Functional brain network architecture supporting the learning of social networks in humans.
Functional brain network architecture supporting the learning of social networks in humans.
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DOI：
10.1016/j.neuroimage.2019.116498
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发表时间：
2020-04-15
期刊：
影响因子：
5.7
通讯作者：
Bassett DS
中科院分区：
文献类型：
--
作者：
Tompson SH;Kahn AE;Falk EB;Vettel JM;Bassett DS
大多数人都有幸生活在结构丰富的社会群体中。然而，人类如何获得这些社会结构的知识以成功驾驭社会关系仍不清楚。在这里，我们利用网络科学和统计学习的最新进展，通过跨学科的神经影像学研究来解决这一知识差距。具体来说，我们在参与者学习社交网络和非社交网络的社区结构时收集了BOLD MRI数据，以检验这两种网络的学习是否与功能性大脑网络拓扑结构存在差异。我们发现，参与者学习了网络的社区结构，这一点可以从试验在社区之间进行时的反应时间比在社区内进行时的反应时间慢得到证明。学习社会网络的社区结构还表现为，在社区之间过渡时，海马和颞顶连接的功能连通性显著高于在社区内过渡时。此外，与非社交网络相比，默认模式的颞顶叶区域与海马体、体运动和视觉区域的联系更为紧密。总的来说，我们的研究结果确定了社会网络与非社会网络学习的神经生理基础，扩展了我们对社会环境对学习过程影响的认识。更广泛地说，这项工作提供了一种研究社会网络结构学习的经验方法，可以在未来的研究中有效地扩展到其他参与者群体、各种图架构和各种社会背景。
Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how humans acquire knowledge about these social structures to successfully navigate social relationships. Here we address this knowledge gap with an interdisciplinary neuroimaging study drawing on recent advances in network science and statistical learning. Specifically, we collected BOLD MRI data while participants learned the community structure of both social and non-social networks, in order to examine whether the learning of these two types of networks was differentially associated with functional brain network topology. We found that participants learned the community structure of the networks, as evidenced by a slower reaction time when a trial moved between communities than when a trial moved within a community. Learning the community structure of social networks was also characterized by significantly greater functional connectivity of the hippocampus and temporoparietal junction when transitioning between communities than when transitioning within a community. Furthermore, temporoparietal regions of the default mode were more strongly connected to hippocampus, somatomotor, and visual regions for social networks than for non-social networks. Collectively, our results identify neurophysiological underpinnings of social versus non-social network learning, extending our knowledge about the impact of social context on learning processes. More broadly, this work offers an empirical approach to study the learning of social network structures, which could be fruitfully extended to other participant populations, various graph architectures, and a diversity of social contexts in future studies.
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影响因子：
3.5
作者：
Abraham A;Pedregosa F;Eickenberg M;Gervais P;Mueller A;Kossaifi J;Gramfort A;Thirion B;Varoquaux G
通讯作者：
Varoquaux G
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Adolphs, Ralph
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