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

TL;DR

This paper introduces LatentSNA, a Bayesian network analysis method that improves detection of brain biomarkers linked to childhood internalizing psychopathology, revealing star-like brain architectures as unique fingerprints.

Contribution

The study develops a novel inference-based Bayesian framework that enhances power, reduces errors, and uncovers specific brain network patterns associated with childhood internalizing disorders.

Findings

Identifies star-like brain architectures linked to internalizing psychopathology.

Improves prediction accuracy of internalizing traits from brain connectivity.

Provides a robust statistical approach for neuroimaging biomarker detection.

Abstract

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.