Integration of routine QA data into mega-analysis may improve quality and sensitivity of multisite diffusion tensor imaging studies.
Integration of routine QA data into mega-analysis may improve quality and sensitivity of multisite diffusion tensor imaging studies.
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DOI：
10.1002/hbm.23900
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发表时间：
2018-03
影响因子：
4.8
通讯作者：
Voineskos AN
中科院分区：
文献类型：
--
作者：
Kochunov P;Dickie EW;Viviano JD;Turner J;Kingsley PB;Jahanshad N;Thompson PM;Ryan MC;Fieremans E;Novikov D;Veraart J;Hong EL;Malhotra AK;Buchanan RW;Chavez S;Voineskos AN
通过比较精神分裂症患者和对照组的白质完整性，使用多部位扩散张量成像(DTI)分数各向异性(FA)数据，评估了一种减少方法学差异的新的超分析方法。通过回归质量保证(QA)获得的方差和使用Marchenko-Pastur主成分分析(MP-PCA)去噪来减少方法学差异。N=192(119名患者/73名对照)数据集来自三个配备3T磁共振系统的地点：GE MR750、GE HDX和西门子Trio。弥散张量成像包括5个b=0和60个扩散敏感型梯度方向(b=1000 S/mm~2)。使用统一模体每周采集室内DTI质量保证方案数据；因子分析用于提取与SNR和FA相关的两个正交QA因素。它们被用作特定于站点的协变量，以执行大规模分析数据聚合。患者对照差异的效应大小与增强神经成像遗传学荟萃分析(EIGMA)联盟在回归QA方差前后的报告进行了比较。对MP-PCA滤波的影响也进行了评估。QA因素可解释每个部位全脑平均FA值的3-4%的变异。QA因素的回归改善了精神分裂症对全脑平均FA值的影响大小-从Cohen的d=0.53到0.57-并改善了本研究中观察到的FA差异的区域模式与谜之间的一致性，从r=0.54到0.70。应用MP-PCA去噪进一步提高了一致性，r=0.81。通过常规QA和高级去噪方法捕获的方法学差异的回归，导致与大型分析研究更好地吻合。
A novel mega-analytical approach that reduced methodological variance was evaluated using a multi-site diffusion tensor imaging (DTI) fractional anisotropy (FA) data by comparing white matter integrity in people with schizophrenia to controls. Methodological variance was reduced through regression of variance captured from quality assurance (QA) and by using Marchenko-Pastur Principal Component Analysis (MP-PCA) denoising. N=192 (119patients/73controls) datasets were collected at three sites equipped with 3T MRI systems: GE MR750, GE HDx and Siemens Trio. DTI protocol included five b=0 and 60 diffusion-sensitized gradient directions (b=1000 s/mm2). In-house DTI QA protocol data was acquired weekly using a uniform phantom; factor analysis was used to distil into two orthogonal QA factors related to: SNR and FA. They were used as site-specific covariates to perform mega-analytic data aggregation. The effect size of patient-control differences was compared to these reported by the Enhancing Neuro Imaging Genetics Meta Analysis (ENIGMA) consortium before and after regressing QA variance. Impact of MP-PCA filtering was evaluated likewise. QA-factors explained ~3–4% variance in the whole-brain average FA values per site. Regression of QA factors improved the effect size of schizophrenia on whole brain average FA values - from Cohen’s d=0.53 to 0.57 - and improved the agreement between the regional pattern of FA differences observed in this study vs. ENIGMA from r=0.54 to 0.70. Application of MP-PCA-denoising further improved the agreement to r=0.81. Regression of methodological variances captured by routine QA and advanced denoising that led to a better agreement with a large mega-analytic study.
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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：
6.6
作者：
Kochunov, Peter;Hong, L. Elliot
通讯作者：
Hong, L. Elliot
影响因子：
3.1
作者：
Acheson A;Wijtenburg SA;Rowland LM;Winkler A;Mathias CW;Hong LE;Jahanshad N;Patel B;Thompson PM;McGuire SA;Sherman PM;Kochunov P;Dougherty DM
通讯作者：
Dougherty DM
影响因子：
3.5
作者：
Alba-Ferrara LM;de Erausquin GA
通讯作者：
de Erausquin GA
影响因子：
5.7
作者：
Friedman, Lee;Glover, Gary H.
通讯作者：
Glover, Gary H.