Can we predict subject‐specific dynamic cortical thickness maps during infancy from birth?. Issue 6 (15th March 2017)

• Record Type: Journal Article
• Authors: Meng, Yu; Li, Gang; Rekik, Islem; Zhang, Han; Gao, Yaozong; Lin, Weili; Shen, Dinggang
• Is Part Of: Human brain mapping. Volume 38:Issue 6(2017)
• Page Start: 2865
• Page End: 2874
• Publication Date: 2017-03-15
• Subjects: cortical thickness prediction -- longitudinal development -- cortical surface -- infant brain; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.23555
• Languages: English
• ISSNs: 1065-9471

Abstract

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.