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Title: Developing brain connectivity : effects of parcellation scale on network analysis in neonates
Author: Schirmer, Markus D.
Awarding Body: King's College London
Current Institution: King's College London (University of London)
Date of Award: 2015
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Diffusion magnetic resonance imaging (dMRI), tractography and the use of network measures have combined to form an established approach for exploring brain connectivity. When applied to the human brain, a definition of regions of interest (ROIs) which act as network nodes is required. In adults, regions commonly represent brain areas that are assumed to be functionally coherent. During early development however, a complete set and locations of ROIs in the brain is yet to be established. This motivates the use of random parcellation schemes with varying numbers of regions or scales. However, network measures can be scale dependent, making comparisons across multiple scales challenging and hindering group comparisons. To address such scale dependence, network measures are commonly normalised using random surrogate networks which act as a baseline. In this work, the efficacy of commonly used normalisation techniques is determined and new methods for generating randomised surrogate networks are introduced. Furthermore, a subset of measures is derived by investigating inter-measure correlations and the framework is then applied to serial dMRI data of a preterm cohort. It is shown that a new method for generating surrogate networks for normalisation improves on established approaches and eliminates scale dependencies over a local range, allowing for meaningful group comparison. While normalisation may be used for group comparison over a local range, scale dependence can remain over larger ranges. This work shows that the nature of the scale dependence varies between cohorts, and proposes a multiscale framework for group comparison. Using this framework to characterise the scale dependence, it is possible to differentiate the groups of neonates studied. This approach, however, requires the calculation of networks at multiple scales. Therefore the use of a node-merger scheme is also proposed to infer network properties at a coarse scale from a single network estimated at a fine scale. This approach allows for multi-scale group comparison based on a single starting network.
Supervisor: Aljabar, Paul ; Hajnal, Joseph Vilmos Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available