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No description available.

Non-invasive methods to investigate brain function have been used in research laboratories for many decades, however their popularity has increased in recent years given the ease of use and broad application. Such methods have proved valuable in improving our knowledge about numerous areas of basic brain function. Many non-invasive techniques have also been applied to patient groups to allow further identification of pathological mechanisms, but critically a new role has been found for some as biomarkers of disease. Neurodegenerative disease is fast becoming one of the biggest medical problems in the first world. An aging population has caused the relative incidence of many conditions to rise dramatically and studies suggest that this trend will continue. Although our knowledge surrounding these conditions has improved significantly, most remain notoriously difficult to diagnose and to treat. The recent introduction of neuroprotective drugs offers the potential to slow the progression of some diseases. However, to take full advantage of these disease-modifying treatments, administration must occur early in the disease course which fuels the demand for selective and specific diagnostic tests. There is currently a great need to enhance the clinical diagnostic repertoire with reliable, robust and specific biomarkers of neurodegenerative disease. However, careful, rigorous studies are required to validate the use of non-invasive techniques in this role. The same level of care should also be applied to techniques used in basic research; without a fundamental understanding of the mechanisms underpinning these techniques, their utility in the investigation of specific processes or pathways is questionable. This thesis aims to address specific cases to evaluate existing techniques and to screen potential new disease biomarkers.

In many biological systems, evolution has found solutions that balance function and structure with metabolic expense. This certainly seems to be the case in the energetically expensive locomotor system, and so maybe similar efficiency optimisations exist in the central nervous system which is also energy expensive. This notion is tested against three sensory coding systems which have been well characterised, these are monochromatic and chromatic sensitive neurons in the early visual system and sound sensitive neurons of the auditory system. Simple linear models are constructed to make predictions of the optimal receptive fields that balance information coding with energy efficiency. More specifically, synaptic energy efficiency is examined and is found to predict many aspects of luminance and spatiochromatic as well as auditory coding. This approach is extended from the neural level to the higher-level domain of statistical inference where organisms build models of their environment. Balancing predictive power with explanatory simplicity results in superior descriptions of the world

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The adult vertebrate hind brain regulates critical body functions such as sleep, respiration and heart rate. During embryogenesis, the hindbrain forms seven transient swellings called rhombomeres, each with distinct morphology and gene expression patterns. Rhombomere specification and regulation is controlled by transcription factors including members of the Hox family and Krox20. Disruption of these genes changes the number and properties of rhombomeres and leads to neuronal disorganization causing animal lethality. We are using transcriptional profiling and functional validation to reveal rhombornere specific patterns of gene expression regulated by these key transcription factors. We isolated individual rhombomeres from 9.5 dpc mouse embryos using laser capture microscopy and performed transcriptional profiling on either single rhomborneres from wild type hindbrains or whole hindbrains of wild type and Hoxa1, Hoxa2, Hoxb1 and Krox20 mutants. Analyses and validation of these results have uncovered several novel targets and pathways associated with hindbrain specification. For example, Hox genes which are induced by retinoids in turn modulate retinoid metabolism. These feedback loops are important for hindbrain patterning. We also discovered rhombomere-specific genes, genes involved in neurogenesis in the hind brain along with the downstream targets of Hoxa1, Hoxa2, Hoxb1 and Krox20.

Electroencephalography (and the associated Event-Related Potentials, ERPs) anc Functional Magnetic Resonance Imaging (fMRI) are the most widely used functional brain imaging techniques.

Electrophysiological brain activity offers an abundance of information about cognitive brain functionality, heavily exploited in clinical practice and scientific studies. A reliable automated analysis of brain signals has become an urgent need in the context of brain imaging and monitoring. One of the key challenges in this regard is to robustly account for uncertain information inherent to biological data sources. The uncertainty arises mainly out of the complexity and nondeterministic variability of the brain dynamics, and signal acquisition related factors with stochastic characteristics.

