Exploration of shape variation using localized components analysis.

Localized Components Analysis (LoCA) is a new method for describing surface shape variation in an ensemble of objects using a linear subspace of spatially localized shape components. In contrast to earlier methods, LoCA optimizes explicitly for localized components and allows a flexible trade-off between localized and concise representations, and the formulation of locality is flexible enough to incorporate properties such as symmetry. This paper demonstrates that LoCA can provide intuitive presentations of shape differences associated with sex, disease state, and species in a broad range of biomedical specimens, including human brain regions and monkey crania.

Localized measures of callosal atrophy are associated with late-life hypertension: AGES-Reykjavik Study.

Hypertension is highly prevalent in elderly individuals and may be associated with cognitive decline, but the mechanisms by which hypertension may impact brain structure, and thereby modulate the time course of late-life cognitive performance, are not well understood. Therefore we used Localized Components Analysis, a novel computational method, to measure spatially-localized patterns of corpus callosum (CC) atrophy in 28 right-handed female subjects aged 75-79 years in the Age, Gene/Environment Susceptibility-Reykjavik Study (AGES-Reykjavik), a large-scale epidemiological study of aging. Localized callosal atrophy in the posterior midbody and splenium was significantly associated with systolic blood pressure in linear statistical models that controlled for age, while associations between blood pressure and anterior CC atrophy measures were not statistically significant. Additionally, overall measures of global CC atrophy were not significantly associated with blood pressure. The posterior CC may be differentially vulnerable to hypertension-associated atrophy, possibly due to its relatively tenuous vascularization.