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dc.contributor.authorHonnedevasthana Arun, Arush
dc.date.accessioned2021-01-15T09:15:17Z
dc.date.available2021-01-15T09:15:17Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/11343/258710
dc.description© 2020 Arush Honnedevasthana Arun
dc.description.abstractMagnetic resonance imaging (MRI) has revolutionized the way to investigate brain structural connectivity non-invasively. Diffusion MRI can be used to obtain local estimates of the white matter fibre orientations in the brain, which in turn can be used to study changes in the local fibre specific properties and/or in conjunction with fiber-tracking algorithm to reconstruct a representation of the white matter pathways in the brain. In recent years, the Diffusion Tensor model has played an important role in modelling the diffusion of water within white matter bundles. Diffusion tensor derived metrics such as fractional anisotropy (FA) have been used extensively for investigating white matter using approaches such as voxel-based analysis. One of the limitations of the diffusion tensor model is that it is not capable of appropriately modelling regions that have complex fibre architecture (such as crossing fibres). This makes tensor-derived measures unreliable measures to assess the white matter. Recent contributions toward the study of brain asymmetry have suggested asymmetry of brain anatomy and function are observed in the temporal, frontal, and parietal lobes. Several studies have used diffusion tensor model to study asymmetry in various regions of the human brain white matter. However, given the limitations of the tensor model, the nature of any underlying asymmetries remains uncertain. This research aims to provide to provide a more robust characterization of structural white matter asymmetries than those previously derived using the tensor model, by using quantitative measures derived from the spherical deconvolution model, and a whole-brain data-driven statistical inference framework such as Fixel-Based Analysis, that is both sensitive and specific to crossing fibres; we furthermore apply this approach to a state-of-the-art publicly available diffusion MRI dataset.
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dc.subjectdiffusion MRI
dc.subjectMRI
dc.subjectAsymmetry
dc.subjectHealthy human brain
dc.titleCharacterization of white matter asymmetries in the healthy human brain using Diffusion MRI fixel-based analysis
dc.typeMasters Research thesis
melbourne.affiliation.departmentFlorey Department of Neuroscience and Mental Health
melbourne.affiliation.facultyMedicine, Dentistry & Health Sciences
melbourne.thesis.supervisornameFernando Calamante
melbourne.contributor.authorHonnedevasthana Arun, Arush
melbourne.thesis.supervisorothernameRobert Smith
melbourne.tes.fieldofresearch1460306 Image processing
melbourne.tes.fieldofresearch2319999 Other biological sciences not elsewhere classified
melbourne.tes.fieldofresearch3320903 Central nervous system
melbourne.accessrightsOpen Access


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