Statistical Analysis Of Image Features

基于傅里叶变换、小波变换和空间域高斯随机场理论，已经开发了三种不同的统计方法。这三种方法都被应用于正常人和酗酒者的PET和MR图像分析，并在大致相同的大脑区域发现了显著差异。目前对这些主题的研究包括开发一种一维高斯随机场方法来分析fMRI时间序列数据。该方法可用于分析从实验中获得的功能磁共振成像数据，这些实验旨在纳入一个较长的（实验确定的足够长的时间，以估计与获得的数据相关的方差）基线条件，并过渡到另一个激活状态，如药物或酒精管理产生的激活状态。它使用长基线数据来估计与图像内体素的时间数据相关的方差度量，并设置一个统计上严格的激活阈值，尽管数据中已知的时间相关性。该分析技术正在通过模拟和实验数据进行验证。此外，这种分析技术正在被纳入许多实验中，包括一项旨在观察正常受试者中与酒精摄入相关的大脑血流量变化的实验。这是该分析技术的理想演示，基本建立了酒精摄入量的响应曲线。最后，基于传统傅立叶域时间序列分析的时域统计分析得到了发展，并在fMRI血流研究信号定位方面给出了与其他不太严格和可推广的技术相似的结果。这种分析方法有可能(1)定位fMRI激活变化，(2)在没有相关噪声的情况下估计或重建激活信号，(3)在没有事先假设其结构的情况下局部估计血流动力学响应函数，以及(4)检测对多个输入刺激的多个响应。目前，这项技术正被用于研究实验设计，包括旨在引发不同情绪或酒精渴望的视觉刺激幻灯片。此外，该技术已扩展到包含一个完整的复杂一般线性模型（需要在傅立叶域进行统计测试），最多有五个输入刺激（单独或组合）。进一步的开发将允许对多个体素进行统计比较，从而在这个一般线性模型的背景下分析不同条件下（药物/非药物）的实验。

Three different statistical methods have been developed, based on the Fourier transform, the wavelet transform, and the theory of Gaussian random fields in the spatial domain. All three methods have been applied to the analysis of PET and MR images from normal and alcoholic subjects and have identified significant differences in generally the same brain regions. Current research on these topics includes the development of a 1-D Gaussian random field method to analyze fMRI time series data. This methodology can be used to analyze fMRI data acquired from experiments designed to incorporate a long (that is long enough, as determined experimentally, to estimate the variance associated with the acquired data) baseline condition and transition to another activated state such as one produced by drug or alcohol administration. It uses the long baseline data to estimate the variance measure associated with the temporal data from a voxel within the image and sets a statistically rigorous threshold for activation in spite of the known temporal correlation in the data. This analysis technique is being validated with simulated and experimental data. Furthermore, this analysis technique is being incorporated into numerous experiments including one designed to look at the blood flow changes in the brain associated with alcoholic intake in normal subjects. This presents an ideal demonstration of this analysis technique to basically establish a response curve for alcohol intake. Finally, statistical analysis in the temporal domain based on traditional time series analysis in the Fourier domain have been developed and given similar results in terms of localization of the signal in fMRI blood flow studies to other less rigorous and generalizable techniques. This analysis methodology has the potential to (1) localize fMRI activation changes, (2) estimate or reconstruct the activated signal without the associated noise, (3) estimate the hemodynamic response function locally without prior assumptions as to its structure, and (4) detect multiple responses to multiple input stimuli. Currently this technique is being used to study experimental designs including slides of visual stimuli designed to elicit different emotions or alcohol craving. Furthermore this technique has been extended to incorporate a full complex general linear model (required in the Fourier domain for statistical testing) with up to five inputs stimuli (separately or in combinations). Further development will allow statistical comparison of multiple voxels allowing analysis for example of experiments under different conditions (drug/no-drug) within the context of this general linear model.