Background Dance and music are two of the oldest and most universal forms of artistic expression, each requiring extensive training. Prolonged training is known to induce neuroplastic changes and enhance socio-emotional abilities such as empathy. The mirror neuron system (MNS) is associated with empathy; however, few studies systematically explored how dance and music training modulate the core empathy network, including subcortical regions within the limbic MNS. Method Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 25 dancers, 24 musicians and 39 demographically matched control group (both groups are healthy). Core regions within relevant brain networks, along with key subcortical components of limbic MNS, specifically the amygdala and basal ganglia, were defined as regions of interest (ROIs) to build a core empathy network. Graph-theoretical metrics, including within-network degree (WND) and between-network degree (BND), were calculated to evaluate connectivity patterns. These metrics were further correlated with empathy scores. Results Both dancers and musicians exhibited altered connectivity within the core empathy network. Dancers showed increased WND and BND between basal ganglia regions and the ventral attention network, whereas musicians demonstrated enhanced WND between the amygdala and the subcortical network. Notably, both groups exhibited reduced WND between the superior frontal gyrus and the sensorimotor network. The neural changes were correlated with Interpersonal Reactivity Index (IRI) empathy subscale scores, indicating training-related modulations in affective and cognitive empathy. Conclusion Dance and music training induce both distinct and overlapping patterns of neuroplasticity within the core empathy network that are associated with enhanced empathic ability. The findings support the potential of dance and music as systematic intervention programs.
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Abstract Predicting properties from coordinate–category data—sets of vectors paired with categorical information—is fundamental to computational science, manifesting in materials science as predicting formation energies or elastic moduli from crystal structures. While large language models (LLMs) have increasingly been...
Nawaf Alampara, Santiago Miret, Kevin Maik Jablonka· Nature Computational Science· 13 citations
PepPCBench enables a robust evaluation of PFNN-based methods and supports their continued development for peptide-protein structure prediction, and highlights the influence of peptide length, conformational flexibility, and training set similarity on prediction accuracy.
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