title: Subject-specific prediction using nonlinear population modeling: application to early brain maturation from DTI.
abstract: The term prediction implies expected outcome in the future, often based on a model and statistical inference. Longitudinal imaging studies offer the possibility to model temporal change trajectories of anatomy across populations of subjects. In the spirit of subject-specific analysis, such normative models can then be used to compare data from new subjects to the norm and to study progression of disease or to predict outcome. This paper follows a statistical inference approach and presents a framework for prediction of future observations based on past measurements and population statistics. We describe prediction in the context of nonlinear mixed effects modeling (NLME) where the full reference population's statistics (estimated fixed effects, variance-covariance of random effects, variance of noise) is used along with the individual's available observations to predict its trajectory. The proposed methodology is generic in regard to application domains. Here, we demonstrate analysis of early infant brain maturation from longitudinal DTI with up to three time points. Growth as observed in DTI-derived scalar invariants is modeled with a parametric function, its parameters being input to NLME population modeling. Trajectories of new subject's data are estimated when using no observation, only the first or the first two time points. Leave-one-out experiments result in statistics on differences between actual and predicted observations. We also simulate a clinical scenario of prediction on multiple categories, where trajectories predicted from multiple models are classified based on maximum likelihood criteria.
authors.forename: Neda
authors.initials: N
authors.lastname: Sadeghi
authors.forename: P Thomas
authors.initials: PT
authors.lastname: Fletcher
authors.forename: Marcel
authors.initials: M
authors.lastname: Prastawa
authors.forename: John H
authors.initials: JH
authors.lastname: Gilmore
authors.forename: Guido
authors.initials: G
authors.lastname: Gerig
mesh_terms: D000293:Adolescent; D000375:Aging / Q000473:pathology / Q000502:physiology*; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000254:growth & development*; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008953:Models, Anatomic; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D009929:Organ Size / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D055815:Young Adult