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

对社会世界的导航，包括对面部表情的识别，是一项基本而复杂的功能，它是由大脑网络之间的协调互动促进的。面孔在生命早期就成为人类社会环境的重要组成部分，在语言开始之前，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得到充分发展。先前的研究，主要是针对成年人的，已经表明大脑区域的分布式网络促进了情绪面部的处理。然而，在生命早期，人类大脑发生了迅速而深刻的变化，人们对这些大脑区域的组织和功能相互作用知之甚少。为了解决这一重大的知识差距，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孔处理的神经回路，并将系统地描述这些回路在情绪面孔反应中与年龄相关（5到36个月）的组织变化和功能激活。纵向获取的脑电图和同步眼动追踪数据来自约400名正常发育的婴儿，以及在国家科学基金会BRAIN EAGER奖的支持下开发的新型计算工具将用于此目的。这个项目的发现将有助于我们理解在发育中的人类大脑中处理情绪面孔的神经网络，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比，对早期生活中调节情绪面部处理的关键神经回路进行的以网络为重点的研究非常有限。目前尚不清楚发育中的大脑网络是如何补偿不完整的神经成熟来处理情感显著输入的。本项目的总体目标是阐明一大批正常发育的婴儿情绪面部加工的神经网络基础，并将其与相应的行为测量相关联。具体来说，该项目将识别和区分发育中的神经回路的元素，这些元素在生命早期就上线了，对不同的面部表情做出不同的协调反应，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日益分层的神经信息处理。将开发新的信号处理和统计工具，以对这些发育中的大脑网络进行强大的表征。除了显著提高我们对早期识别和描述面部表情能力的神经回路的认识之外，这个项目的发现还可能为早期经历对神经回路的影响提供更好的见解，并可能导致对有发育迟缓风险的婴儿的及时干预，比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，作为该项目一部分开发的计算工具和方法可能具有更广泛的教育影响，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，以进行日益复杂的脑功能研究。

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.