Head, Department of Psychiatry, University of Cambridge and GSK
From investigating data from experimental animals like worms and mice, to MRI data from humans, theoretical approaches to understand the network organization – or connectome – of the brain are vital if we are to understand how the brain behaves in health and disease.
By graph theoretical analysis of connectivity matrices derived from magnetic resonance imaging (MRI) it has been shown that human connectomes have a complex topology. Human brains are also parsimoniously “wired”, with a strong bias towards short physical distances between connected regions. Similar topological and parsimonious properties have been demonstrated in the worm, C. elegans, and mouse connectomes.
Research to understand the network phenotypes of brain connectivity is vital to understanding neurodevelopmental disorders, like schizophrenia. We need to understand how human brain networks develop normally in young people. For example, in adolescence, as changes in network properties are associated with the over-expression of schizophrenia risk genes during this period. It seems that network science is in a strong position to understand more completely both the nearly-universal principles and the specific biological details of connectomes, with implications for the future of brain network medicine.
Key Learning Objectives:
Introduction to graph theoretical methods for connectomics
Learning about about complex topology and relationship to anatomical constraints
Examples of work linking network topology to cognitive function, gene expression, clinical disorders and brain development