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This dissertation, Magnetic Resonance Imaging Investigation of Brain Networks by Shi, Cheng, 程实, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Brain operates on a network level. Magnetic resonance imaging (MRI) provides structural and functional images noninvasively with large field of view and at high spatial resolution and thus assumes an extremely valuable role in studying brain networks. The objectives of this doctoral work were to develop and apply novel MRI methods on human and rodent brains, for in vivo and global assessments of functional brain networks at resting and task-evoked states. Firstly, the feasibility of passband balanced steady-state free precession (bSSFP) imaging for distortion-free and high-resolution resting-state fMRI (rsfMRI) was investigated. Resting-state networks (RSNs) derived from bSSFP images were shown spatially and spectrally comparable to those derived from conventional gradient-echo echo-planar imaging (GE-EPI) with considerable intra- and inter-subject reproducibility. High-resolution bSSFP corresponded well to the anatomical images, with RSNs exquisitely co-localized to gray matter. Furthermore, RSNs at areas of severe susceptibility were proved accessible including human anterior prefrontal cortex and rat piriform cortex. These findings demonstrated for the first time that passband bSSFP approach can be a promising alternative to GE-EPI for rsfMRI. It offers distortion-free and high-resolution RSNs and is potentially suited for high field studies. Secondly, to examine the macrovascular contributions to the spatial and spectral prosperities of resting-state networks, spin-echo echo-planar imaging (SE-EPI) with moderate diffusion weighting (DW) was proposed for rsfMRI. SE and DW suppressed the extravascular and intravascular contributions from macrovessels respectively. Significantly lower functional connectivity strength was observed in the posterior cingulate cortex of the default mode network derived from DW SE-EPI data comparing to that derived from SE-EPI, suggesting a confounding role played by the intravascular component from large veins, whereas no significant spectral difference was detected. Therefore, the DW SEEPI approach for rsfMRI may assist in better identifying and interpreting largescale brain networks with future improvement in temporal resolution by acceleration techniques and in sensitivity at higher field. Thirdly, rsfMRI was performed to evaluate the intrinsic functional networks in the corresponding anatomical visual brain connections traced by Mn-enhanced MRI (MEMRI). Strengths of resting-state functional connectivity appeared to couple with structural connectivity in MEMRI, demonstrating the sensitivity of these structural and functional connectivity MRI techniques for assessing the neuroarchitecture, neurophysiology and structural-functional relationships in the visual brain in vivo.Fourthly, the hypothesis that a regional activation identified via general linear model analysis of fMRI data reflects the summation of multiple distinct networks that carry different functional purposes was tested. Overlapping frontoparietal networks engaged in a simple single-digit multiplication task were found and their functional roles were evaluated through independent components analysis and contributive source analysis. Future studies incorporating different arithmetic tasks and resting state will shed more light upon how brain accomplishes arithmetic and more complex tasks in general. Lastly, benefiting from higher SNR, better spatial and temporal resolution at higher fiel

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