Paper Info
Title
Dynamic neural network approach to human emotion: an analysis based on sliding time windows
Abstract
Emotion is a key motivational factor of a person strivings for health and well-being. Understanding neural networks supporting different types of emotion bears far-reaching implications for mental health. Recent studies suggest that emotional processing is associated with a large number of brain regions. However, the precise functional connectivity (FC) of these regions in investigations of emotional processing are largely unknown. To address this issue, we recruited 359 participants who completed emotional-related measures including the Positive and Negative Affect Schedule (PANAS) the Self-Compassion Scale, while scanned with resting-state functional magnetic resonance images (fMRI). Here, we proposed a novel psychological characteristics analysis framework by using a dynamic sliding window method to characterize the nature of resting-state functional connectivity in the human brain, in relation to the static FC method. The comparison results showed that the dynamic FC method produced the better performance, compared to the static FC method. The global network analyses across all 6 possible connectivity matrices further demonstrated that the dynamically hemispheric asymmetry best predicted emotional processing. The dynamic FC method was evaluated on the three emotional labels - positive emotion, negative emotion, self-compassion and the best prediction performance was consistently observed in the dynamically hemispheric asymmetric FC.
Year
DOI
Venue
2021
10.1109/ICCICC53683.2021.9811310
2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)
Keywords
DocType
ISBN
neural network,sliding time windows,emotion,human brain,resting-state fMRI
Conference
978-1-6654-2120-1
Citations 
PageRank 
References 
0
0.34
3
Authors
6
Name
Order
Citations
PageRank
Jingqi Wang100.34
Gen Shi200.34
Ning Ma300.34
Yang Sun400.68
Xuesong Li594.33
Jie Sui610.68