Abstract Autism spectrum disorder is a pervasive developmental disorder with heterogeneous symptomatology. Currently, subjective evaluations of behavioral symptoms determine diagnosis and treatment, as the neurological interactions that underpin autism remain largely unknown. This study investigates the relationship between effective connectivity (EC) among resting-state networks and autism symptomatology in a large dataset. EC is estimated with spectral dynamic causal modeling for functional magnetic resonance imaging and then related to Autism Diagnostic Observation Schedule scores through parametric empirical Bayes and statistical correlation analyses. Furthermore, EC is used innovatively in machine learning for individual-level prediction. Group-level analyses reveal multiple connections associated with symptom domains, including the newly identified effective connection from the lateral visual network to the posterior default mode network, which is consistently negatively correlated with scores for restricted and repetitive behaviors. Despite the identified group-level associations between EC and symptom domains, EC shows limited generalizability in symptom prediction and heterogeneity in feature importance at the individual level. These findings suggest the presence of subgroups within the spectrum, where informative connectivity patterns vary between individuals.
Schielen et al. (Wed,) studied this question.