Functional magnetic resonance imaging (fMRI) has provided invaluable insight into our understanding of human behavior. However, large inter-individual differences in both brain anatomy and functional localization after anatomical alignment remain a major limitation in conducting group analyses and performing population-level inference. This paper addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain systems by spatially transforming each subject's functional data to a common reference map. Our proposed Bayesian functional registration approach allows us to assess differences in brain function across subjects and individual differences in activation topology. It combines intensity-based and feature-based information into an integrated framework and allows inference to be performed on the transformation via the posterior samples. We evaluate the method in a simulation study and apply it to data from a study of thermal pain. We find that the proposed approach provides increased sensitivity for group-level inference.

翻译：功能磁共振成像(fMRI)为我们对人类行为的理解提供了宝贵的洞察力,然而,在解剖结合后大脑解剖和功能定位方面的重大个人差异仍然是进行群体分析和进行人口层次推断的一个主要限制,本文件通过开发和验证新的计算技术来解决这一问题,通过将每个主体的功能数据空间转换为共同参考地图,减少功能大脑系统中个人之间的不匹配。我们提议的巴伊西亚功能登记方法使我们能够评估不同主体的大脑功能差异,以及激活地形方面的个人差异。它将基于强度和特征的信息结合到一个综合框架中,并允许通过远地点样本对转换进行推断。我们在模拟研究中评估方法,并将其应用于热疼痛研究的数据。我们发现,拟议的方法提高了群体层次推断的敏感性。