Wavelet based multi-scale shape features on arbitrary surfaces for cortical thickness discrimination.
Year: 2012
Type: article
Abstract: early signs of diseases, the corresponding statistical differences at the group level invariably become weaker and increasingly hard to identify. Indeed, after a multiple comparisons correction is adopted (to account for correlated statistical tests over all surface points), very few regions may survive. In contrast to hypothesis tests on point-wise measurements, in this paper, we make the case for performing statistical analysis on multi-scale shape descriptors that characterize the local topological context of the signal around each surface vertex. Our descriptors are based on recent results from harmonic analysis, that show how wavelet theory extends to non-Euclidean settings (i.e., irregular weighted graphs). We provide strong evidence that these descriptors successfully pick up group-wise differences, where traditional methods either fail or yield unsatisfactory results. Other than this primary application, we show how the framework allows performing cortical surface smoothing in the native space without mappint to a unit sphere.
Source: PubMed
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: AD classification algorithms using ADNI multi-modal image data, Maximizing Power in AD Clinical Trials via Multimodal Machine Learning, Institutional Clinical and Translational Science Award (UW-Madison)(UL1), The Effect of Calorie Restriction on Brain Aging, Institutional Clinical and Translational Science Award, Wisconsin Alzheimer's Disease Research Center, Dietary Restriction and Aging in Rhesus Monkeys, The Longitudinal Course of Imaging Biomarkers in People At Risk for Alzheimer’s Disease, Wisconsin National Primate Research Center Support
OpenAlex ID: W2145720158