Predicting 'Brainage' in late childhood to adolescence (6-17yrs) using structural MRI, morphometric similarity, and machine learning.
Predicting 'Brainage' in late childhood to adolescence (6-17yrs) using structural MRI, morphometric similarity, and machine learning.
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利用结构MRI、形态相似性和机器学习预测儿童期晚期至青春期（6-17岁）的“脑龄”。
DOI：
10.1038/s41598-023-42414-5
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发表时间：
2023-09-20
影响因子：
4.6
通讯作者：
中科院分区：
文献类型：
--
作者：
大脑发育经常使用结构MRI进行研究。最近，研究已经使用统计学习和健康儿童的大规模成像数据库的组合来预测结构MRI的个体年龄。这种数据驱动的、预测的“脑容量”通常与受试者的实际年龄不同，这种差异是个体差异的潜在衡量标准。很少有研究利用结构MRI数据的高阶或连接组表示来进行这种Bravelet方法。我们利用形态相似性作为结构MRI的网络级方法来生成年龄的预测模型。我们使用形态相似性对这些新的Brazilian方法进行了基准测试，以更典型的单一特征（即，皮质厚度）方法。我们发现，这些新的方法并没有优于皮质厚度或皮质体积的措施。所有的模型都显着偏置的年龄，但强大的运动混淆。主要结果表明，虽然形态相似性映射可能是一种新的方式来利用额外的信息，从T1加权结构MRI超出个人功能，在一个Braidian框架的背景下，形态相似性不提供更准确的预测年龄。形态学相似性作为结构MRI的网络水平方法，可能不适合以这种方式研究健康参与者大脑发育的个体差异。
Brain development is regularly studied using structural MRI. Recently, studies have used a combination of statistical learning and large-scale imaging databases of healthy children to predict an individual’s age from structural MRI. This data-driven, predicted ‘Brainage’ typically differs from the subjects chronological age, with this difference a potential measure of individual difference. Few studies have leveraged higher-order or connectomic representations of structural MRI data for this Brainage approach. We leveraged morphometric similarity as a network-level approach to structural MRI to generate predictive models of age. We benchmarked these novel Brainage approaches using morphometric similarity against more typical, single feature (i.e., cortical thickness) approaches. We showed that these novel methods did not outperform cortical thickness or cortical volume measures. All models were significantly biased by age, but robust to motion confounds. The main results show that, whilst morphometric similarity mapping may be a novel way to leverage additional information from a T1-weighted structural MRI beyond individual features, in the context of a Brainage framework, morphometric similarity does not provide more accurate predictions of age. Morphometric similarity as a network-level approach to structural MRI may be poorly positioned to study individual differences in brain development in healthy participants in this way.
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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：
64.8
作者：
通讯作者：
--
DOI：
10.1111/jcpp.12838
发表时间：
2018-04
期刊：
Journal of child psychology and psychiatry, and allied disciplines
影响因子：
--
作者：
Batalle D;Edwards AD;O'Muircheartaigh J
通讯作者：
O'Muircheartaigh J
影响因子：
11
作者：
Di Martino, A.;Yan, C-G;Li, Q.;Denio, E.;Castellanos, F. X.;Alaerts, K.;Anderson, J. S.;Assaf, M.;Bookheimer, S. Y.;Dapretto, M.;Deen, B.;Delmonte, S.;Dinstein, I.;Ertl-Wagner, B.;Fair, D. A.;Gallagher, L.;Kennedy, D. P.;Keown, C. L.;Keysers, C.;Lainhart, J. E.;Lord, C.;Luna, B.;Menon, V.;Minshew, N. J.;Monk, C. S.;Mueller, S.;Mueller, R. A.;Nebel, M. B.;Nigg, J. T.;O'Hearn, K.;Pelphrey, K. A.;Peltier, S. J.;Rudie, J. D.;Sunaert, S.;Thioux, M.;Tyszka, J. M.;Uddin, L. Q.;Verhoeven, J. S.;Wenderoth, N.;Wiggins, J. L.;Mostofsky, S. H.;Milham, M. P.
通讯作者：
Milham, M. P.
影响因子：
4.8
作者：
Ball, Gareth;Beare, Richard;Seal, Marc L.
通讯作者：
Seal, Marc L.