identity recognition. RESULTS: Groups significantly differed on social impairments (p < .001), with youth with ASD scoring highest followed by PBTS and lastly TD youth. Youth with ASD performed significantly worse on the two measures of facial processing, while TD youth and PBTS were not statistically different. The association of facial expression recognition and social impairments was moderated by group, such that PBTS with higher levels of social impairment performed worse on the expression task compared to TD and ASD groups (p < .01, η2 = 0.07). CONCLUSIONS: Variability in face processing may be uniquely important to the social challenges of PBTS compared to other neurodevelopmental populations. Future directions include prospectively examining associations between facial expression recognition and social difficulties in PBTS and face processing training as an intervention for PBTS.

Profiles of Autonomic Activity in Autism Spectrum Disorder with and without Anxiety.

Both anxiety and autism spectrum disorder (ASD) are associated with atypical physiological activity. Few studies have systematically assessed the resting physiological activity in ASD with co-occurring anxiety disorders. We tested 75 participants divided in four groups: youth with ASD, with (ASD + Anxiety = 22, 6F, 12.29 ± 2.83 years old) and without co-occurring anxiety (ASD Alone = 15, 6F, 11.59 ± 2.85 years old) and compared their physiological profile with that of matched typically developing controls (TDC) with (Anxiety Alone = 16, 6F, 11.24 ± 3.36 years old) and without co-occurring anxiety disorders (TDC = 22, 8F, 11.88 ± 2.88 years old). Results indicated reduced sympathetic and parasympathetic activity at rest in ASD as compared to TDC youth. ASD + Anxiety and Anxiety Alone groups showed different sympathetic, but similar parasympathetic activity. These findings suggest that autonomic profile-based approaches may advance research, diagnosis, and treatment of ASD and anxiety.

Discovering Synchronized Subsets of Sequences: A Large Scale Solution.

Finding the largest subset of sequences (i.e., time series) that are correlated above a certain threshold, within large datasets, is of significant interest for computer vision and pattern recognition problems across domains, including behavior analysis, computational biology, neuroscience, and finance. Maximal clique algorithms can be used to solve this problem, but they are not scalable. We present an approximate, but highly efficient and scalable, method that represents the search space as a union of sets called ϵ-expanded clusters, one of which is theoretically guaranteed to contain the largest subset of synchronized sequences. The method finds synchronized sets by fitting a Euclidean ball on ϵ-expanded clusters, using Jung's theorem. We validate the method on data from the three distinct domains of facial behavior analysis, finance, and neuroscience, where we respectively discover the synchrony among pixels of face videos, stock market item prices, and dynamic brain connectivity data. Experiments show that our method produces results comparable to, but up to 300 times faster than, maximal clique algorithms, with speed gains increasing exponentially with the number of input sequences.

Computer Vision Analysis of Reduced Interpersonal Affect Coordination in Youth With Autism Spectrum Disorder.

Atypical social-emotional reciprocity is a core feature of autism spectrum disorder (ASD) but can be difficult to operationalize. One measurable manifestation of reciprocity may be interpersonal coordination, the tendency to align the form and timing of one's behaviors (including facial affect) with others. Interpersonal affect coordination facilitates sharing and understanding of emotional cues, and there is evidence that it is reduced in ASD. However, most research has not measured this process in true social contexts, due in part to a lack of tools for measuring dynamic facial expressions over the course of an interaction. Automated facial analysis via computer vision provides an efficient, granular, objective method for measuring naturally occurring facial affect and coordination. Youth with ASD and matched typically developing youth participated in cooperative conversations with their mothers and unfamiliar adults. Time-synchronized videos were analyzed with an open-source computer vision toolkit for automated facial analysis, for the presence and intensity of facial movements associated with positive affect. Both youth and adult conversation partners exhibited less positive affect during conversations when the youth partner had ASD. Youth with ASD also engaged in less affect coordination over the course of conversations. When considered dimensionally across youth with and without ASD, affect coordination significantly predicted scores on rating scales of autism-related social atypicality, adaptive social skills, and empathy. Findings suggest that affect coordination is an important interpersonal process with implications for broader social-emotional functioning. This preliminary study introduces a promising novel method for quantifying moment-to-moment facial expression and emotional reciprocity during natural interactions. LAY SUMMARY: This study introduces a novel, automated method for measuring social-emotional reciprocity during natural conversations, which may improve assessment of this core autism diagnostic behavior. We used computerized methods to measure facial affect and the degree of affect coordination between conversation partners. Youth with autism displayed reduced affect coordination, and reduced affect coordination predicted lower scores on measures of broader social-emotional skills.

