Abstract
The goal of this R03 is to leverage computational linguistics to identify sensitive, efficient, and scalable measures
of social communication that can be used to inform intervention efforts for transition-aged youth on the autism
spectrum. Such measures would yield significant benefits for both (a) quantifying post-intervention clinical
outcome and (b) precision tailoring for future interventions. By applying sophisticated computational linguistic
approaches specifically designed to assess social communication in autistic youth (developed by MPI Parish-
Morris and Co-I Cho) to a rich pre-existing dataset from a completed RCT of the START intervention model
(extracted from 421 video-recorded conversations from 35 participants collected across 6 timepoints by MPI
Vernon), the proposed project will significantly advance the goal of objective, efficient, and scalable
clinical outcome measurement for youth with autism. Our collaborative research teams will use
computational linguistics to identify objective, quantitative predictors of clinical intervention outcomes in autistic
youth and chart individual trajectories of change.
We hypothesize that (a) youth assigned to the START intervention group will demonstrate significant gains in
linguistic markers of social phenotype in comparison to waitlist controls, (b) across the entire START cohort,
modeling individual trajectories of growth in linguistic markers of social phenotype will identify subgroups with
unique baseline factors that moderate the slope of change, and (c) linguistic markers that predict social
communication success will vary by speaker (male vs. female) and context (same-sex vs opposite-sex
conversations). We will also validate our linguistic features as social communication outcome metrics over time.
We also anticipate that identified vocal/linguistic features will have high convergent validity with existing
measures of social communication outcome and social impression ratings, and high discriminant validity with
measures of restricted/repetitive behaviors and interests.
This R03 proposal addresses NIDCD Strategic Plan Theme 3 to promote a precision medicine approach to
prevention, diagnosis, and treatment of conditions that impact speech and communicative functioning, such as
autism. A key output of this project will be validated computational linguistic features that can be used for clinical
outcome measurement in autism. These metrics hold promise for both (a) efficiently and objectively quantifying
post-intervention clinical outcome in autism and (b) guiding precision tailoring for future intervention
implementations. Thus, the proposed project fills an important knowledge gap that could significantly advance
the goal of objective, efficient, and scalable clinical outcome measurement and intervention response monitoring
for transition-aged autistic youth. This proposal has high
