  Research in machine learning for autism spectrum disorder (ASD)
classification bears the promise to improve clinical diagnoses. However, recent
studies in clinical imaging have shown the limited generalization of biomarkers
across and beyond benchmark datasets. Despite increasing model complexity and
sample size in neuroimaging, the classification performance of ASD remains far
away from clinical application. This raises the question of how we can overcome
these barriers to develop early biomarkers for ASD. One approach might be to
rethink how we operationalize the theoretical basis of this disease in machine
learning models. Here we introduced unsupervised graph representations that
explicitly map the neural mechanisms of a core aspect of ASD, deficits in
dyadic social interaction, as assessed by dual brain recordings, termed
hyperscanning, and evaluated their predictive performance. The proposed method
differs from existing approaches in that it is more suitable to capture social
interaction deficits on a neural level and is applicable to young children and
infants. First results from functional near-infrared spectroscopy data indicate
potential predictive capacities of a task-agnostic, interpretable graph
representation. This first effort to leverage interaction-related deficits on
neural level to classify ASD may stimulate new approaches and methods to
enhance existing models to achieve developmental ASD biomarkers in the future.
