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To improve the statistical power for imaging biomarker detection, we proposea latent variable-based statistical network analysis (LatentSNA) that combinesbrain functional connectivity with internalizing psychopathology, implementingnetwork science in a generative statistical process to preserve theneurologically meaningful network topology in the adolescents and childrenpopulation. The developed inference-focused generative Bayesian framework (1)addresses the lack of power and inflated Type II errors in current analyticapproaches when detecting imaging biomarkers, (2) allows unbiased estimation ofbiomarkers' influence on behavior variants, (3) quantifies the uncertainty andevaluates the likelihood of the estimated biomarker effects against chance and(4) ultimately improves brain-behavior prediction in novel samples and theclinical utilities of neuroimaging findings. We collectively model multi-statefunctional networks with multivariate internalizing profiles for 5,000 to 7,000children in the Adolescent Brain Cognitive Development (ABCD) study withsufficiently accurate prediction of both children internalizing traits andfunctional connectivity, and substantially improved our ability to explain theindividual internalizing differences compared with current approaches. Wesuccessfully uncover large, coherent star-like brain functional architecturesassociated with children's internalizing psychopathology across multiplefunctional systems and establish them as unique fingerprints for childhoodinternalization.

Inference-based statistical network analysis uncovers star-like brain functional architectures for internalizing psychopathology in children