虚假关系
神经影像学
计算机科学
统计能力
人工智能
差异(会计)
模式
再现性
磁共振弥散成像
数据集
集合(抽象数据类型)
计算机视觉
扫描仪
模式识别(心理学)
数据挖掘
磁共振成像
机器学习
统计
数学
神经科学
心理学
医学
放射科
社会学
业务
会计
社会科学
程序设计语言
作者
Jean‐Philippe Fortin,Nicholas Cullen,Yvette I. Sheline,Warren D. Taylor,Irem Aselcioglu,Phil Adams,Crystal Cooper,Maurizio Fava,Patrick J. McGrath,Melvin G. McInnis,Ramin V. Parsey,Mary L. Phillips,Madhukar H. Trivedi,Myrna M. Weissman,Russell T. Shinohara
摘要
Abstract With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such unwanted sources of variation, which we refer to as “scanner effects”, can hinder the detection of imaging features associated with clinical covariates of interest and cause spurious findings. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements, across a total of 11 scanners. We propose a set of general tools for visualizing and identifying scanner effects that are generalizable to other modalities. We then propose to use ComBat, a technique adopted from the genomics literature and recently applied to diffusion tensor imaging data, to combine and harmonize cortical thickness values across scanners. We show that ComBat removes unwanted sources of scan variability while simultaneously increasing the power and reproducibility of subsequent statistical analyses. We also show that ComBat is useful for combining imaging data with the goal of studying life-span trajectories in the brain.
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