Evidence against the "normalization" prediction of the early brain overgrowth hypothesis of autism.

BACKGROUND: The frequently cited Early Overgrowth Hypothesis of autism spectrum disorder (ASD) postulates that there is overgrowth of the brain in the first 2 years of life, which is followed by a period of arrested growth leading to normalized brain volume in late childhood and beyond. While there is consistent evidence for early brain overgrowth, there is mixed evidence for normalization of brain volume by middle childhood. The outcome of this debate is important to understanding the etiology and neurodevelopmental trajectories of ASD. METHODS: Brain volume was examined in two very large single-site samples of children, adolescents, and adults. The primary sample comprised 456 6-25-year-olds (ASD n = 240, typically developing controls (TDC) n = 216), including a large number of females (n = 102) and spanning a wide IQ range (47-158). The replication sample included 175 males. High-resolution T1-weighted anatomical MRI images were examined for group differences in total brain, cerebellar, ventricular, gray, and white matter volumes. RESULTS: The ASD group had significantly larger total brain, cerebellar, gray matter, white matter, and lateral ventricular volumes in both samples, indicating that brain volume remains enlarged through young adulthood, rather than normalizing. There were no significant age or sex interactions with diagnosis in these measures. However, a significant diagnosis-by-IQ interaction was detected in the larger sample, such that increased brain volume was related to higher IQ in the TDCs, but not in the ASD group. Regions-of-significance analysis indicated that total brain volume was larger in ASD than TDC for individuals with IQ less than 115, providing a potential explanation for prior inconsistent brain size results. No relationships were found between brain volume and measures of autism symptom severity within the ASD group. LIMITATIONS: Our cross-sectional sample may not reflect individual changes over time in brain volume and cannot quantify potential changes in volume prior to age 6. CONCLUSIONS: These findings challenge the "normalization" prediction of the brain overgrowth hypothesis by demonstrating that brain enlargement persists across childhood into early adulthood. The findings raise questions about the clinical implications of brain enlargement, since we find that it neither confers cognitive benefits nor predicts increased symptom severity in ASD.

Reliability and validity of the Pediatric Anxiety Rating Scale modified for autism spectrum disorder.

LAY ABSTRACT: Many youth with autism spectrum disorder have anxiety, but it can be difficult to assess anxiety with existing measures. We modified the Pediatric Anxiety Rating Scale for youth with autism spectrum disorder and tested the new measure in a group of 116 youth (age: 5-17 years) with autism spectrum disorder. The Pediatric Anxiety Rating Scale for youth with autism spectrum disorder is an interview that a clinician usually completes with the child and parent together. We modified the interview questions and scoring instructions based on feedback from parents of children with autism spectrum disorder and from a panel of experts in autism spectrum disorder and anxiety. Unlike many other anxiety measures, the Pediatric Anxiety Rating Scale for youth with autism spectrum disorder relies less on a child's verbal expression of anxiety and more on signs that a parent can easily observe. Training clinicians to administer and score the Pediatric Anxiety Rating Scale for youth with autism spectrum disorder was uncomplicated, and raters showed excellent agreement on video-recorded interviews. Youth who were not currently in treatment for anxiety had stable Pediatric Anxiety Rating Scale for youth with autism spectrum disorder scores with repeat measurement over a 1-month period. The Pediatric Anxiety Rating Scale for youth with autism spectrum disorder is a useful clinician-rated measure of anxiety in youth with autism spectrum disorder and fills a gap for assessing anxiety in this population.