标题： 基于功能磁共振成像的空间选择性与跨条件预测 显示英文标题

标题： On spatial selectivity and prediction across conditions with fMRI

摘要： 功能神经成像领域的研究人员大多使用激活坐标来制定假设。相反，我们提议使用完整的统计图象来定义感兴趣区域（ROI）。本文介绍了两种机器学习方法，迁移学习和选择迁移，它们被比较了识别与两个功能任务相关的脑激活图之间共同模式的能力。我们提供了一些这些相似性的初步量化，并表明选择迁移可以设定一个空间尺度，从而产生比迁移学习更具体于感兴趣的上下文的ROI。特别是，当区分不同的视觉任务时，选择迁移能很好地勾勒出诸如视觉单词形态区等众所周知的区域。 显示英文摘要

摘要： Researchers in functional neuroimaging mostly use activation coordinates to formulate their hypotheses. Instead, we propose to use the full statistical images to define regions of interest (ROIs). This paper presents two machine learning approaches, transfer learning and selection transfer, that are compared upon their ability to identify the common patterns between brain activation maps related to two functional tasks. We provide some preliminary quantification of these similarities, and show that selection transfer makes it possible to set a spatial scale yielding ROIs that are more specific to the context of interest than with transfer learning. In particular, selection transfer outlines well known regions such as the Visual Word Form Area when discriminating between different visual tasks.