High correlations between MRI brain volume measurements based on NeuroQuant® and FreeSurfer
High correlations between MRI brain volume measurements based on NeuroQuant® and FreeSurfer
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DOI：
10.1016/j.pscychresns.2018.05.007
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发表时间：
2018-08-30
影响因子：
2.3
通讯作者：
Bigler, Erin D.
中科院分区：
文献类型：
--
作者：
Ross, David E.;Ochs, Alfred L.;Bigler, Erin D.
NeuroQuant（R）（NQ）和FreeSurfer（FS）是测量MRI脑体积的常用计算机自动化程序。以前，他们被报道有很高的方法间的可靠性，但往往大方法间的影响大小的差异。我们假设线性转换可以用来减少大的效应量。这项研究是我们以前报告的研究的延伸。我们对60名受试者（包括正常对照、创伤性脑损伤患者和阿尔茨海默病患者）进行了NQ和FS脑体积测量。我们同时使用两种统计方法来开发将FS卷转换为NQ卷的方法：传统线性回归和贝叶斯线性回归。对于这两种方法，我们使用回归分析来开发FS体积的线性变换，以使它们更类似于NQ体积。基于传统线性回归的FS到NQ转换导致小到中等的效应量。基于贝叶斯线性回归的转换导致所有效应量都非常小。据我们所知，这是第一份报告描述了一种方法，用于转换FS到NQ数据，以实现高可靠性和低效应量差异。像贝叶斯回归这样的机器学习方法可能比传统方法更有用。
NeuroQuant (R) ( NQ) and FreeSurfer (FS) are commonly used computer-automated programs for measuring MRI brain volume. Previously they were reported to have high intermethod reliabilities but often large intermethod effect size differences. We hypothesized that linear transformations could be used to reduce the large effect sizes. This study was an extension of our previously reported study. We performed NQ and FS brain volume measurements on 60 subjects (including normal controls, patients with traumatic brain injury, and patients with Alzheimer's disease). We used two statistical approaches in parallel to develop methods for transforming FS volumes into NQ volumes: traditional linear regression, and Bayesian linear regression. For both methods, we used regression analyses to develop linear transformations of the FS volumes to make them more similar to the NQ volumes. The FS-to-NQ transformations based on traditional linear regression resulted in effect sizes which were small to moderate. The transformations based on Bayesian linear regression resulted in all effect sizes being trivially small. To our knowledge, this is the first report describing a method for transforming FS to NQ data so as to achieve high reliability and low effect size differences. Machine learning methods like Bayesian regression may be more useful than traditional methods.