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基于栈式循环神经网络的血液动力学状态估计方法

姚垚 冀俊忠

姚垚, 冀俊忠. 基于栈式循环神经网络的血液动力学状态估计方法. 自动化学报, 2020, 46(5): 991-1003. doi: 10.16383/j.aas.2018.c170541
引用本文: 姚垚, 冀俊忠. 基于栈式循环神经网络的血液动力学状态估计方法. 自动化学报, 2020, 46(5): 991-1003. doi: 10.16383/j.aas.2018.c170541
YAO Yao, JI Jun-Zhong. Estimation of Hemodynamic States Based on Stacked Recurrent Neural Network in fMRI Time Series. ACTA AUTOMATICA SINICA, 2020, 46(5): 991-1003. doi: 10.16383/j.aas.2018.c170541
Citation: YAO Yao, JI Jun-Zhong. Estimation of Hemodynamic States Based on Stacked Recurrent Neural Network in fMRI Time Series. ACTA AUTOMATICA SINICA, 2020, 46(5): 991-1003. doi: 10.16383/j.aas.2018.c170541

基于栈式循环神经网络的血液动力学状态估计方法

doi: 10.16383/j.aas.2018.c170541
基金项目: 

国家自然科学基金 61672065

国家自然科学基金 61375059

详细信息
    作者简介:

    姚垚   北京工业大学信息学部博士研究生. 2014年获得北京工业大学理学学士学位.主要研究方向为计算智能, 深度学习和脑科学. E-mail:yaoyao1314@emails.bjut.edu.cn

    通讯作者:

    冀俊忠   北京工业大学教授. 2004年获北京工业大学计算机应用技术专业博士学位, 2005年和2010年分别在挪威科技大学、纽约州立大学布法罗分校做访问学者.主要研究方向为机器学习, 计算智能, 生物信息学和脑科学.本文通信作者. E-mail: jjz01@bjut.edu.cn

Estimation of Hemodynamic States Based on Stacked Recurrent Neural Network in fMRI Time Series

Funds: 

National Natural Science Foundation of China 61672065

National Natural Science Foundation of China 61375059

More Information
    Author Bio:

    YAO Yao Ph. D. candidate at the the Faculty of Information Technology Beijing University of Technology. He received his bachelor degree from Beijing University of Technology in 2014. His research interest covers computation intelligence, deep learning and brain science

    Corresponding author: JI Jun-Zhong Professor at Beijing University of Technology. He received his Ph. D. degree in computer science and application technology from Beijing University of Technology in 2004. He was a visiting scholar at Norwegian University of Science and Technology in 2005 and State University of New York at Buffalo in 2010, respectively. His research interest covers machine learning, computation intelligence, bioinformatics and brain science. Corresponding author of this paper
  • 摘要: 利用fMRI数据准确地估计血液动力学状态, 能得到一种更接近神经元层面的大脑活动的客观表示, 这将促进人们对大脑运行机理的深刻理解, 推动脑认知的进一步发展.迄今为止, 人们已经提出了许多血液动力学状态估计方法.然而, 这些方法大都只考虑了相邻时刻血液动力学状态之间的关系, 忽视了更深层次的时序特征.而对模型参数先验信息的需求也使一些方法在实际应用中受到了限制.为此, 本文提出了一种基于循环神经网络的血液动力学状态估计新方法.首先, 利用血液动力学模型中非线性函数的反函数建立BOLD信号与血液动力学状态之间的映射关系, 并构建模型的反演过程.然后, 采用一种堆叠三个RNN模块的栈式神经网络结构来拟合这种映射关系, 使其能够以BOLD信号作为输入, 得到血液动力学状态的估计值.最后, 在仿真数据上验证新方法的性能.实验结果表明:与一些代表算法相比, 新方法能够更合理地提取fMRI数据中的时间特性, 有效地拟合BOLD信号与血液动力学状态之间的动态非线性关系.
    Recommended by Associate Editor TAN Ying
    1)  本文责任编委 谭营
  • 图  1  基本循环神经网络结构

    Fig.  1  A simple recurrent neural network

    图  2  血液动力学模型反演过程

    Fig.  2  The inversion process of the hemodynamic model

    图  3  栈式循环神经网络结构

    Fig.  3  An illustration of the proposed SRNN

    图  4  随机生成的神经元活动

    Fig.  4  The random generated neural activity

    图  5  随机生成的血液动力学状态及BOLD信号

    Fig.  5  The random generated hemodynamic states and the BOLD signal

    图  6  RNN模块与LSTM模块结果对比

    Fig.  6  The comparison of RNN module and the LSTM module

    图  7  各算法对比结果

    Fig.  7  The result of different approaches

    图  8  DEM、SCKS以及SRNN的实验结果对比

    Fig.  8  The results of different approaches for estimation of hemodynamic states

    图  9  血液动力学模型参数先验值对算法DEM与SCKS性能的影响

    Fig.  9  The influence of prior knowledge on model parameters on DEM and SCKS

    表  1  血液动力学模型参数默认值

    Table  1  The default value of hemodynamic model parameters

    Parameters Description Default
    $ \epsilon $ Neural efficiency 0.50
    $ \kappa $ Rate of signal decay 0.65
    $ \gamma $ Rate of flow dependent elimination 0.41
    $ \tau $ Hemodynamic transit time 0.98
    $ \alpha $ Grubb's exponent 0.32
    $ E_0 $ Resting oxygen extraction fraction 0.34
    $ V_0 $ Resting blood volume fraction 0.08
    下载: 导出CSV

    表  2  时间性能比较

    Table  2  The comparison of time performance

    方法 训练复杂度 运算复杂度 训练时间(s) 运算时间(s)
    DEM[16] $ - $ $ {\rm O}(K_D T n_s^3) $ $ - $ $ 16.3 $
    SCKS[17] $ - $ $ {\rm O}(2K_{SC} T n_p^3) $ $ - $ $ 35.7 $
    NARX[24] $ {\rm O}(K_N T m n_{hn}) $ $ {\rm O}(K_N T n_{hn}) $ 11 367 0.09
    SRNN $ {\rm O}(4K_{SR} T m n_{hs}) $ $ {\rm O}(4K_{SR} T n_{hs}) $ 1 831 0.2
    下载: 导出CSV
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  • 收稿日期:  2017-09-25
  • 录用日期:  2018-01-20
  • 刊出日期:  2020-06-01

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