Human brain is one of the most complex system in nature and its working mechanisms are the most intriguing questions being pursued in science. Imaging connectomics provide a new and powerful framework to map the brain 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 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 the construction and characterization methods of large-scale brain networks based on multiple imaging techniques and graph theory, validate their reliability and reproducibility, and elucidate their underlying microstructural and neurophysiological substrates;
2) Study the formation, degeneration, dysfunction and modulation principles of large-scale brain networks using normal development, aging and brain disorders as experimental models, and establish connectome-based imaging biomarkers for disease diagnosis and treatment evaluation in neuropsychiatric disorders such as ADHD and depression;
3) Develop graph-based brain network analysis and visualization tools as well as computing cluster platform 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.