It has been recently shown that deep learning models such as convolutional neural networks (CNN), deep belief networks (DBN) and recurrent neural networks (RNN), exhibited remarkable ability in modeling and representing fMRI data for the understanding of functional activities and networks because of their superior data representation capability and wide availability of effective deep learning tools. For example, spatial and/or temporal patterns of functional brain activities embedded in fMRI data can be effectively characterized and modeled by a variety of CNN/DBN/RNN deep learning models as shown in recent studies. However, it has been rarely investigated whether it is possible to directly infer hierarchical brain networks from volumetric fMRI data using deep learning models such as DBN. The perceived difficulties of such studies include very large number of input variables, very large number of training parameters, the lack of effective software tools, the challenge of results interpretation, and etc. To bridge these technical gaps, we designed a novel volumetric sparse deep belief network (VS-DBN) model and implemented it through the popular TensorFlow open source platform to reconstruct hierarchical brain networks from volumetric fMRI data based on the Human Connectome Project (HCP) 900 subjects release. Our experimental results showed that a large number of interpretable and meaningful brain networks can be robustly reconstructed from HCP 900 subjects in a hierarchical fashion, and importantly, these brain networks exhibit reasonably good consistency and correspondence across multiple HCP task-based fMRI datasets. Our work contributed a new general deep learning framework for inferring multiscale volumetric brain networks and offered novel insights into the hierarchical organization of functional brain architecture.

中文翻译：

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通过体积稀疏深度信念网络 (VS-DBN) 对分层大脑网络进行建模

最近的研究表明，卷积神经网络 (CNN)、深度信念网络 (DBN) 和循环神经网络 (RNN) 等深度学习模型在建模和表示 fMRI 数据以理解功能活动和网络方面表现出卓越的能力，因为其卓越的数据表示能力和有效的深度学习工具的广泛可用性。例如，嵌入在 fMRI 数据中的功能性大脑活动的空间和/或时间模式可以通过各种 CNN/DBN/RNN 深度学习模型有效地表征和建模，如最近的研究所示。然而，很少有人研究是否可以使用深度学习模型（如 DBN）从体积 fMRI 数据中直接推断分层大脑网络。此类研究的困难包括大量输入变量、大量训练参数、缺乏有效的软件工具、结果解释的挑战等。为了弥合这些技术差距，我们设计了一种新颖的体积稀疏深度信念网络 (VS-DBN) 模型，并通过流行的 TensorFlow 开源平台实现，以基于人类连接组项目 (HCP) 900 受试者发布的体积 fMRI 数据重建分层大脑网络。我们的实验结果表明，可以从 HCP 900 受试者以分层方式稳健地重建大量可解释且有意义的大脑网络，重要的是，这些大脑网络在多个基于 HCP 任务的 fMRI 数据集之间表现出相当好的一致性和对应性。