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.

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.

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.

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.

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 (GABA_A) receptor δ subunit and deficient GABAergic tonic inhibition could be associated with symptoms of FXS. Gaboxadol (OV101) is a δ-subunit-selective, extrasynaptic GABA_A 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.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s11689-020-09349-8.

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.

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/.

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.

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.

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.

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.

Graph Neural Network for Interpreting Task-fMRI Biomarkers

Finding the biomarkers associated with ASD is helpful for understanding the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment. A promising approach to identify biomarkers is using Graph Neural Networks (GNNs), which can be used to analyze graph structured data, i.e. brain networks constructed by fMRI. One way to interpret important features is through looking at how the classification probability changes if the features are occluded or replaced. The major limitation of this approach is that replacing values may change the distribution of the data and lead to serious errors. Therefore, we develop a 2-stage pipeline to eliminate the need to replace features for reliable biomarker interpretation. Specifically, we propose an inductive GNN to embed the graphs containing different properties of task-fMRI for identifying ASD and then discover the brain regions/sub-graphs used as evidence for the GNN classifier. We first show GNN can achieve high accuracy in identifying ASD. Next, we calculate the feature importance scores using GNN and compare the interpretation ability with Random Forest. Finally, we run with different atlases and parameters, proving the robustness of the proposed method. The detected biomarkers reveal their association with social behaviors and are consistent with those reported in the literature. We also show the potential of discovering new informative biomarkers. Our pipeline can be generalized to other graph feature importance interpretation problems.

Invertible Network for Classification and Biomarker Selection for ASD

Determining biomarkers for autism spectrum disorder (ASD) is crucial to understanding its mechanisms. Recently deep learning methods have achieved success in the classification task of ASD using fMRI data. However, due to the black-box nature of most deep learning models, it's hard to perform biomarker selection and interpret model decisions. The recently proposed invertible networks can accurately reconstruct the input from its output, and have the potential to unravel the black-box representation. Therefore, we propose a novel method to classify ASD and identify biomarkers for ASD using the connectivity matrix calculated from fMRI as the input. Specifically, with invertible networks, we explicitly determine the decision boundary and the projection of data points onto the boundary. Like linear classifiers, the difference between a point and its projection onto the decision boundary can be viewed as the explanation. We then define the importance as the explanation weighted by the gradient of prediction w.r.t the input, and identify biomarkers based on this importance measure. We perform a regression task to further validate our biomarker selection: compared to using all edges in the connectivity matrix, using the top 10% important edges we generate a lower regression error on 6 different severity scores. Our experiments show that the invertible network is both effective at ASD classification and interpretable, allowing for discovery of reliable biomarkers.

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.

Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery

Discovering imaging biomarkers for autism spectrum disorder (ASD) is critical to help explain ASD and predict or monitor treatment outcomes. Toward this end, deep learning classifiers have recently been used for identifying ASD from functional magnetic resonance imaging (fMRI) with higher accuracy than traditional learning strategies. However, a key challenge with deep learning models is understanding just what image features the network is using, which can in turn be used to define the biomarkers. Current methods extract biomarkers, i.e., important features, by looking at how the prediction changes if "ignoring" one feature at a time. However, this can lead to serious errors if the features are conditionally dependent. In this work, we go beyond looking at only individual features by using Shapley value explanation (SVE) from cooperative game theory. Cooperative game theory is advantageous here because it directly considers the interaction between features and can be applied to any machine learning method, making it a novel, more accurate way of determining instance-wise biomarker importance from deep learning models. A barrier to using SVE is its computational complexity: 2 N given N features. We explicitly reduce the complexity of SVE computation by two approaches based on the underlying graph structure of the input data: 1) only consider the centralized coalition of each feature; 2) a hierarchical pipeline which first clusters features into small communities, then applies SVE in each community. Monte Carlo approximation can be used for large permutation sets. We first validate our methods on the MNIST dataset and compare to human perception. Next, to insure plausibility of our biomarker results, we train a Random Forest (RF) to classify ASD/control subjects from fMRI and compare SVE results to standard RF-based feature importance. Finally, we show initial results on ranked fMRI biomarkers using SVE on a deep learning classifier for the ASD/control dataset.

