Individual differences in neural circuits underlying emotional regulation, motivation, and decision-making are implicated in many psychiatric illnesses. Interindividual variability in these circuits may manifest, at least in part, as individual differences in impulsivity. Impulsivity reflects a tendency towards rapid, unplanned reactions to internal or external stimuli without considering potential negative consequences, coupled with difficulty inhibiting responses. Here, we use multivariate machine learning approaches (brain-based predictive models) to explore the neural bases of impulsivity. We consider multiple impulsivity measures, neuroanatomical features (cortical thickness, surface area, and gray matter volume, as well as non-cortical gray matter volume), and sexes (females and males) in a large sample of youth from the Adolescent Brain Cognitive Development (ABCD) Study at baseline (n = 8630), two-year follow-up (n = 5998), four-year follow-up (n = 4844), and six-year follow-up (n = 3100). Using brain-based predictive models, we demonstrate that regional variations in cortical thickness, surface area, and gray matter volume significantly predict self-reported impulsivity measures, with associations varying across impulsivity dimensions and developmental timepoints. Impulsivity broadly maps onto default mode, limbic, ventral attention, and visual networks, as well as cerebellar and brain stem structures. While many relationships are stable across sexes and developmental time points, others exhibit sex effects and dynamic changes. These results suggest that neuroanatomy is linked to self-reported impulsivity in youth and highlight the complexity of these relationships across measures, features, sexes, and time points. This work also emphasizes the importance of adopting a multivariate and sex-specific approach in neuroimaging and behavioral research.
Dhamala et al. (Tue,) studied this question.