OBJECTIVE 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 evidence on hierarchical organization of functional brain networks. METHOD This paper introduces the Hybrid Spatiotemporal Deep Learning (HSDL), by jointly using Deep Belief Networks (DBN) and Deep LASSO to reveal the temporal hierarchical features and spatial hierarchical maps of brain networks based on the Human Connectome Project (HCP) 900 fMRI datasets. Briefly, the key idea of HSDL is to extract the weights between two adjacent layers of DBN, which are then treated as the hierarchical dictionaries for Deep LASSO to identify the corresponding hierarchical spatial maps. CONCLUSION 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. SIGNIFICANCE Our proposed novel hybrid deep model is employed 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.

中文翻译：

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混合时空深度学习揭示了功能性大脑网络的分层组织。

目的长期以来，大脑功能的分层组织一直是神经科学领域的一个公认概念，但是很少有人证明这种分层的宏观功能网络是如何在人脑中实际组织的。在这项研究中，为回答这个问题，我们提出了一种新颖的方法，以提供功能性大脑网络的分层组织的证据。方法本文通过结合深度信念网络（DBN）和深度LASSO来介绍混合时空深度学习（HSDL），以基于人类Connectome项目（HCP）900 fMRI数据集揭示大脑网络的时间分层特征和空间分层图。简而言之，HSDL的关键思想是提取DBN的两个相邻层之间的权重，然后将其作为Deep LASSO的分层词典，以识别相应的分层空间图。结论我们的结果表明，数十个功能网络的时空方面均表现出多尺度的特性，这些特性可以基于现有的计算工具和神经科学知识很好地表征和解释。意义我们提议的新型混合深度模型可用于提供第一个有洞察力的机会，利用人脑基于任务的fMRI信号揭示时间序列和功能性脑网络的潜在层次结构。结论我们的结果表明，数十个功能网络的时空方面均表现出多尺度的特性，这些特性可以基于现有的计算工具和神经科学知识很好地表征和解释。意义我们提议的新型混合深度模型可用于提供第一个有洞察力的机会，利用人脑基于任务的fMRI信号揭示时间序列和功能性脑网络的潜在层次结构。结论我们的结果表明，数十个功能网络的时空方面均表现出多尺度的特性，这些特性可以基于现有的计算工具和神经科学知识很好地表征和解释。意义我们提议的新型混合深度模型可用于提供第一个有洞察力的机会，利用人脑基于任务的fMRI信号揭示时间序列和功能性脑网络的潜在层次结构。