Limited generalizability of multivariate brain-based dimensions of child psychiatric symptoms
Bing Xu, Lorenza Dall’Aglio, John C. Flournoy, Gerda Bortsova, Brenden Tervo‐Clemmens, Paul F. Collins, Marleen de Bruijne, Mónica Luciana, André F. Marquand, Hao Wang, Henning Tiemeier, Ryan L. Muetzel
Abstract
Multivariate machine learning techniques are a promising set of tools for identifying complex brain-behavior associations. However, failure to replicate results from these methods across samples has hampered their clinical relevance. Here we aimed to delineate dimensions of brain functional connectivity that are associated with child psychiatric symptoms in two large and independent cohorts: the Adolescent Brain Cognitive Development (ABCD) Study and the Generation R Study (total n = 6935). Using sparse canonical correlations analysis, we identified two brain-behavior dimensions in ABCD: attention problems and aggression/rule-breaking behaviors. Importantly, out-of-sample generalizability of these dimensions was consistently observed in ABCD, suggesting robust multivariate brain-behavior associations. Despite this, out-of-study generalizability in Generation R was limited. These results highlight that the degrees of generalizability can vary depending on the external validation methods employed as well as the datasets used, emphasizing that biomarkers will remain elusive until models generalize better in true external settings.