Sample size estimates for well-powered cross-sectional cortical thickness studies.
Sample size estimates for well-powered cross-sectional cortical thickness studies.
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DOI：
10.1002/hbm.22120
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发表时间：
2013-11
影响因子：
4.8
通讯作者：
Jackson, Graeme D.
中科院分区：
文献类型：
--
作者：
Pardoe, Heath R.;Abbott, David F.;Jackson, Graeme D.
皮质厚度图是分析受试者之间神经解剖学差异的一种广泛使用的方法。我们将能量分析方法应用于一系列图像处理参数，以得出一个模型，该模型允许研究人员计算确保良好的横断面皮质厚度研究所需的受试者数量。98例对照组(女性53例，年龄29.1±9.7岁)用Freesurfer 5.0进行了0.9 mm各向同性T1加权3DMPRAGE MRI扫描。能量分析使用来自共同记录的皮质厚度图的顶点方向方差估计，系统地改变处理参数。采用遗传规划的方法建立了描述样本大小与加工参数之间关系的模型。该模型在4个阿尔茨海默病神经成像计划对照数据集上进行了验证(平均126.5名受试者/站点，年龄76.6±5.0岁)。每组大约50名受试者被要求检测0.25 mm的厚度差异；每组不到10名受试者被要求检测1 mm的差异(双面测试，10 mm平滑，α=0.05)。样本大小估计在皮质表面是不同的。该模型使用来自其他四个数据集的独立数据，得出的样本量预测在实验确定的2-6%以内。将模型参数与各测点数据进行拟合，估计误差小于2%。导出的模型为研究人员提供了一个简单的工具来计算在强大的皮质厚度分析中应该包括多少受试者。
Cortical thickness mapping is a widely used method for the analysis of neuroanatomical differences between subject groups. We applied power analysis methods over a range of image processing parameters to derive a model that allows researchers to calculate the number of subjects required to ensure a well-powered cross-sectional cortical thickness study. 0.9-mm isotropic T1-weighted 3D MPRAGE MRI scans from 98 controls (53 females, age 29.1 ± 9.7 years) were processed using Freesurfer 5.0. Power analyses were carried out using vertex-wise variance estimates from the coregistered cortical thickness maps, systematically varying processing parameters. A genetic programming approach was used to derive a model describing the relationship between sample size and processing parameters. The model was validated on four Alzheimer’s Disease Neuroimaging Initiative control datasets (mean 126.5 subjects/site, age 76.6 ± 5.0 years). Approximately 50 subjects per group are required to detect a 0.25-mm thickness difference; less than 10 subjects per group are required for differences of 1 mm (two-sided test, 10 mm smoothing, α = 0.05). Sample size estimates were heterogeneous over the cortical surface. The model yielded sample size predictions within 2–6% of that determined experimentally using independent data from four other datasets. Fitting parameters of the model to data from each site reduced the estimation error to less than 2%. The derived model provides a simple tool for researchers to calculate how many subjects should be included in a well-powered cortical thickness analysis.
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