Dang Shilpa, Chaudhury Santanu, Lall Brejesh, Roy Prasun Kumar
Electrical Engineering Department, Indian Institute of Technology Delhi, New Delhi 110016, India.
Electrical Engineering Department, Indian Institute of Technology Delhi, New Delhi 110016, India; Director, Central Electronics Engineering Research Institute, Pilani 333031, India.
J Neurosci Methods. 2017 Feb 15;278:87-100. doi: 10.1016/j.jneumeth.2016.12.019. Epub 2017 Jan 5.
Effective connectivity (EC) analysis of neuronal groups using fMRI delivers insights about functional-integration. However, fMRI signal has low-temporal resolution due to down-sampling and indirectly measures underlying neuronal activity.
The aim is to address above issues for more reliable EC estimates. This paper proposes use of autoregressive hidden Markov model with missing data (AR-HMM-md) in dynamically multi-linked (DML) framework for learning EC using multiple fMRI time series. In our recent work (Dang et al., 2016), we have shown how AR-HMM-md for modelling single fMRI time series outperforms the existing methods. AR-HMM-md models unobserved neuronal activity and lost data over time as variables and estimates their values by joint optimization given fMRI observation sequence.
The effectiveness in learning EC is shown using simulated experiments. Also the effects of sampling and noise are studied on EC. Moreover, classification-experiments are performed for Attention-Deficit/Hyperactivity Disorder subjects and age-matched controls for performance evaluation of real data. Using Bayesian model selection, we see that the proposed model converged to higher log-likelihood and demonstrated that group-classification can be performed with higher cross-validation accuracy of above 94% using distinctive network EC which characterizes patients vs.
The full data EC obtained from DML-AR-HMM-md is more consistent with previous literature than the classical multivariate Granger causality method.
The proposed architecture leads to reliable estimates of EC than the existing latent models.
This framework overcomes the disadvantage of low-temporal resolution and improves cross-validation accuracy significantly due to presence of missing data variables and autoregressive process.
利用功能磁共振成像（fMRI）对神经元群体进行有效连接（EC）分析可提供有关功能整合的见解。然而，由于下采样，fMRI信号具有低时间分辨率，并且间接测量潜在的神经元活动。
目的是解决上述问题以获得更可靠的EC估计。本文提出在动态多链接（DML）框架中使用具有缺失数据的自回归隐马尔可夫模型（AR-HMM-md），通过多个fMRI时间序列来学习EC。在我们最近的工作（Dang等人，2016年）中，我们已经展示了用于对单个fMRI时间序列进行建模的AR-HMM-md如何优于现有方法。AR-HMM-md将未观察到的神经元活动和随时间丢失的数据建模为变量，并根据fMRI观测序列通过联合优化来估计它们的值。
使用模拟实验展示了学习EC的有效性。还研究了采样和噪声对EC的影响。此外，针对注意力缺陷多动障碍受试者和年龄匹配的对照组进行了分类实验，以评估真实数据的性能。使用贝叶斯模型选择，我们发现所提出的模型收敛到更高的对数似然，并证明使用区分患者与对照组的独特网络EC，可以以高于94%的交叉验证准确率进行组分类。
从DML-AR-HMM-md获得的全数据EC比经典的多元格兰杰因果关系方法更符合先前的文献。
所提出的架构比现有的潜在模型能得出更可靠的EC估计。
该框架克服了低时间分辨率的缺点，并且由于存在缺失数据变量和自回归过程，显著提高了交叉验证准确率。
