title: Learning-based subject-specific estimation of dynamic maps of cortical morphology at missing time points in longitudinal infant studies.
abstract: Longitudinal neuroimaging analysis of the dynamic brain development in infants has received increasing attention recently. Many studies expect a complete longitudinal dataset in order to accurately chart the brain developmental trajectories. However, in practice, a large portion of subjects in longitudinal studies often have missing data at certain time points, due to various reasons such as the absence of scan or poor image quality. To make better use of these incomplete longitudinal data, in this paper, we propose a novel machine learning-based method to estimate the subject-specific, vertex-wise cortical morphological attributes at the missing time points in longitudinal infant studies. Specifically, we develop a customized regression forest, named dynamically assembled regression forest (DARF), as the core regression tool. DARF ensures the spatial smoothness of the estimated maps for vertex-wise cortical morphological attributes and also greatly reduces the computational cost. By employing a pairwise estimation followed by a joint refinement, our method is able to fully exploit the available information from both subjects with complete scans and subjects with missing scans for estimation of the missing cortical attribute maps. The proposed method has been applied to estimating the dynamic cortical thickness maps at missing time points in an incomplete longitudinal infant dataset, which includes 31 healthy infant subjects, each having up to five time points in the first postnatal year. The experimental results indicate that our proposed framework can accurately estimate the subject-specific vertex-wise cortical thickness maps at missing time points, with the average error less than 0.23 mm. Hum Brain Mapp 37:4129-4147, 2016. © 2016 Wiley Periodicals, Inc.
authors.affiliation: Department of Computer Science, University of North Carolina at Chapel Hill, North Carolina.|Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina.
authors.forename: Yu
authors.initials: Y
authors.lastname: Meng
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina. dgshen@med.unc.edu.
authors.forename: Gang
authors.initials: G
authors.lastname: Li
authors.affiliation: Department of Computer Science, University of North Carolina at Chapel Hill, North Carolina.|Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina.
authors.forename: Yaozong
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina.
authors.forename: Weili
authors.initials: W
authors.lastname: Lin
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina. gang_li@med.unc.edu.|Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea. gang_li@med.unc.edu.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D002540:Cerebral Cortex / Q000000981:diagnostic imaging* / Q000254:growth & development; D002675:Child, Preschool; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007223:Infant; D008137:Longitudinal Studies; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods; D059906:Neuroimaging / Q000379:methods*; D009929:Organ Size; D012044:Regression Analysis
keywords: cortical surface; cortical thickness; early brain development; longitudinal study; missing data completion