Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models.
Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models.
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DOI：
10.1016/j.neuroimage.2022.119699
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发表时间：
2022-12-01
期刊：
影响因子：
5.7
通讯作者：
Marquand A
中科院分区：
文献类型：
--
作者：
Bayer JMM;Dinga R;Kia SM;Kottaram AR;Wolfers T;Lv J;Zalesky A;Schmaal L;Marquand A
规范建模的潜力，使个性化的预测从神经影像学数据已经超越了病例对照方法的推论。然而，位点效应常常以复杂的方式与感兴趣的变量相混淆，并且可能对规范模型的估计产生偏差，这阻碍了规范模型在大型多位点神经成像数据集上的应用。在本研究中，我们建议通过将这些站点效应作为随机效应纳入分层贝叶斯模型来适应这些站点效应。我们比较了线性和非线性层次贝叶斯模型在模拟年龄对皮质厚度影响方面的表现。在我们的实验中，我们使用了570名健康个体的数据，这些数据来自于自闭症脑成像数据交换（ABIDE）数据集。此外，我们使用来自自闭症患者的数据来测试我们的模型是否能够在去除位点效应的同时保留临床有用的信息。我们将提出的单阶段分层贝叶斯方法与几种常用的协调技术进行了比较，这些协调技术通常用于处理使用两阶段回归的加性和乘法位点效应，包括回归出位点和使用ComBat协调位点，无论是否明确保留与年龄和性别相关的方差作为感兴趣的生物变异。此外，我们还根据原始数据进行了预测，这些数据中没有包含站点。提出的分层贝叶斯方法在多指标下的预测性能最好。除此之外，得到的z分数显示很少或没有残留位点效应，但仍然保留了临床有用的信息。相比之下，回归模型和战斗模型的性能特别差，其中年龄和性别没有明确建模。在所有两个阶段的协调模型中，预测的尺度都很差，损失了90%以上的原始方差。我们的研究结果显示了层次贝叶斯回归方法在神经成像数据中适应位点变化的价值，这为协调技术提供了一种替代方法。虽然我们提出的方法可能具有广泛的实用性，但我们的方法特别适合于规范性建模，其中主要兴趣是对主体间变化的准确建模和对参考模型偏差的统计量化。
The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy individuals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from individuals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance related to age and sex as biological variation of interest. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90 % of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modelling where the primary interest is in accurate modelling of inter-subject variation and statistical quantification of deviations from a reference model.
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影响因子：
5.7
作者：
Jiao, Yun;Chen, Rong;Ke, Xiaoyan;Chu, Kangkang;Lu, Zuhong;Herskovits, Edward H.
通讯作者：
Herskovits, Edward H.
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.
DOI：
10.1098/rspa.1937.0109
发表时间：
1937-05-01
影响因子：
--
作者：
Bartlett, MS
通讯作者：
Bartlett, MS
影响因子：
3.7
作者：
Khundrakpam, Budhachandra S.;Lewis, John D.;Evans, Alan C.
通讯作者：
Evans, Alan C.
影响因子：
5.9
作者：
Bethlehem, Richard A. I.;Seidlitz, Jakob;Lombardo, Michael V.
通讯作者：
Lombardo, Michael V.