PREDICTION OF TREATMENT OUTCOME FOR AUTISM FROM STRUCTURE OF THE BRAIN BASED ON SURE INDEPENDENCE SCREENING

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and behavioral treatment interventions have shown promise for young children with ASD. However, there is limited progress in understanding the effect of each type of treatment. In this project, we aim to detect structural changes in the brain after treatment and select structural features associated with treatment outcomes. The difficulty in building large databases of patients who have received specific treatments and the high dimensionality of medical image analysis problems are the challenges in this work. To select predictive features and build accurate models, we use the sure independence screening (SIS) method. SIS is a theoretically and empirically validated method for ultra-high dimensional general linear models, and it achieves both predictive accuracy and correct feature selection by iterative feature selection. Compared with step-wise feature selection methods, SIS removes multiple features in each iteration and is computationally efficient. Compared with other linear models such as elastic-net regression, support vector regression (SVR) and partial least squares regression (PSLR), SIS achieves higher accuracy. We validated the superior performance of SIS in various experiments: First, we extract brain structural features from FreeSurfer, including cortical thickness, surface area, mean curvature and cortical volume. Next, we predict different measures of treatment outcomes based on structural features. We show that SIS achieves the highest correlation between prediction and measurements in all tasks. Furthermore, we report regions selected by SIS as biomarkers for ASD.

Clinical Development of Targeted Fragile X Syndrome Treatments: An Industry Perspective

Fragile X syndrome (FXS) is the leading known cause of inherited intellectual disability and autism spectrum disorder. It is caused by a mutation of the fragile X mental retardation 1 (FMR1) gene, resulting in a deficit of fragile X mental retardation protein (FMRP). The clinical presentation of FXS is variable, and is typically associated with developmental delays, intellectual disability, a wide range of behavioral issues, and certain identifying physical features. Over the past 25 years, researchers have worked to understand the complex relationship between FMRP deficiency and the symptoms of FXS and, in the process, have identified several potential targeted therapeutics, some of which have been tested in clinical trials. Whereas most of the basic research to date has been led by experts at academic institutions, the pharmaceutical industry is becoming increasingly involved with not only the scientific community, but also with patient advocacy organizations, as more promising pharmacological agents are moving into the clinical stages of development. The objective of this review is to provide an industry perspective on the ongoing development of mechanism-based treatments for FXS, including identification of challenges and recommendations for future clinical trials.

Prediction of severity and treatment outcome for ASD from fMRI

Autism spectrum disorder (ASD) is a complex neurodevelop-mental syndrome. Early diagnosis and precise treatment are essential for ASD patients. Although researchers have built many analytical models, there has been limited progress in accurate predictive models for early diagnosis. In this project, we aim to build an accurate model to predict treatment outcome and ASD severity from early stage functional magnetic resonance imaging (fMRI) scans. The difficulty in building large databases of patients who have received specific treatments and the high dimensionality of medical image analysis problems are challenges in this work. We propose a generic and accurate two-level approach for high-dimensional regression problems in medical image analysis. First, we perform region-level feature selection using a predefined brain parcellation. Based on the assumption that voxels within one region in the brain have similar values, for each region we use the bootstrapped mean of voxels within it as a feature. In this way, the dimension of data is reduced from number of voxels to number of regions. Then we detect predictive regions by various feature selection methods. Second, we extract voxels within selected regions, and perform voxel-level feature selection. To use this model in both linear and non-linear cases with limited training examples, we apply two-level elastic net regression and random forest (RF) models respectively. To validate accuracy and robustness of this approach, we perform experiments on both task-fMRI and resting state fMRI datasets. Furthermore, we visualize the influence of each region, and show that the results match well with other findings.

Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder. Finding the biomarkers associated with ASD is extremely helpful to understand the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment. Although Deep Neural Networks (DNNs) have been applied in functional magnetic resonance imaging (fMRI) to identify ASD, understanding the data driven computational decision making procedure has not been previously explored. Therefore, in this work, we address the problem of interpreting reliable biomarkers associated with identifying ASD; specifically, we propose a 2-stage method that classifies ASD and control subjects using fMRI images and interprets the saliency features activated by the classifier. First, we trained an accurate DNN classifier. Then, for detecting the biomarkers, different from the DNN visualization works in computer vision, we take advantage of the anatomical structure of brain fMRI and develop a frequency-normalized sampling method to corrupt images. Furthermore, in the ASD vs. control subjects classification scenario, we provide a new approach to detect and characterize important brain features into three categories. The biomarkers we found by the proposed method are robust and consistent with previous findings in the literature. We also validate the detected biomarkers by neurological function decoding and comparing with the DNN activation maps.

Learning Generalizable Recurrent Neural Networks from Small Task-fMRI Datasets

Deep learning has become the new state-of-the-art for many problems in image analysis. However, large datasets are often required for such deep networks to learn effectively. This poses a difficult challenge for many medical image analysis problems in which only a small number of subjects are available, e.g., patients undergoing a new treatment. In this work, we propose a number of approaches for learning generalizable recurrent neural networks from smaller task-fMRI datasets: 1) a resampling method for ROI-based fMRI analysis to create augmented data; 2) inclusion of a small number of non-imaging variables to provide subject-specific initialization of the recurrent neural network; and 3) selection of the most generalizable model from multiple reinitialized training runs using criteria based on only training loss. Using cross-validation to assess model performance, we demonstrate the effectiveness of the proposed methods to train recurrent neural networks from small datasets to predict treatment outcome for children with autism spectrum disorder (N = 21) and classify autistic vs. typical control subjects (N = 40) from task-fMRI scans.

Improving social skills in children with ASD using a long-term, in-home social robot

Social robots can offer tremendous possibilities for autism spectrum disorder (ASD) interventions. To date, most studies with this population have used short, isolated encounters in controlled laboratory settings. Our study focused on a 1-month, home-based intervention for increasing social communication skills of 12 children with ASD between 6 and 12 years old using an autonomous social robot. The children engaged in a triadic interaction with a caregiver and the robot for 30 min every day to complete activities on emotional storytelling, perspective-taking, and sequencing. The robot encouraged engagement, adapted the difficulty of the activities to the child's past performance, and modeled positive social skills. The system maintained engagement over the 1-month deployment, and children showed improvement on joint attention skills with adults when not in the presence of the robot. These results were also consistent with caregiver questionnaires. Caregivers reported less prompting over time and overall increased communication.

Clinical Features of Children With Autism Who Passed 18-Month Screening

OBJECTIVES

We compared sex-stratified developmental and temperamental profiles at 18 months in children screening negative for autism spectrum disorder (ASD) on the Modified Checklist for Autism in Toddlers (M-CHAT) but later receiving diagnoses of ASD (false-negative group) versus those without later ASD diagnoses (true-negative group).

METHODS

We included 68 197 screen-negative cases from the Norwegian Mother and Child Cohort Study (49.1% girls). Children were screened by using the 6 critical items of the M-CHAT at 18 months. Groups were compared on domains of the Ages and Stages Questionnaire and the Emotionality Activity Sociability Temperament Survey.

RESULTS

Despite passing M-CHAT screening at 18 months, children in the false-negative group exhibited delays in social, communication, and motor skills compared with the true-negative group. Differences were more pronounced in girls. However, with regard to shyness, boys in the false-negative group were rated as more shy than their true-negative counterparts, but girls in the false-negative group were rated as less shy than their counterparts in the true-negative group.

CONCLUSIONS

This is the first study to reveal that children who pass M-CHAT screening at 18 months and are later diagnosed with ASD exhibit delays in core social and communication areas as well as fine motor skills at 18 months. Differences appeared to be more pronounced in girls. With these findings, we underscore the need to enhance the understanding of early markers of ASD in boys and girls, as well as factors affecting parental report on early delays and abnormalities, to improve the sensitivity of screening instruments.

