An imbalance in excitatory/inhibitory transmission, due to alterations in GABAergic signaling, has been suggested as an underlying cause of autism. These studies argue that the GABAergic system represents a viable therapeutic target for autism. Indeed, the GABA-B receptor agonist R-baclofen has been shown to ameliorate social deficits and repetitive behaviors in two mouse models of autism1. However, baclofen is known to exhibit relatively poor blood-brain-barrier permeability, arguing for the development of drug treatments more accessible to the central nervous system. Yet such drug discovery efforts are complicated by a lack of suitable radiotracers for imaging GABA-B receptors.

Autism spectrum disorder (ASD), affecting 1 in 68 children in the U.S., is a significant unresolved public health concern. The clinical presentations of ASD can be quite broad, and recent evidence points to many different genetic causes. This heterogeneity could lead to a scientific and clinical impasse; each cause of ASD has its own disrupted mechanisms and requires its own unique treatment. However, a more optimistic interpretation, for which there is now accumulating evidence, is that many different primary causes of ASD actually converge on a limited subset of biochemical pathways in nerve cells that mediate cell growth and function. Demonstrating that such a mechanistic convergence exists would be a significant step forward for the field.

Chromosomal microarray analysis (CMA) is a robust technology officially recommended for children with autism spectrum disorders (ASDs). However, CMA genetic testing might raise ethical, legal and social issues such as stigmatization, health insurance concerns and test misconceptions. Therefore, it is essential to understand what might motivate or inhibit test decisions among parents of children with ASD.

Sensitive, standardized, reliable measures of outcomes for clinical trials attempting to change behavior and function in children and adults with autism spectrum disorder (ASD) have been extremely limited to date. Previous clinical trials have often relied on a single measure to quantify change, determined change in one domain or context (i.e., language or solely parent report or assessments only in the clinic), or used an approach measure with limited capability for replication, thus reducing the scope of the research.

How children recognize emotion from facial expressions, understand others’ perspectives, reason through social problems and regulate emotion is critical to their social success. These social cognition skill areas are often affected in children with autism spectrum disorders (ASDs). They are also frequent targets of both behavioral and medical interventions for ASD. There are no reliable, comprehensive, easy-to-use, scalable and standardized assessment tools available to measure responsiveness to treatment in each of these social domains among children with ASD.

One of the most important objectives of autism spectrum disorder (ASD) interventions is to improve spoken communicative language skills. Across all ages and levels of language, quantitative changes in the amount of intelligible speech, the length of speaker turns and better conversational turn-taking are key measures of advances in spoken language. Changes in these measures not only signify that a treatment is effective in improving language itself, but also leads to improvements in social communication.

Very early interventions for autism spectrum disorders (ASDs) have the potential to improve long-term outcomes by targeting emotion regulation, social learning and social communication. However, treatment studies currently rely upon long-term measures that are often far removed from the underlying targets of treatment and that are rather insensitive to short-term treatment effects. Measuring short-term outcomes would improve treatment research by speeding discovery and validation of early interventions, but such an approach is currently limited by diagnostic tools that are insensitive to small changes in behavior.

Research assessing the effectiveness of treatments in autism spectrum disorder (ASD) is limited by a lack of scalable and quantifiable autism-specific treatment response measures. Assessing treatment success would be benefited by the development of response measures that can be administered ‘blindly’ and are sensitive enough to capture change over short periods of time, flexible enough to be used across studies and are standardized in order to be comparable across sites.

Individuals with autism spectrum disorder (ASD) have difficulties engaging in social interactions, including problems making eye contact with other people and sharing attention with others about objects in their environment (joint attention). Many interventions aim to allow individuals to more effectively use gaze during social interactions. Currently, there is a shortage of objective, quick and reliable measurements that can detect changes in social communication behaviors. Without good measures to capture change, it is almost impossible to determine if a treatment is effective or is helping to ameliorate ASD symptoms. In addition, measures that are used to determine change are often time intensive, making them impractical to use in many clinical settings.