Brain Age Prediction: A Comparison between Machine Learning Models Using Brain Morphometric Data.
Brain Age Prediction: A Comparison between Machine Learning Models Using Brain Morphometric Data.
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DOI：
10.3390/s22208077
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发表时间：
2022-10-21
期刊：
影响因子：
--
通讯作者：
Lee WH
中科院分区：
文献类型：
--
作者：
Han J;Kim SY;Lee J;Lee WH
大脑结构形态随着年龄的增长而变化，使用大脑形态特征预测一个人的年龄可以帮助检测异常的衰老过程。基于神经成像的大脑年龄被广泛用于量化个体的大脑健康，作为与正常大脑老化轨迹的偏离。机器学习方法正在扩大准确的大脑年龄预测的潜力，但由于机器学习算法的多样性，这是一个挑战。在这里，我们的目的是比较机器学习模型的性能，这些模型用于使用来自结构磁共振成像扫描的大脑形态测量来估计大脑年龄。我们评估了27个机器学习模型，应用于来自人类连接组项目（HCP，n = 1113，年龄范围22-37），剑桥老龄化和神经科学中心（Cam-CAN，n = 601，年龄范围18-88）和图像信息提取（IXI，n = 567，年龄范围19-86）的三个独立数据集。使用交叉验证和一个看不见的测试集在每个样本中评估性能。该模型实现的平均绝对误差为2.75-3.12，7.08-10.50，和8.04-9.86岁，以及皮尔森的相关系数为0.11-0.42，0.64-0.85，和0.63-0.79之间的预测脑年龄和实际年龄的HCP，凸轮CAN，和IXI样本，分别。我们发现在相同数据类型上训练的模型之间的性能存在很大差异，这表明模型的选择会在大脑预测的年龄上产生相当大的差异。此外，在三个数据集，正则化线性回归算法实现了类似的性能，非线性和集成算法。我们的研究结果表明，正则化的线性算法是有效的非线性和集成算法的大脑年龄预测，同时显着降低计算成本。我们的研究结果可以作为未来使用应用于脑形态测量数据的机器学习模型来改善大脑年龄预测的起点和定量参考。
Brain structural morphology varies over the aging trajectory, and the prediction of a person’s age using brain morphological features can help the detection of an abnormal aging process. Neuroimaging-based brain age is widely used to quantify an individual’s brain health as deviation from a normative brain aging trajectory. Machine learning approaches are expanding the potential for accurate brain age prediction but are challenging due to the great variety of machine learning algorithms. Here, we aimed to compare the performance of the machine learning models used to estimate brain age using brain morphological measures derived from structural magnetic resonance imaging scans. We evaluated 27 machine learning models, applied to three independent datasets from the Human Connectome Project (HCP, n = 1113, age range 22–37), the Cambridge Centre for Ageing and Neuroscience (Cam-CAN, n = 601, age range 18–88), and the Information eXtraction from Images (IXI, n = 567, age range 19–86). Performance was assessed within each sample using cross-validation and an unseen test set. The models achieved mean absolute errors of 2.75–3.12, 7.08–10.50, and 8.04–9.86 years, as well as Pearson’s correlation coefficients of 0.11–0.42, 0.64–0.85, and 0.63–0.79 between predicted brain age and chronological age for the HCP, Cam-CAN, and IXI samples, respectively. We found a substantial difference in performance between models trained on the same data type, indicating that the choice of model yields considerable variation in brain-predicted age. Furthermore, in three datasets, regularized linear regression algorithms achieved similar performance to nonlinear and ensemble algorithms. Our results suggest that regularized linear algorithms are as effective as nonlinear and ensemble algorithms for brain age prediction, while significantly reducing computational costs. Our findings can serve as a starting point and quantitative reference for future efforts at improving brain age prediction using machine learning models applied to brain morphometric data.
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影响因子：
11.1
作者：
Baecker L;Garcia-Dias R;Vieira S;Scarpazza C;Mechelli A
通讯作者：
Mechelli A
影响因子：
5.7
作者：
Franke, Katja;Ziegler, Gabriel;Gaser, Christian
通讯作者：
Gaser, Christian
影响因子：
4.8
作者：
Anatürk M;Kaufmann T;Cole JH;Suri S;Griffanti L;Zsoldos E;Filippini N;Singh-Manoux A;Kivimäki M;Westlye LT;Ebmeier KP;de Lange AG
通讯作者：
de Lange AG
脑年龄预测：使用基于区域和体素的形态学数据进行机器学习模型的比较。
影响因子：
4.8
作者：
Baecker L;Dafflon J;da Costa PF;Garcia-Dias R;Vieira S;Scarpazza C;Calhoun VD;Sato JR;Mechelli A;Pinaya WHL
通讯作者：
Pinaya WHL
影响因子：
5.7
作者：
Dale, AM;Fischl, B;Sereno, MI
通讯作者：
Sereno, MI