The eTHINK Study: Cognitive and Behavioral Outcomes in Children with Hemophilia.

OBJECTIVE: To assess cognitive, behavioral, and adaptive functions in children and young adults with hemophilia treated according to contemporary standards of care. STUDY DESIGN: eTHINK was a US-based, prospective, cross-sectional, observational study (September 2018 through October 2019). Males (aged 1-21 years) with hemophilia A or B of any severity, with or without inhibitors, were eligible. Participants underwent neurological examinations and age-appropriate neuropsychological assessments, including standardized tests/ratings scales of early development, cognition, emotional/behavioral adjustment, and adaptive skills. RESULTS: 551 males with hemophilia A (n=433) or B (n=101) were enrolled. Performance on cognitive tests was largely comparable with that of age-matched US population norms, although participants in certain age groups (4-5 and 10-21 years) performed worse on measures of attention and processing speed. Furthermore, adolescents and young adults and those with comorbid attention-deficit/hyperactivity disorder (ADHD; n=64) reported more adaptive and executive function problems in daily life. Incidence of ADHD in adolescents (21%) was higher than expected in the general population. CONCLUSIONS: In general, males with hemophilia demonstrated age-appropriate intellectual, behavioral, and adaptive development. However, specific patient/age groups showed poorer attention performance and concerns for executive and adaptive development. This study established a normative data set for monitoring neurodevelopment in individuals with hemophilia and highlights the importance of screening and intervention for challenges with cognitive and adaptive skills in this population.

An adapted clinical global Impression of improvement for use in Angelman syndrome: Validation analyses utilizing data from the NEPTUNE study.

PURPOSE: Angelman Syndrome (AS) is a rare, severe neurogenetic disorder that causes symptoms such as intellectual disability and motor impairments and is typically diagnosed in early childhood. The complexity and heterogeneity of AS confound characterization of disease severity and pose unique challenges when determining an individual's response to treatment. There is therefore a substantial unmet need for rating scales specifically designed for complex conditions such as AS. To address this, the Clinical Global Impressions (CGI) scale, which has components for both symptom severity (CGI-S) and improvement (CGI-I) was specifically adapted to measure severity (CGI-S-AS) and improvement (CGI-I-AS) in AS. METHODS: The modified CGI-S/I-AS was used in the NEPTUNE trial of gaboxadol for the treatment of AS. Here we report on the validation of the CGI-I-AS using data from NEPTUNE and discuss insights for its potential use in future trials. RESULTS: Improvements in the CGI-I-AS rating tended to be consistent with changes on other relevant rating scales. Sleep-related symptoms were particularly well represented, while communication-related symptoms were not. CONCLUSIONS: Our validation analysis of the CGI-I-AS demonstrates its usefulness along with possible areas of improvement. The CGI-I-AS is a potential tool for use in other trials of AS drug candidates, and the process for its development can serve as a road map for the development of assessment tools for other neuropsychiatric disorders with similar complexities and heterogeneity.

Modeling idiopathic autism in forebrain organoids reveals an imbalance of excitatory cortical neuron subtypes during early neurogenesis.

Idiopathic autism spectrum disorder (ASD) is highly heterogeneous, and it remains unclear how convergent biological processes in affected individuals may give rise to symptoms. Here, using cortical organoids and single-cell transcriptomics, we modeled alterations in the forebrain development between boys with idiopathic ASD and their unaffected fathers in 13 families. Transcriptomic changes suggest that ASD pathogenesis in macrocephalic and normocephalic probands involves an opposite disruption of the balance between excitatory neurons of the dorsal cortical plate and other lineages such as early-generated neurons from the putative preplate. The imbalance stemmed from divergent expression of transcription factors driving cell fate during early cortical development. While we did not find genomic variants in probands that explained the observed transcriptomic alterations, a significant overlap between altered transcripts and reported ASD risk genes affected by rare variants suggests a degree of gene convergence between rare forms of ASD and the developmental transcriptome in idiopathic ASD.

NGLY1 deficiency: a prospective natural history study.

