Computational, Cognitive & Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London, UK.
Department of Biomedical Engineering, King's College London, London, UK.
Neuroimage. 2017 Dec;163:115-124. doi: 10.1016/j.neuroimage.2017.07.059. Epub 2017 Jul 29.
Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of 'brain-predicted age' as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. CNN accurately predicted chronological age using GM (correlation between brain-predicted age and chronological age r = 0.96, mean absolute error [MAE] = 4.16 years) and raw (r = 0.94, MAE = 4.65 years) data. This was comparable to GPR brain-predicted age using GM data (r = 0.95, MAE = 4.66 years). Brain-predicted age was a heritable phenotype for all models and input data (h ≥ 0.5). Brain-predicted age showed high test-retest reliability (intraclass correlation coefficient [ICC] = 0.90-0.99). Multi-centre reliability was more variable within high ICCs for GM (0.83-0.96) and poor-moderate levels for WM and raw data (0.51-0.77). Brain-predicted age represents an accurate, highly reliable and genetically-influenced phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings.
机器学习分析神经影像学数据可以准确预测健康人群的年龄。与认知障碍和疾病相关的大脑老化偏差已被确定。在这里，我们试图通过基于深度学习的预测建模方法（特别是卷积神经网络 (CNN)），并应用于预处理和原始 T1 加权 MRI 数据，进一步确立“大脑预测年龄”作为大脑老化过程中个体差异的生物标志物的可信度。首先，我们旨在使用大量健康成年人的数据集 (N = 2001) 证明 CNN 大脑预测年龄的准确性。接下来，我们试图使用同卵双胞胎和异卵双胞胎女性样本 (N = 62) 确定大脑预测年龄的遗传性。第三，我们使用两个样本（扫描内 N = 20；扫描间 N = 11）检查大脑预测年龄的测试-再测试和多中心可靠性。在所有数据集上，生成了 CNN 大脑预测年龄，并与高斯过程回归 (GPR) 方法进行了比较。输入数据是通过统计参数映射 (SPM) 生成的灰质 (GM) 或白质 (WM) 容积图，或原始数据。CNN 使用 GM (大脑预测年龄与实际年龄之间的相关性 r = 0.96，平均绝对误差 [MAE] = 4.16 岁) 和原始数据 (r = 0.94，MAE = 4.65 岁) 准确预测实际年龄。这与使用 GM 数据的 GPR 大脑预测年龄相当 (r = 0.95，MAE = 4.66 岁)。对于所有模型和输入数据，大脑预测年龄都是可遗传的表型 (h ≥ 0.5)。大脑预测年龄具有很高的测试-再测试可靠性 (组内相关系数 [ICC] = 0.90-0.99)。多中心可靠性在 GM 的高 ICC（0.83-0.96）内变化较大，而在 WM 和原始数据中的 ICC 较低（0.51-0.77）。大脑预测年龄是一种准确、高度可靠且受遗传影响的表型，具有作为大脑老化生物标志物的潜力。此外，可以在原始 T1-MRI 数据上准确生成年龄预测，大大减少了对新数据的计算时间，使该过程更接近在临床环境中提供有关大脑健康的实时信息。
