State-base dynamic functional connectivity analysis of fMRI data during facial emotional processing

Area of Science:

• Neuroscience
• Cognitive Science
• Psychology

Background:

• Emotion is fundamental to human interaction and communication.
• Emotional processing (EP) involves complex brain network dynamics.
• Understanding the temporal dynamics of EP remains a significant research challenge.

Purpose of the Study:

• To investigate alterations in dynamic functional connectivity (dFC) during an emotional processing task.
• To identify distinct brain connectivity states and their temporal dynamics using functional magnetic resonance imaging (fMRI).
• To explore state-dependent changes in regional connectivity across different emotional conditions.

Main Methods:

• Utilized fMRI data from 100 healthy participants in the Human Connectome Project (HCP).
• Applied sliding window correlation (SWC) and k-means clustering for dFC analysis.
• Employed a hidden Markov model (HMM) to analyze transition probabilities and dwell times between identified connectivity states.

Main Results:

• Identified three distinct dFC states, with significant differences in transition probabilities involving frontoparietal, limbic, and visual networks.
• Observed state-dependent alterations in within- and between-network connectivity during face and shape conditions.
• Key bilateral regions across limbic, default mode, frontoparietal, visual, temporal-parietal, and subcortical networks showed significant dFC changes.

Conclusions:

• The study successfully identified distinct dynamic functional connectivity states during emotional processing.
• Findings highlight the temporal dynamics of brain networks, particularly frontoparietal, limbic, and visual systems, in emotional tasks.
• This research provides a novel framework for understanding the complex temporal organization of brain activity underlying emotional processing.