ArticleBehavioral and brain functions : BBF2026
Rich-club organization and functional brain network metrics during facial emotional processing: a task-based fMRI study.
Article in Behavioral and brain functions : BBF, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundEmotion 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.
methodsIn 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.
resultsOur 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.
conclusionsEP 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.