Designed for doctoral candidates in neuroscience and related fields, this course will show you how network theory is useful to analyse your data, giving you hands-on experience on neuroimaging modeling from sMRI, DWI, fMRI, PET, EEG and MEG data. Importantly the same principles can be applied to other types of data such as multi-omics or clinical variables. Participants will construct, visualize, and quantify brain networks and will be able to build their own software with graphical user interface just by changing a few lines of code.
After the course, the doctoral student shall have obtained a thorough knowledge about core concepts about network neuroscience. This includes to be able to: 1) create network models; 2) apply and interpret network measures calculated from models (centrality measures, shortest paths, community detection etc); 3) implement a network analysis and visualize the results; 4) show understanding about how network models have been applied within the neurosciences; 5) show understanding about how network models relate to theory; 6) apply recent developments within network neuroscience including multilayer connectivity and deep learning analyses of brain networks
Basic knowledge of brain imaging