N-glycanase 1 (NGLY1) deficiency is a debilitating, ultra-rare autosomal recessive disorder caused by loss of function of NGLY1, a cytosolic enzyme that deglycosylates other proteins. It is characterized by severe global developmental delay and/or intellectual disability, hyperkinetic movement disorder, transient elevation of transaminases, (hypo)alacrima and progressive, diffuse, length-dependent sensorimotor polyneuropathy. A prospective natural history study (NHS) was conducted to elucidate clinical features and disease course. Twenty-nine participants were enrolled (15 onsite, 14 remotely) and followed for up to 32 months, representing ~29% of the ~100 patients identified worldwide. Participants exhibited profound developmental delays, with almost all developmental quotients below 20 on the Mullen Scales of Early Learning, well below the normative score of 100. Increased difficulties with sitting and standing suggested decline in motor function over time. Most patients presented with (hypo)alacrima and reduced sweat response. Pediatric quality of life was poor except for emotional function. Language/communication and motor skill problems including hand use were reported by caregivers as the most bothersome symptoms. Levels of the substrate biomarker, GlcNAc-Asn (aspartylglucosamine; GNA), were consistently elevated in all participants over time, independent of age. Liver enzymes were elevated for some participants but improved especially in younger patients and did not reach levels indicating severe liver disease. Three participants died during the study period. Data from this NHS informs selection of endpoints and assessments for future clinical trials for NGLY1 deficiency interventions. Potential endpoints include GNA biomarker levels, neurocognitive assessments, autonomic and motor function (particularly hand use), (hypo)alacrima and quality of life.

Feasibility and Acceptability of Delivering Pivotal Response Treatment for Autism Spectrum Disorder via Telehealth: Pilot Pre-Post Study.

BACKGROUND: Pivotal response treatment (PRT), an evidence-based and parent-delivered intervention, is designed to improve social communication in autistic individuals. OBJECTIVE: The aim of this study was to assess the feasibility, acceptability, and clinical effects of an online model of PRT delivered via MindNest Health, a telehealth platform that aims to provide self-directed and engaging online modules, real-time coaching and feedback, and accessible stepped-care to large populations of parents seeking resources for their autistic children. METHODS: Male and female autistic children, aged 2-7 years with single-word to phrase-level speech, and their parents were eligible to participate in the study. Families were randomized to the online parent training condition or control condition. The online component of the intervention consisted of eight 20-minute online courses of content describing parent training principles in PRT. Four 1-hour videoconferences were held after course 1, course 3, course 5, and course 8. Parents were given 1-2 weeks to complete each course. Parents completed the Client Credibility Questionnaire (CCQ) at week 2 and at the study endpoint, as well as the Behavioral Intervention Rating Scale (BIRS) at the study endpoint to assess parental expectancies, and treatment acceptability and effectiveness. RESULTS: Nine of 14 participants completed the study curriculum in the online parent training condition, and 6 of 12 participants completed the control condition. Thus, a total of 58% (15/26) participants across both groups completed the study curriculum by study closure. Within the online parent training condition, there was a significant increase in mean CCQ total scores, from 25.38 (SD 3.25) at baseline to 27.5 (SD 3.74) at study endpoint (P=.04); mean CCQ confidence scores, from 6.0 (SD 1.07) at baseline to 6.75 (SD 0.89) at study endpoint (P=.02); and mean CCQ other improvement scores, from 5.25 (SD 0.89) at baseline to 6.25 (SD 1.28) at study endpoint (P=.009). Within the control condition, a modest increase in mean CCQ scores was noted (Confidence, difference=+0.25; Recommend, difference=+0.25; Total Score, difference=+0.50), but the differences were not statistically significant (Confidence P=.38, Recommend P=.36, Total Score P=.43). Among the 11 parents who completed the BIRS at the study endpoint, 82% (n=9) endorsed that they slightly agree or agree with over 93% of the Acceptability factor items on the BIRS. CONCLUSIONS: The feasibility of this online treatment is endorsed by the high rate of online module completion and attendance to videoconferences within the online parent training group. Acceptability of treatment is supported by strong ratings on the CCQ and significant improvements in scores, as well as strong ratings on the BIRS. This study's small sample size limits the conclusions that can be drawn; however, the PRT MindNest Health platform holds promise to support parents of autistic children who are unable to access traditional, in-person parent-mediated interventions for their child.

