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首页> 外文期刊>NeuroImage >Prediction of individual subject's age across the human lifespan using diffusion tensor imaging: A machine learning approach
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Prediction of individual subject's age across the human lifespan using diffusion tensor imaging: A machine learning approach

机译：使用扩散张量成像预测整个人类寿命中个体受试者的年龄：一种机器学习方法

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Diffusion tensor imaging has the potential to be used as a neuroimaging marker of natural ageing and assist in elucidating trajectories of cerebral maturation and ageing. In this study, we applied a multivariate technique relevance vector regression (RVR) to predict individual subject's age using whole brain fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) from a cohort of 188 subjects aged 4-85. years. High prediction accuracy as derived from Pearson correlation coefficient of actual versus predicted age (FA - r= 0.870 p. <. 0.0001; MD - r= 0.896 p. <. 0.0001; AD - r= 0.895 p. <. 0.0001; RD - r= 0.899 p. <. 0.0001) was achieved. Cerebral white-matter regions that contributed to these predictions include; corpus callosum, cingulum bundles, posterior longitudinal fasciculus and the cerebral peduncle. A post-hoc analysis of these regions showed that FA follows a nonlinear rational-quadratic trajectory across the lifespan peaking at approximately 21.8. years. The MD, RD and AD volumes were particularly useful for making predictions using grey matter cerebral regions. These results suggest that diffusion tensor imaging measurements can reliably predict individual subject's age and demonstrate that FA cerebral maturation and ageing patterns follow a non-linear trajectory with a noteworthy peaking age. These data will contribute to the understanding of neurobiology of cerebral maturation and ageing. Most notably, from a neuropsychiatric perspective our results may allow differentiation of cerebral changes that may occur due to natural maturation and ageing, and those due to developmental or neuropsychiatric disorders. ?Machine-learning is used to predict age using whole-brain diffusion tensor images.?A cross-validation approach is used to separate training and testing datasets.?White matter follows a rational-quadratic trajectory peaking at 21.8years.?Diffusivity in grey-matter tissue increases with maturation and ageing.
机译：扩散张量成像有潜力用作自然衰老的神经影像标记，并有助于阐明大脑成熟和衰老的轨迹。在这项研究中，我们运用多变量技术相关性向量回归（RVR），使用来自整个研究组的全脑分数各向异性（FA），平均扩散率（MD），轴向扩散率（AD）和径向扩散率（RD）来预测个体受试者的年龄。 188位年龄在4-85岁之间的受试者。年份。从实际年龄与预测年龄的皮尔逊相关系数得出的高预测准确性（FA-r = 0.870 p。<0.0001; MD-r = 0.896 p。<0.0001; AD-r = 0.895 p。<0.0001; RD- r = 0.899 p。<0.0001）。有助于这些预测的脑白质区域包括： call体，扣带束，后纵筋膜和脑梗。对这些区域的事后分析表明，FA在整个寿命周期内遵循非线性的有理二次轨迹，峰值约为21.8。年份。 MD，RD和AD量对于使用灰质脑区域进行预测特别有用。这些结果表明，扩散张量成像测量可以可靠地预测个体受试者的年龄，并证明FA脑的成熟和衰老模式遵循具有明显峰值年龄的非线性轨迹。这些数据将有助于理解脑成熟和衰老的神经生物学。最值得注意的是，从神经精神病学的角度来看，我们的结果可以区分因自然成熟和衰老而发生的脑部变化，以及由于发育性或神经精神病性障碍而引起的脑部变化。使用机器学习来使用全脑扩散张量图像预测年龄。使用交叉验证方法来分离训练和测试数据集。白质遵循21.8年的有理二次轨迹峰值。 -组织随着成熟和衰老而增加。

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