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A mutual information based framework for the analysis of multiple-subject fMRI data

机译：一个基于互信息的框架，用于多主题fMRI数据分析

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Functional magnetic resonance imaging (fMRI) is a non-invasive method of obtaining images of neural activity in response to a stimulus. The reverse process, decoding fMRI images to infer the underlying stimulus relating to a single subject, is a challenging area of work. The complexity greatly increases when the decoding process has to be sufficiently generic to cover multiple subjects and studies. In order to decode neural images with high degrees of accuracy, the process relies on an extensive database of neural signatures. This paper proposes a framework to generate the elements of the database. We also outline the process involved in decomposing an fMRI dataset into independent subsets corresponding to the neural signatures of the stimuli. Each subset is described in a standard format, based on mutual information derived from Regions of Interest, which in turn are derived by co-registering with a standard atlas.
机译：功能磁共振成像（fMRI）是一种非侵入性方法，可响应刺激获取神经活动的图像。对fMRI图像进行解码以推断与单个对象有关的潜在刺激的反向过程是一项具有挑战性的工作。当解码过程必须足够通用以覆盖多个主题和研究时，复杂性会大大增加。为了高度准确地解码神经图像，该过程依赖于神经特征的广泛数据库。本文提出了一个框架来生成数据库的元素。我们还概述了将fMRI数据集分解为与刺激的神经特征相对应的独立子集所涉及的过程。每个子集均以标准格式描述，基于从关注区域派生的互信息，而互取信息又是通过与标准地图集共同注册而得出的。

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