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis.

Understanding which brain regions are related to a specific neurological disorder or cognitive stimuli has been an important area of neuroimaging research. We propose BrainGNN, a graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI) and discover neurological biomarkers. Considering the special property of brain graphs, we design novel ROI-aware graph convolutional (Ra-GConv) layers that leverage the topological and functional information of fMRI. Motivated by the need for transparency in medical image analysis, our BrainGNN contains ROI-selection pooling layers (R-pool) that highlight salient ROIs (nodes in the graph), so that we can infer which ROIs are important for prediction. Furthermore, we propose regularization terms-unit loss, topK pooling (TPK) loss and group-level consistency (GLC) loss-on pooling results to encourage reasonable ROI-selection and provide flexibility to encourage either fully individual- or patterns that agree with group-level data. We apply the BrainGNN framework on two independent fMRI datasets: an Autism Spectrum Disorder (ASD) fMRI dataset and data from the Human Connectome Project (HCP) 900 Subject Release. We investigate different choices of the hyper-parameters and show that BrainGNN outperforms the alternative fMRI image analysis methods in terms of four different evaluation metrics. The obtained community clustering and salient ROI detection results show a high correspondence with the previous neuroimaging-derived evidence of biomarkers for ASD and specific task states decoded for HCP. Our code is available at https://github.com/xxlya/BrainGNN_Pytorch.

Gaboxadol in Fragile X Syndrome: A 12-Week Randomized, Double-Blind, Parallel-Group, Phase 2a Study.

Background: Fragile X syndrome (FXS), the most common single-gene cause of intellectual disability and autism spectrum disorder (ASD), is caused by a >200-trinucleotide repeat expansion in the 5' untranslated region of the fragile X mental retardation 1 (FMR1) gene. Individuals with FXS can present with a range of neurobehavioral impairments including, but not limited to: cognitive, language, and adaptive deficits; ASD; anxiety; social withdrawal and avoidance; and aggression. Decreased expression of the γ-aminobutyric acid type A (GABAA) receptor δ subunit and deficient GABAergic tonic inhibition could be associated with symptoms of FXS. Gaboxadol (OV101) is a δ-subunit-selective, extrasynaptic GABAA receptor agonist that enhances GABAergic tonic inhibition, providing the rationale for assessment of OV101 as a potential targeted treatment of FXS. No drug is approved in the United States for the treatment of FXS. Methods: This 12-weeks, randomized (1:1:1), double-blind, parallel-group, phase 2a study was designed to assess the safety, tolerability, efficacy, and optimal daily dose of OV101 5 mg [once (QD), twice (BID), or three-times daily (TID)] when administered for 12 weeks to adolescent and adult men with FXS. Safety was the primary study objective, with key assessments including treatment-emergent adverse events (TEAEs), treatment-related adverse events leading to study discontinuation, and serious adverse events (SAEs). The secondary study objective was to evaluate the effect of OV101 on a variety of problem behaviors. Results: A total of 23 participants with FXS (13 adolescents, 10 adults) with moderate-to-severe neurobehavioral phenotypes (Full Scale Intelligence Quotient, 41.5 ± 3.29; ASD, 82.6%) were randomized to OV101 5 mg QD (n = 8), 5 mg BID (n = 8), or 5 mg TID (n = 7) for 12 weeks. OV101 was well tolerated across all 3 treatment regimens. The most common TEAEs were upper respiratory tract infection (n = 4), headache (n = 3), diarrhea (n = 2), and irritability (n = 2). No SAEs were reported. Improvements from baseline to end-of-treatment were observed on several efficacy endpoints, and 60% of participants were identified as treatment responders based on Clinical Global Impressions-Improvement. Conclusions: Overall, OV101 was safe and well tolerated. Efficacy results demonstrate an initial signal for OV101 in individuals with FXS. These results need to be confirmed in a larger, randomized, placebo-controlled study with optimal outcomes and in the most appropriate age group. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT03697161.

Brief Report: Preliminary Evidence of the N170 as a Biomarker of Response to Treatment in Autism Spectrum Disorder.

