BAYESIAN FUNCTIONAL REGISTRATION OF FMRI ACTIVATION MAPS.

BAYESIAN FUNCTIONAL REGISTRATION OF FMRI ACTIVATION MAPS.
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DOI:
10.1214/21-aoas1562
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发表时间:
2022-09
期刊:
The annals of applied statistics
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其他
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功能性磁共振成像(fMRI)为我们理解人类行为提供了宝贵的见解。然而,在进行组分析和进行群体水平推断时,解剖对齐后脑解剖结构和功能定位的个体间差异较大仍然是一个主要限制。本文通过开发和验证一种新的计算技术来解决这个问题,该技术通过将每个受试者的功能数据空间转换到一个共同的参考图来减少功能脑系统中个体之间的不对准。我们提出的贝叶斯功能配准方法使我们能够评估不同受试者的大脑功能差异和激活拓扑的个体差异。它将基于强度和基于特征的信息结合到一个集成的框架中,并允许通过后验样本对变换进行推理。我们在模拟研究中评估该方法,并将其应用于热痛研究的数据。我们发现,所提出的方法提供了更高的灵敏度组级推理。
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 subjects 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.
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