• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Frontiers in Neuroinformatics >A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data
【24h】

A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data

机译：一种基于任务的功能磁共振成像中信号采样的字典学习方法，用于减少大数据

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size. Therefore, how to reduce the data size but without losing important information becomes a more and more pressing issue. To address this problem, we propose a signal sampling approach for significant fMRI data reduction before performing structurally-guided dictionary learning and sparse coding of whole brain's fMRI data. We compared the proposed structurally guided sampling method with no sampling, random sampling and uniform sampling schemes, and experiments on the Human Connectome Project (HCP) task fMRI data demonstrated that the proposed method can achieve more than 15 times speed-up without sacrificing the accuracy in identifying task-evoked functional brain networks.
机译：fMRI大数据的指数增长为研究人员提供了探索功能性大脑网络的前所未有的机会。然而，由于缺乏有效且有效的工具来处理此类fMRI大数据，因此尚未充分探索此机会。一个主要挑战是计算能力仍然落后于大型fMRI数据库的增长，例如，对于平均大小的fMRI数据库，执行字典学习和全脑fMRI数据的稀疏编码需要花费很多时间。因此，如何减小数据大小却又不丢失重要信息成为越来越紧迫的问题。为了解决这个问题，我们提出了一种信号采样方法，用于在执行结构引导的字典学习和全脑fMRI数据的稀疏编码之前，显着减少fMRI数据。我们比较了所提出的无抽样，随机抽样和统一抽样方案的结构指导抽样方法，并且对人类Connectome Project（HCP）任务fMRI数据进行的实验表明，所提出的方法可以实现超过15倍的提速，而不会牺牲准确性在识别任务诱发的功能性大脑网络中。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号