Harmonization of multi-site diffusion tensor imaging data.
Harmonization of multi-site diffusion tensor imaging data.
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DOI：
10.1016/j.neuroimage.2017.08.047
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发表时间：
2017-11-01
期刊：
影响因子：
5.7
通讯作者：
Shinohara RT
中科院分区：
文献类型：
--
作者：
Fortin JP;Parker D;Tunç B;Watanabe T;Elliott MA;Ruparel K;Roalf DR;Satterthwaite TD;Gur RC;Gur RE;Schultz RT;Verma R;Shinohara RT
扩散张量成像(DTI)是一种成熟的磁共振成像(MRI)技术，用于研究脑白质的微结构变化。与许多其他成像方式一样，DTI图像受到扫描仪之间技术差异的影响，这种差异阻碍了跨成像站点、扫描仪和时间的图像比较。使用两种不同扫描仪采集的205名健康受试者的分数各向异性(FA)和平均弥散率(MD)图，我们表明DTI测量具有高度的站点特异性，强调在执行下游统计分析之前需要校正站点影响。我们首先展示了证据表明，组合来自多个地点的DTI数据，如果不统一，可能会适得其反，并对推断产生负面影响。然后，我们提出并比较了几种DTI数据的协调方法，结果表明，基因组学中流行的批量效应校正工具Fighting在建模和消除FA和MD图中不必要的站点间变异性方面表现最好。使用年龄作为感兴趣的生物表型，我们表明战斗既保留了生物多样性，又消除了SITE引入的不必要的变异。最后，我们评估了在地点和年龄之间存在不同程度混淆的情况下的不同协调方法，以及对小样本研究的稳健性测试。
Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer from technical between-scanner variation that hinders comparisons of images across imaging sites, scanners and over time. Using fractional anisotropy (FA) and mean diffusivity (MD) maps of 205 healthy participants acquired on two different scanners, we show that the DTI measurements are highly site-specific, highlighting the need of correcting for site effects before performing downstream statistical analyses. We first show evidence that combining DTI data from multiple sites, without harmonization, may be counter-productive and negatively impacts the inference. Then, we propose and compare several harmonization approaches for DTI data, and show that ComBat, a popular batch-effect correction tool used in genomics, performs best at modeling and removing the unwanted inter-site variability in FA and MD maps. Using age as a biological phenotype of interest, we show that ComBat both preserves biological variability and removes the unwanted variation introduced by site. Finally, we assess the different harmonization methods in the presence of different levels of confounding between site and age, in addition to test robustness to small sample size studies.
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影响因子：
5.7
作者：
Jahanshad, Neda;Kochunov, Peter V.;Sprooten, Emma;Mandl, Rene C.;Nichols, Thomas E.;Almasy, Laura;Blangero, John;Brouwer, Rachel M.;Curran, Joanne E.;de Zubicaray, Greig I.;Duggirala, Ravi;Fox, Peter T.;Hong, L. Elliot;Landman, Bennett A.;Martin, Nicholas G.;McMahon, Katie L.;Medland, Sarah E.;Mitchell, Braxton D.;Olvera, Rene L.;Peterson, Charles P.;Starr, John M.;Sussmann, Jessika E.;Toga, Arthur W.;Wardlaw, Joanna M.;Wright, Margaret J.;Pol, Hilleke E. Hulshoff;Bastin, Mark E.;McIntosh, Andrew M.;Deary, Ian J.;Thompson, Paul M.;Glahn, David C.
通讯作者：
Glahn, David C.
影响因子：
3.5
作者：
Garyfallidis E;Brett M;Amirbekian B;Rokem A;van der Walt S;Descoteaux M;Nimmo-Smith I;Dipy Contributors
通讯作者：
Dipy Contributors
影响因子：
5.7
作者：
Jenkinson, M;Bannister, P;Smith, S
通讯作者：
Smith, S
影响因子：
2.1
作者：
Gagnon-Bartsch, Johann A.;Speed, Terence P.
通讯作者：
Speed, Terence P.
影响因子：
3.3
作者：
Alexander, AL;Hasan, KM;Parker, DL
通讯作者：
Parker, DL