Statistical Analysis Of Image Features

图像特征统计分析

• 批准号: 7591914
• 负责人: Daniel W Hommer
• 金额: $ 47.07万
• 依托单位: NATIONAL INSTITUTE ON ALCOHOL ABUSE AND ALCOHOLISM
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7591914
• 关键词: Accounting; Age; Alcohol consumption; Alcohols; Blood flow; Blur; Brain; Brain region; Complex; Condition; Data; Development; Diagnostic; Emotions; Experimental Designs; Fourier Transform; Functional Magnetic Resonance Imaging; Image; Image Analysis; Individual; Intake; Investigation; Linear Models; Localized; Magnetic Resonance Imaging; Measures; Methodology; Methods; Noise; Numbers; Outcome; Pharmaceutical Preparations; Positron-Emission Tomography; Property; Research; Series; Signal Transduction; Simulate; Slide; Statistical Methods; Stimulus; Structure; Techniques; Testing; Time; Time Series Analysis; alcohol craving; base; design; experience; hemodynamics; interest; problem drinker; research study; response; statistics; theories; visual stimulus

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.

基于傅立叶变换，小波变换和空间域中高斯随机场的理论，已经开发了三种不同的统计方法。所有三种方法都应用于对正常受试者和酒精受试者的PET和MR图像的分析，并确定了通常相同大脑区域的显着差异。当前对这些主题的研究包括开发一种1-D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旨在结合长时间的实验中获得的fMRI数据（如实验上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，例如由药物或酒精给药产生的方差。它使用较长的基线数据来估计与图像中体元相关的时间数据相关的方差度量，并设置一个统计上严格的阈值以激活，尽管数据中存在已知的时间相关性。该分析技术正在通过模拟和实验数据进行验证。此外，该分析技术正在纳入许多实验中，包括旨在研究正常受试者中酒精摄入量相关的大脑的血流变化。这为该分析技术提供了理想的证明，以基本建立酒精摄入的响应曲线。最后，已经开发了基于傅立叶域中传统时间序列分析的时间域中的统计分析，并在fMRI血流研究中的信号定位与其他不太严格且可推广的技术相似。该分析方法有可能（1）定位fMRI激活变化，（2）估计或重建无相关噪声的激活信号，（3）估算本地血液动力学响应函数在没有对其结构的事先假设的情况下进行，并且（4）检测到对多重输入刺激的多个响应。目前，该技术用于研究实验设计，包括旨在引起不同情绪或渴望酒精的视觉刺激幻灯片。此外，该技术已扩展为结合了最多五个输入刺激（分别或组合）的完整复杂的一般线性模型（在傅立叶域中需要进行统计测试）。进一步的发展将允许对多个体素进行统计比较，从而可以在此通用线性模型的背景下在不同条件（药物/NO-drug）下进行实验分析。