Do in-scanner tasks outperform rest for predicting autistic traits using functional connectivity data?

Autism is a heterogeneous condition, and functional magnetic resonance imaging-based studies have advanced understanding of neurobiological correlates of autistic features. Nevertheless, little work has focused on the optimal brain states to reveal brain-phenotype relationships. In addition, there is a need to better understand the relevance of attentional abilities in mediating autistic features. Here, we used connectome-based predictive modelling to identify brain-behavior relationships. We analyzed four datasets to determine scanning conditions that can boost prediction of clinically relevant phenotypes and assess generalizability. Across all four samples, we observed successful prediction. Specifically, in dataset one, a sample of youth with autism and neurotypical participants (n = 63), we found that a sustained attention task (the gradual onset continuous performance task) resulted in high prediction performance of autistic traits compared to a free-viewing social attention task and a resting-state condition. In dataset two (n = 25), we observed the predictive network model of autistic traits generated from the sustained attention task generalized to predict measures of attention in neurotypical adults. In datasets three and four, we determined the same predictive network model of autistic traits from dataset one further generalized to predict measures of social responsiveness in data from the Autism Brain Imaging Data Exchange (n = 229) and the Healthy Brain Network (n = 643). We further generated predictive models of social responsiveness in the Healthy Brain Network sample, finding task-based models outperformed rest-based models. A consensus model from the Healthy Brain network subsequently generalized to predict ADOS scores in dataset one. In sum, our data suggest that an in-scanner sustained attention challenge can help delineate robust markers of autistic traits and support the continued investigation of the optimal brain states under which to predict phenotypes in psychiatric conditions.