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BVAR-Connect: A Variational Bayes Approach to Multi-Subject Vector Autoregressive Models for Inference on Brain Connectivity Networks.
Neuroinformatics ( IF 3 ) Pub Date : 2020-06-05 , DOI: 10.1007/s12021-020-09472-w
Jeong Hwan Kook 1 , Kelly A Vaughn 2 , Dana M DeMaster 2 , Linda Ewing-Cobbs 2 , Marina Vannucci 1
Affiliation  

In this paper we propose BVAR-connect, a variational inference approach to a Bayesian multi-subject vector autoregressive (VAR) model for inference on effective brain connectivity based on resting-state functional MRI data. The modeling framework uses a Bayesian variable selection approach that flexibly integrates multi-modal data, in particular structural diffusion tensor imaging (DTI) data, into the prior construction. The variational inference approach we develop allows scalability of the methods and results in the ability to estimate subject- and group-level brain connectivity networks over whole-brain parcellations of the data. We provide a brief description of a user-friendly MATLAB GUI released for public use. We assess performance on simulated data, where we show that the proposed inference method can achieve comparable accuracy to the sampling-based Markov Chain Monte Carlo approach but at a much lower computational cost. We also address the case of subject groups with imbalanced sample sizes. Finally, we illustrate the methods on resting-state functional MRI and structural DTI data on children with a history of traumatic injury.



中文翻译:

BVAR-Connect:用于大脑连接网络推理的多主题向量自回归模型的变分贝叶斯方法。

在本文中,我们提出了BVAR-connect,是一种基于贝叶斯多主体向量自回归(VAR)模型的变分推理方法,用于基于静止状态功能MRI数据来推理有效的大脑连通性。建模框架使用贝叶斯变量选择方法,该方法将多模态数据(特别是结构扩散张量成像(DTI)数据)灵活地集成到现有结构中。我们开发的变分推理方法可实现方法的可扩展性,并具有在数据全脑分割的情况下估算受试者和小组级大脑连接网络的能力。我们提供了供公众使用的用户友好型MATLAB GUI的简要说明。我们评估模拟数据的效果,在这里,我们证明了所提出的推理方法可以达到与基于采样的马尔可夫链蒙特卡洛方法相当的精度,但计算成本却低得多。我们还将解决样本量不平衡的主题组的情况。最后,我们举例说明了有外伤史的儿童的静息状态功能性MRI和结构性DTI数据的方法。

更新日期:2020-06-05
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