Department of Psychiatry, University of Jena, Jena, Germany.
Neuroimage. 2010 Apr 15;50(3):883-92. doi: 10.1016/j.neuroimage.2010.01.005. Epub 2010 Jan 11.
The early identification of brain anatomy deviating from the normal pattern of growth and atrophy, such as in Alzheimer's disease (AD), has the potential to improve clinical outcomes through early intervention. Recently, Davatzikos et al. (2009) supported the hypothesis that pathologic atrophy in AD is an accelerated aging process, implying accelerated brain atrophy. In order to recognize faster brain atrophy, a model of healthy brain aging is needed first. Here, we introduce a framework for automatically and efficiently estimating the age of healthy subjects from their T(1)-weighted MRI scans using a kernel method for regression. This method was tested on over 650 healthy subjects, aged 19-86 years, and collected from four different scanners. Furthermore, the influence of various parameters on estimation accuracy was analyzed. Our age estimation framework included automatic preprocessing of the T(1)-weighted images, dimension reduction via principal component analysis, training of a relevance vector machine (RVM; Tipping, 2000) for regression, and finally estimating the age of the subjects from the test samples. The framework proved to be a reliable, scanner-independent, and efficient method for age estimation in healthy subjects, yielding a correlation of r=0.92 between the estimated and the real age in the test samples and a mean absolute error of 5 years. The results indicated favorable performance of the RVM and identified the number of training samples as the critical factor for prediction accuracy. Applying the framework to people with mild AD resulted in a mean brain age gap estimate (BrainAGE) score of +10 years.
早期识别大脑解剖结构偏离正常生长和萎缩模式，如阿尔茨海默病（AD），通过早期干预有可能改善临床结果。最近，Davatzikos 等人（2009 年）支持 AD 病理性萎缩是加速衰老过程的假设，这意味着大脑萎缩加速。为了识别更快的大脑萎缩，首先需要一个健康大脑衰老的模型。在这里，我们介绍了一种使用核回归方法自动且高效地从 T1 加权 MRI 扫描中估计健康受试者年龄的框架。该方法在超过 650 名年龄在 19 至 86 岁之间的健康受试者中进行了测试，这些受试者来自四个不同的扫描仪。此外，还分析了各种参数对估计准确性的影响。我们的年龄估计框架包括 T1 加权图像的自动预处理、通过主成分分析进行降维、用于回归的相关向量机（RVM；Tipping，2000）的训练，以及最后从测试样本中估计受试者的年龄。该框架被证明是一种可靠、与扫描仪无关且高效的健康受试者年龄估计方法，在测试样本中，估计年龄与实际年龄之间的相关性 r=0.92，平均绝对误差为 5 岁。结果表明 RVM 表现良好，并确定了训练样本数量是预测准确性的关键因素。将该框架应用于轻度 AD 患者，得出的平均大脑年龄差距估计（BrainAGE）得分为+10 岁。