PREDICTION OF PIVOTAL RESPONSE TREATMENT OUTCOME WITH TASK FMRI USING RANDOM FOREST AND VARIABLE SELECTION

Behavior intervention has shown promise for treatment for young children with autism spectrum disorder (ASD). However, current therapeutic decisions are based on trial and error, often leading to suboptimal outcomes. We propose an approach that employs task-based fMRI for early outcome prediction. Our strategy is based on the general linear model (GLM) and a random forest, combined with feature selection techniques. GLM analysis is performed on each voxel to get t-statistic of contrast between two tasks. Due to the high dimensionality of predictor variables, feature selection is crucial for accurate prediction. Thus we propose a two-step feature selection method: a "shadow" method to select all-relevant variables, followed by a stepwise method to select minimal-optimal set of variables for prediction. A few columns of random noise are generated and added as shadow variables. Regression based on the random forest is performed, and permutation importance of each variable is estimated. Candidate voxels with higher importance than the shadow are kept. Surviving voxels are fed into stepwise variable selection methods. We test both forward and backward stepwise selection. Our method was validated on a dataset of 20 children with ASD using leave-one-out cross-validation, and compared to other standard regression methods. The proposed pipeline generated highest accuracy.

2-CHANNEL CONVOLUTIONAL 3D DEEP NEURAL NETWORK (2CC3D) FOR FMRI ANALYSIS: ASD CLASSIFICATION AND FEATURE LEARNING

In this paper, we propose a new whole brain fMRI-analysis scheme to identify autism spectrum disorder (ASD) and explore biological markers in ASD classification. To utilize both spatial and temporal information in fMRI, our method investigates the potential benefits of using a sliding window over time to measure temporal statistics (mean and standard deviation) and using 3D convolutional neural networks (CNNs) to capture spatial features. The sliding window created 2-channel images, which were used as inputs to the 3D CNN. From the outputs of the 3D CNN convolutional layers, ASD related fMRI spatial features were directly deciphered. Input formats and sliding window parameters were investigated in our study. The power of aligning 2-channel images was shown in our proposed method. Compared with traditional machine learning classification models, our proposed 2CC3D method increased mean F-scores over 8.5%.

COMBINING PHENOTYPIC AND RESTING-STATE FMRI DATA FOR AUTISM CLASSIFICATION WITH RECURRENT NEURAL NETWORKS

Accurate identification of autism spectrum disorder (ASD) from resting-state functional magnetic resonance imaging (rsfMRI) is a challenging task due in large part to the heterogeneity of ASD. Recent work has shown better classification accuracy using a recurrent neural network with rsfMRI time-series as inputs. However, phenotypic features, which are often available and likely carry predictive information, are excluded from the model, and combining such data with rsfMRI into the recurrent neural network is not a straightforward task. In this paper, we present several methodologies for incorporating phenotypic data with rsfMRI into a single deep learning framework for classifying ASD. We test the proposed architectures using a cross-validation framework on the large, heterogeneous first cohort from the Autism Brain Imaging Data Exchange. Our best model achieved an accuracy of 70.1%, outperforming prior work.

Brief report: Reduced anxiety following Pivotal Response Treatment in young children with Autism Spectrum Disorder

Up to 40% of children with Autism Spectrum Disorder (ASD) exhibit co-occurring anxiety symptoms. Despite recent success in mitigating anxiety symptoms in school-aged children with ASD (mean age >9 years) using adapted versions of Cognitive Behavioural Therapy, little is known about potential treatment outcomes for younger children. To address the gap in the literature, this open-label study evaluated change in anxiety following a 16-week open-label trial of Pivotal Response Treatment (PRT) in children with ASD aged 4-8 years. PRT is a behavioural treatment based on the principles of Applied Behaviour Analysis and has a primary aim of increasing social communication skills in children with ASD through natural reinforcements. To minimise conflation of anxiety and other co-occurring symptoms such as disruptive behaviour and attention-deficit hyperactivity disorder, we measured anxiety using the autism anxiety subscale of the Child and Adolescent Symptom Inventory (CASI) devised by Sukhodolsky et al. (2008). We observed significant anxiety reduction over 16-weeks of PRT. Furthermore, anxiety reduction was independent of changes in autism symptom severity. This study shows promising results for PRT as an intervention for reducing anxiety in young children with ASD.

Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks

Functional magnetic resonance imaging (fMRI) has helped characterize the pathophysiology of autism spectrum disorders (ASD) and carries promise for producing objective biomarkers for ASD. Recent work has focused on deriving ASD biomarkers from resting-state functional connectivity measures. However, current efforts that have identified ASD with high accuracy were limited to homogeneous, small datasets, while classification results for heterogeneous, multi-site data have shown much lower accuracy. In this paper, we propose the use of recurrent neural networks with long short-term memory (LSTMs) for classification of individuals with ASD and typical controls directly from the resting-state fMRI time-series. We used the entire large, multi-site Autism Brain Imaging Data Exchange (ABIDE) I dataset for training and testing the LSTM models. Under a cross-validation framework, we achieved classification accuracy of 68.5%, which is 9% higher than previously reported methods that used fMRI data from the whole ABIDE cohort. Finally, we presented interpretation of the trained LSTM weights, which highlight potential functional networks and regions that are known to be implicated in ASD.

Pivotal response treatment for autism spectrum disorder: current perspectives

Pivotal response treatment (PRT) is an evidence-based behavioral intervention based on applied behavior analysis principles aimed to improve social communication skills in individuals with autism spectrum disorder (ASD). PRT adopts a more naturalistic approach and focuses on using a number of strategies to help increase children's motivation during intervention. Since its conceptualization, PRT has received much empirical support for eliciting therapeutic gains in greater use of functional social communication skills in individuals with ASD. Building upon the empirical evidence supporting PRT, recent advancements have increasingly turned to using interdisciplinary research integrating neuroimaging techniques and behavioral measures to help identify objective biomarkers of treatment, which have two primary purposes. First, neuroimaging results can help characterize how PRT may elicit change, and facilitate partitioning of the heterogeneous profiles of neural mechanisms underlying similar profile of behavioral changes observed over PRT. Second, neuroimaging provides an objective means to both map and track how biomarkers may serve as reliable and sensitive predictors of responder profiles to PRT, assisting clinicians to identify who will most likely benefit from PRT. Together, a better understanding of both mechanisms of change and predictors of responder profile will help PRT to serve as a more precise and targeted intervention for individuals with ASD, thus moving towards the goal of precision medicine and improving quality of care. This review focuses on the recent emerging neuroimaging evidences supporting PRT, offering current perspectives on the importance of interdisciplinary research to help clinicians better understand how PRT works and predict who will respond to PRT.

Distinct neural bases of disruptive behavior and autism symptom severity in boys with autism spectrum disorder

Background

Disruptive behavior in autism spectrum disorder (ASD) is an important clinical problem, but its neural basis remains poorly understood. The current research aims to better understand the neural underpinnings of disruptive behavior in ASD, while addressing whether the neural basis is shared with or separable from that of core ASD symptoms.

Methods

Participants consisted of 48 male children and adolescents: 31 ASD (7 had high disruptive behavior) and 17 typically developing (TD) controls, well-matched on sex, age, and IQ. For ASD participants, autism symptom severity, disruptive behavior, anxiety symptoms, and ADHD symptoms were measured. All participants were scanned while viewing biological motion (BIO) and scrambled motion (SCR). Two fMRI contrasts were analyzed: social perception (BIO > SCR) and Default Mode Network (DMN) deactivation (fixation > BIO). Age and IQ were included as covariates of no interest in all analyses.

Results

First, the between-group analyses on BIO > SCR showed that ASD is characterized by hypoactivation in the social perception circuitry, and ASD with high or low disruptive behavior exhibited similar patterns of hypoactivation. Second, the between-group analyses on fixation > BIO showed that ASD with high disruptive behavior exhibited more restricted and less DMN deactivation, when compared to ASD with low disruptive behavior or TD. Third, the within-ASD analyses showed that (a) autism symptom severity (but not disruptive behavior) was uniquely associated with less activation in the social perception regions including the posterior superior temporal sulcus and inferior frontal gyrus; (b) disruptive behavior (but not autism symptom severity) was uniquely associated with less DMN deactivation in the medial prefrontal cortex (MPFC) and lateral parietal cortex; and (c) anxiety symptoms mediated the link between disruptive behavior and less DMN deactivation in both anterior cingulate cortex (ACC) and MPFC, while ADHD symptoms mediated the link primarily in ACC.

