A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain...

Area of Science:

• Neuroscience
• Developmental Psychology
• Computational Psychiatry

Background:

• Emotion perception is crucial for cognitive and affective development, involving complex brain circuits.
• Understanding the developmental trajectory of these emotion circuits is key to explaining adolescent emotional changes.

Purpose of the Study:

• To advance the understanding of emotion-related functional connectivity (FC) from association to causation.
• To develop and validate a novel computational model for analyzing brain network development.

Main Methods:

• Proposed a Bayesian incorporated linear non-Gaussian acyclic model (BiLiNGAM) integrating association models.
• Validated the BiLiNGAM model using fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) comprising 855 individuals (ages 8-22).
• Analyzed developmental changes in intra- and intermodular connectivity and identified emotion-related brain hubs.

Main Results:

• The BiLiNGAM model demonstrated stable and accurate performance, particularly with small sample sizes.
• Network analysis revealed significant developmental patterns in emotion-related connectivity across five adolescent stages.
• Identified and categorized key emotion-related brain hubs as 'in-hubs' and 'out-hubs', uncovering unique developmental structures and group-specific patterns.

Conclusions:

• The study provides a novel causal framework for analyzing emotion network development.
• Findings offer a directed functional connectivity template for brain organization underlying adolescent emotion processing.
• This research contributes to understanding the neural basis of emotional development during adolescence.