کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
3071947 1188688 2014 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multi-resolutional shape features via non-Euclidean wavelets: Applications to statistical analysis of cortical thickness
ترجمه فارسی عنوان
ویژگی های شکل چند حلقه ای از طریق موجک های غیر اقلیدسی: برنامه های کاربردی برای تجزیه و تحلیل آماری ضخامت قشر
کلمات کلیدی
موجک غیر اقلیدسی، تجزیه و تحلیل چندگانه، موجک گراف، تجزیه و تحلیل شکل، تبعیض ضخامت قشر بیماری آلزایمر
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب شناختی
چکیده انگلیسی


• Multi-resolutional shape descriptor for signals on surfaces for statistical analysis
• Highly sensitive to statistical group analysis in a population of subjects
• Demonstration of significant improvements in results on two distinct datasets
• Provides open source implementation of the framework

Statistical analysis on arbitrary surface meshes such as the cortical surface is an important approach to understanding brain diseases such as Alzheimer's disease (AD). Surface analysis may be able to identify specific cortical patterns that relate to certain disease characteristics or exhibit differences between groups. Our goal in this paper is to make group analysis of signals on surfaces more sensitive. To do this, we derive multi-scale shape descriptors that characterize the signal around each mesh vertex, i.e., its local context, at varying levels of resolution. In order to define such a shape descriptor, we make use of recent results from harmonic analysis that extend traditional continuous wavelet theory from the Euclidean to a non-Euclidean setting (i.e., a graph, mesh or network). Using this descriptor, we conduct experiments on two different datasets, the Alzheimer's Disease NeuroImaging Initiative (ADNI) data and images acquired at the Wisconsin Alzheimer's Disease Research Center (W-ADRC), focusing on individuals labeled as having Alzheimer's disease (AD), mild cognitive impairment (MCI) and healthy controls. In particular, we contrast traditional univariate methods with our multi-resolution approach which show increased sensitivity and improved statistical power to detect a group-level effects. We also provide an open source implementation.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 93, Part 1, June 2014, Pages 107–123
نویسندگان
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