In this talk I will discuss research with the goal of building models of brain anatomy. The neuronanatomical structures of interest can be broadly subdivided into two categories - cortical and non-cortical. Cortical structures (particularly the cerbral cortex) are typically highly folded, thin sheets of gray matter. Functionally, the cerebral cortex has been shown to have a "columnar" architecture. For this reason, we construct surface-based models for analysis of cortical properties. The construction of such models is a difficult task due to the high degree of folding of the cortical manifold in conjunction with the limited (~ 1 mm) resolution of current neuroimaging technologies. Once constructed, the cortical models can be deformed for morphometry, visualization and registration purposes. I will show some results of this type of analysis, including the morphometric changes that the cortex undergoes in disorders such as schizophrenia, Alzheimer's disease, and Huntington's disease, as well as healthy aging.
A different set of techniques have been developed for the construction of models of subcortical structures. Here, we model the segmentation as an anisotropic nonstationary Markov Random Field. The anisotropy lets us model the local spatial relationships that exist between neuroanatomical structures (e.g. hippocampus is anterior and inferior to amygdala), while the nonstationarity facilitates the encoding of inhomogeneous properties of the tissue within a structure. This approach is based on extracting the relevant model parameters from a manually labeled training set, and has been shown to be comparable in accuracy to the manual labeling. Finally, I will discuss our ongoing efforts to link microstructural features of the brain typically only visible under a microscope, with macrostructural properties visible on standard scans. This linkage provides an objective means to assess the accuracy of various coordinate systems, and to optimize the alignment of homologous areas across subjects.
Audio (MP3 File, Podcast Ready)
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