Neuroanatomical prediction of individual anxiety problems level using machine learning models: A population-based cohort study of young adults

Anxiety, a mental state in healthy individuals, is characterized by apprehension of potential future threats. Though the neurobiological basis of anxiety has been investigated widely in the clinical populations, the underly mechanism of neuroanatomical correlates with anxiety level in healthy young adults is still unclear. In this study, 1080 young adults were enrolled from the Human Connectome Project Young Adult dataset, and machine learning-based elastic net regression models with cross validation, together with linear mix effects (LME) models were adopted to investigate whether the neuroanatomical profiles of structural magnetic resonance imaging indicators associated with anxiety level in healthy young adults. We found multi-region neuroanatomical profiles predicted anxiety problems level and it was still robust in an out-of-sample. The neuroanatomical profiles had widespread brain nodes, including the dorsal lateral prefrontal cortex, supramarginal gyrus, and entorhinal cortex, which implicated in the default mode network and frontoparietal network. This finding was further supported by LME models, which showed significant univariate associations between brain nodes with anxiety. In sum, it's a large sample size study with multivariate analysis methodology to provide evidence that individual anxiety problems level can be predicted by machine learning-based models in healthy young adults. The neuroanatomical signature including hub nodes involved theoretically relevant brain networks robustly predicts anxiety, which could aid the assessment of potential high-risk of anxiety individuals.