Digital Behavioral Phenotyping Detects Atypical Pattern of Facial Expression in Toddlers with Autism

Abstract

Commonly used screening tools for autism spectrum disorder (ASD) generally rely on subjective caregiver questionnaires. While behavioral observation is more objective, it is also expensive, time-consuming, and requires significant expertise to perform. As such, there remains a critical need to develop feasible, scalable, and reliable tools that can characterize ASD risk behaviors. This study assessed the utility of a tablet-based behavioral assessment for eliciting and detecting one type of risk behavior, namely, patterns of facial expression, in 104 toddlers (ASD N = 22) and evaluated whether such patterns differentiated toddlers with and without ASD. The assessment consisted of the child sitting on his/her caregiver's lap and watching brief movies shown on a smart tablet while the embedded camera recorded the child's facial expressions. Computer vision analysis (CVA) automatically detected and tracked facial landmarks, which were used to estimate head position and facial expressions (Positive, Neutral, All Other). Using CVA, specific points throughout the movies were identified that reliably differentiate between children with and without ASD based on their patterns of facial movement and expressions (area under the curves for individual movies ranging from 0.62 to 0.73). During these instances, children with ASD more frequently displayed Neutral expressions compared to children without ASD, who had more All Other expressions. The frequency of All Other expressions was driven by non-ASD children more often displaying raised eyebrows and an open mouth, characteristic of engagement/interest. Preliminary results suggest computational coding of facial movements and expressions via a tablet-based assessment can detect differences in affective expression, one of the early, core features of ASD. LAY SUMMARY: This study tested the use of a tablet in the behavioral assessment of young children with autism. Children watched a series of developmentally appropriate movies and their facial expressions were recorded using the camera embedded in the tablet. Results suggest that computational assessments of facial expressions may be useful in early detection of symptoms of autism.

Keywords: autism; computer vision; early detection; facial expressions; risk behaviors.

Figure 1.

Example of movie stimuli. Developmentally appropriate movies consisted of cascading bubbles, a mechanical bunny, animal puppets interacting with each other, and a split screen showing a woman singing nursery rhymes on one side and dynamic, noise-making toys on the other side.

Figure 2.

Time series of odds ratios for the association between the mean expression probability or proportion attending and ASD diagnosis. Using the “Rhymes” movie as one illustrative example, lines depict the odds of meeting criteria for ASD (OR > 1) or being in the non-ASD comparison group (OR < 1) for each of the outcomes of interest for each 3 sec time bin across the movie. Points with error bars are intervals that were selected by the LASSO regression models and included in the final logistic model. The blue window depicts a segment of the movie where there were differential emotional responses between the ASD and non-ASD children. The green window depicts a segment of the movie in which there was no difference in emotional responses between the groups.

Figure 3.

Receiver-operating characteristics (ROC) curves. ROC curves were calculated for predictive ability of expression-specific LASSO selected interval parameters for facial expressions and attention to stimulus for each movie independently.

Figure 4.

Analysis of Other Facial Expression. The 4 panels on the left depict heat maps of aligned landmarks across ASD and non-ASD participants when they were exhibiting Neutral and other facial expressions (the color bar indicates proportion of frames where landmarks were displayed in a given image location). The single panel on the right is an example of the landmark distances explored.