Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age.
Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age.
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DOI：
10.3389/fnagi.2023.1249415
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发表时间：
2023
影响因子：
4.8
通讯作者：
中科院分区：
文献类型：
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应用人工智能（AI）将全脑磁共振图像（MRI）总结为有效的“大脑年龄”指标，可以提供关于大脑在衰老过程中如何与各种因素（例如遗传和生活方式）相互作用的整体、个性化和客观的视图。使用深度学习（DL）进行脑年龄预测已被广泛用于量化人类大脑的发育状态，但其在生物医学领域的更广泛应用却因需要大样本和复杂的可解释性而受到批评。动物模型，即恒河猴，为理解人类大脑提供了一个独特的视角——人类大脑是一个衰老模式相似的物种，环境和生活方式因素更容易控制。然而，在动物模型中应用深度学习方法会遇到数据不足的问题，因为与数千个人类 MRI 相比，动物大脑 MRI 的可用性有限。我们证明，迁移学习可以缓解样本量问题，从 8,859 个人脑 MRI 迁移预先训练的 AI 模型，提高了猴脑年龄估计的准确性和稳定性。当传输 3D ResNet [平均绝对误差 (MAE) = 1.83 年] 和 2D 全局局部变换器 (MAE = 1.92 年) 模型时，出现了最高的精度和稳定性。我们的模型将额叶白质确定为猴脑年龄预测的最重要特征，这与之前的组织学发现一致。这是第一个基于深度学习的、解剖学上可解释的、自适应的大脑年龄估计器，可以将人工智能技术的应用范围扩大到各种动物或疾病样本，并扩大非人类灵长类动物大脑整个生命周期的研究机会。
The application of artificial intelligence (AI) to summarize a whole-brain magnetic resonance image (MRI) into an effective “brain age” metric can provide a holistic, individualized, and objective view of how the brain interacts with various factors (e.g., genetics and lifestyle) during aging. Brain age predictions using deep learning (DL) have been widely used to quantify the developmental status of human brains, but their wider application to serve biomedical purposes is under criticism for requiring large samples and complicated interpretability. Animal models, i.e., rhesus monkeys, have offered a unique lens to understand the human brain - being a species in which aging patterns are similar, for which environmental and lifestyle factors are more readily controlled. However, applying DL methods in animal models suffers from data insufficiency as the availability of animal brain MRIs is limited compared to many thousands of human MRIs. We showed that transfer learning can mitigate the sample size problem, where transferring the pre-trained AI models from 8,859 human brain MRIs improved monkey brain age estimation accuracy and stability. The highest accuracy and stability occurred when transferring the 3D ResNet [mean absolute error (MAE) = 1.83 years] and the 2D global-local transformer (MAE = 1.92 years) models. Our models identified the frontal white matter as the most important feature for monkey brain age predictions, which is consistent with previous histological findings. This first DL-based, anatomically interpretable, and adaptive brain age estimator could broaden the application of AI techniques to various animal or disease samples and widen opportunities for research in non-human primate brains across the lifespan.
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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.
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影响因子：
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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：
Doshi, Jimit;Erus, Guray;Ou, Yangming;Gaonkar, Bilwaj;Davatzikos, Christos
通讯作者：
Davatzikos, Christos