A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence

• URL: http://arxiv.org/abs/2006.12618v1
• Date: Tue, 16 Jun 2020 21:35:12 GMT
• Title: A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence
• Authors: Aiying Zhang, Gemeng Zhang, Biao Cai, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun and Yu-Ping Wang
• Abstract summary: The ability of emotion identification begins in infancy and continues to develop throughout childhood and adolescence. Our previous study delineated the trajectory of brain functional connectivity (FC) from late childhood to early adulthood during emotion identification tasks. We proposed a Bayesian incorporated linear non-Gaussian acyclic model (BiLiNGAM), which incorporated our previous association model into the prior estimation pipeline.
• Score: 27.39669536270664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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