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by primary difficulties in social function. Individuals with ASD display slowed neural processing of faces, as indexed by the latency of the N170, a face-sensitive event-related potential. Currently, there are no objective biomarkers of ASD useful in clinical care or research. Efficacy of behavioral treatment is currently evaluated through subjective clinical impressions. To explore whether the N170 might have utility as an objective index of treatment response, we examined N170 before and after receipt of an empirically validated behavioral treatment in children with ASD. Method: Electroencephalography (EEG) data were obtained on a preliminary cohort of preschool-aged children with ASD before and after a 16-week course of PRT and in a subset of participants in waitlist control (16-weeks before the start of PRT) and follow-up (16-weeks after the end of PRT). EEG was recorded while participants viewed computer-generated faces with neutral and fearful affect. Results: Significant reductions in N170 latency to faces were observed following 16 weeks of PRT intervention. Change in N170 latency was not observed in the waitlist-control condition. Conclusions: This exploratory study offers suggestive evidence that N170 latency may index response to behavioral treatment. Future, more rigorous, studies in larger samples are indicated to evaluate whether the N170 may be useful as a biomarker of treatment response.

Functional Network Development in Sagittal Craniosynostosis Treated With Whole Vault Cranioplasty.

INTRODUCTION: In this study, the authors seek to clarify the neurological changes before and after whole vault cranioplasty (WVC) in patients born with sagittal craniosynostosis. METHODS: A case control study design was performed that included thirty functional MRI scans, from 25 individual patients. Functional MRI and diffusion tension imaging data were analyzed with BioImageSuite (Yale University, USA). 9 functional brain networks were analyzed, with appropriate correlated functional regions of the brain and utilized for analysis. RESULTS: Comparing functional MRI the infants after WVC versus infants before WVC group, the after WVC group demonstrated an increased connectivity in the left frontoparietal, secondary (V2), and third (V3) visual networks (P < 0.001). The right frontoparietal (RFPN) had decreased connectivity (P < 0.001). There is also a decrease and increase in anisotropy in the cingulum and precuneus despite surgery, respectively (P < 0.05). Adolescents treated with WVC compared to controls, demonstrated an increased connectivity in the salience and decreased connectivity in the RFPN relative to adolescent controls. CONCLUSIONS: Patients born with sagittal craniosynostosis have different connections in infancy in most of the defined cerebral networks compared to controls. After surgery, there are specific connectivity changes that occur in the RFPN, left frontoparietal, V2, and V3 networks, which are areas associated with executive function and emotional control. Changes identified in white matter tract microstructure connections could be influential in changes in functional connectivity. Although, as a child with sagittal craniosynostosis develops, much of the abnormal network connections, seen in infancy preoperatively, corrects to some degree after surgery. However, some aberrancies in the salience and RFPN networks remain potentially affecting executive functioning.

Development of an adapted Clinical Global Impression scale for use in Angelman syndrome.

BACKGROUND: The Clinical Global Impression-Severity (CGI-S) and CGI-Improvement (CGI-I) scales are widely accepted tools that measure overall disease severity and change, synthesizing the clinician's impression of the global state of an individual. Frequently employed in clinical trials for neuropsychiatric disorders, the CGI scales are typically used in conjunction with disease-specific rating scales. When no disease-specific rating scale is available, the CGI scales can be adapted to reflect the specific symptom domains that are relevant to the disorder. Angelman syndrome (AS) is a rare, clinically heterogeneous condition for which there is no disease-specific rating scale. This paper describes efforts to develop standardized, adapted CGI scales specific to AS for use in clinical trials. METHODS: In order to develop adapted CGI scales specific to AS, we (1) reviewed literature and interviewed caregivers and clinicians to determine the most impactful symptoms, (2) engaged expert panels to define and operationalize the symptom domains identified, (3) developed detailed rating anchors for each domain and for global severity and improvement ratings, (4) reviewed the anchors with expert clinicians and established minimally clinically meaningful change for each symptom domain, and (5) generated mock patient vignettes to test the reliability of the resulting scales and to standardize rater training. This systematic approach to developing, validating, and training raters on a standardized, adapted CGI scale specifically for AS is described herein. RESULTS: The resulting CGI-S/I-AS scales capture six critical domains (behavior, gross and fine motor function, expressive and receptive communication, and sleep) defined by caregivers and expert clinicians as the most challenging for patients with AS and their families. CONCLUSIONS: Rigorous training and careful calibration for clinicians will allow the CGI-S/-I-AS scales to be reliable in the context of randomized controlled trials. The CGI-S/-I-AS scales are being utilized in a Phase 3 trial of gaboxadol for the treatment of AS.

Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis.

Understanding how certain brain regions relate to a specific neurological disorder has been an important area of neuroimaging research. A promising approach to identify the salient regions is using Graph Neural Networks (GNNs), which can be used to analyze graph structured data, e.g. brain networks constructed by functional magnetic resonance imaging (fMRI). We propose an interpretable GNN framework with a novel salient region selection mechanism to determine neurological brain biomarkers associated with disorders. Specifically, we design novel regularized pooling layers that highlight salient regions of interests (ROIs) so that we can infer which ROIs are important to identify a certain disease based on the node pooling scores calculated by the pooling layers. Our proposed framework, Pooling Regularized-GNN (PR-GNN), encourages reasonable ROI-selection and provides flexibility to preserve either individual- or group-level patterns. We apply the PR-GNN framework on a Biopoint Autism Spectral Disorder (ASD) fMRI dataset. We investigate different choices of the hyperparameters and show that PR-GNN outperforms baseline methods in terms of classification accuracy. The salient ROI detection results show high correspondence with the previous neuroimaging-derived biomarkers for ASD.

Graph Embedding Using Infomax for ASD Classification and Brain Functional Difference Detection.

Significant progress has been made using fMRI to characterize the brain changes that occur in ASD, a complex neuro-developmental disorder. However, due to the high dimensionality and low signal-to-noise ratio of fMRI, embedding informative and robust brain regional fMRI representations for both graph-level classification and region-level functional difference detection tasks between ASD and healthy control (HC) groups is difficult. Here, we model the whole brain fMRI as a graph, which preserves geometrical and temporal information and use a Graph Neural Network (GNN) to learn from the graph-structured fMRI data. We investigate the potential of including mutual information (MI) loss (Infomax), which is an unsupervised term encouraging large MI of each nodal representation and its corresponding graph-level summarized representation to learn a better graph embedding. Specifically, this work developed a pipeline including a GNN encoder, a classifier and a discriminator, which forces the encoded nodal representations to both benefit classification and reveal the common nodal patterns in a graph. We simultaneously optimize graph-level classification loss and Infomax. We demonstrated that Infomax graph embedding improves classification performance as a regularization term. Furthermore, we found separable nodal representations of ASD and HC groups in prefrontal cortex, cingulate cortex, visual regions, and other social, emotional and execution related brain regions. In contrast with GNN with classification loss only, the proposed pipeline can facilitate training more robust ASD classification models. Moreover, the separable nodal representations can detect the functional differences between the two groups and contribute to revealing new ASD biomarkers.

Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results.

Deep learning models have shown their advantage in many different tasks, including neuroimage analysis. However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required. The time and cost for acquisition and annotation in assembling, for example, large fMRI datasets make it difficult to acquire large numbers at a single site. However, due to the need to protect the privacy of patient data, it is hard to assemble a central database from multiple institutions. Federated learning allows for population-level models to be trained without centralizing entities' data by transmitting the global model to local entities, training the model locally, and then averaging the gradients or weights in the global model. However, some studies suggest that private information can be recovered from the model gradients or weights. In this work, we address the problem of multi-site fMRI classification with a privacy-preserving strategy. To solve the problem, we propose a federated learning approach, where a decentralized iterative optimization algorithm is implemented and shared local model weights are altered by a randomization mechanism. Considering the systemic differences of fMRI distributions from different sites, we further propose two domain adaptation methods in this federated learning formulation. We investigate various practical aspects of federated model optimization and compare federated learning with alternative training strategies. Overall, our results demonstrate that it is promising to utilize multi-site data without data sharing to boost neuroimage analysis performance and find reliable disease-related biomarkers. Our proposed pipeline can be generalized to other privacy-sensitive medical data analysis problems. Our code is publicly available at: https://github.com/xxlya/Fed_ABIDE/.

Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity.

