The Infant Sibling Study

In 2021 the Early Detection Lab concluded 18 years of longitudinal data collection for the Infant Sibling Study. This project, funded by the National Institute of Mental Health (NIMH), followed the development of younger siblings of children with ASD or typical development who were enrolled in the study as infants and participated in follow up data collection at regular intervals through childhood and adolescence. One goal of the study was to identify the first signs of ASD in the infant and toddler years to enable earliest diagnosis and treatment at a time in development when the brain is most malleable. Another goal was to develop new instruments that could identify ASD signs in infancy. Our team designed and tested a new video-based measure, called the Video-referenced Infant Rating System for Autism (VIRSA), for earlier identification of autism. Following children into their school age years allowed us to evaluate the stability of diagnoses over time and also identify later occurring conditions such as learning disabilities, ADHD, and anxiety.

Here are some of the findings generated by the Infant Sibling Study thus far:

  • The chances that a younger sibling of a child with autism will develop autism themselves is much higher than was originally thought, around 20%.
  • Not many infants show signs of autism before their first birthday, and symptoms usually begin developing in the 2nd year of life. Timing of symptom onset is variable, with some children showing red flags for ASD earlier than other children. Many children who developed ASD demonstrated declines in their social-communication over time, such as diminishing eye contact and interest in people during the 2nd year of life.
  • Some of the earliest appearing signs of ASD are not responding to name consistently, being more interested in objects than in interacting with people, delayed onset of gestures like pointing, and unusual use of objects, such as staring at them for prolonged periods, rotating them, or spinning them.
  • Accurate and stable diagnoses of autism can be made as early as 18 months of age.
  • Parent ratings on the VIRSA, a video-based screener for ASD, distinguished children who eventually received an ASD diagnosis from those who were typically developing beginning at 12 months of age. This finding validated the utility of the VIRSA as an early screening tool for ASD. We are continuing to examine the accuracy of this measure through a large community-based screening study.
  • 30-40% of siblings of children with autism, who don’t develop autism themselves, show some behavioral, social, learning, or language challenges by the time they reach elementary school. ADHD is one of the most common of these difficulties seen in siblings of children with autism.

Early ASD Detection Using Remote Telehealth Methods

Ongoing research in the Ozonoff Lab examines distance methods for the early detection of ASD using online screeners (Online Developmental Screening Study) and tele-assessments done via video links to families in their homes (Tele-Assessment of Development Study). The goal of this research is to advance early detection methods for ASD and improve access to diagnostic services for families who live far away from metropolitan centers or in medically under-served communities.

In collaboration with colleagues from Engineering and Computer Science, we are testing another innovative method of utilizing video for ASD detection in combination with machine learning (Machine Learning Approaches to Automated Detection of ASD). Machine learning is an application of artificial intelligence in which computer programs “learn” and adjust themselves in response to training data to which they are exposed, improving performance and generalization to novel data without being explicitly programmed. We are using videos collected from the longitudinal Infant Sibling Study as training inputs to develop machine-learning algorithms for automatic detection of ASD-related behaviors. This will lay the foundation for future attempts to develop video-based mobile applications for ASD recognition, which require validated classifiers that can recognize behavioral events central to early detection of ASD. The ultimate goal of the project is to develop low-cost, low-burden measures that capitalize on new technologies, including mobile platforms, video, and machine learning methods, to detect ASD risk in infancy. Such measures would have significant public health applications, including screening large community-based samples and longitudinally tracking development in pediatric settings to identify children requiring evaluation. Identification of ASD in infancy would afford treatment at an optimal age, when the brain is most malleable, which could lessen disability and possibly prevent the emergence of later-appearing symptoms.