Conclusions

In boys with ASD, disruptive behavior has a neural basis in reduced DMN deactivation, which is distinct and separable from that of core ASD symptoms, with the latter characterized by hypoactivation in the social perception circuitry. These differential neurobiological markers may potentially serve as neural targets or predictors for interventions when treating disruptive behavior vs. core symptoms in ASD.

Electronic supplementary material

The online version of this article (doi:10.1186/s11689-017-9183-z) contains supplementary material, which is available to authorized users.

Brain responses to biological motion predict treatment outcome in young children with autism

Autism spectrum disorders (ASDs) are common yet complex neurodevelopmental disorders, characterized by social, communication and behavioral deficits. Behavioral interventions have shown favorable results-however, the promise of precision medicine in ASD is hampered by a lack of sensitive, objective neurobiological markers (neurobiomarkers) to identify subgroups of young children likely to respond to specific treatments. Such neurobiomarkers are essential because early childhood provides a sensitive window of opportunity for intervention, while unsuccessful intervention is costly to children, families and society. In young children with ASD, we show that functional magnetic resonance imaging-based stratification neurobiomarkers accurately predict responses to an evidence-based behavioral treatment-pivotal response treatment. Neural predictors were identified in the pretreatment levels of activity in response to biological vs scrambled motion in the neural circuits that support social information processing (superior temporal sulcus, fusiform gyrus, amygdala, inferior parietal cortex and superior parietal lobule) and social motivation/reward (orbitofrontal cortex, insula, putamen, pallidum and ventral striatum). The predictive value of our findings for individual children with ASD was supported by a multivariate pattern analysis with cross validation. Predicting who will respond to a particular treatment for ASD, we believe the current findings mark the very first evidence of prediction/stratification biomarkers in young children with ASD. The implications of the findings are far reaching and should greatly accelerate progress toward more precise and effective treatments for core deficits in ASD.

Pivotal Response Treatment: Case Reports

The current paper provides an overview of an evidence-based treatment, Pivotal Response Treatment (PRT), for autism spectrum disorder (ASD). The paper describes PRT principles and then illustrates the approach using two case reports. The children are preschool-aged children with high-functioning ASD. They were participating in a four-month clinical trial of PRT. At the start of treatment, they presented with significant social communication impairments, including a minimal understanding of reciprocity, limited play skills, and repetitive behaviors and speech. The paper outlines how behavioral treatment goals were identified and then how activities were designed, using principles of PRT, to target skill acquisition. Following the treatment course, both children made substantial and meaningful gains in social communication skill development.

A functional neuroimaging study of fusiform response to restricted interests in children and adolescents with autism spectrum disorder

Background

While autism spectrum disorder (ASD) is characterized by both social communication deficits and restricted and repetitive patterns of behavior and interest, literature examining possible neural bases of the latter class of symptoms is limited. The fusiform face area (FFA) is a region in the ventral temporal cortex that not only shows preferential responsiveness to faces but also responds to non-face objects of visual expertise. Because restricted interests in ASD are accompanied by high levels of visual expertise, the objective of this study was to determine the extent to which this region responds to images related to restricted interests in individuals with ASD, compared to individuals without ASD who have a strong hobby or interest.

Methods

Children and adolescents with and without ASD with hobbies or interests that consumed a pre-determined minimum amount of time were identified, and the intensity, frequency, and degree of interference of these interests were quantified. Each participant underwent functional magnetic resonance imaging (fMRI) while viewing images related to their personal restricted interests (in the ASD group) or strong interest or hobby (in the comparison group). A generalized linear model was used to compare the intensity and spatial extent of fusiform gyrus response between groups, controlling for the appearance of faces in the stimuli.

Results

Images related to interests and expertise elicited response in FFA in both ASD and typically developing individuals, but this response was more robust in ASD.

Conclusions

These findings add neurobiological support to behavioral observations that restricted interests are associated with enhanced visual expertise in ASD, above and beyond what would be expected for simply a strong interest. Further, the results suggest that brain regions associated with social functioning may not be inherently less responsive in ASD, but rather may be recruited by different environmental stimuli. This study contributes to our understanding of the neural basis of restricted interests in ASD and may provide clues toward developing novel interventions.

