Closing the life-cycle of normative modeling using federated hierarchical Bayesian regression.
Closing the life-cycle of normative modeling using federated hierarchical Bayesian regression.
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DOI：
10.1371/journal.pone.0278776
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
Marquand, Andre F.
中科院分区：
文献类型：
--
作者：
Kia, Seyed Mostafa;Huijsdens, Hester;Rutherford, Saige;de Boer, Augustijn;Dinga, Richard;Wolfers, Thomas;Berthet, Pierre;Mennes, Maarten;Andreassen, Ole A.;Westlye, Lars T.;Beckmann, Christian F.;Marquand, Andre F.
在过去十年中，临床神经影像学数据的可用性大幅增长，为研究临床队列的异质性提供了前所未有的规模。规范模型是一种新兴的统计工具，用于解剖复杂脑疾病的异质性。然而，由于医疗数据隐私问题和处理有害变化（如图像采集过程中的可变性）的困难，其应用在技术上仍然具有挑战性。在这里，我们使用大量的多中心神经成像数据集来解决在大量人群中估计参考规范模型的问题。为此，我们引入了一个使用层次贝叶斯回归（HBR）的联邦概率框架来完成规范建模的生命周期。该模型提供了学习、更新和适应分散神经成像数据的模型参数的可能性。我们的实验结果证实了与目前的标准方法相比，HBR在大型多位点神经成像数据集上获得更准确的规范范围方面的优势。此外，我们的方法提供了在局部数据集甚至是非常小样本量的数据集上重新校准和重用学习模型的可能性。所提出的方法将促进规范建模作为一种医疗工具的应用，用于筛选受复杂疾病（如精神障碍）影响的个体的生物学偏差。
Clinical neuroimaging data availability has grown substantially in the last decade, providing the potential for studying heterogeneity in clinical cohorts on a previously unprecedented scale. Normative modeling is an emerging statistical tool for dissecting heterogeneity in complex brain disorders. However, its application remains technically challenging due to medical data privacy issues and difficulties in dealing with nuisance variation, such as the variability in the image acquisition process. Here, we approach the problem of estimating a reference normative model across a massive population using a massive multi-center neuroimaging dataset. To this end, we introduce a federated probabilistic framework using hierarchical Bayesian regression (HBR) to complete the life-cycle of normative modeling. The proposed model provides the possibilities to learn, update, and adapt the model parameters on decentralized neuroimaging data. Our experimental results confirm the superiority of HBR in deriving more accurate normative ranges on large multi-site neuroimaging datasets compared to the current standard methods. In addition, our approach provides the possibility to recalibrate and reuse the learned model on local datasets and even on datasets with very small sample sizes. The proposed method will facilitate applications of normative modeling as a medical tool for screening the biological deviations in individuals affected by complex illnesses such as mental disorders.
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影响因子：
4.7
作者：
Casey BJ;Cannonier T;Conley MI;Cohen AO;Barch DM;Heitzeg MM;Soules ME;Teslovich T;Dellarco DV;Garavan H;Orr CA;Wager TD;Banich MT;Speer NK;Sutherland MT;Riedel MC;Dick AS;Bjork JM;Thomas KM;Chaarani B;Mejia MH;Hagler DJ Jr;Daniela Cornejo M;Sicat CS;Harms MP;Dosenbach NUF;Rosenberg M;Earl E;Bartsch H;Watts R;Polimeni JR;Kuperman JM;Fair DA;Dale AM;ABCD Imaging Acquisition Workgroup
通讯作者：
ABCD Imaging Acquisition Workgroup
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
5.7
作者：
Fischl, Bruce
通讯作者：
Fischl, Bruce
探索人类大脑功能的科学
DOI：
10.1073/pnas.0911855107
发表时间：
2010-03-09
影响因子：
11.1
作者：
Biswal, Bharat B.;Mennes, Maarten;Milham, Michael P.
通讯作者：
Milham, Michael P.
影响因子：
5.7
作者：
Bookheimer SY;Salat DH;Terpstra M;Ances BM;Barch DM;Buckner RL;Burgess GC;Curtiss SW;Diaz-Santos M;Elam JS;Fischl B;Greve DN;Hagy HA;Harms MP;Hatch OM;Hedden T;Hodge C;Japardi KC;Kuhn TP;Ly TK;Smith SM;Somerville LH;Uğurbil K;van der Kouwe A;Van Essen D;Woods RP;Yacoub E
通讯作者：
Yacoub E