Identifying the Hierarchical Emotional Areas in the Human Brain Through Information Fusion

TL;DR

This paper proposes a novel method using multi-source information fusion and graph machine learning to identify hierarchical emotional areas in the human brain, advancing understanding of emotion processing.

Contribution

It introduces a new approach that models interactions among multiple brain regions, overcoming traditional pairwise analysis limitations, to reveal hierarchical emotional areas.

Findings

Hierarchical emotional areas facilitate emotion perception and psychological operations.

Identified brain regions correspond to different levels of emotional processing.

Results support the psychological constructionist hypothesis of emotion.

Abstract

The brain basis of emotion has consistently received widespread attention, attracting a large number of studies to explore this cutting-edge topic. However, the methods employed in these studies typically only model the pairwise relationship between two brain regions, while neglecting the interactions and information fusion among multiple brain regions$\unicode{x2014}$one of the key ideas of the psychological constructionist hypothesis. To overcome the limitations of traditional methods, this study provides an in-depth theoretical analysis of how to maximize interactions and information fusion among brain regions. Building on the results of this analysis, we propose to identify the hierarchical emotional areas in the human brain through multi-source information fusion and graph machine learning methods. Comprehensive experiments reveal that the identified hierarchical emotional areas, from lower to higher levels, primarily facilitate the fundamental process of emotion perception, the construction of basic psychological operations, and the coordination and integration of these operations. Overall, our findings provide unique insights into the brain mechanisms underlying specific emotions based on the psychological constructionist hypothesis.