Brain age prediction: A comparison between machine learning models using region- and voxel-based morphometric data.
Brain age prediction: A comparison between machine learning models using region- and voxel-based morphometric data.
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大脑年龄预测：使用基于区域和基于体素的形态测量数据的机器学习模型之间的比较。
DOI：
10.1002/hbm.25368
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发表时间：
2021-06-01
影响因子：
4.8
通讯作者：
Pinaya WHL
中科院分区：
文献类型：
--
作者：
Baecker L;Dafflon J;da Costa PF;Garcia-Dias R;Vieira S;Scarpazza C;Calhoun VD;Sato JR;Mechelli A;Pinaya WHL
大脑形态在衰老过程中会有所不同，使用大脑特征预测一个人的年龄可以帮助检测衰老过程中的异常。现有的关于这种“大脑年龄预测”的研究在方法和数据类型方面差异很大，因此目前最准确和最普遍的方法论方法尚不清楚。因此，我们使用UK Biobank数据集（N = 10，824，年龄范围47-73）来比较机器学习模型支持向量回归，相关向量回归和高斯过程回归对全脑区域或基于体素的结构磁共振成像数据的性能，通过主成分分析进行或不进行降维。通过交叉验证以及独立测试集在验证集中评估性能。这些模型的平均绝对误差在3.7到4.7年之间，其中那些在体素水平数据上训练的模型表现最好。总体而言，我们观察到在相同数据类型上训练的模型之间的性能差异很小，这表明输入数据的类型对性能的影响比模型选择更大。所有代码都在网上提供，希望这将有助于未来的研究。我们比较了机器学习模型支持向量回归，相关向量回归和高斯过程回归，用于使用不同类型的形态测量输入和超过10，000名受试者的样本量进行脑年龄预测。不同模型的平均绝对误差范围为3.7至4.7年。数据输入类型（区域或体素水平）对性能的影响大于模型的选择。
Brain morphology varies across the ageing trajectory and the prediction of a person's age using brain features can aid the detection of abnormalities in the ageing process. Existing studies on such “brain age prediction” vary widely in terms of their methods and type of data, so at present the most accurate and generalisable methodological approach is unclear. Therefore, we used the UK Biobank data set (N = 10,824, age range 47–73) to compare the performance of the machine learning models support vector regression, relevance vector regression and Gaussian process regression on whole‐brain region‐based or voxel‐based structural magnetic resonance imaging data with or without dimensionality reduction through principal component analysis. Performance was assessed in the validation set through cross‐validation as well as an independent test set. The models achieved mean absolute errors between 3.7 and 4.7 years, with those trained on voxel‐level data with principal component analysis performing best. Overall, we observed little difference in performance between models trained on the same data type, indicating that the type of input data had greater impact on performance than model choice. All code is provided online in the hope that this will aid future research. We compared the machine learning models support vector regression, relevance vector regression and Gaussian process regression for brain age prediction using different types of morphometric input and sample sizes of more than 10,000 subjects. The mean absolute error across the different models ranged from 3.7 to 4.7 years. The type of data input (region‐ or voxel‐level) had a greater impact on performance than the choice of model.
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影响因子：
11.2
作者：
Cole, James H.;Leech, Robert;Sharp, David J.
通讯作者：
Sharp, David J.
影响因子：
5.7
作者：
Becker, Benjamin Gutierrez;Klein, Tassilo;Wachinger, Christian
通讯作者：
Wachinger, Christian
影响因子：
4.1
作者：
Fjell, Anders M.;Walhovd, Kristine B.
通讯作者：
Walhovd, Kristine B.
影响因子：
3.7
作者：
Esteban, Oscar;Birman, Daniel;Gorgolewski, Krzysztof J.
通讯作者：
Gorgolewski, Krzysztof J.
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