Hong Kong Baptist University (HKBU) Research Cluster on Data Analytics and Artificial Intelligence in X

Complex cortical wave propagation: analysis and modeling
Principal Investigatgor: Prof. Changsong ZHOU ( Department of Physics )
Co-Investigatgor: Prof. Thomas Knopfel ( Department of Medicine, Imperial College London, UK )

Investigating complex neural dynamical patterns and the organizing mechanisms are crucial both for understanding brain functioning and disorders and developing brain-inspired new paradigms in computing and machine learning and other applications of neural dynamics. Oscillations are ubiquitous in neural activity. During sleep or anesthesia and also quiet wakefulness, cerebral cortex is dominated by apparently synchronized large-scale slow oscillations alternating between periods of neuronal firing (Up states) and periods of silence (Down states). However, due to the limited spatial resolution of EEG and the limited spatial coverage by LFP array, a comprehensive characterization and understanding of the dynamical nature of cortical slow waves and their formation mechanisms on the underlying complex network is still elusive.

This collaborative project between experimental and computational groups aims to address these important questions by large-scale data analysis and dynamical modeling. We will systematically investigate the emergence of complex waves in different brain states of mice based on unique high resolution whole-cortex voltage imaging data from collaborator Prof. Knöpfel (Imperial College London). By applying the phase velocity field methods combining optical flow methods in computer vision and turbulence theories in complex physical systems, we will analyze different types of wave patterns (e.g., plane waves, synchrony, source, sink, saddle (spiral) waves), the switching dynamics among them, the relationship of wave evolution paths with the large-scale connectivity properties and the dependence of the wave patterns on brain states from anesthesia to wakefulness. We will then develop dynamical neural network models by incorporating recently established brain connectome to elucidate the mechanisms underlying the emergence of complex waves and their propagation dynamics.


  • Develop methods of pattern formation to analyze the emergence of different types of complex wave patterns, including plane waves, synchrony, sink, source, spiral waves and saddle patterns, and the switching and interaction dynamics among the different pattern types and the wave propagation paths over the whole cortex.
  • Study the relationship between the complex wave patterns, phase singularities and the propagation paths with the underlying brain cortical networks properties, for example, the initiation of waves by hubs and the propagation within and between network modules. Investigate how the wave properties change with the brain states from anesthesia to wakefulness.
  • Build coarse-grained excitable model of large-scale cortical network based on experimentally observed excitable features of local circuits and elucidate the mechanism underlying the emergence and propagation of waves and the formation of large-scale slow oscillations in the brain network in the anesthesia state.
  • Build neuronal network model to study how the mechanism of wave propagation features depends on the excitation-inhibition interaction in neural circuit due to the modulations by anesthesia.

Grant Support:

This project is supported by the National Science Foundation of China (Project 11975194).

For further information on this research topic, please contact Prof. Changsong ZHOU.