Investigating the functional neural network dynamics of cognitive-affective action planning during threat processing

Area of Science:

• Neuroscience
• Cognitive Neuroscience
• Network Science

Background:

• Understanding the neural basis of social threat processing is crucial.
• Previous research has identified various brain regions involved in threat detection.
• The dynamic functional organization of intrinsic connectivity networks (ICNs) during social threat remains less understood.

Purpose of the Study:

• To investigate the functional organization of neural networks during social threat processing.
• To examine both local and global connectivity metrics within and between ICNs.
• To identify specific ICNs and network dynamics associated with threat perception.

Main Methods:

• Functional neuroimaging data from tasks involving threatening faces and screams.
• Graph theory metrics (participation coefficient, betweenness centrality, global efficiency, modularity) applied to a 412-node brain template.
• Mass univariate linear mixed effects models and multivariate support vector machine classification across 8 ICNs in 124 participants.

Main Results:

• A diffuse pattern of strengthened intra-network connectivity characterized social threat processing.
• Univariate analyses showed a significant involvement of the somatomotor network, suggesting motor preparation.
• Multivariate classification revealed diffuse participation from limbic, default, visual, attentional, control, and somatomotor ICNs.

Conclusions:

• Intra- and inter-network functional organization plays a key role in the response to social threat.
• Findings highlight the dynamic nature of brain networks during threat processing.
• Implications for understanding network perspectives in threat-related research.