Multivariate analytical approaches for investigating brain-behavior relationships - Durham, E. Leighton; Ghanem, Karam; Stier, Andrew J.; Cardenas-Iniguez, Carlos; Reimann, Gabrielle E.; Jeong, Hee Jung; Dupont, Randolph M.; Dong, Xiaoyu; Moore, Tyler M.; Berman, Marc G.; Lahey, Benjamin B.; Bzdok, Danilo; et al. (Frontiers in Neuroscience, 2023)
Many studies of brain-behavior relationships rely on univariate approaches in which each variable of interest is tested independently, which do not allow for the simultaneous investigation of multiple correlated variables. Alternatively, multivariate approaches allow for examining relationships between psychopathology and neural substrates simultaneously. There are multiple multivariate methods to choose from that each have assumptions which can affect the results; however, many studies employ one method without a clear justification for its selection.Additionally, there are few studies illustrating how differences between methods manifest in This is a provisional file, not the final typeset article examining brain-behavior relationships. We used data from 9,027 9-to 10-year-old children from the Adolescent Brain Cognitive Development SM Study (ABCD Study ® ) to examine brain-behavior relationships with three commonly used multivariate approaches: canonical correlation analysis (CCA), partial least squares correlation (PLSC), and partial least squares regression (PLSR). We examined the associations between psychopathology dimensions including general psychopathology, attention-deficit/hyperactivity symptoms, conduct problems, and internalizing symptoms with regional brain volumes. The results show that the three methods all yield slightly different interpretations regarding the relationship between child psychopathology and brain volume. In demonstrating the divergence between these approaches, we exemplify the importance of carefully considering the method's underlying assumptions when choosing a multivariate approach to delineate brain-behavior relationships.