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Storytelling-an ancient way for humans to share individual experiences with others-has been found to induce neural alignment among listeners. In exploring the dynamic fluctuations in listener-listener (LL) coupling throughout stories, we uncover a significant correlation between LL coupling and lagged speaker-listener (lag-SL) coupling over time. Using the analogy of neural pattern (dis)similarity as distances between participants, we term this phenomenon the "herding effect." Like a shepherd guiding a group of sheep, the more closely listeners mirror the speaker's preceding brain activity patterns (higher lag-SL similarity), the more tightly they cluster (higher LL similarity). This herding effect is particularly pronounced in brain regions where neural alignment among listeners tracks with moment-by-moment behavioral ratings of narrative content engagement. By integrating LL and SL neural coupling, this study reveals a dynamic, multibrain functional network between the speaker and the audience, with the unfolding narrative content playing a mediating role in network configuration.
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http://dx.doi.org/10.1093/scan/nsae059 | DOI Listing |
Disabil Rehabil Assist Technol
September 2025
School of Drama, Film and Television, Shenyang Conservatory of Music, Shenyang, China.
This study examines how choral singing functions as a mechanism for sustaining ritual practice and reinforcing cultural identity. By integrating perspectives from musicology, social psychology, and cognitive science, it explores how collective vocal performance supports emotional attunement, group cohesion, and symbolic memory in culturally diverse contexts. A mixed-methods approach was applied, combining ethnographic observation, survey-based data, and cognitive measures with AI-informed frameworks such as voice emotion recognition and neural synchrony modeling.
View Article and Find Full Text PDFMol Omics
September 2025
Laboratory of Structural Bioinformatics and Computational Biology, Federal University of Rio Grande do Sul, Av. Bento Gonçalves, 9500, Porto Alegre 91501-970, RS, Brazil.
The integration of multimodal single-cell omics data is a state-of-art strategy for deciphering cellular heterogeneity and gene regulatory mechanisms. Recent advances in single-cell technologies have enabled the comprehensive characterization of cellular states and their interactions. However, integrating these high-dimensional and heterogeneous datasets poses significant computational challenges, including batch effects, sparsity, and modality alignment.
View Article and Find Full Text PDFJ Biomater Sci Polym Ed
September 2025
Department of Bioengineering, Faculty of Chemical and Metallurgical Engineering, Yildiz Technical University, Turkey.
Biodegradable biosensors represent a transformative advancement in sustainable sensing technology, offering an environmentally friendly and biocompatible alternative to traditional sensors. This review examines recent advancements, material innovations, degradation mechanisms, and application areas of biodegradable biosensors within the biomedical and environmental sectors. Natural and synthetic biodegradable polymers, such as chitosan, silk fibroin, alginate, PLA, PLGA, and PVA, are assessed for their functional contributions to sensing platforms.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
September 2025
In multiagent systems, learning optimal behavior policies for individual agents remains a challenging yet crucial task. While recent research has made strides in this area, the issue of when agents should maintain consistent behaviors with one another is still not adequately addressed. This article proposes a novel approach to enable agents to autonomously decide whether their behaviors should align with those of their peers by leveraging intrinsic rewards to optimize their policies.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
The tumor microenvironment is a dynamic eco system where cellular interactions drive cancer progression. However, inferring cell-cell communication from non-spatial scRNA-seq data remains challenging due to incomplete li gand-receptor databases and noisy cell type annotations. H ere, we propose scGraphDap, a graph neural network frame work that integrates functional state pseudo-labels and graph structure learning to improve both cell type annotation an d CCC inference.
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