Human brain is one of the most complex system in nature and its topological mechanisms are the most intriguing questions being pursued in neuroscience. Imaging connectomics provide a new and powerful framework to map the brain’s structural and functional connectivity patterns and its relationship to cognition in normal and pathological conditions. The main goal of our research is to understand how the human brain networks or connectomes in vivo are topologically (dis)organized in health and disease. Specifically, we focus on the macro-level of brain networks using a variety of neuroimaging modalities, with the following directions:
1) Develop connectome construction and characterization methods based on advanced neuroimaging techniques and graph theory, validate the reliability and reproducibility, and elucidate the underlying microstructural and neurophysiological substrates;
2) Investigate connectome development from infancy to early childhood, in particular focusing on establishing age-related brain atlases and understanding network growth patterns and potential principles for connectome development;
3) Investigate connectome dysfunction in brain disorders such as ADHD, autism, and depression, in particular focusing on exploring network-level dysfunctional mechanism and establishing connectome-based imaging biomarker for disease diagnosis and treatment evaluation;
4) Develop graph-based brain network analysis and visualization tools as well as computing cluster platforms for imaging connectomics.
We particularly welcome students with backgrounds of computer science, electronic engineering, complex networks, physics, mathematics, or psychology to join our team.
Much of our research occurs at the IDG/McGovern Institute for Brain Research, Beijing Normal University, and at the National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University.