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.

翻译：为提升影像生物标志物检测的统计功效，我们提出一种基于潜变量的统计网络分析方法（LatentSNA），该方法将脑功能连接与内化精神病理学相结合，在生成式统计过程中实现网络科学，以保留青少年与儿童群体中具有神经学意义的网络拓扑结构。所开发的以推断为核心的生成式贝叶斯框架能够：（1）解决当前分析方法在检测影像生物标志物时存在的统计功效不足与II类错误膨胀问题；（2）实现生物标志物对行为变异影响的无偏估计；（3）量化不确定性并评估所估计生物标志物效应相对于随机水平的可能性；（4）最终提升对新样本脑-行为预测的准确性以及神经影像学发现的临床适用性。我们利用青少年脑认知发展（ABCD）研究中5,000至7,000名儿童的多状态功能网络与多变量内化特征进行联合建模，在准确预测儿童内化特质与功能连接的同时，较现有方法显著提升了对个体内化差异的解释能力。我们成功揭示了与儿童内化精神病理学相关的多个功能系统中大规模、连贯的星形脑功能架构，并将其确立为儿童内化过程的独特指纹特征。