Human Cognitive and Behavioral Science — Request for Applications

The objective of the Human Cognitive and Behavioral RFA was to better understand the cognitive and behavioral foundations of ASD, and to support basic science studies in humans with clear value for improving outcome measures and treatment options. It prioritized research that produces foundational knowledge about the neurobehavioral differences associated with ASD. These projects were expected to inform or relate to the development and refinement of tools needed for translational efforts such as biomarkers and outcome measures. Special emphasis was placed on objective, quantitative measures that may be used in conjunction with standardized clinical measures and genomic information to better characterize phenotypic and neurobiological variability within and across individuals with ASD.

We encouraged studies that capitalize on approaches that are informed by recent advances in computer vision, machine learning and speech processing, as well as psychophysics and non-invasive neuroscience techniques (e.g., EEG and MRI). SFARI has a strong interest in developmentally focused studies in areas that include, but are not limited to, communicative, social and ritualistic/stereotyped behavior, as well as sensory and motor function. SFARI also recognizes the importance of domains of function, such as attention, learning and memory, and sleep, in influencing core ASD diagnostic domains. While applications may have proposed laboratory-based measures, we were especially interested in real-world, scalable and quantitative measures of behavior (e.g., wearable devices and other methods of digital phenotyping).

We also encouraged proposals that not only quantify specific phenotypes, but also may contribute to the development of scalable innovative technologies for improving interventions and supports in humans. Successful applications would include a clear rationale for how the digital technology could be deployed to increase, maintain or improve quantifiable functional outcomes in individuals with autism; for example, machine learning approaches that could amplify measurable gains from intensive behavioral interventions would be of interest.

SFARI considered the following as out-of-scope for this RFA: studies with a primary focus on developing new clinical rating scales, or survey-based assessments, or online adaptations of traditional paper and pencil tests or existing assessments, such as cognitive (IQ) or social-cognitive tests. In addition, we discouraged applications with a primary focus on psychosocial factors, or those proposing interventional clinical trials. Applications primarily focused on infrastructure or recruitment support of cohorts were also considered unresponsive.