A Comparison of Distributional Considerations with Statistical Analysis of Resting State fMRI at 3T and 7T.
A Comparison of Distributional Considerations with Statistical Analysis of Resting State fMRI at 3T and 7T.
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分布考虑因素与 3T 和 7T 静息态 fMRI 统计分析的比较。
DOI：
10.1117/12.911307
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Landman,BennettA
中科院分区：
文献类型：
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作者：
Yang,Xue;Holmes,MarthaJ;Newton,AllenT;Morgan,VictoriaL;Landman,BennettA
超高场7T磁共振成像（MRI）通过比传统的1.5T和3T MRI扫描仪更高的信噪比和对比度，为人脑内的功能活动提供了前所未有的空间分辨率。然而，生理和成像伪影的影响也大大增加。传统的统计参数映射理论的基础上的分布特性代表的数据采集在较低的领域可能是不够的新的7T数据。在这里，我们研究了基于两个7T和一个3T协议的模型拟合残差。我们发现，模型残差在7T相对于3T的非高斯性更强。通过具有峰值不均匀性问题的区域的成像切片（例如，7T海马体的中脑采集）显示出视觉上更高程度的失真，沿着有空间相关的和极值的峰度（非高斯性的量度）。先前已通过估计回归误差的协方差矩阵解决了3T数据的伪影影响。我们进一步扩展了自回归模型的鲁棒估计方法，并评估了这种技术相对于传统推断的定性影响。基于经典假设与稳健假设的推论之间存在明显的统计显著性差异，这表明基于高斯假设的推论受到有关其功率和有效性的实际（以及理论）关注。因此，现代统计方法，如本文提出的鲁棒自回归模型，是适当的，适合于超高场功能磁共振成像的推理。
Ultra-high field 7T magnetic resonance imaging (MRI) offers potentially unprecedented spatial resolution of functional activity within the human brain through increased signal and contrast to noise ratios over traditional 1.5T and 3T MRI scanners. However, the effects physiological and imaging artifacts are also greatly increased. Traditional statistical parametric mapping theories based on distributional properties representative of data acquired at lower fields may be inadequate for new 7T data. Herein, we investigate the model fitting residuals based on two 7T and one 3T protocols. We find that model residuals are substantively more non-Gaussian at 7T relative to 3T. Imaging slices that passed through regions with peak inhomogeneity problems (e.g., mid-brain acquisitions for the 7T hippocampus) exhibited visually higher degrees of distortion along with spatially correlated and extreme values of kurtosis (a measure of non-Gaussianity). The impacts of artifacts have been previously addressed for 3T data by estimating the covariance matrix of the regression errors. We further extend the robust estimation approach for autoregressive models and evaluate the qualitative impacts of this technique relative to traditional inference. Clear differences in statistical significance are shown between inferences based on classical versus robust assumptions, which suggest that inferences based on Gaussian assumptions are subject to practical (as well as theoretical) concerns regarding their power and validity. Hence, modern statistical approaches, such as the robust autoregressive model posed herein, are appropriate and suitable for inference with ultra-high field functional magnetic resonance imaging.