do Nascimento Diego Carvalho, Santos da Silva José Roberto, Ara Anderson, Sato João Ricardo, Costa Lilia
Departamento de Matemática, Facultad de Ingeniería, Universidad de Atacama, Copiapó, Chile.
Department of Statistics, Federal University of Bahia, Salvador, Brazil.
Front Comput Neurosci. 2023 Jul 27;17:1132160. doi: 10.3389/fncom.2023.1132160. eCollection 2023.
Interpersonal neural synchronization (INS) demands a greater understanding of a brain's influence on others. Therefore, brain synchronization is an even more complex system than intrasubject brain connectivity and must be investigated. There is a need to develop novel methods for statistical inference in this context.
In this study, motivated by the analysis of fNIRS hyperscanning data, which measure the activity of multiple brains simultaneously, we propose a two-step network estimation: Tabu search local method and global maximization in the selected subgroup [partial conditional directed acyclic graph (DAG) + multiregression dynamic model]. We illustrate this approach in a dataset of two individuals who are playing the violin together.
This study contributes new tools to the social neuroscience field, which may provide new perspectives about intersubject interactions. Our proposed approach estimates the best probabilistic network representation, in addition to providing access to the time-varying parameters, which may be helpful in understanding the brain-to-brain association of these two players.
The illustration of the violin duo highlights the time-evolving changes in the brain activation of an individual influencing the other one through a data-driven analysis. We confirmed that one player was leading the other given the ROI causal relation toward the other player.
人际神经同步（INS）需要更深入地理解大脑对他人的影响。因此，大脑同步是一个比个体内大脑连接更为复杂的系统，必须加以研究。在这种情况下，需要开发新的统计推断方法。
在本研究中，受功能近红外光谱超扫描数据（可同时测量多个大脑的活动）分析的启发，我们提出了一种两步网络估计方法：禁忌搜索局部方法和在选定子组中的全局最大化[部分条件有向无环图（DAG）+多元回归动态模型]。我们在一个两人一起拉小提琴的数据集上展示了这种方法。
本研究为社会神经科学领域贡献了新工具，可能为主体间互动提供新视角。我们提出的方法除了能够获取时变参数外，还能估计最佳概率网络表示，这可能有助于理解这两位演奏者之间的脑对脑关联。
小提琴二重奏的示例通过数据驱动分析突出了个体大脑激活随时间的变化如何影响另一个个体。我们通过感兴趣区域（ROI）对另一个演奏者的因果关系证实了一个演奏者在引领另一个演奏者。
