Early identification of infants at risk for autism spectrum disorder (ASD) is a huge challenge because neither genetic nor neuroimaging methods have revealed robust early pathological markers for developing social and/or communication challenges. Additionally, clinical screenings are primarily based on the severity of post-infancy social-communication (S-C) differences, leading to relatively late diagnosis and inhibiting early interventions. While parents, practitioners, teachers, clinicians and researchers often cite motor anomalies, from birth, in children later diagnosed with ASD, the sensitive, longitudinal measures to establish trajectories in early motor development and their cascading interactions with emerging S-C abilities are missing.
Gillian Forrester and her colleagues are employing high-frequency longitudinal data collection because research demonstrates that infant groups at high and low risk of ASD may demonstrate differences at one time point which may no longer be visible at a later time point, but the nature of when (e.g., sensitive stages) and why (e.g., compensatory mechanisms) changes occur in the intervening time is unknown. The team will use wearable technology and intelligent toys to capture high-frequency, longitudinal samples of motor development and standardized modules of cognitive batteries to evaluate S-C abilities in infants from birth.
Forrester and her team will evaluate weekly motor behaviors in infants aged 0–12 months and biweekly S-C abilities between ages 0–18 months. High frequency motor data will be derived from video and motion sensors embedded in baby bodysuits and toys used in participants’ homes. Motor ability of infants derived from sensors will be validated against standardized clinical general movement assessments. The investigation focuses on (1) how different motor trajectories unfold, (2) how motor trajectories interact with S-C through early development in both a linear and nonlinear fashion and (3) whether the variation in these developmental trajectories is discernible between groups of infants at low and high risk for ASD.
High-frequency behavioral sampling is required to reveal early risk markers for specifically targeted interventions to reduce the severity of developing symptoms associated with ASD, thereby increasing the quality of the lives of infants and their families.
- Computerized assessment of motor imitation (CAMI): Advancing the validity and scalability of a promising phenotypic biomarker for autism
- Home-based system for biobehavioral recording of individuals with autism
- Pathogenic insight into autism from novel mapping of behavioral dynamics in the infant-caregiver dyad from 0 to 6 months
- Quantitative and remote methods to study early cognitive development and heterogeneity in autism