Multiscale Integration of Brain Imaging and Physiological Signals: Network and AI-driven Approaches

Recent advances in physiological monitoring, neuroimaging , wearable technologies, and artificial intelligence have created unprecedented opportunities to investigate interactions among (neuro)physiological systems across multiple spatial and temporal scales. However, integrating heterogeneous data sources remains a major challenge.This Research Topic focuses on network- and AI-driven approaches for integrating multimodal brain imaging and physiological signals. We welcome contributions spanning neuroimaging, electrophysiology, cardiovascular and autonomic physiology, wearable sensing, multimodal data fusion, graph learning, multilayer networks, and machine learning. The goal is to advance our understanding of how complex physiological interactions give rise to function, adaptation, and disease, while promoting new computational frameworks and biomarkers for precision medicine.We welcome original research, methods, reviews, and perspectives on themes including, but not limited to:• Multimodal integration of neuroimaging with electrophysiological, cardiovascular, autonomic, and behavioral signals• Wearable and mobile sensing for continuous, multi-system monitoring• Multimodal data fusion across spatial and temporal scales• Graph learning, multilayer and higher-order networks for biological and physiological data• Machine learning approaches for modelling cross-system interactions and directionality• Network-based and multimodal biomarkers of health, ageing, adaptation, and disease Topic editors: Vesna Vuksanovic, Jordi Solé-Casals, Mirjana M. Platiša Source: https://www.frontiersin.org/research-topics/84496/multiscale-integration-of-brain-imaging-and-physiological-signals-network-and-ai-driven-approaches