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An increasing ageing population presents the challenge of understanding how healthy ageing and dementia affect brain function. Improved understanding of healthy ageing can provide a baseline against which to examine Mild. - Cognitive Impairment (MCI) and Alzheimer's disease (AD) in which there is a great need for better characterisation and early diagnostic tools. Examining sensory processing can reveal how the ageing brain copes and reacts to changes in peripheral sensory deficits and can inform theories of cognitive ageing. Additionally sensory processing has largely been neglected in the examination of MCI and AD, and may provide further understanding of the early stages of the disease processes, unaffected by the educational and cultural confounds that can often hamper measures of higher level processing. This thesis explored sensory processing in healthy ageing, MCI and AD with two distinct aims: 1) to test and further inform cognitive theories of healthy ageing and 2) to identify changes in sensory processing due to MCI and AD that may be further developed as diagnostic markers. Electroencephalography was used to measure sensory processing across four paradigms designed to examine visual, auditory and audiovisual processing. Early sensory evoked potentials and the mismatch negativity response were measured in healthy younger and older adults, MC! patients and AD patients. -The results indicated that older adults experience modality specific changes in sensory processing, with patterns of a frontal lobe inhibitory deficit emerging in auditory processing, and cortical compensation to reduced peripheral input in visual processing. AD patients showed deficits in sensory association area processing that manifested as reduced audiovisual binding and reduced visual evoked potentials. MC! patients showed high intra and inter individual variability in visual processing and a broad attentional deficit in evoked potentials across both auditory and visual modalities. This thesis demonstrates that the healthily ageing brain adapts to changes in peripheral sensory processing in an adaptive, compensatory manner that is specific to the sensory modality examined. Both MCI and AD show specific changes in sensory processing that with future improvements in individual subject analyses provide considerable potential for improved early classification and diagnosis.

The initial experiment used a new ABM questionnaire to test the ability of a group of 21 elderly adults and 20 patients with minimal to mild AD to retrieve personal episodic and semantic memories from across the life span. The test revealed that AD patients were significantly impaired compared to elderly adults on all aspects of the questionnaire compared to controls, especially for the retrieval of ‘true’ episodic memory, which was defined as uniquely detailed information that enables one to re-experience an event. There was a significant temporal gradient in retrieval of semantic memory but not for episodic memory. The results suggested that there was a dissociation between episodic and semantic ABM. The neural segregation of the episodic and semantic aspects of ABM was then explored using a paradigm based on the questionnaire during fMRI scanning. Twelve elderly and eleven young adults were tested.  The results suggested that retrieval of the episodic aspect was linked to activation in the regions of the right frontal lobe, whereas retrieval of the semantic aspect was associated with activation of left frontal lobe areas.  Elderly participants showed a decrease in activation of the right frontal lobe region during episodic retrieval compared to younger adults.  The proposal that this area could be linked to autobiographical delusions was then explored.  A new test that comprised of a discursive and a questionnaire section that could provoke confabulation revealed that AD patients made significantly more confabulations on both sections of the test than elderly adults. Through a series of experiments, it was found that the confabulation produced was not due to an overload in the memory capacity of AD patients or due to executive or attentional deficits. Rather it was found that the tendency to confabulate was linked to a deficit in the retrieval of the episodic aspect of ABM.  Thus, the results from the experiments suggest that memory distortions in ABM in AD could be linked to a deficit in episodic memory and impaired reality monitoring, due to dysfunction of  regions of the right frontal lobe.

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Non-rigid image registration is an important tool for analysing morphometric differences in subjects with Alzheimer's disease from structural magnetic resonance images of the brain. This thesis describes a novel probabilistic approach to non-rigid registration of medical images, and explores the benefits of its use in this area of neuroimaging. Many image registration approaches have been developed for neuroimaging. The vast majority suffer from two limitations: Firstly, the trade-off between image fidelity and regularisation requires selection. Secondly, only a point-estimate of the mapping between images is inferred, overlooking the presence of uncertainty in the estimation. This thesis introduces a novel probabilistic non-rigid registration model and inference scheme. This framework allows the inference of the parameters that control the level of regularisation, and data fidelity in a data-driven fashion. To allow greater flexibility, this model is extended to allow the level of data fidelity to vary across space. A benefit of this approach, is that the registration can adapt to anatomical variability and other image acquisition differences. A further advantage of the proposed registration framework is that it provides an estimate of the distribution of probable transformations. Additional novel contributions of this thesis include two proposals for exploiting the estimated registration uncertainty. The first of these estimates a local image smoothing filter, which is based on the registration uncertainty. The second approach incorporates the distribution of transformations into an ensemble learning scheme for statistical prediction. These techniques are integrated into standard frameworks for morphometric analysis, and are demonstrated to improve the ability to distinguish subjects with Alzheimer's disease from healthy controls.