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 Jun 15;285:33-44. doi: 10.1016/j.jneumeth.2017.05.009. Epub 2017 May 8.
Determination of effective connectivity (EC) among brain regions using fMRI is helpful in understanding the underlying neural mechanisms. Dynamic Bayesian Networks (DBNs) are an appropriate class of probabilistic graphical temporal-models that have been used in past to model EC from fMRI, specifically order-one.
NEW-METHOD: High-order DBNs (HO-DBNs) have still not been explored for fMRI data. A fundamental problem faced in the structure-learning of HO-DBN is high computational-burden and low accuracy by the existing heuristic search techniques used for EC detection from fMRI. In this paper, we propose using dynamic programming (DP) principle along with integration of properties of scoring-function in a way to reduce search space for structure-learning of HO-DBNs and finally, for identifying EC from fMRI which has not been done yet to the best of our knowledge. The proposed exact search-&-score learning approach HO-DBN-DP is an extension of the technique which was originally devised for learning a BN's structure from static data (Singh and Moore, 2005).
The effectiveness in structure-learning is shown on synthetic fMRI dataset. The algorithm reaches globally-optimal solution in appreciably reduced time-complexity than the static counterpart due to integration of properties. The proof of optimality is provided.
The results demonstrate that HO-DBN-DP is comparably more accurate and faster than currently used structure-learning algorithms used for identifying EC from fMRI. The real data EC from HO-DBN-DP shows consistency with previous literature than the classical Granger Causality method.
Hence, the DP algorithm can be employed for reliable EC estimates from experimental fMRI data.
使用功能磁共振成像（fMRI）确定脑区之间的有效连接性（EC）有助于理解潜在的神经机制。动态贝叶斯网络（DBNs）是一类合适的概率图形时间模型，过去已被用于从fMRI中对EC进行建模，特别是一阶模型。
高阶DBNs（HO-DBNs）尚未用于fMRI数据。HO-DBN结构学习中面临的一个基本问题是，用于从fMRI检测EC的现有启发式搜索技术计算负担重且准确性低。在本文中，我们提出使用动态规划（DP）原理，并结合评分函数的属性，以减少HO-DBNs结构学习的搜索空间，最终从fMRI中识别EC，据我们所知，这尚未实现。所提出的精确搜索与评分学习方法HO-DBN-DP是最初为从静态数据学习贝叶斯网络（BN）结构而设计的技术的扩展（Singh和Moore，2005）。
在合成fMRI数据集上展示了结构学习的有效性。由于属性的整合，该算法在比静态对应算法明显降低的时间复杂度下达到全局最优解。提供了最优性证明。
结果表明，HO-DBN-DP比目前用于从fMRI识别EC的结构学习算法更准确、更快。与经典格兰杰因果关系方法相比，HO-DBN-DP得到的真实数据EC与先前文献显示出一致性。
因此，DP算法可用于从实验性fMRI数据中进行可靠的EC估计。
