Dynamic changes in neural circuitry underlying emotional face processing in early life: network re-organization and functional interactions

社交世界的导航，包括面部表情的识别，是一项重要而复杂的功能，它由大脑网络之间的协调互动促进。面孔在生命早期就成为人类社会环境的重要组成部分，在语言出现之前，婴儿依赖于他们阅读面孔的能力，这表明潜在的神经回路可能在生命早期就已经充分发育。以前的研究，主要是在成年人中，已经表明大脑区域的分布式网络促进了情绪化的面部处理。然而，人们对这些大脑区域在生命早期的组织和功能相互作用知之甚少，这是人类大脑快速而深刻变化的时期。为了解决这一知识上的重大差距，Stamoulis博士和她在波士顿儿童医院/哈佛医学院的合作者将研究涉及情绪面部处理的神经回路，并将系统地描述年龄相关（5至36个月）的组织变化和这些回路对情绪面部的功能激活。纵向采集的EEG和同步眼动跟踪数据来自约400名典型发育中的婴儿，在国家科学基金会BRAIN EAGER奖的支持下开发的新型计算工具将用于此目的。该项目的发现将有助于我们理解发育中的人类大脑中参与处理情感面孔的神经网络，以及识别这些网络的年龄不变和经验依赖方面。与大量的行为研究相反，在早期生活中介导情绪面部处理的关键神经回路的网络研究非常有限。目前尚不清楚发育中的大脑网络如何补偿不完全的神经成熟，以处理情绪上显著的输入。该项目的首要目标是阐明在一个大的队列中的典型发育的婴儿的情绪面部处理的神经网络基板，并将它们与相应的行为措施。具体来说，该项目将识别和区分在生命早期上线的发育中的神经回路的元素，并在响应不同的面部表情时进行差异协调，并将系统地描述这些元素中与年龄相关的拓扑和功能变化如何促进越来越多的分层神经信息处理。将开发新的信号处理和统计工具，用于对这些正在发展的大脑网络进行鲁棒表征。除了显着提高我们对神经回路的了解，这些神经回路是我们在生命早期识别和表征面部表情的能力的基础，该项目的发现还可以提供对早期经历对神经回路影响的更好见解，并可能导致对有发育迟缓风险的婴儿进行及时干预，例如那些在压力环境中长大的婴儿。最后，鉴于网络神经科学是该领域研究的一个相对较新的方面，作为该项目的一部分开发的计算工具和方法可能会产生更广泛的教育影响，并可能为下一代认知神经科学家的科学准备做出重大贡献，以应对日益复杂的大脑功能研究。

Navigation of the social world, including recognition of facial expressions, is an essential and complex function that is facilitated by coordinated interactions between brain networks. Faces become a critical part of the human social environment early in life and prior to the onset of language infants depend on their ability to read faces, suggesting that the underlying neural circuitry may be sufficiently developed early in life. Previous studies, predominantly in adults, have shown that a distributed network of brain regions facilitates emotional face processing. However little is known about the organization and functional interactions of these brain regions in early life, a period of rapid and profound changes in the human brain. To address this significant gap in knowledge, Dr. Stamoulis and her collaborators at Boston Children's Hospital/Harvard Medical School will investigate the neural circuits involved in emotional face processing and will systematically characterize age-related (5 to 36 months) changes in the organization and functional activation of these circuits in response to emotional faces. Longitudinally acquired EEG and simultaneous eye-tracking data from ~400 typically developing infants and novel computational tools developed with the support of a National Science Foundation BRAIN EAGER award will be used for this purpose. Findings from this project will facilitate our understanding of the neural networks involved in processing emotional faces in the developing human brain, and the identification of age-invariant and experience-dependent aspects of these networks. In contrast to a large volume of behavioral studies, there are very limited network-focused investigations of the critical neural circuitry that mediates emotional face processing in early life. It is currently unclear how developing brain networks compensate for incomplete neural maturation to process emotionally salient inputs. The overarching goal of this project is to elucidate the neuronal network substrates of emotional face processing in a large cohort of typically developing infants and correlate them with corresponding behavioral measures. Specifically, this project will identify and distinguish elements of the developing neural circuitry that come on line early in life and are differentially coordinated in response to distinct facial expressions, and will systematically characterize how age-related topological and functional changes in these elements facilitate increasingly hierarchical neural information processing. Novel signal processing and statistical tools will be developed for robust characterization of these developing brain networks. In addition to significantly improving our knowledge of the neural circuitry underlying our ability to recognize and characterize facial expressions early in life, findings from this project may also provide improved insights into the impact of early experiences on neural circuitry and may lead to timely interventions for infants at risk of developmental delays, such as those growing up in stressful environments. Finally, given that network neuroscience is a relatively new aspect of research in the field, computational tools and approaches developed as part of this project may have a broader educational impact and may substantially contribute to the scientific preparation of next-generation cognitive neuroscientists for increasingly sophisticated investigations of brain function.