July 1-3, 2020
The brain is increasingly understood as a complex network, composed of nerve cells and brain areas, linked by a dense web of connections and interacting in dynamic patterns that underpin behavior and cognition. This course provides an introduction to the foundations and current topics in “network neuroscience,” an emerging field that studies the structure and dynamics of brain networks. The course covers fundamentals of neuroscience that are relevant to network approaches, including anatomical to functional connectivity constructed with a variety of measurement techniques and across many scales, from neurons to whole-brain systems. Emphasis will be on highlighting how network science has helped us understand the architecture of brain networks, for example, by discussing the role of geometry, structure-function relationships, network growth and generative models, network control, dynamic networks, and communication processes. Additional topics address current applications of network models to describe and model changes in brain structure and function during development and aging, in health and disease.
University of Cambridge
Students should be familiar with basic network terminology and have a basic level of mathematical and programming proficiency, such as the ability to navigate and interact with large data sets in Matlab. Background in basic neuroscience (anatomy, physiology, imaging, cognition and behavior) is a plus.
This course will be largely conceptual, emphasizing the nature of neurobiological data and the ability of network models to provide biological insight. Throughout the course, we will provide hands-on examples and experience with analyzing open-access brain network data. The computational portion of the course will mainly rely on in-class demonstrations, as well as exercises and a set of brief projects based on data sets provided in class.
Students should have a laptop with Matlab installed.
About the instructors
After receiving an undergraduate degree in biochemistry, Olaf Sporns earned a PhD in Neuroscience at Rockefeller University and then conducted postdoctoral work at The Neurosciences Institute in New York and San Diego. Currently he is the Robert H. Shaffer Chair, a Distinguished Professor, and a Provost Professor in the Department of Psychological and Brain Sciences at Indiana University in Bloomington. Sporns holds adjunct appointments in the School of Informatics, Computing and Engineering, and in the School of Medicine. In addition to over 240 peer-reviewed publications, he is the author of two books, “Networks of the Brain” and “Discovering the Human Connectome.” He is the Founding Editor of “Network Neuroscience”, a journal published by MIT Press. Sporns was awarded a John Simon Guggenheim Memorial Fellowship in 2011, elected Fellow of the American Association for the Advancement of Science in 2013, and received the Patrick Suppes Prize in Psychology/Neuroscience, awarded by the American Philosophical Society in 2017.
His main research area is theoretical and computational neuroscience, with a focus on complex brain networks. His work is focused on the study of brain connectivity and networks (connectomics), including patterns of anatomical projections, and the role of connectivity in shaping brain dynamics and function. He addresses these problems through analysis and modeling of empirical data derived from a variety of animal models as well as noninvasive imaging of the human brain. Using a broad set of tools and techniques from the area of complex systems and networks, he aims to identify organizing principles that underlie the brain’s structural and functional organization. Sporns applies these computational and network techniques to data and problems in the areas of brain development, comparative anatomy and evolution, cognitive performance, and in applications to various clinical disorders.
Petra Vértes’ research applies tools from physics, engineering and network science to fundamental problems in neuroscience and mental health. In particular, she is interested in the structure-function relationship in brain networks, from the microscopic scale of neurons to the large-scale connectivity of brain regions, in both health and disease. Insights into these questions are not only fascinating in their own right but have important implications for our understanding and therapeutic approaches to cognitive impairments associated with psychiatric disorders, brain injury and ageing.
In addition to human brain networks derived from neuroimaging Vértes works with a wide variety of multivariate data, from microarray to protein interaction networks, flow cytometry and data from wearable devices. She also studies simpler organisms, such as C. elegans, which provide a testbed for methodological innovations as well as insights into generalisable aspects of brain organisation, brain development, network dysfunction and repair.
Petra Vértes leads the Systems and Computational Neuroscience group in the Department of Psychiatry at the University of Cambridge. She is also a fellow at the Alan Turing Institute, the UK’s national institute for data science and machine learning. She received a master’s degree in theoretical physics and a PhD in artificial neural networks from the University of Cambridge. She is the recipient of multiple fellowships in bioinformatics and mental health, as well as the Foreign Policy magazine’s prize for Top 100 Global Thinkers in 2016. She is also one of the co-founders and organizers of the Cambridge Networks Network (CNN), a forum for academics across different disciplines who share an interest in Network Science.