一种用于多主题fMRI数据聚合的卷积自编码器
A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation
摘要：
寻找最有效的方法来聚合多学科的功能磁共振成像数据是一个长期存在的和具有挑战性的问题。由于每个受试者的数据稀缺以及大脑解剖结构和功能反应在受试者之间的差异性，它在当代人类认知的fMRI研究中越来越受到关注。最近的潜在因素模型的工作表明，在这项任务中有希望的结果，但这种方法并不保留在大脑中的空间局部性。我们研究了两种方法相结合的因素模型和探照灯为基础的分析，以聚合多学科的功能磁共振成像数据，同时保持空间局部性的想法。首先，我们直接将最近的因子方法（称为共享响应模型）与探照灯分析相结合。然后，我们设计了一个多视图卷积自动编码器相同的任务。这两种方法都保留了空间局部性，与标准探照灯分析和应用于整个大脑的共享响应模型相比，具有竞争力或更好的性能。我们还报告了一个系统设计，以处理训练卷积自动编码器的计算挑战。
英文摘要：
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.
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