Multi-resolutional shape features via non-Euclidean wavelets: Applications to statistical analysis of cortical thickness

• Published in: NeuroImage (Orlando, Fla.) Vol. 93; no. 1; pp. 107 - 123
• Main Authors: Kim, Won Hwa, Singh, Vikas, Chung, Moo K., Hinrichs, Chris, Pachauri, Deepti, Okonkwo, Ozioma C., Johnson, Sterling C.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.06.2014; Elsevier; Elsevier Limited
• Subjects: Adult and adolescent clinical studies; Aged; Alzheimer Disease - pathology; Alzheimer's disease; Biological and medical sciences; Biomarkers; Cerebral Cortex - anatomy & histology; Cerebral Cortex - pathology; Cortical thickness discrimination; Data Interpretation, Statistical; Degenerative and inherited degenerative diseases of the nervous system. Leukodystrophies. Prion diseases; Female; Gender; Graph wavelets; Graphs; Humans; Hypotheses; Hypothesis testing; Male; Medical sciences; Multi-resolution analysis; Neurology; Noise; Non-Euclidean wavelet; Organic mental disorders. Neuropsychology; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; Shape analysis; Studies; Wavelet Analysis; Graph wavelets; Multi-resolution analysis; Shape analysis; Alzheimer's disease; Non-Euclidean wavelet; Cortical thickness discrimination; Nervous system diseases; Discrimination; Alzheimer disease; Central nervous system disease; Degenerative disease; Cerebral disorder
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2014.02.028

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. •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