Estimation of Hemodynamic States Based on Stacked Recurrent Neural Network in fMRI Time Series
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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.Abstract: The estimation of underlying hemodynamic states from the fMRI data can provide an objective representation of the brain activity at the neuronal level, which can contribute to the understanding of the brain operation mechanism and the development of the brain cognition research. So far, many methods have been proposed for estimating hemodynamic states from the fMRI data. However, most of these methods are limited to the further consideration of the temporal characteristic in the hemodynamic model. In addition, they require the prior knowledge on hemodynamic model parameter values, which are not measurable in the actual situation. Therefore, this paper presents a new approach based on recurrent neural network (RNN) to carry out the estimation of hemodynamic states, which employs RNN to extract the temporal features inherent in fMRI time series. Firstly, the inversion process has been constructed by the inversions of nonlinear functions in the hemodynamic model, which map the BOLD signal to hemodynamic states. Then, a novel neural network architecture called stacked recurrent neural network (SRNN) is used for estimating hemodynamic states with BOLD signals by approximating the mapping relations. Finally, the experimental results on the simulated data have shown that the new approach can not only capture the temporal characteristic in the fMRI data, but also can model the nonlinear relationship between hemodynamic states dynamically.
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Key words:
- Recurrent neural network (RNN) /
- functional magnetic resonance imaging (fMRI) /
- hemodynamic model /
- hemodynamic state /
- neural activity
1) 本文责任编委 谭营 -
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 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 
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