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首页> 外文期刊>Psychiatry Research. Neuroimaging >High correlations between MRI brain volume measurements based on NeuroQuant (R) and FreeSurfer
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High correlations between MRI brain volume measurements based on NeuroQuant (R) and FreeSurfer

机译：基于神经夸（R）和FreeSurfer的MRI脑体积测量之间的高相关性

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摘要

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.
机译：神经夸（R）（NQ）和FreSurfer（FS）通常是用于测量MRI脑体积的计算机自动化程序。以前据报道他们具有高间歇性的可靠性，但往往大的间面效应大小差异。我们假设线性变换可用于减少大效果大小。本研究是我们先前报告的研究的延伸。我们对60项受试者进行了NQ和FS脑体积测量（包括正常对照，创伤性脑损伤患者，以及阿尔茨海默病的患者）。我们使用两个统计方法并行，以开发将FS卷转换为NQ卷的方法：传统的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卷的线性变换，使其更类似于NQ卷。基于传统线性回归的FS-to-NQ变换导致效果大小为中等。基于贝叶斯线性回归的变换导致所有效果大小庞大。为了我们的知识，这是第一个描述用于将FS转换为NQ数据的方法，以实现高可靠性和低效果大小差异。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。

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