  To improve the statistical power for imaging biomarker detection, we propose
a latent variable-based statistical network analysis (LatentSNA) that combines
brain functional connectivity with internalizing psychopathology, implementing
network science in a generative statistical process to preserve the
neurologically meaningful network topology in the adolescents and children
population. The developed inference-focused generative Bayesian framework (1)
addresses the lack of power and inflated Type II errors in current analytic
approaches when detecting imaging biomarkers, (2) allows unbiased estimation of
biomarkers' influence on behavior variants, (3) quantifies the uncertainty and
evaluates the likelihood of the estimated biomarker effects against chance and
(4) ultimately improves brain-behavior prediction in novel samples and the
clinical utilities of neuroimaging findings. We collectively model multi-state
functional networks with multivariate internalizing profiles for 5,000 to 7,000
children in the Adolescent Brain Cognitive Development (ABCD) study with
sufficiently accurate prediction of both children internalizing traits and
functional connectivity, and substantially improved our ability to explain the
individual internalizing differences compared with current approaches. We
successfully uncover large, coherent star-like brain functional architectures
associated with children's internalizing psychopathology across multiple
functional systems and establish them as unique fingerprints for childhood
internalization.
