Moo K. Chung is an Associate Professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin, Madison and is also affiliated with the Department of Statistics and Waisman Laboratory for Brain Imaging and Behavior. He has received the Vilas Associate Award for his research in applied topology to medical imaging, the Editor's Award for best paper published in the Journal of Speech, Language, and Hearing Research for a paper that analyzed CT images, and a National Institutes of Health (NIH) Brain Initiative Award for work on persistent homological brain network analysis. He has written numerous papers in computational neuroimaging and two previous books on computation on brain image analysis.
Inhaltsangabe
1. Statistical preliminary 2. Brain network nodes and edges 3. Graph theory 4. Correlation networks 5. Big brain network data 6. Network simulations 7. Persistent homology 8. Diffusion on graphs 9. Sparse networks 10. Brain network distances 11. Combinatorial inference for networks 12. Series expansion of connectivity matrices 13. Dynamic network models.
