Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, Georgia.
School of Automation, Northwestern Polytechnical University, Xi'an, P.R. China.
Brain Connect. 2020 Mar;10(2):72-82. doi: 10.1089/brain.2019.0701. Epub 2020 Mar 5.
Hierarchical organization of brain function has been an established concept in the neuroscience field for a long time, however, it has been rarely demonstrated how such hierarchical macroscale functional networks are actually organized in the human brain. In this study, to answer this question, we propose a novel methodology to provide an evidence of hierarchical organization of functional brain networks. This article introduces the hybrid spatiotemporal deep learning (HSDL), by jointly using deep belief networks (DBNs) and deep least absolute shrinkage and selection operator (LASSO) to reveal the temporal hierarchical features and spatial hierarchical maps of brain networks based on the Human Connectome Project 900 functional magnetic resonance imaging (fMRI) data sets. Briefly, the key idea of HSDL is to extract the weights between two adjacent layers of DBNs, which are then treated as the hierarchical dictionaries for deep LASSO to identify the corresponding hierarchical spatial maps. Our results demonstrate that both spatial and temporal aspects of dozens of functional networks exhibit multiscale properties that can be well characterized and interpreted based on existing computational tools and neuroscience knowledge. Our proposed novel hybrid deep model is used to provide the first insightful opportunity to reveal the potential hierarchical organization of time series and functional brain networks, using task-based fMRI signals of human brain.
大脑功能的层次结构在神经科学领域已经是一个长期确立的概念，但很少有研究表明人类大脑中的这种层次化的宏观功能网络是如何组织的。在这项研究中，为了解决这个问题，我们提出了一种新的方法来提供功能脑网络层次组织的证据。本文介绍了混合时空深度学习（HSDL），通过联合使用深度置信网络（DBNs）和深度最小绝对收缩和选择算子（LASSO），基于人类连接组计划的 900 个功能磁共振成像（fMRI）数据集，揭示了脑网络的时间层次特征和空间层次图谱。简而言之，HSDL 的关键思想是提取 DBN 两个相邻层之间的权重，然后将其作为深层 LASSO 的层次字典，以识别相应的层次空间图谱。我们的结果表明，数十个功能网络的空间和时间方面都表现出多尺度特性，可以基于现有的计算工具和神经科学知识进行很好的描述和解释。我们提出的新型混合深度模型被用于提供第一个有见地的机会，揭示基于人类大脑任务功能磁共振成像信号的时间序列和功能脑网络的潜在层次组织。
