Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning.
Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning.
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DOI：
10.1038/s41598-022-06459-2
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发表时间：
2022-02-23
影响因子：
4.6
通讯作者：
Montillo A
中科院分区：
文献类型：
--
作者：
Mellema CJ;Nguyen KP;Treacher A;Montillo A
自闭症谱系障碍（ASD）是第四大常见的神经发育障碍，患病率为1/160。准确的诊断依赖于专家，但这样的人很少。这导致人们对机器学习（ML）模型的开发越来越感兴趣，这些模型可以整合来自功能和结构MRI（fMRI和sMRI）的神经成像特征，以帮助揭示ASD的中枢神经系统改变特征。我们优化并比较了12个最流行和最强大的ML模型的性能。每个都使用fMRI和sMRI特征的15种不同组合进行单独训练，并使用无偏模型搜索进行优化。深度学习模型以最高的诊断准确率预测ASD，并很好地推广到其他MRI数据集。我们的模型在IMPAC数据集的测试数据诊断中达到了最先进的80% ROC曲线下面积（AUROC）;在外部ABIDE I和ABIDE II数据集上达到了86%和79%的AUROC（在监督域自适应后进一步提高到93%和90%）。性能最高的模型鉴定了用于准确ASD诊断的可重复的推定生物标志物，与已知的ASD标志物以及新的小脑生物标志物雅阁。这种可重复性使人们相信它们在定义和使用一组真正可推广的ASD生物标志物方面的巨大潜力，这将促进对ASD神经元变化的科学理解。
Autism spectrum disorder (ASD) is the fourth most common neurodevelopmental disorder, with a prevalence of 1 in 160 children. Accurate diagnosis relies on experts, but such individuals are scarce. This has led to increasing interest in the development of machine learning (ML) models that can integrate neuroimaging features from functional and structural MRI (fMRI and sMRI) to help reveal central nervous system alterations characteristic of ASD. We optimized and compared the performance of 12 of the most popular and powerful ML models. Each was separately trained using 15 different combinations of fMRI and sMRI features and optimized with an unbiased model search. Deep learning models predicted ASD with the highest diagnostic accuracy and generalized well to other MRI datasets. Our model achieves state-of-the-art 80% area under the ROC curve (AUROC) in diagnosis on test data from the IMPAC dataset; and 86% and 79% AUROC on the external ABIDE I and ABIDE II datasets (with further improvement to 93% and 90% after supervised domain adaptation). The highest performing models identified reproducible putative biomarkers for accurate ASD diagnosis in accord with known ASD markers as well as novel cerebellar biomarkers. Such reproducibility lends credence to their tremendous potential for defining and using a set of truly generalizable ASD biomarkers that will advance scientific understanding of neuronal changes in ASD.
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影响因子：
3.5
作者：
Abraham A;Pedregosa F;Eickenberg M;Gervais P;Mueller A;Kossaifi J;Gramfort A;Thirion B;Varoquaux G
通讯作者：
Varoquaux G
使用功能连接对脑部疾病进行分类和预测：有希望但具有挑战性
影响因子：
4.3
作者：
Du Y;Fu Z;Calhoun VD
通讯作者：
Calhoun VD
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：
5.1
作者：
Chaitra, N.;Vijaya, P. A.;Deshpande, Gopikrishna
通讯作者：
Deshpande, Gopikrishna
影响因子：
17.7
作者：
Allen, G;Courchesne, E
通讯作者：
Courchesne, E