Quantitative and remote methods to study early cognitive development and heterogeneity in autism
- Awarded: 2021
- Award Type: Human Cognitive and Behavioral Science Award
- Award #: 874889
Language difficulties and intellectual disability affect approximately one-third of individuals with autism spectrum disorder (ASD) and predict quality of life for adults with ASD. Because ASD is not typically diagnosed until 2–3 years of age, very little is known about the first two years of life, arguably the most critical stages of language and cognitive development.
To better understand this key developmental period and to potentially identify early predictors of ASD, researchers often prospectively study infant-siblings of diagnosed children (“baby-sibs”) who are more likely to receive a diagnosis. These studies have been hampered by small sample sizes and the need for in-person assessments, thereby limiting accessibility and generalizability of the findings. Recent developments in remote administration of tasks to assess cognitive development and in computer vision software to automate video-coding of infant looking patterns have created a unique opportunity to study a large sample of baby sibs. Elena Tenenbaum, Shafali Jeste and team plan to capitalize on these advances to increase knowledge about early cognitive development in ASD.
Established laboratory measures of cognitive development have identified remarkable perceptual abilities within the first year of life that predict developmental outcomes. In the current project, Tenenbaum, Jeste and team plan to test the feasibility of a remote battery to assess cognitive development using these existing measures. This project draws on four exceptional resources: (1) the Remote Infant Studies of Early Learning (RISE) Battery, a set of tasks compiled by experts in cognitive development and ASD (2) the Lookit Platform, an innovative tool developed at the Massachusetts Institute of Technology (MIT) for the remote administration of infant cognitive development tasks1, (3) iCatcher, a new computer vision tool for automated assessment of infants’ looking behavior2 and (4) SPARK, a registry of individuals with autism and their families.
Tenenbaum and Jeste’s team of developmental, clinical and computer vision experts will determine whether tasks from the RISE Battery that assess attention, memory, prediction, multimodal sensory processing and language processing produce consistent results when administered at home. They will also assess feasibility and test-retest reliability of the RISE Battery in typically developing infants and baby sibs. Finally, the team will assess convergent and predicative validity of the RISE Battery for developmental and language outcomes.
This work has important implications for our understanding of the foundational stages of development that likely account for significant phenotypic variability in ASD and will guide clinically relevant assessments in early infancy that can inform early intervention and ultimately improve outcomes in infants with atypical development.
- Electroencephalography and eye-tracking measures as scalable biomarker-based predictors of ASD in high-risk infants
- Tracking intervention effects with eye-tracking
- Biomarkers of emotion regulation, social response and social attention in autism
- Home-based system for biobehavioral recording of individuals with autism
- Multisite validation study of eye-tracking-based measures of autism symptom severity
- Objective measures of social interactions via wearable cameras