Open Collections

ROI-based brain functional connectivity using fMRI : regional signal representation, modelling and analysis

University of British Columbia

Inferring brain functional connectivity from functional magnetic resonance imaging (fMRI) data extends our understanding of systems-level functional organization of the brain. Functional connectivity can be assessed at the individual voxel or Regions of Interest (ROI) level, with pros and cons of each approach. This thesis focuses on addressing fundamental problems associated with ROI-based brain functional connectivity inference, including regional signal representation, brain functional connectivity modelling and brain functional connectivity analysis. Functional connectivity involving brainstem ROIs has been rarely studied. We propose a novel framework for brainstem-cortical functional connectivity modelling where the regional signal of brainstem nuclei is estimated by Partial Least Squares and connections between brainstem nuclei and other cortical/subcortical brain regions are reliably estimated by partial correlation. We then apply the proposed framework to assess functional connectivity of one particular brainstem nucleus - the pedunculopontine nucleus (PPN), which is important for ambulation, and is affected in diseases putting people at risk for falls (e.g., Parkinson’s Disease). A key issue for ROI-based brain functional connectivity assessment is how to summarize the information contained in the voxels of a given ROI. Currently, the signals from the same ROI voxels are simply averaged, neglecting any inhomogeneity in each ROI and assuming that the same voxels will interact with different ROIs in a similar manner. In this thesis, we develop a novel method of representing ROI activity and estimating brain functional connectivity that takes the regionally-specific nature of brain activity, the spatial location of concentrated activity, and activity in other ROIs into account. Finally, to facilitate the interpretation of the estimated brain functional connectivity networks, we propose the use of dynamic graph theoretical measures (e.g., the newly introduced graph spectral metric, Fiedler value) as potential MRI-related biomarkers. The proposed methods were applied to real fMRI datasets, with a primary focus on Parkinson’s disease. The proposed methods demonstrated enhanced robustness of brain functional connection estimation, with potential use in disease assessment and treatment evaluation. More broadly, this thesis suggests that brain functional connectivity offers a promising avenue for non-invasive and quantitative assessment of neurological diseases.

Text

2019-12-18

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/72848

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/