dissertationNeurodegenerative diseases are an increasing health care problem in the United States. Quantitative neuroimaging provides a noninvasive method to illuminate individual variations in brain structure to better understand and diagnose these disorders. The overall objective of this research is to develop novel clinical tools that summarize and quantify changes in brain shape to not only help better understand age-appropriate changes but also, in the future, to dissociate structural changes associated with aging from those caused by dementing neurodegenerative disorders. Because the tools we will develop can be applied for individual assessment, achieving our goals could have a significant clinical impact. An accurate, practical objective summary measure of the brain pathology would augment current subjective visual interpretation of structural magnetic resonance images. Fractal dimension is a novel approach to image analysis that provides a quantitative measure of shape complexity describing the multiscale folding of the human cerebral cortex. Cerebral cortical folding reflects the complex underlying architectural features that evolve during brain development and degeneration including neuronal density, synaptic proliferation and loss, and gliosis. Building upon existing technology, we have developed innovative tools to compute global and local (voxel-wise and regional) cerebral cortical fractal dimensions and voxel-wise cortico-fractal surfaces from high-contrast MR images. Our previous research has shown that fractal dimension correlates with cognitive function and changes during the course of normal aging. We will now apply unbiased diffeomorphic atlasing methodology to dramatically improve the alignment of complex cortical surfaces. Our novel methods will create more accurate, detailed geometrically averaged images to take into account the intragroup differences and make statistical inferences about spatiotemporal changes in shape of the cerebral cortex across the adult human lifespan
