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Background <p>Emotion is a complex psychological phenomenon involving both arousal and valence. emotional processing (EP) refers to the ability to perceive and interpret emotional stimuli, such as facial expressions or vocal cues.</p> Methods <p>In this study, we investigated functional connectivity (FC), graph-theoretical network measures, and rich-club organization during EP using task-based functional magnetic resonance imaging (fMRI) data from 100 healthy participants from the Human Connectome Project (HCP). Mean time series were extracted from 264 regions of interest (ROIs) defined by the Power Atlas, encompassing 10 large-scale functional networks. Pairwise Pearson correlation coefficients were computed to generate FC matrices, which were then thresholded using the orthogonal minimal spanning tree (OMST) method to form adjacency matrices. These matrices served as the basis for calculating global and local network metrics and analyzing rich-club organization. Permutation-based paired t-tests (<i>p</i> &lt; 0.05, 1000 permutations) and family wise error (FWE)-corrected were used to identify significant differences between face and shape conditions.</p> Results <p>Our findings indicate a significant modulation of neural activity between the face and shape conditions during facial EP. Significant differences in FC, network metrics, and rich-club organization were observed at both ROI and network levels. High-level cognitive networks exhibited stronger positive correlations, whereas low-level perceptual networks showed increased anticorrelations. Global network measures, including modularity, mean local efficiency, and clustering coefficient, were increased, indicating enhanced functional segregation. Simultaneously, selective rich-club connectivity among hubs in dorsal attention, frontoparietal, visual, somatomotor, and subcortical networks suggests preserved network integration at the mesoscale level. These findings uniquely combine rich-club organization with graph-theoretical measures across 10 large-scale networks, providing novel insights into hub coordination under emotional demands.</p> Conclusions <p>EP induces reorganization of brain networks, enhancing functional specialization while selectively modulating hub regions to maintain efficient integration. These results offer deeper insight into neural mechanisms underlying emotional cognition and may help explain connectivity alterations in emotional and affective disorders.</p>

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Rich-club organization and functional brain network metrics during facial emotional processing: a task-based fMRI study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月16日

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  • Maryam Gholam Tamimi,
  • Mohammad Reza Daliri

摘要

Background

Emotion is a complex psychological phenomenon involving both arousal and valence. emotional processing (EP) refers to the ability to perceive and interpret emotional stimuli, such as facial expressions or vocal cues.

Methods

In this study, we investigated functional connectivity (FC), graph-theoretical network measures, and rich-club organization during EP using task-based functional magnetic resonance imaging (fMRI) data from 100 healthy participants from the Human Connectome Project (HCP). Mean time series were extracted from 264 regions of interest (ROIs) defined by the Power Atlas, encompassing 10 large-scale functional networks. Pairwise Pearson correlation coefficients were computed to generate FC matrices, which were then thresholded using the orthogonal minimal spanning tree (OMST) method to form adjacency matrices. These matrices served as the basis for calculating global and local network metrics and analyzing rich-club organization. Permutation-based paired t-tests (p < 0.05, 1000 permutations) and family wise error (FWE)-corrected were used to identify significant differences between face and shape conditions.

Results

Our findings indicate a significant modulation of neural activity between the face and shape conditions during facial EP. Significant differences in FC, network metrics, and rich-club organization were observed at both ROI and network levels. High-level cognitive networks exhibited stronger positive correlations, whereas low-level perceptual networks showed increased anticorrelations. Global network measures, including modularity, mean local efficiency, and clustering coefficient, were increased, indicating enhanced functional segregation. Simultaneously, selective rich-club connectivity among hubs in dorsal attention, frontoparietal, visual, somatomotor, and subcortical networks suggests preserved network integration at the mesoscale level. These findings uniquely combine rich-club organization with graph-theoretical measures across 10 large-scale networks, providing novel insights into hub coordination under emotional demands.

Conclusions

EP induces reorganization of brain networks, enhancing functional specialization while selectively modulating hub regions to maintain efficient integration. These results offer deeper insight into neural mechanisms underlying emotional cognition and may help explain connectivity alterations in emotional and affective disorders.