Lambert, C.P.; (2012) Multimodal segmentation of deep cortical structures. Doctoral thesis , UCL (University College London).

Abstract

The organisation of the human cortex is characterised by macroscopically defined areas consisting of functionally distinct subunits, each connected to an array of local and distant targets forming distinctive networks. Classically, these structures have been parcellated according to ex vivo cytochemical and connectivity properties. However, the emergent flaw with this approach is the presence of significant inter-hemispheric and inter-individual anatomical variability. By exploiting several MRI modalities, a similar approach to sub-regional parcellation can be applied in vivo across large numbers of individuals. Using diffusion tensor imaging (DTI), probabilistic tractography can be used to generate a representation of the white matter pathways originating from or passing through a single voxel. By quantifying the degree of similarity between different tract distributions, regional parcellation can be achieved through several algorithms. These have previously been used on regions such as the thalamus and basal ganglia. However, due to computational limitations, it is normal practice to apply dimension reduction tactics prior to parcellation, thereby generating an upper bound on the degree of accuracy that can be achieved. I have set out to further this pre-existing framework by developing methods to analyse and cluster massive matrices without down-sampling data, thereby generating a prior free, bottom-up approach to regional parcellation based on regional connectivity. I have applied this approach to several areas including the sub-thalamic nucleus, amygdala and human brainstem. Several fundamental properties and limitations of the technique are revealed, and additional methods developed to further improve the white matter parcellation. This includes a novel method of multichannel segmentation, which was applied to the human brainstem and cortex. The new tissue classes were used both for quantitative analysis, and also to improve DTI based segmentation. Throughout, the findings are extrapolated to examine a variety of neuropathological scenarios, including symptom networks, pre-clinical diagnosis and therapeutic interventions such as deep brain stimulation.

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