Electronic supplementary material

The online version of this article (doi:10.1186/s11689-016-9149-6) contains supplementary material, which is available to authorized users.

Improvements in social and adaptive functioning following short-duration PRT program: a clinical replication

Pivotal Response Treatment (PRT) is an empirically validated behavioral treatment for individuals with autism spectrum disorders (ASD). The purpose of the current study was to assess the efficacy of PRT for ten cognitively-able preschool-aged children with ASD in the context of a short-duration (4-month) treatment model. Most research on PRT used individual behavioral goals as outcome measures, but the current study utilized standardized assessments of broader-based social communication and adaptive skills. The children made substantial gains; however, magnitude and consistency of response across measures were variable. The results provide additional support for the efficacy of PRT as well as evidence for improvements in higher-order social communication and adaptive skill development within the context of a short-duration PRT model.

Early-emerging social adaptive skills in toddlers with autism spectrum disorders: an item analysis

Individuals with ASD have significant impairments in adaptive skills, particularly adaptive socialization skills. The present study examined the extent to which 20 items from the Vineland Adaptive Behavior Scales-Socialization Domain differentiated between ASD and developmentally delayed (DD) groups. Participants included 108 toddlers with ASD or DD under the age of 3 years. Nine of the 20 items significantly distinguished the groups. The ASD group demonstrated significantly weaker socialization skills, including deficits in basic social behaviors. The results support the notion that (a) socialization deficits in ASD impact foundational social skills typically emerging in the first year of life, (b) examination of specific social adaptive behaviors contribute to differential diagnosis, and (c) foundational social behaviors should be targeted for intervention.

Neural mechanisms of improvements in social motivation after pivotal response treatment: two case studies

Pivotal response treatment (PRT) is an empirically validated behavioral treatment that has widespread positive effects on communication, behavior, and social skills in young children with autism spectrum disorder (ASD). For the first time, functional magnetic resonance imaging was used to identify the neural correlates of successful response to PRT in two young children with ASD. Baseline measures of social communication, adaptive behavior, eye tracking and neural response to social stimuli were taken prior to treatment and after 4 months of PRT. Both children showed striking gains on behavioral measures and also showed increased activation to social stimuli in brain regions utilized by typically developing children. These results suggest that neural systems supporting social perception are malleable through implementation of PRT.

Neural signatures of autism

Functional magnetic resonance imaging of brain responses to biological motion in children with autism spectrum disorder (ASD), unaffected siblings (US) of children with ASD, and typically developing (TD) children has revealed three types of neural signatures: (i) state activity, related to the state of having ASD that characterizes the nature of disruption in brain circuitry; (ii) trait activity, reflecting shared areas of dysfunction in US and children with ASD, thereby providing a promising neuroendophenotype to facilitate efforts to bridge genomic complexity and disorder heterogeneity; and (iii) compensatory activity, unique to US, suggesting a neural system-level mechanism by which US might compensate for an increased genetic risk for developing ASD. The distinct brain responses to biological motion exhibited by TD children and US are striking given the identical behavioral profile of these two groups. These findings offer far-reaching implications for our understanding of the neural systems underlying autism.

Differentiating between autism spectrum disorders and other developmental disabilities in children who failed a screening instrument for ASD

This study compared behavioral presentation of toddlers with autistic spectrum disorders (ASD) and toddlers with global developmental delay (DD) or developmental language disorder (DLD) who display some characteristics of ASD using the diagnostic algorithm items from the Autism Diagnostic Observation Schedule, Generic (ADOS), the Childhood Autism Rating Scale (CARS), and Modified Checklist for Autism in Toddlers (M-CHAT). To date, 195 children have failed the M-CHAT and have been diagnosed with ASD, DD or DLD. Children with ASD had prominent and consistent impairments in socialization skills, especially joint attention skills and were more impaired in some aspects of communication, play, and sensory processing. Children with ASD and children with DD/DLD shared common features, but certain behavioral markers differentiated the two groups.