Brain Connectivity and Connectomics
Full course description
This course introduces the fields of human brain connectivity and connectomics. The human brain is one of the largest and most complex biological networks known to exist. It contains about 85 billion neurons each making on average ten thousand connections with other neurons. Today, the map or annotated graph of all connections in the brain is called the connectome and the emerging field of connectomics endeavours to measure and understand the connectome. It has become increasingly clear over a century of neuroscience endeavours since Ramon y Cajal that the particular organisation of brain connectivity plays a crucial role in enabling human abilities. Two general principles of this organisation became clear early on and remain important to this day: i) the multi-scale organization of brain connectivity (from macroscale white matter organization to microscale cortical circuits) and ii) the interplay between structure and function (with structure determining function and function driving structural plasticity). With recent advances in methods, neuroimaging investigations of human perception and cognition are increasingly interpreted in terms of connectivity, inter-areal interactions and cortical circuit computations. This course will discuss both structural connectivity and functional interactions, with an emphasis on the human brain, and how these can be measured and analysed in cognitive neuroscience experiments. The different spatial and temporal scales at which connectivity is organized will be treated in depth, with an emphasis on neuroanatomy of layered cortical circuits and the large scale organization of white matter fiber tracts.
Students are able to understand:
Structural connectivity, Functional connectivity, Effective connectivity, Resting state experiments and networks, Layers in the neocortex, Cytoarchitecture, Myeloarchitecture, Receptor architecture, Canonical cortical microcircuits, Cortical computation, Realistic neural network models, Diffusion MRI tractography and connectomics, Graph analysis, Connectivity analyses in fMRI and M/EEG, Independent Component Analysis , Granger causality, Dynamic Causal Modeling, Histology and microscopy, Tracer studies, Polarized Light Imaging, White matter organization, Myelination, White matter plasticity.