title: FADTTS: functional analysis of diffusion tensor tract statistics.
abstract: The aim of this paper is to present a functional analysis of a diffusion tensor tract statistics (FADTTS) pipeline for delineating the association between multiple diffusion properties along major white matter fiber bundles with a set of covariates of interest, such as age, diagnostic status and gender, and the structure of the variability of these white matter tract properties in various diffusion tensor imaging studies. The FADTTS integrates five statistical tools: (i) a multivariate varying coefficient model for allowing the varying coefficient functions in terms of arc length to characterize the varying associations between fiber bundle diffusion properties and a set of covariates, (ii) a weighted least squares estimation of the varying coefficient functions, (iii) a functional principal component analysis to delineate the structure of the variability in fiber bundle diffusion properties, (iv) a global test statistic to test hypotheses of interest, and (v) a simultaneous confidence band to quantify the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of FADTTS. We apply FADTTS to investigate the development of white matter diffusivities along the splenium of the corpus callosum tract and the right internal capsule tract in a clinical study of neurodevelopment. FADTTS can be used to facilitate the understanding of normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles. The advantages of FADTTS compared with the other existing approaches are that they are capable of modeling the structured inter-subject variability, testing the joint effects, and constructing their simultaneous confidence bands. However, FADTTS is not crucial for estimation and reduces to the functional analysis method for the single measure.
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. htzhu@email.unc.edu
authors.forename: Hongtu
authors.initials: H
authors.lastname: Zhu
authors.forename: Linglong
authors.initials: L
authors.lastname: Kong
authors.forename: Runze
authors.initials: R
authors.lastname: Li
authors.forename: Martin
authors.initials: M
authors.lastname: Styner
authors.forename: Guido
authors.initials: G
authors.lastname: Gerig
authors.forename: Weili
authors.initials: W
authors.lastname: Lin
authors.forename: John H
authors.initials: JH
authors.lastname: Gilmore
mesh_terms: D000465:Algorithms; D016880:Anisotropy; D001921:Brain / Q000033:anatomy & histology / Q000254:growth & development; D003198:Computer Simulation; D056324:Diffusion Tensor Imaging / Q000379:methods* / Q000706:statistics & numerical data*; D005260:Female; D005865:Gestational Age; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007223:Infant; D020772:Internal Capsule / Q000033:anatomy & histology; D016018:Least-Squares Analysis; D008297:Male; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D009434:Neural Pathways / Q000033:anatomy & histology / Q000254:growth & development; D016011:Normal Distribution; D025341:Principal Component Analysis; D012727:Sex Characteristics; D035501:Uncertainty