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<p>Emotion is present in all aspects of human life and serves as a crucial foundation for communication and interaction. Emotional processing (EP) is a complex phenomenon involving dynamic interactions among various brain regions. Despite significant progress in EP research, important challenges remain—particularly in understanding the temporal dynamics of emotion. In this study, we investigated alterations in dynamic functional connectivity (dFC) patterns during an emotional processing task, using fMRI data from 100 healthy participants in the Human Connectome Project (HCP). The brain was parcellated into 90 regions of interest (ROIs) and grouped into six networks and ten well-known brain regions using the AAL atlas. We applied dFC analysis based on sliding window correlation (SWC) and k-means clustering to identify discrete connectivity states. To define the optimum number of states, we employed non-supervised validity criteria silhouette measure. Additionally, we estimated mean dwell times and transition probability matrices between states in both face and shape conditions using a hidden Markov model (HMM). Within these states, we observed state-dependent alterations in within and between regional connectivity between the face and shape conditions. Our findings revealed three distinct dFC states and among them, dFC state with the most significant differences in probability of transitions included brain regions involved in, frontoparietal, limbic and visual networks. Across all three states, several key bilateral regions exhibited significant changes in dFC, involved in limbic (amygdala, hippocampus, parahippocampal and rectus), default mode (anterior cingulate gyrus, median cingulate gyrus, posterior cingulate gyrus and angular), frontoparietal (inferior parietal gyrus, superior parietal gyrus, and middle frontal gyrus), visual (inferior occipital gyrus, fusiform, cuneus, precuneus, lingual and calcarine), temporal-parietal (paracentral lobule, precentral, postcentral, superior temporal gyrus, temporal pole superior and insula), and subcortical (caudate, putamen, pallidum and thalamus) networks. Also, we identified three dFC states between ten brain regions —frontal-central-parietal, frontal-temporal-occipital, and global state.</p>

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

  • 期刊论文

  • 发表日期：

    2025年9月24日

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  • Maryam Gholam Tamimi,
  • Mohammad Reza Daliri

摘要

Emotion is present in all aspects of human life and serves as a crucial foundation for communication and interaction. Emotional processing (EP) is a complex phenomenon involving dynamic interactions among various brain regions. Despite significant progress in EP research, important challenges remain—particularly in understanding the temporal dynamics of emotion. In this study, we investigated alterations in dynamic functional connectivity (dFC) patterns during an emotional processing task, using fMRI data from 100 healthy participants in the Human Connectome Project (HCP). The brain was parcellated into 90 regions of interest (ROIs) and grouped into six networks and ten well-known brain regions using the AAL atlas. We applied dFC analysis based on sliding window correlation (SWC) and k-means clustering to identify discrete connectivity states. To define the optimum number of states, we employed non-supervised validity criteria silhouette measure. Additionally, we estimated mean dwell times and transition probability matrices between states in both face and shape conditions using a hidden Markov model (HMM). Within these states, we observed state-dependent alterations in within and between regional connectivity between the face and shape conditions. Our findings revealed three distinct dFC states and among them, dFC state with the most significant differences in probability of transitions included brain regions involved in, frontoparietal, limbic and visual networks. Across all three states, several key bilateral regions exhibited significant changes in dFC, involved in limbic (amygdala, hippocampus, parahippocampal and rectus), default mode (anterior cingulate gyrus, median cingulate gyrus, posterior cingulate gyrus and angular), frontoparietal (inferior parietal gyrus, superior parietal gyrus, and middle frontal gyrus), visual (inferior occipital gyrus, fusiform, cuneus, precuneus, lingual and calcarine), temporal-parietal (paracentral lobule, precentral, postcentral, superior temporal gyrus, temporal pole superior and insula), and subcortical (caudate, putamen, pallidum and thalamus) networks. Also, we identified three dFC states between ten brain regions —frontal-central-parietal, frontal-temporal-occipital, and global state.