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本文引用的文献

1
A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics.对头微运动对功能连接组学影响的区域变异进行全面评估。
Neuroimage. 2013 Aug 1;76:183-201. doi: 10.1016/j.neuroimage.2013.03.004. Epub 2013 Mar 15.
2
Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data.在控制静息状态功能连接磁共振成像数据中的微运动后,检测到 ADHD 亚型的独特神经特征。
Front Syst Neurosci. 2013 Feb 4;6:80. doi: 10.3389/fnsys.2012.00080. eCollection 2012.
3
Morning-evening variation in human brain metabolism and memory circuits.人类大脑代谢和记忆回路的早晚变化。
J Neurophysiol. 2013 Mar;109(5):1444-56. doi: 10.1152/jn.00651.2012. Epub 2012 Nov 28.
4
Toward reliable characterization of functional homogeneity in the human brain: preprocessing, scan duration, imaging resolution and computational space.为了可靠地描述人类大脑功能同质性:预处理、扫描持续时间、成像分辨率和计算空间。
Neuroimage. 2013 Jan 15;65:374-86. doi: 10.1016/j.neuroimage.2012.10.017. Epub 2012 Oct 17.
5
An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.一种改进的框架,用于在静息态功能连接数据预处理中进行混杂回归和滤波,以控制运动伪影。
Neuroimage. 2013 Jan 1;64:240-56. doi: 10.1016/j.neuroimage.2012.08.052. Epub 2012 Aug 25.
6
Steps toward optimizing motion artifact removal in functional connectivity MRI; a reply to Carp.功能连接磁共振成像中优化运动伪影去除的步骤;对卡普的回应。
Neuroimage. 2013 Aug 1;76:439-41. doi: 10.1016/j.neuroimage.2012.03.017. Epub 2012 Mar 13.
7
Trouble at rest: how correlation patterns and group differences become distorted after global signal regression.静息态的困扰:全脑信号回归后相关模式和组间差异如何发生扭曲。
Brain Connect. 2012;2(1):25-32. doi: 10.1089/brain.2012.0080.
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Empirical evaluations of slice-timing, smoothing, and normalization effects in seed-based, resting-state functional magnetic resonance imaging analyses.基于种子的静息态功能磁共振成像分析中片层时间、平滑和归一化效应的实证评估。
Brain Connect. 2011;1(5):401-10. doi: 10.1089/brain.2011.0018. Epub 2011 Dec 2.
9
Characterizing variation in the functional connectome: promise and pitfalls.描述功能连接组的变异性:前景与陷阱。
Trends Cogn Sci. 2012 Mar;16(3):181-8. doi: 10.1016/j.tics.2012.02.001. Epub 2012 Feb 15.
10
Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth.在扫描过程中头部运动对多种功能连接测量的影响:对青少年神经发育研究的相关性。
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标准化内在大脑:实现 1000 个功能连接组个体间变异性的稳健测量。

Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes.

机构信息

Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA.

出版信息

Neuroimage. 2013 Oct 15;80:246-62. doi: 10.1016/j.neuroimage.2013.04.081. Epub 2013 Apr 28.

DOI:10.1016/j.neuroimage.2013.04.081
PMID:23631983
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4074397/
Abstract

As researchers increase their efforts to characterize variations in the functional connectome across studies and individuals, concerns about the many sources of nuisance variation present and their impact on resting state fMRI (R-fMRI) measures continue to grow. Although substantial within-site variation can exist, efforts to aggregate data across multiple sites such as the 1000 Functional Connectomes Project (FCP) and International Neuroimaging Data-sharing Initiative (INDI) datasets amplify these concerns. The present work draws upon standardization approaches commonly used in the microarray gene expression literature, and to a lesser extent recent imaging studies, and compares them with respect to their impact on relationships between common R-fMRI measures and nuisance variables (e.g., imaging site, motion), as well as phenotypic variables of interest (age, sex). Standardization approaches differed with regard to whether they were applied post-hoc vs. during pre-processing, and at the individual vs. group level; additionally they varied in whether they addressed additive effects vs. additive+multiplicative effects, and were parametric vs. non-parametric. While all standardization approaches were effective at reducing undesirable relationships with nuisance variables, post-hoc approaches were generally more effective than global signal regression (GSR). Across approaches, correction for additive effects (global mean) appeared to be more important than for multiplicative effects (global SD) for all R-fMRI measures, with the exception of amplitude of low frequency fluctuations (ALFF). Group-level post-hoc standardizations for mean-centering and variance-standardization were found to be advantageous in their ability to avoid the introduction of artifactual relationships with standardization parameters; though results between individual and group-level post-hoc approaches were highly similar overall. While post-hoc standardization procedures drastically increased test-retest (TRT) reliability for ALFF, modest reductions were observed for other measures after post-hoc standardizations-a phenomena likely attributable to the separation of voxel-wise from global differences among subjects (global mean and SD demonstrated moderate TRT reliability for these measures). Finally, the present work calls into question previous observations of increased anatomical specificity for GSR over mean centering, and draws attention to the near equivalence of global and gray matter signal regression.

摘要

随着研究人员加大努力,从多个研究和个体的角度来描述功能连接组的变化,人们对目前存在的许多干扰变量及其对静息态 fMRI(R-fMRI)测量的影响的担忧继续增加。尽管在一个站点内可能存在大量的变化,但将数据聚合到多个站点的努力,例如 1000 个功能连接组计划(FCP)和国际神经影像学数据共享倡议(INDI)数据集,放大了这些担忧。本研究借鉴了微阵列基因表达文献中常用的标准化方法,以及在较小程度上借鉴了最近的成像研究,并比较了它们对常见 R-fMRI 测量值与干扰变量(例如成像站点、运动)之间关系的影响,以及感兴趣的表型变量(年龄、性别)。标准化方法在是否应用于事后处理与预处理期间、在个体与组水平上有所不同;此外,它们还在是否解决了加性效应与加性+乘法效应、参数与非参数方面有所不同。虽然所有的标准化方法都能有效地减少与干扰变量的不良关系,但事后处理方法通常比全局信号回归(GSR)更有效。在所有的 R-fMRI 测量中,对于所有的 R-fMRI 测量,校正加性效应(全局均值)似乎比校正乘法效应(全局标准差)更重要,除了低频波动幅度(ALFF)外。发现针对均值中心化和方差标准化的组水平事后标准化方法在避免与标准化参数引入人为关系方面具有优势;尽管个体和组水平事后处理方法之间的结果总体上非常相似。虽然事后标准化程序极大地提高了低频波动幅度的测试-重测(TRT)可靠性,但在事后标准化后,其他测量的可靠性略有降低——这一现象可能归因于从全局差异中分离出体素间的差异(全局均值和标准差对这些测量具有中等的 TRT 可靠性)。最后,本研究对 GSR 相对于均值中心化的解剖学特异性增加的先前观察提出了质疑,并提请注意全局和灰质信号回归的近乎等同性。

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