▶ Structure-function coupling
Connectivity analysis is a representative approach for quantifying the strength of interconnections among brain regions or networks. Given the close relationship between brain structure and function, investigating structure–function coupling using multimodal connectivity features is a key area of research. We develop novel methods for integrating structural and functional brain information and explore their underlying biological mechanisms.
▶ Connectome gradients
The connectome gradient is a low-dimensional representation of connectivity data. By leveraging manifold learning techniques, we project high-dimensional connectivity data into lower-dimensional spaces to capture the principal axes of whole-brain organization. Providing a novel and continuous reference frame for studying macroscale connectivity, these techniques enable effective visualization of the principles underlying cortical organization.
▶ Functional dynamics
The human brain is an inherently dynamic system that undergoes spontaneous fluctuations in activity over time. To capture these dynamic transitions in brain function, we employ methods such as the sliding window approach, hidden Markov models, and anomaly detection techniques. Our goal is to provide a more nuanced understanding of how brain networks interact in response to cognitive and behavioral demands.
