Exploring Brain Networks via Structured Sparse Representation of fMRI Data

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• 关键词：Sparse representation ; Dictionary learning ; Group sparsity ; Functional networks
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9900
• 期：1
• 页码：55-62
• 全文大小：1,969 KB
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• 作者单位：Qinghua Zhao (18) (20)
  Jianfeng Lu (18)
  Jinglei Lv (19) (20)
  Xi Jiang (20)
  Shijie Zhao (19) (20)
  Tianming Liu (20)
  
  18. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
  20. Cortical Architecture Imaging and Discovery, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
  19. School of Automation, Northwestern Polytechnical University, Xi’an, China
  
• 丛书名：Medical Image Computing and Computer-Assisted Intervention ¨C MICCAI 2016
• ISBN：978-3-319-46720-7
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9900

文摘

Investigating functional brain networks and activities using sparse representation of fMRI data has received significant interests in the neuroimaging field. It has been reported that sparse representation is effective in reconstructing concurrent and interactive functional brain networks. However, previous data-driven reconstruction approaches rarely simultaneously take consideration of anatomical structures, which are the substrate of brain function. Furthermore, it has been rarely explored whether structured sparse representation with anatomical guidance could facilitate functional networks reconstruction. To address this problem, in this paper, we propose to reconstruct brain networks using the anatomy-guided structured multi-task regression (AGSMR) in which 116 anatomical regions from the AAL template as prior knowledge are employed to guide the network reconstruction. Using the publicly available Human Connectome Project (HCP) Q1 dataset as a test bed, our method demonstrated that anatomical guided structure sparse representation is effective in reconstructing concurrent functional brain networks.