title: Sparse decomposition and modeling of anatomical shape variation.
abstract: Recent advances in statistics have spawned powerful methods for regression and data decomposition that promote sparsity, a property that facilitates interpretation of the results. Sparse models use a small subset of the available variables and may perform as well or better than their full counterparts if constructed carefully. In most medical applications, models are required to have both good statistical performance and a relevant clinical interpretation to be of value. Morphometry of the corpus callosum is one illustrative example. This paper presents a method for relating spatial features to clinical outcome data. A set of parsimonious variables is extracted using sparse principal component analysis, producing simple yet characteristic features. The relation of these variables with clinical data is then established using a regression model. The result may be visualized as patterns of anatomical variation related to clinical outcome. In the present application, landmark-based shape data of the corpus callosum is analyzed in relation to age, gender, and clinical tests of walking speed and verbal fluency. To put the data-driven sparse principal component method into perspective, we consider two alternative techniques, one where features are derived using a model-based wavelet approach, and one where the original variables are regressed directly on the outcome.
authors: Department of Informatics and Mathematical Modelling, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark. kas@imm.dtu.dk
authors: Karl
authors: K
authors: Sjöstrand
authors: Egill
authors: E
authors: Rostrup
authors: Charlotte
authors: C
authors: Ryberg
authors: Rasmus
authors: R
authors: Larsen
authors: Colin
authors: C
authors: Studholme
authors: Hansjoerg
authors: H
authors: Baezner
authors: Jose
authors: J
authors: Ferro
authors: Franz
authors: F
authors: Fazekas
authors: Leonardo
authors: L
authors: Pantoni
authors: Domenico
authors: D
authors: Inzitari
authors: Gunhild
authors: G
authors: Waldemar
mesh_terms: D000367:Age Factors; D003337:Corpus Callosum / Q000033:anatomy & histology* / Q000502:physiology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008954:Models, Biological; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis / Q000379:methods*; D011597:Psychomotor Performance; D012044:Regression Analysis; D012737:Sex Factors; D013060:Speech