Finding the most effective way to aggregate multi-subject fMRI data is a
long-standing and challenging problem. It is of increasing interest in
contemporary fMRI studies of human cognition due to the scarcity of data per
subject and the variability of brain anatomy and functional response across
subjects. Recent work on latent factor models shows promising results in this
task but this approach does not preserve spatial locality in the brain. We
examine two ways to combine the ideas of a factor model and a searchlight based
analysis to aggregate multi-subject fMRI data while preserving spatial
locality. We first do this directly by combining a recent factor method known
as a shared response model with searchlight analysis. Then we design a
multi-view convolutional autoencoder for the same task. Both approaches
preserve spatial locality and have competitive or better performance compared
with standard searchlight analysis and the shared response model applied across
the whole brain. We also report a system design to handle the computational
challenge of training the convolutional autoencoder.

这篇研究论文探讨了如何在保留脑部区域性的同时聚合多主体 fMRI 数据。通过组合共享响应模型和搜索光分析等方法来实现该目标，并介绍了一个系统性设计来解决卷积自编码器的计算挑战。