Heterogeneous presentation of a neurological disorder suggests potential differences in the underlying pathophysiological changes that occur in the brain. We propose to model heterogeneous patterns of functional network differences using a demographic-guided attention (DGA) mechanism for recurrent neural network models for prediction from functional magnetic resonance imaging (fMRI) time-series data. The context computed from the DGA head is used to help focus on the appropriate functional networks based on individual demographic information. We demonstrate improved classification on 3 subsets of the ABIDE I dataset used in published studies that have previously produced state-of-the-art results, evaluating performance under a leave-one-site-out cross-validation framework for better generalizeability to new data. Finally, we provide examples of interpreting functional network differences based on individual demographic variables.

Efficient Shapley Explanation For Features Importance Estimation Under Uncertainty.

Complex deep learning models have shown their impressive power in analyzing high-dimensional medical image data. To increase the trust of applying deep learning models in medical field, it is essential to understand why a particular prediction was reached. Data feature importance estimation is an important approach to understand both the model and the underlying properties of data. Shapley value explanation (SHAP) is a technique to fairly evaluate input feature importance of a given model. However, the existing SHAP-based explanation works have limitations such as 1) computational complexity, which hinders their applications on high-dimensional medical image data; 2) being sensitive to noise, which can lead to serious errors. Therefore, we propose an uncertainty estimation method for the feature importance results calculated by SHAP. Then we theoretically justify the methods under a Shapley value framework. Finally we evaluate our methods on MNIST and a public neuroimaging dataset. We show the potential of our method to discover disease related biomarkers from neuroimaging data.

ESTIMATING REPRODUCIBLE FUNCTIONAL NETWORKS ASSOCIATED WITH TASK DYNAMICS USING UNSUPERVISED LSTMS.

We propose a method for estimating more reproducible functional networks that are more strongly associated with dynamic task activity by using recurrent neural networks with long short term memory (LSTMs). The LSTM model is trained in an unsupervised manner to learn to generate the functional magnetic resonance imaging (fMRI) time-series data in regions of interest. The learned functional networks can then be used for further analysis, e.g., correlation analysis to determine functional networks that are strongly associated with an fMRI task paradigm. We test our approach and compare to other methods for decomposing functional networks from fMRI activity on 2 related but separate datasets that employ a biological motion perception task. We demonstrate that the functional networks learned by the LSTM model are more strongly associated with the task activity and dynamics compared to other approaches. Furthermore, the patterns of network association are more closely replicated across subjects within the same dataset as well as across datasets. More reproducible functional networks are essential for better characterizing the neural correlates of a target task.

Discrepancies between parent and child ratings of anxiety in children with autism spectrum disorder.

Co-occurring anxiety is common in children with autism spectrum disorder (ASD). However, inconsistencies across parent and child reports of anxiety may complicate the assessment of anxiety in this population. The present study examined parent and child anxiety ratings in children with ASD with and without anxiety disorders and tested the association between parent-child anxiety rating discrepancy and ASD symptom severity. Participants included children aged 8-16 years in three diagnostic groups: ASD with co-occurring anxiety disorders (ASD + Anxiety; n = 34), ASD without co-occurring anxiety disorders (ASD; n = 18), and typically developing healthy controls (TD; n = 50). Parents and children completed ratings of child anxiety using the Multidimensional Anxiety Rating Scale. Patterns of parent and child anxiety ratings differed among the three groups, with parent ratings exceeding child ratings only in the ASD + Anxiety group. Parents reported higher levels of child anxiety in the ASD + Anxiety versus ASD group, whereas children reported comparable levels of anxiety in the two groups. Among children with ASD, ASD symptom severity was positively associated with the degree to which parent ratings exceeded child ratings. Results suggest that children with ASD and co-occurring anxiety disorders endorse some anxiety symptoms but may underreport overall levels of anxiety. In addition, ASD symptom severity might increase discrepancies in parent-child anxiety ratings. These findings suggest a unique and valuable role of child anxiety ratings and suggest that both parent and child anxiety ratings should be considered in light of children's ASD symptom severity and used to guide further assessment. Autism Res 2020, 13: 93-103. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Children with autism spectrum disorder (ASD) commonly experience anxiety; yet, their perceptions of their anxiety might differ from their parents' perceptions. This study found that, while children with ASD and anxiety disorders acknowledge some anxiety, their parents report them as having higher levels of anxiety. Also, child and parent perceptions of anxiety may differ more for children with more severe ASD symptoms. How these findings may guide research and clinical practice is discussed.

