Much effort has been made to better understand the complex integration of distinct parts of the human brain using functional magnetic resonance imaging (fMRI). Altered functional connectivity between brain regions is associated with many neurological and mental illnesses, such as Alzheimer and Parkinson diseases, addiction, and depression. In computational science, Bayesian networks (BN) have been used in a broad range of studies to model complex data set in the presence of uncertainty and when expert prior knowledge is needed. However, little is done to explore the use of BN in connectivity analysis of fMRI data. In this paper, we present an up-to-date literature review and methodological details of connectivity analyses using BN, while highlighting caveats in a real-world application. We present a BN model of fMRI dataset obtained from sixty healthy subjects performing the stop-signal task (SST), a paradigm widely used to investigate response inhibition. Connectivity results are validated with the extant literature including our previous studies. By exploring the link strength of the learned BN's and correlating them to behavioral performance measures, this novel use of BN in connectivity analysis provides new insights to the functional neural pathways underlying response inhibition.

中文翻译：

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大脑功能连接分析中的贝叶斯网络模型

为了更好地理解使用功能性磁共振成像 (fMRI) 的人脑不同部分的复杂整合，已经做出了很多努力。大脑区域之间功能连接的改变与许多神经和精神疾病有关，例如阿尔茨海默病和帕金森病、成瘾和抑郁症。在计算科学中，贝叶斯网络 (BN) 已被广泛用于在存在不确定性和需要专家先验知识的情况下对复杂数据集进行建模的研究。然而，很少有人探索 BN 在 fMRI 数据的连通性分析中的使用。在本文中，我们展示了使用 BN 进行连接分析的最新文献综述和方法论细节，同时强调了实际应用中的注意事项。我们提出了从 60 名执行停止信号任务 (SST) 的健康受试者获得的 fMRI 数据集的 BN 模型，这是一种广泛用于研究反应抑制的范式。连接结果已通过现有文献（包括我们以前的研究）进行验证。通过探索学习到的 BN 的链接强度并将它们与行为表现测量相关联，这种 BN 在连接分析中的新用途为反应抑制的功能性神经通路提供了新的见解。