For decades, a variety of task-based functional MRI (tfMRI) data analysis approaches have been developed, including the general linear model (GLM), sparse representations, and independent component analysis (ICA). However, these methods are mainly shallow models and are limited in faithfully modeling the complex, diverse, and concurrent spatial–temporal functional brain activities. Recently, recurrent neural networks (RNNs) have demonstrated great superiority in modeling temporal dependency of signals, while autoencoder models have been proven to be effective in automatically estimating the optimal representations of the original data. These characteristics of RNNs and autoencoders naturally meet the requirement of modeling hemodynamic response patterns in tfMRI data. Thus, we propose a novel unsupervised framework of deep recurrent autoencoder (DRAE) for modeling hemodynamic response patterns in this article. The basic idea of the DRAE model is to combine the deep RNN and the autoencoder to automatically characterize the meaningful functional brain networks and corresponding diverse and complex hemodynamic response patterns simultaneously. The experimental results demonstrate the superiority of the proposed DRAE model in automatically estimating the diverse and complex hemodynamic response patterns.

中文翻译：

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通过深度循环自动编码器模拟大脑多样化和复杂的血流动力学反应模式

几十年来，已经开发了各种基于任务的功能性 MRI (tfMRI) 数据分析方法，包括一般线性模型 (GLM)、稀疏表示和独立分量分析 (ICA)。然而，这些方法主要是浅层模型，并且在忠实地模拟复杂、多样和并发的时空功能性大脑活动方面存在局限性。最近，循环神经网络 (RNN) 在对信号的时间依赖性建模方面表现出极大的优势，而自编码器模型已被证明在自动估计原始数据的最佳表示方面是有效的。RNN 和自动编码器的这些特性自然满足了在 tfMRI 数据中对血流动力学响应模式进行建模的要求。因此，我们在本文中提出了一种新的深度循环自动编码器 (DRAE) 的无监督框架，用于对血液动力学反应模式进行建模。DRAE 模型的基本思想是结合深度 RNN 和自编码器，同时自动表征有意义的功能性大脑网络和相应的多样化和复杂的血流动力学反应模式。实验结果证明了所提出的 DRAE 模型在自动估计多样化和复杂的血流动力学响应模式方面的优越性。