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Multimodal fusion for assessing functional segregation and integration in the human brain

University of British Columbia

Mental and neurological diseases account for a major portion of the global disease burden. Neuroimaging has greatly contributed to the characterization and understanding of such disorders by enabling noninvasive investigation of the human brain. Among neuroimaging technologies, magnetic resonance imaging (MRI) stands out as a relatively widespread and safe imaging modality that can be sensitized to capture different aspects of the brain. Historically, MRI studies have investigated anatomy or function of the brain in isolation, which created an apparent dichotomy. In this thesis, we aim to bridge this divide using novel multimodal techniques. In particular, we present techniques to reconcile information regarding anatomical and functional connectivity (AC and FC) in the brain estimated from diffusion MRI (dMRI) and functional MRI (fMRI) data, respectively. Our first contribution is to show that the consistency between AC and FC is understated when standard analysis methods are used. We illustrate how the estimation of AC can be improved to increase the AC-FC consistency, which facilitates a more meaningful fusion of these two types of information. Specifically, we propose to improve AC estimation by the use of a dictionary based super-resolution approach to increase the spatial resolution in dMRI, reconstructing the white matter tracts using global tractography instead of conventional streamline tractography, and quantifying AC using fiber count as the metric. Our second contribution is to develop novel multimodal approaches for investigating functional segregation and integration in the human brain. We show that task fMRI data can be fused with dMRI and resting state fMRI data to mitigate the effects of noise and deconfound the effects of spontaneous fluctuations in brain activity on activation detection. Further, we show that sensitivity in unraveling the modular structure of the brain can be increased by fusing dMRI and fMRI data. Our results collectively suggest that combining dMRI and fMRI data outperforms classical unimodal analyses in understanding the brain's organization, bringing us one step closer to understanding the most complex organ in the human body.

Text

2016-03-02

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/57075

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/