Can we predict subject-specific dynamic cortical thickness maps during infancy from birth?
Can we predict subject-specific dynamic cortical thickness maps during infancy from birth?
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DOI：
10.1002/hbm.23555
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发表时间：
2017-06
影响因子：
4.8
通讯作者：
Shen D
中科院分区：
文献类型：
--
作者：
Meng Y;Li G;Rekik I;Zhang H;Gao Y;Lin W;Shen D
了解人类大脑皮层的早期动态发育仍然是一个具有挑战性的问题。皮质厚度作为大脑皮层最重要的形态属性之一，是正常神经发育和神经精神疾病的敏感指标，但其出生后早期发育在很大程度上仍未被探索。在这项研究中，我们研究了神经发育科学中的一个关键问题：我们能否根据婴儿出生时可用的 MRI 数据来预测其皮质厚度图的未来动态发育？如果这是可能的，我们也许能够更好地建模和理解早期大脑发育，并及早发现婴儿期异常的大脑发育。为此，我们开发了一种新颖的基于学习的方法，称为动态组装回归森林（DARF），根据新生儿 MRI 特征来预测出生后第一年皮质厚度图的发展。我们将我们的方法应用于 15 名健康婴儿，并预测了他们在 3、6、9 和 12 个月大时的皮质厚度图，平均绝对误差分别为 0.209mm、0.332mm、0.340mm 和 0.321mm。此外，我们发现预测精度是区域特异性的，单峰皮层的精度较高，而高阶关联皮层的精度相对较低，这可能与其差异化的发育模式有关。额外的实验还表明，使用更多的早期时间点进行预测可以进一步显着提高预测精度。
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 0.209mm, 0.332mm, 0.340mm, and 0.321mm. 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.