ABSTRACT Anxiety is a diffuse condition ranging from milder manifestations to severe disorders, impacting individuals' lives significantly. Specific sensitive periods such as adolescence and young adulthood are particularly vulnerable to anxious states, often associated with psychological traits like impulsivity, aggression and varying coping strategies. The goal of the present study is to address the need for a comprehensive analysis of trait anxiety by employing Parallel ICA, a data fusion machine learning technique, in a sample of young individuals divided into a lower anxiety group ( n = 252) and a higher anxiety group ( n = 302), aiming to identify the joint grey–white matter networks characterizing higher versus lower trait anxiety. Additionally, we aim to characterize higher anxiety individuals for their usage of maladaptive coping strategies, and other affective dimensions. In higher anxious individuals, we identified a fronto‐parieto‐cerebellar network with decreased grey matter concentration, linked to bodily awareness and threat modulation, and a parieto‐temporal network with increased white matter concentration, emphasizing insula and precuneus role. At the psychological level, we found higher stress, cognitive and motor impulsivity and avoidance/emotional coping in higher anxious individuals. These findings may enhance the understanding of anxiety's neural underpinnings in young individuals, supporting early interventions.
Baggio et al. (Fri,) studied this question.