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Inferring Macroscopic Brain Connectomes via Group-Sparse Factorization
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- Author / Creator
- Farzane Aminmansour
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Mapping the macrostructural connectivity of the living human brain is one of the primary goals of neuroscientists who study connectomics. The reconstruction of a brain's structural connectivity, aka its connectome, typically involves applying expert analysis to diffusion-weighted magnetic resonance imaging (dMRI). A data-driven approach -- inferring the underlying model from data -- could overcome the limitations of such human-based approaches and improve precision mappings for a novel brain. In this work, we explore a framework that facilitates applying learning algorithms to automatically extract brain connectomes. Using a tensor encoding to unify the representation of brain structure and diffusion information, we design a constrained objective function with a group-regularizer that prefers a biologically plausible structure for each bundle of neuronal axons, called a fascicle. We show that the objective is convex and has a unique solution, ensuring identifiable connectomes for an individual brain. We develop an efficient optimization strategy for this extremely high-dimensional sparse problem, by reducing the number of parameters using a greedy algorithm, called GreedyOrientation, designed specifically for the problem. We show that GreedyOrientation significantly improves on a standard greedy algorithm, called Orthogonal Matching Pursuit. We confirm that our method works effectively by reconstructing structural connectivity of two major tracts. We conclude with an analysis of the solutions found by our method, showing it can accurately reconstruct the diffusion information while maintaining contiguous fascicles with smooth direction changes.
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- Graduation date
- Spring 2020
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- Type of Item
- Thesis
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- Degree
- Master of Science
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- License
- Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.