Montreal Neurological Institute, McGill University, Montreal, Canada.
Montreal Neurological Institute, McGill University, Montreal, Canada.
Neuroimage. 2018 Jun;173:341-350. doi: 10.1016/j.neuroimage.2018.02.050. Epub 2018 Mar 1.
Knowing the maturational schedule of typical brain development is critical to our ability to identify deviations from it; such deviations have been related to cognitive performance and even developmental disorders. Chronological age can be predicted from brain images with considerable accuracy, but with limited spatial specificity, particularly in the case of the cerebral cortex. Methods using multi-modal data have shown the greatest accuracy, but have made limited use of cortical measures. Methods using complex measures derived from voxels throughout the brain have also shown great accuracy, but are difficult to interpret in terms of cortical development. Measures based on cortical surfaces have yielded less accurate predictions, suggesting that perhaps cortical maturation is less strongly related to chronological age than is maturation of deep white matter or subcortical structures. We question this suggestion. We show that a simple metric based on the white/gray contrast at the inner border of the cortex is a good predictor of chronological age. We demonstrate this in two large datasets: the NIH Pediatric Data, with 832 scans of typically developing children, adolescents, and young adults; and the Pediatric Imaging, Neurocognition, and Genetics data, with 760 scans of individuals in a similar age-range. Further, our usage of an elastic net penalized linear regression model reveals the brain regions which contribute most to age-prediction. Moreover, we show that the residuals of age-prediction based on this white/gray contrast metric are not merely random errors, but are strongly related to IQ, suggesting that this metric is sensitive to aspects of brain development that reflect cognitive performance.
了解典型大脑发育的成熟时间表对于我们识别其偏差的能力至关重要;这种偏差与认知表现甚至发育障碍有关。大脑图像可以相当准确地预测年龄,但空间特异性有限,特别是在大脑皮层的情况下。使用多模态数据的方法具有最高的准确性,但对皮层测量的使用有限。使用从整个大脑体素中得出的复杂测量方法也显示出很高的准确性,但难以根据皮层发育进行解释。基于皮层表面的测量方法得出的预测结果不太准确,这表明皮层成熟与实际年龄的相关性可能不如深部白质或皮质下结构成熟强。我们对此表示怀疑。我们表明,基于皮层内边界处的白/灰质对比度的简单度量是实际年龄的良好预测指标。我们在两个大型数据集上证明了这一点:NIH 儿科数据,包含 832 名正常发育的儿童、青少年和年轻人的扫描图像;以及儿科成像、神经认知和遗传学数据,包含 760 名年龄相似的个体的扫描图像。此外,我们使用弹性网惩罚线性回归模型揭示了对年龄预测贡献最大的大脑区域。此外,我们表明,基于该白/灰质对比度度量的年龄预测的残差不仅是随机误差,而且与智商密切相关,这表明该度量对反映认知表现的大脑发育方面敏感。