The human brain is certainly the most complex biological system as it contains a network of more than l0 to the power of 11 individual nerve cells and interconnections. Fibre tractography using diffusion MR imaging is a promising non-invasive method for reconstructing the 3D fibre architecture of the human brain white matter in vivo. Despite the great potential, white matter tractography is relatively immature. At the current resolution of diffusion MR images, researchers agree that more than one third of imaging voxels in human brain white matter contain crossing fibre bundles. Generally, conventional diffusion tensor imaging (DTI) fibre tracking approaches have difficulties in crossing regions. Also, noise and other artefacts associated with diffusion MR data lead to uncertainty in the estimates of fib re orientation directions. Furthennore. each fib re tracking method has limitations due to the algorithmic approach that they follow and the assumptions they make. This thesis presents novel probabilistic based fibre tracking algorithms aiming to tackle a number of limitations of existing fibre tracking algorithms. Fibre clustering is a key step towards tract-based, quantitative analysis of white matter. Clustering algorithms analyse a collection of fibre curves in 3D and delineate them into anatomically distinct fibre tracts groups. In this thesis, a probabilistic framework is developed and the framework al lows for the clustering of sets of cunres In curve space. This thesis describes a number of original contributions to the field. First, a novel statistical framework is developed for improved fibre tractography and a quantitative analysis tool is introduced for probabilistic tracking methods using the statistical measures. The goal is to elucidate problems with existing detenninistic and probabilistic algorithms used to process diffusion MR images and propose solutions and methods through a new framework. Subsequently, random-walk and modelbased bootstrapping algorithms are developed using a two-tensor field to quantify the uncertainty of fibre orientation and probabilistic fibre tractography. A further problem tackled here is resolving crossing fibre configurations, a major concern in diffusion MR imaging, using data that can be routinely acquired in a clinical setting. Finally, a new probabilistic clustering algorithm is introduced using regression mixtures and the result of clustering is the probabilistic assignment of the fibre trajectories to each cluster. The tract geometry model is estimated using fitted parameters of the probabilistic clustering algorithm. Local reconstruction, tracking results, segmentation and quantitative analysis are shown on simulated datasets, on a hardware phantom and on multiple human brain datasets.

Optic nerves are an appropriate and widely employed model used to study the function and the pathophysiology of central white matter. This thesis investigates ischaemic injury mechanisms in mature and immature white matter, using isolated adult and neonatal Wistar rat and balb-c mouse optic nerve. A central theme to this work is that both myelinated and nonmyelinated central white matter injury is a partially glutamate-dependent process. Electrophysiology was used to record the compound action potential (CAP) under normal and pathological conditions in both myelinated and premyelinated (post-natal day 2: P2) optic nerves. Following a period of oxygen and glucose deprivation (OGD), both white matters were susceptible to excitotoxicity; mediated by over-activation of N-methyl D-aspartate type glutamate receptors (NMDA-Rs). The previously described higher tolerance of mature mouse optic nerve to OGD was eliminated by exogenous stimulation of NMDA-Rs via direct perfusion with agonists during OGD. My data reconcile earlier contradictions in the literature regarding the significance of NMDA-Rs for ischaemic injury in white matter in the two animals. A second major finding; ischaemic injury in P2 RONs was completely prevented by the NMDARs antagonist MK-801. Interestingly, both MK-801 and a second antagonist, memantine, were toxic to P2 RONs when perfused under control conditions. The presence of NMDA-Rs on premyelinated axons was confirmed by immuno-staining. Neurotransmitters other than glutamate, such as GABA and glycine may also play a role in ischaemic injury of P2, with GABA and glycine receptor block being particularly protective of the CAP against damage. Electron-micrographs of pre-myelinated optic nerve axons and glia confirmed the data collected by electrophysiological recording of the CAP. These findings show that ischaemic damage of immature white matter is mediated largely by NMDA-Rs and that other neurotransmitter receptors also contribute to injury.