Ashkan Faghiri

Title: Representing the human brain data using networks that change with time

Time: Friday, March 10th, 3:00 PM
Location: CSIP library (room 5126), 5th floor, Centergy one building

Bio: Ashkan Faghiri is a postdoctoral fellow at the Center for Translational Research in Neuroimaging and Data Science (TReNDS). In his current position, he is working on developing new methodologies to analyze the collective behavior of the human brain. He got his PhD from the school of electrical and computer engineering at the Georgia Institute of Technology. He received his MS in electrical engineering from the Sharif university of technology in his home country, Iran.

Abstract: The human brain is a complex and dynamic system, and our many measurements of this organ can be used to capture these two properties. We often need to perform extensive processing on these measurements before we can analyze and discuss the complexity and dynamism of the human brain. One modeling/representation approach that has been used extensively to capture the complexity of the data is based on networks which enables us to construct connectivity between different parts of the brain. Using networks enables us to capture collective aspects of the human brain that cannot be deduced if we only look at different parts of the brain in isolation. In addition, to explore the dynamic aspect of the human brain, recently we have been using networks that change with time to model the data. There are many methods to construct time-resolved networks from the data, with sliding window Pearson correlation (SWPC) possibly being the most commonly used approach, partly because of its simplicity. In this talk, I discuss some of the issues with this method and introduce some new methodologies that try to remedy some of the shortcomings of SWPC. These include a method that replaces the windowing part of SWPC with a filter bank to enable us to capture the whole spectrum of the brain connectivity. Another method uses the idea behind single-sideband modulation to improve the estimation of the time resolved connectivity. Finally, I present a method that enables us to go beyond pairwise connectivity when constructing the network. Using this method, we can estimate connectivity between more than two nodes.