Data-driven Network Neuroscience
Mathematics Department, and Computational and Data-Enabled Science and Engineering Program, University at Buffalo
In the past decade, the field of neuroscience has benefited from myriad experimental advances, allowing researchers to explore the brain at across multiple spatial and temporal scales with ever increasing resolution and sensitivity. Along with this wave of new information has come a need to develop novel methods and techniques for visualizing, quantifying, and comparing data across scales and modalities. Network theory - the art of mapping physical systems to mathematical graphs - provides an attractive methodology to study the brain, but also requires the development of novel techniques to identify network nodes and connections from large data sets. In this talk, I’ll discuss some of the different types of data sets that are studied in network neuroscience and how we are working to extract network topology from diverse modalities ranging from single neuron imaging to whole brain fMRI datasets. Finally, I’ll highlight why new training programs such as the Computational and Data-Enabled Science and Engineering Program at the University at Buffalo are crucial in order to provide the field of neuroscience with a new generation of researchers that are specifically trained with a skillset that allows them to work at the border of experiment and theory in order to quantify and compare large datasets and provide new insights into brain function.