BME PhD Thesis Defense: Understanding Whole Brain Activity through Brain Network Models
Date: Nov 4th 2020
Time: 12-1pm
Link: https://gatech.bluejeans.com/8078952945
Meeting Info: No Password
Advisor:
Dr. Shella Keilholz
Department of Biomedical Engineering
Georgia Institute of Technology and Emory University
Committee Members:
Dr. Lena Ting
Department of Biomedical Engineering
Georgia Institute of Technology and Emory University
Dr. Christopher Rozell
Department of Electrical Engineering
Georgia Institute of Technology
Dr. Madeleine Hackney
Department of Medicine
Emory University
Dr. Bruce Crosson
Department of Neurology
Emory University
Dr. Sergey Plis
School of Computer Science
Georgia State University
Title: Understanding Whole Brain Activity through Brain Network Models
Abstract:
The central nervous system coordinates many neural subpopulations connected via macroscale white matter architecture and surface cortical connections in order to produce complex behavior depending on environmental cues. The activity occurs over many different scales, from the information transfer between individual neurons at the synapse level, to macroscale coordination of neural populations used to maximize information transfer between specialized brain regions. The whole brain activity measured through functional Magnetic Resonance Imaging (fMRI), allows us to observe how these large neural populations over time. Researchers have recently developed a set of Brain Network models (BNMs), that have simulated brain activity using the macroscale white matter structure and different models for activity in local neural populations. These simulations have been able to reproduce properties of brain signals measured via fMRI especially those averaged over long periods of time. This has generated a lot of interest, because these models can be constructed from individual estimates of the structural network and are perhaps a step towards an individualized models of brain activity and would have useful clinical implications. To find a good BNM to fit the individual fMRI data, however, is a difficult problem as BNMs represent a large family of mathematical models. Moreover, a large set of BNMs have reproduced time averaged metrics that have been used so far to compare the models with the fMRI data. In this thesis, we extend previous work on BNM research by establishing new dynamic metrics that would allow us to better differentiate between BNMs simulations on how well they reproduce measured fMRI dynamics (Chapter 2). In Chapter 3, we directly compare transient short-term trajectories by synchronizing the outputs of a BNM in relation to observed fMRI timeseries using a novel Machine Learning Algorithm, Neural Ordinary Differential Equations (ODE). Finally, we show that the Neural ODE can be used as its own stand-alone generative model and is able to simulate the most realistic fMRI signals so far (Chapter 4). In short, we demonstrate that we have made progress in developing and quantifying BNMs and advanced the research of more realistic whole brain simulations.
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