title: Can we predict subject-specific dynamic cortical thickness maps during infancy from birth?
abstract: Understanding the early dynamic development of the human cerebral cortex remains a challenging problem. Cortical thickness, as one of the most important morphological attributes of the cerebral cortex, is a sensitive indicator for both normal neurodevelopment and neuropsychiatric disorders, but its early postnatal development remains largely unexplored. In this study, we investigate a key question in neurodevelopmental science: can we predict the future dynamic development of cortical thickness map in an individual infant based on its available MRI data at birth? If this is possible, we might be able to better model and understand the early brain development and also early detect abnormal brain development during infancy. To this end, we develop a novel learning-based method, called Dynamically-Assembled Regression Forest (DARF), to predict the development of the cortical thickness map during the first postnatal year, based on neonatal MRI features. We applied our method to 15 healthy infants and predicted their cortical thickness maps at 3, 6, 9, and 12 months of age, with respectively mean absolute errors of 0.209 mm, 0.332 mm, 0.340 mm, and 0.321 mm. Moreover, we found that the prediction precision is region-specific, with high precision in the unimodal cortex and relatively low precision in the high-order association cortex, which may be associated with their differential developmental patterns. Additional experiments also suggest that using more early time points for prediction can further significantly improve the prediction accuracy. Hum Brain Mapp 38:2865-2874, 2017. © 2017 Wiley Periodicals, Inc.
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.|Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
authors.forename: Yu
authors.identifier: 0000-0003-0013-2102
authors.initials: Y
authors.lastname: Meng
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
authors.forename: Gang
authors.initials: G
authors.lastname: Li
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
authors.forename: Islem
authors.initials: I
authors.lastname: Rekik
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
authors.forename: Han
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.|Department of Computer Science, University of North Carolina at Chapel Hill, 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, 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, Chapel Hill, North Carolina.|Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D000367:Age Factors; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000033:anatomy & histology* / Q000000981:diagnostic imaging / Q000254:growth & development*; D005260:Female; D005865:Gestational Age; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007223:Infant; D007231:Infant, Newborn; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D008297:Male; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests
keywords: cortical surface; cortical thickness prediction; infant brain; longitudinal development