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In Vivo quantification of complex neurite configurations using magnetic resonance imaging

Tariq, Maira; (2018) In Vivo quantification of complex neurite configurations using magnetic resonance imaging. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Abstract

Axons and dendrites, collectively termed neurites, are part of the brain microstructure and are integral for normal brain function. Configurations of neurites like axonal fanning/bending or dendritic arbour complexity, which manifest as anisotropic orientation dispersion, are known to be very important during normal brain development and function. In vivo studies to characterise how these configurations change for normal development/ageing, function and pathology can help understand the precise changes in microstructure underlying these processes. This can facilitate advancement in the field of neuroscience and development of better diagnosis and prognosis of various brain diseases and disorders. This thesis concerns the development and evaluation of such an imaging technique, which characterises complex configurations of neurites in the human brain. The proposed technique presents a novel marker of neurite morphology, which specifically quantifies anisotropic orientation dispersion, using in vivo diffusion-weighted magnetic resonance imaging (DW-MRI). This is done using standard image acquisition, making the technique feasible for neuroscience and clinical applications. The work presented is the first to enable in vivo quantification of anisotropic orientation dispersion. The first part of the thesis involves the development of the technique to enable quantification of anisotropic orientation dispersion and establishing its in vivo feasibility. We call the proposed technique Bingham-NODDI. The proposed technique is based on an existing DW-MRI technique called neurite orientation dispersion and density imaging (NODDI), that provides useful indices of neurite morphology, but can not characterise anisotropic orientation dispersion. The in vivo feasibility of the proposed model is established by thoroughly evaluating the inherent accuracy and precision of its indices, using in silico and in vivo data. In the second part, the proposed technique is applied to a larger cohort to demonstrate its applicability to a greater number of subjects. Normative values of the indices of the proposed technique are reported across the white matter (WM) of the normal human population and power calculations carried out to help design future studies using the technique. Finally, a test-retest imaging data of multiple subjects is used to establish repeatability and reliability of the indices of the proposed technique. Comparison to a standard DW-MRI model shows that the technique can quantify inter-subject differences but is less robust to noise. The third part assesses if the proposed technique can characterise the anisotropic orientation dispersion in the grey matter (GM). This is because the assessment in the first two parts shows that for standard imaging data, the technique can only capture anisotropic dispersion in the WM. This is done using high-resolution ex vivo and in vivo DW-MRI data, which provides better sensitivity to the neurites in the GM. The aim of this PhD was to develop a model to characterise anisotropic orienta- tion dispersion of neurites, which can ultimately be applied to neuroscience and clinical studies. The proposed work provides an exciting development for better research in neuroscience, by making a step towards improved characterisation of neurites, using standard imaging acquisitions. This will help in making key links between the precise changes in microstructure that result in brain development, ageing and pathology.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: In Vivo quantification of complex neurite configurations using magnetic resonance imaging
Event: UCL (University College London)
Open access status: An open access version is available from UCL Discovery
Language: English
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10046378
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