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A Bayesian method for inference of effective connectivity in brain networks for detecting the Mozart effect
Computers in Biology and Medicine ( IF 7.7 ) Pub Date : 2020-10-15 , DOI: 10.1016/j.compbiomed.2020.104055
Rik J C van Esch 1 , Shengling Shi 1 , Antoine Bernas 1 , Svitlana Zinger 1 , Albert P Aldenkamp 2 , Paul M J Van den Hof 1
Affiliation  

Several studies claim that listening to Mozart music affects cognition and can be used to treat neurological conditions like epilepsy. Research into this Mozart effect has not addressed how dynamic interactions between brain networks, i.e. effective connectivity, are affected. The Granger-causality analysis is often used to infer effective connectivity. First, we investigate if a new method, Bayesian topology identification, can be used as an alternative. Both methods are evaluated on simulation data, where the Bayesian method outperforms the Granger-causality analysis in the inference of connectivity graphs of dynamic networks, especially for short data lengths. In the second part, the Bayesian method is extended to enable the inference of changes in effective connectivity between groups of subjects. Next, we apply both methods to fMRI scans of 16 healthy subjects, who were scanned before and after the exposure to Mozart's sonata K448 at least 2 hours a day for 7 days. Here, we investigate if the effective connectivity of the subjects significantly changed after listening to Mozart music. The Bayesian method detected changes in effective connectivity between networks related to cognitive processing and control in the connection from the central executive to the superior sensori-motor network, in the connection from the posterior default mode to the fronto-parietal right network, and in the connection from the anterior default mode to the dorsal attention network. This last connection was only detected in a subgroup of subjects with a longer listening duration. Only in this last connection, an effect was found by the Granger-causality analysis.



中文翻译:

用于检测莫扎特效应的脑网络有效连通性的贝叶斯方法

多项研究声称,听莫扎特音乐会影响认知,并可用于治疗癫痫症等神经系统疾病。对这种莫扎特效应的研究尚未解决大脑网络之间的动态交互(即有效连接性)如何受到影响。格兰杰因果关系分析通常用于推断有效的连通性。首先,我们调查是否可以将贝叶斯拓扑识别这一新方法用作替代方法。两种方法都在仿真数据上进行评估,其中贝叶斯方法在推断动态网络的连通性图方面优于格兰杰因果分析,尤其是对于较短的数据长度而言。在第二部分中,对贝叶斯方法进行了扩展,以推断对象组之间有效连接的变化。下一个,我们将这两种方法应用于16名健康受试者的fMRI扫描,这些受试者在每天至少2小时,连续7天暴露于莫扎特奏鸣曲K448之前和之后进行扫描。在这里,我们调查了听莫扎特音乐后,对象的有效连接性是否发生了显着变化。贝叶斯方法检测到与认知处理和控制相关的网络之间的有效连接的变化,该连接在从中央执行器到上层感觉运动网络的连接中,在从后默认模式到额顶右侧网络的连接中以及在从前默认模式到背侧注意网络的连接。仅在听力持续时间较长的受试者子组中检测到了最后一个连接。仅在最后这一联系中,格兰杰因果关系分析才发现了一种影响。

更新日期:2020-11-04
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