人类连接体项目
人工智能
计算机科学
连接体
层级组织
功能磁共振成像
深度学习
Lasso(编程语言)
系统神经科学
机器学习
神经科学
模式识别(心理学)
功能连接
心理学
万维网
经济
管理
中枢神经系统
少突胶质细胞
髓鞘
作者
Wei Zhang,Shijie Zhao,Xintao Hu,Qinglin Dong,Heng Huang,Shu Zhang,Yu Zhao,Haixing Dai,Fangfei Ge,Lei Guo,Tianming Liu
出处
期刊:Brain connectivity
[Mary Ann Liebert]
日期:2020-02-14
卷期号:10 (2): 72-82
被引量:19
标识
DOI:10.1089/brain.2019.0701
摘要
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.
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