Surface shape analysis with an application to brain surface asymmetry in schizophrenia.
Some methods for the statistical analysis of surface shapes and asymmetry are introduced. We focus on a case study where magnetic resonance images of the brain are available from groups of 30 schizophrenia patients and 38 controls, and we investigate large-scale brain surface shape differences. Key aspects of shape analysis are to remove nuisance transformations by registration and to identify which parts of one object correspond with the parts of another object. We introduce maximum likelihood and Bayesian methods for registering brain images and providing large-scale correspondences of the brain surfaces. Brain surface size-and-shape analysis is considered using random field theory, and also dimension reduction is carried out using principal and independent components analysis. Some small but significant differences are observed between the the patient and control groups. We then investigate a particular type of asymmetry called torque. Differences in asymmetry are observed between the control and patient groups, which add strength to other observations in the literature. Further investigations of the midline plane location in the 2 groups and the fitting of nonplanar curved midlines are also considered.
Brignell
Christopher J
CJ
School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK.
Dryden
Ian L
IL
Gattone
S Antonio
SA
Park
Bert
B
Leask
Stuart
S
Browne
William J
WJ
Flynn
Sean
S
D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D056808:Biostatistics / Q000379:methods*; D001921:Brain / Q000033:anatomy & histology / Q000473:pathology*; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D025341:Principal Component Analysis; D012559:Schizophrenia / Q000473:pathology*; D016008:Statistical Distributions; D013499:Surface Properties