Computational Approaches for Mapping the Human Connectome

Seminar
Wednesday, March 07, 2018
12:00 PM to 1:00 PM
EER 1.518
Free and open to the public

The human connectome is a graph representation of the brain’s functional and structural architectures. In this graph, nodes represent brain areas and edges represent either anatomical connections (fibers) or synchrony between physiological measures of brain activity that occurred at those areas. This latter method has been called “functional connectivity” based on the assumption that the measured synchrony is a result of the implicated brain areas working together to perform some function. Estimating the functional interactions between brain areas and mapping them to corresponding inter-individual variability (i.e. age, sex, IQ, disease state, disease severity) are “big data” problems that require very large numbers of high quality datasets and the tools and resources to efficiently analyze them. In this talk, I will discuss some of the computational challenges inherent in brain connectivity research and discuss the tools and algorithms developed in the Computational Neuroimaging Lab (CNL) for addressing them. These include classifier and feature selection for identifying functional connectivity based biomarkers of disease, unsupervised identification of brain areas to be used as graph nodes, multivariate regression to decode ongoing brain activity, and brain computer interfaces for probing network dynamics. Additionally I will discuss CNL’s ongoing open science initiatives that include developing software pipelines for performing high capacity data processing and analysis, as well as, aggregating and sharing large-scale neuroimaging datasets. The overarching goal of these initiatives is to make tools and data accessible to a wider audience of data science researchers so as to accelerate the pace of discovery in neuroimaging neuroscience.

x x

Speaker

R. Cameron Craddock

Dell Medical School

R. Cameron Craddock, PhD, is a computer engineer who combines an extensive knowledge of MRI acquisition and analysis methods with computational sciences to research the impact of development and mental health disorders on brain function. He obtained his undergraduate and graduate degrees in the department of Electrical and Computer Engineering at Georgia Tech and completed post-doctoral fellowships at Baylor College of Medicine and the Virginia Tech Carilion Research Institute. Dr. Craddock spent 5 years in New York City working at the Child Mind Institute and Nathan S. Kline Institute for Psychiatric Research before joining the Department of Diagnostic Medicine in the Dell Medical School at The University of Texas at Austin in 2017.