Altered Neural Connectivity in Females, But Not Males with Autism: Preliminary Evidence for the Female Protective Effect from a Quality-Controlled Diffusion Tensor Imaging Study.

Previous studies using diffusion tensor imaging (DTI) to investigate white matter (WM) structural connectivity have suggested widespread, although inconsistent WM alterations in autism spectrum disorder (ASD), such as greater reductions in fractional anisotropy (FA). However, findings may lack generalizability because: (a) most have focused solely on the ASD male brain phenotype, and not sex-differences in WM integrity; (b) many lack stringent and transparent data quality control such as controlling for head motion in analysis. This study addressed both issues by using Tract-Based Spatial Statistics (TBSS) to separately compare WM differences in 81 ASD (56 male, 25 female; 4-21 years old) and 39 typically developing (TD; 23 males, 16 females; 5-18 years old) children and young people, carefully group-matched on sex, age, cognitive abilities, and head motion. ASD males and females were also matched on autism symptom severity. Two independent-raters completed a multistep scan quality assurance to remove images that were significantly distorted by motion artifacts before analysis. ASD females exhibited significant widespread reductions in FA compared to TD females, suggesting altered WM integrity. In contrast, no significant localized or widespread WM differences were found between ASD and TD males. This study highlights the importance of data quality control in DTI, and outlines important sex-differences in WM alterations in ASD females. Future studies can explore the extent to which neural structural differences might underlie sex-differences in ASD behavioral phenotype, and guide clinical interventions to be tailored toward the unique needs of ASD females and males. Autism Res 2019, 12: 1472-1483. © 2019 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc. LAY SUMMARY: Previous Diffusion Tensor Imaging (DTI) studies have found atypical brain structural connectivity in males with autism, although findings are inconclusive in females with autism. To investigate potential sex-differences, we studied males and females with and without autism who showed a similar level of head movement during their brain scan. We found that females with autism had widespread atypical neural connectivity than females without autism, although not in males, highlighting sex-differences.

Reduced Amygdala-Prefrontal Functional Connectivity in Children With Autism Spectrum Disorder and Co-occurring Disruptive Behavior.

BACKGROUND: Disruptive behaviors are prevalent in children with autism spectrum disorder (ASD) and often cause substantial impairments. However, the underlying neural mechanisms of disruptive behaviors remain poorly understood in ASD. In children without ASD, disruptive behavior is associated with amygdala hyperactivity and reduced connectivity with the ventrolateral prefrontal cortex (vlPFC). This study examined amygdala reactivity and connectivity in children with ASD with and without co-occurring disruptive behavior disorders. We also investigated differential contributions of externalizing behaviors and callous-unemotional traits to variance in amygdala connectivity and reactivity. METHODS: This cross-sectional study involved behavioral assessments and neuroimaging in three groups of children 8 to 16 years of age: 18 children had ASD and disruptive behavior, 20 children had ASD without disruptive behavior, and 19 children were typically developing control participants matched for age, gender, and IQ. During functional magnetic resonance imaging, participants completed an emotion perception task of fearful versus calm faces. Task-specific changes in amygdala reactivity and connectivity were examined using whole-brain, psychophysiological interaction, and multiple regression analyses. RESULTS: Children with ASD and disruptive behavior showed reduced amygdala-vlPFC connectivity compared with children with ASD without disruptive behavior. Externalizing behaviors and callous-unemotional traits were associated with amygdala reactivity to fearful faces in children with ASD after controlling for suppressor effects. CONCLUSIONS: Reduced amygdala-vlPFC connectivity during fear processing may differentiate children with ASD and disruptive behavior from children with ASD without disruptive behavior. The presence of callous-unemotional traits may have implications for identifying differential patterns of amygdala activity associated with increased risk of aggression in ASD. These findings suggest a neural mechanism of emotion dysregulation associated with disruptive behavior in children with ASD.