Abstract:
: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.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Kim WH,Singh V,Chung MK,Hinrichs C,Pachauri D,Okonkwo OC,Johnson SC,Alzheimer's Disease Neuroimaging Initiative.doi
10.1016/j.neuroimage.2014.02.028subject
Has Abstractpub_date
2014-06-01 00:00:00pages
107-23eissn
1053-8119issn
1095-9572pii
S1053-8119(14)00139-6journal_volume
93 Pt 1pub_type
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