2016Alycia Halladay
Podcast: Having autism is challenging, but being a minority with autism is extra challenging
Background: Autism spectrum disorder (ASD) encompasses a complex manifestation of symptoms that include deficits in social interaction and repetitive or stereotyped interests and behaviors. In keeping with the increasing recognition of the dimensional characteristics of ASD symptoms and the categorical nature of a diagnosis, we sought to delineate the neural mechanisms of ASD symptoms based on the functional connectivity of four known neural networks (i.e., default mode network, dorsal attention network, salience network, and executive control network).
Methods: We leveraged an open data resource (Autism Brain Imaging Data Exchange) providing resting-state functional magnetic resonance imaging data sets from 90 boys with ASD and 95 typically developing boys. This data set also included the Social Responsiveness Scale as a dimensional measure of ASD traits. Seed-based functional connectivity was paired with linear regression to identify functional connectivity abnormalities associated with categorical effects of ASD diagnosis, dimensional effects of ASD-like behaviors, and their interaction.
Results: Our results revealed the existence of dimensional mechanisms of ASD uniquely affecting each network based on the presence of connectivity-behavioral relationships; these were independent of diagnostic category. However, we also found evidence of categorical differences (i.e., diagnostic group differences) in connectivity strength for each network as well as categorical differences in connectivity-behavioral relationships (i.e., diagnosis-by-behavior interactions), supporting the coexistence of categorical mechanisms of ASD.
Conclusions: Our findings support a hybrid model for ASD characterization that includes a combination of categorical and dimensional brain mechanisms and provide a novel understanding of the neural underpinnings of ASD.
Keywords: Autism spectrum disorder; Default mode network; Dimensional measures; Functional connectivity; Resting-state fMRI; Social cognition.
Background: Peer mediated intervention (PMI) is a promising practice used to increase social skills in children with autism spectrum disorder (ASD). PMIs engage typically developing peers as social models to improve social initiations, responses, and interactions.
Method: The current study is a systematic review examining PMIs for children and adolescents with ASD conducted using group designs. Five studies met the pre-specified review inclusion criteria: four randomized controlled trials and one pre- and post-test design.
Results: Four of the studies were conducted in school settings, whereas one study was conducted in a camp setting. The studies all reported that participants improved in social skills (e.g., social initiations, social responses, social communication) post intervention. Additionally, sustainment, generalization, and fidelity of implementation were examined.
Conclusion: PMI is a promising approach to address social skills in children with ASD, and this approach can be conducted in meaningful real-word contexts, such as schools. Limitations of the studies as well as future directions are discussed.
Keywords: Autism spectrum disorder; Peer-mediated interventions; Systematic review.
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that would benefit from low-cost and reliable improvements to screening and diagnosis. Human language technologies (HLTs) provide one possible route to automating a series of subjective decisions that currently inform “Gold Standard” diagnosis based on clinical judgment. In this paper, we describe a new resource to support this goal, comprised of 100 20-minute semi-structured English language samples labeled with child age, sex, IQ, autism symptom severity, and diagnostic classification. We assess the feasibility of digitizing and processing sensitive clinical samples for data sharing, and identify areas of difficulty. Using the methods described here, we propose to join forces with researchers and clinicians throughout the world to establish an international repository of annotated language samples from individuals with ASD and related disorders. This project has the potential to improve the lives of individuals with ASD and their families by identifying linguistic features that could improve remote screening, inform personalized intervention, and promote advancements in clinically-oriented HLTs.
Keywords: annotation; autism spectrum disorder; collection; data distribution; language resources; quality control.
Objective: This study compared use of and associated expenditures for Medicaid-reimbursed school-based and out-of-school services for children with autism spectrum disorder (ASD) and those with other psychiatric disorders.
Methods: Philadelphia County Medicaid claims were used to identify children ages five to 17 who received behavioral health services through Medicaid any time between October 2008 and September 2009 (N=24,271). Children were categorized into four diagnostic groups: autism spectrum disorder (ASD), conduct disorder or oppositional defiant disorder (conduct-ODD), attention-deficit hyperactivity disorder (ADHD), and other psychiatric disorders. Logistic regression analysis compared use of in-school and out-of-school behavioral health services between children with ASD and children with other psychiatric disorders. Generalized linear models with gamma distribution were used to estimate differences in Medicaid expenditures for in-school and out-of-school services and total Medicaid expenditures for both service types by disorder, with adjustments for age, sex, and race-ethnicity.
Results: The most common diagnosis was ADHD (40%); 35% had other psychiatric disorders, 21% had conduct-ODD, and 4% had ASD. A significantly greater proportion of children with ASD (52%) received in-school behavioral health services (conduct-ODD, 5%; ADHD, 8%; and other psychiatric disorders, 1.7%) Per-child expenditures for both school-based and out-of-school behavioral health services were significantly higher for children with ASD than for children in the other groups.
Conclusions: Medicaid represents an important source of in-school and out-of-school care for children with ASD and their families. States that expand Medicaid under the Affordable Care Act should give careful consideration to covering school-based mental health services for children with ASD.
Introduction of the National Institute of Mental Health’s Research Domain Criteria and revision of diagnostic classification for Autism Spectrum Disorder in the latest diagnostic manual call for a new way of conceptualizing heterogeneous ASD features. We propose a novel conceptualization of ASD, borrowing from the schizophrenia literature in clustering ASD features along positive, negative, and cognitive dimensions. We argue that this dimensional conceptualization can offer improved ability to classify, diagnose, and treat, to apply and predict response to treatment, and to explore underlying neural and genetic alterations that may contribute to particular feature clusters. We suggest the proposed conceptualization can advance the field in a manner that may prove clinically and biologically useful for understanding and addressing heterogeneity within ASD.
Keywords: Autism spectrum disorder; Classification; Diagnosis; Heterogeneity; RDoC; Symptoms.