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首页> 外文会议>Conference on Physiology and Function: Methods, Systems, and Applications Feb 16-18, 2003 San Diego, California, USA >A Novel Information Theoretic and Bayesian Approach for fMRI data Analysis
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A Novel Information Theoretic and Bayesian Approach for fMRI data Analysis

机译：fMRI数据分析的新型信息理论和贝叶斯方法

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摘要

Functional Magnetic Resonance Imaging (fMRI) is a powerful technique for studying the working of the human brain. This overall goals of the project are to devlop a novel method for the analysis of fMRI data in order to discover the activation of a network of regions involving most likely the hippocampus, parietal cortex and cerebellum as a person is navigating in a virtual environment. Spatially sensitive voxels are extracted by selecting voxels that have high mutual information. Each of these extracted voxels is then used to create a response curve for the stimulus of interest, in this case spatial location. Following the voxel extraction stage, the set of extracted voxel tune series would be treated as a population and used to predict the location of the subject at any randomly selected time in the experiment. The population of voxels essentially "votes" with their current activity. The approach used for prediction is the Bayesian reconstruction method. The ability to predict the location of a subject in the virtual environment based on brain signals will be useful in developing a physiological understanding of spatial cognition in virtual environments.
机译：功能磁共振成像（fMRI）是研究人脑工作的强大技术。该项目的总体目标是开发一种新颖的方法来分析fMRI数据，以发现人在虚拟环境中航行时最可能涉及海马，顶叶皮层和小脑的区域网络的激活。通过选择具有较高互信息的体素来提取空间敏感体素。然后，将这些提取的体素中的每一个用于为感兴趣的刺激（在这种情况下为空间位置）创建响应曲线。在体素提取阶段之后，将提取的体素曲调序列集视为总体，并用于预测实验中任意随机选择的时间对象的位置。体素总体上以其当前活动进行“投票”。用于预测的方法是贝叶斯重建方法。基于脑信号预测对象在虚拟环境中的位置的能力将有助于发展对虚拟环境中空间认知的生理理解。

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