论文标题
贝叶斯掺入线性非高斯无环模型,用于多个有向图估计,以研究青春期的大脑情绪电路的发展
A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence
论文作者
论文摘要
情感感知对于涉及分布的脑电路的情感和认知发展至关重要。情绪识别的能力始于婴儿期,并在整个童年和青春期继续发展。了解大脑情绪电路的发展可能有助于我们解释青春期观察到的情绪变化。我们先前的研究描述了在情绪识别任务中,从儿童晚期到成年期的大脑功能连通性(FC)的轨迹。在这项工作中,我们努力加深我们从关联到因果关系的理解。我们提出了一个贝叶斯合并的线性非高斯无环模型(Bilingam),该模型将我们先前的关联模型纳入了先前的估计管道。特别是,它可以在不同的发育阶段共同估计多个年龄组的多个定向无环图(DAG)。模拟结果表明,在各种设置上,尤其是样本量很小(高维情况)时,更稳定和准确的性能。然后,我们应用于费城神经发育队列(PNC)的真实数据分析。其中包括855名8-22岁的人分为五个不同的青少年阶段。我们的网络分析揭示了与情绪相关的内部和模块间连通性的发展,并确定了几个与情绪相关的集线器。我们将枢纽进一步归类为两种类型:内枢纽和外线,是接收和分发信息的中心。还发现了几种独特的发展中心结构和特定组的模式。我们的发现有助于提供对人脑情绪发展的因果理解。
Emotion perception is essential to affective and cognitive development which involves distributed brain circuits. The ability of emotion identification begins in infancy and continues to develop throughout childhood and adolescence. Understanding the development of brain's emotion circuitry may help us explain the emotional changes observed during adolescence. Our previous study delineated the trajectory of brain functional connectivity (FC) from late childhood to early adulthood during emotion identification tasks. In this work, we endeavour to deepen our understanding from association to causation. We proposed a Bayesian incorporated linear non-Gaussian acyclic model (BiLiNGAM), which incorporated our previous association model into the prior estimation pipeline. In particular, it can jointly estimate multiple directed acyclic graphs (DAGs) for multiple age groups at different developmental stages. Simulation results indicated more stable and accurate performance over various settings, especially when the sample size was small (high-dimensional cases). We then applied to the analysis of real data from the Philadelphia Neurodevelopmental Cohort (PNC). This included 855 individuals aged 8-22 years who were divided into five different adolescent stages. Our network analysis revealed the development of emotion-related intra- and inter- modular connectivity and pinpointed several emotion-related hubs. We further categorized the hubs into two types: in-hubs and out-hubs, as the center of receiving and distributing information. Several unique developmental hub structures and group-specific patterns were also discovered. Our findings help provide a causal understanding of emotion development in the human brain.