This subproject is one of many research subprojects utilizing theresources provided by a Center grant funded by NIH/NCRR. The subproject andinvestigator (PI) may have received primary funding from another NIH source,and thus could be represented in other CRISP entries. The institution listed isfor the Center, which is not necessarily the institution for the investigator.Dementia is the broad term applied to the progressive decline in cognitive function due to disease or damage beyond that of natural aging. The decline can be correlated with changes in brain structure and there is evidence to suggest that these patterns of change uniquely characterise the type of the disease. As an example, the path of neurodegeneration in Alzheimer's disease (AD) exhibits a general progression originating in the transentorhinal cortex through to an accelerated deterioration of the hippocampus years before the clinical diagnosis of the disease. This form of dementia only then spreads cortically to the rest of the temporal, frontal and parietal lobes, sparing the sensorimotor corticies. In contrast, neurodegeneration in HIV/AIDS affects the temporal, limbic and associated cortices first before attacking the primary sensorimotor and visual corticies. While this evidence of structural change from dementia differentiates the disease from normal aging, the early detection of these changes is difficult, as they are generally too mild, diffuse or topologically complex to be recognized by visual inspection of medical images (MR, PET or SPECT). Quantitatively however, there are a range of techniques to determine the rates of change of brain volume as a whole or in part for an example). A technique that is being increasingly applied is tensor-based morphology (TBM) which generates maps of the rates of change in brain volume, more commonly from sequential MR images. The success of TBM can be attributed to the high resolution of MRI machines and the increased ability of registration algorithms to gain a point to point correspondence in sequential MR images; the results providing valuable information on the progression of atrophy in the brain. The algorithm is very well suited to tracking the path of neurodegeneration, at monitoring the effectiveness of drug trials and could go on to aid as a diagnostic tool in clinical applications. A limitation of this method is though, that it requires sequential images of the same individual, allowing the possible disease to progress without diagnosis. TBM can also be used to determine the difference an individual's brain is from an average brain. The method is the same as in the serial MR case but instead of registering the brain scan to the same individual some time in the past, it is registered to some standard template. The results provide differences in neurological structure from your individual to the template. The registration across a population is generally more difficult resulting in a larger errors, though generally successful. Making sense of the result is more difficult than the serial MR study also, as the variation in structural anatomy across a population of aged healthy brains is large. This makes it difficult to determine if any change is indicative of dementia, a result of an increased level of normal atrophy or simply due to a variation in the structure of the two brains.This method of registering an individual's brain to a standard template is not futile however; as the patterns of atrophy are relatively consistent across a single disease, the footprint of the disease, if present, should be recognisable even in its earliest onset. So, while it is not obvious from inspection, there is an underlying pattern of atrophy that is consistently different to the templat e image . What is required is a method to characterise this complex pattern of a trophy. The method we are proposing is an extension to TBM by building a statistical deformation model of normal aging, and then to train an algorithm to identify when an atrophy pattern steps out of that of normal aging.
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