BRAIN EAGER: Robust longitudinal characterization of brain oscillations in the first 3 years of life

人的大脑在生命的前三年经历快速而深刻的变化，伴随着新的认知技能的出现，包括记忆和识别面孔和物体、获取词汇以及将注意力集中在手头的任务上等。 虽然如此丰富的功能需要多个大脑区域的协调，但人们对不同大脑区域的大脑电活动变化如何与行为变化相关联知之甚少。这种知识差距的部分原因是主要的脑成像方法——功能磁共振成像（fMRI）——所带来的方法学限制。 fMRI不仅要求婴儿或幼儿在观察过程中保持静止或睡眠状态，而且无法以高时间分辨率（毫秒）直接测量神经元活动的快速变化。 随着最近技术和计算的进步，克服这些技术障碍并获得行为婴儿脑电活动的直接测量已经成为可能。在美国国家科学基金会的支持下，波士顿儿童医院/哈佛医学院认知神经科学实验室的 Stamoulis 博士及其同事将获得难得的机会，利用多种先进的计算方法，系统地表征神经信号中与发育相关的变化，这些神经信号源自 3 至 36 个月大的婴儿和幼儿的纵向 hdEEG 数据。这项研究将提供有关大脑在早期发育过程中如何变化的基本信息。该项目的研究结果预计还将帮助教育家庭了解早期经历如何塑造大脑并促进认知功能，并将激发新课程和教学材料的开发，以教育学生、研究人员和临床医生了解认知发展的行为和神经机制之间的关系。该项目是一项雄心勃勃的尝试，通过分析从同一婴儿在生命的前三年收集的高密度脑电图（hdEEG）数据，使用源定位和不同功能大脑内部和之间的神经振荡频率分析，来描述人类大脑发育中的变化。地区。 该研究将集中于表征早期发育过程中多个时间点的不同空间位置的脑电信号的振荡波形。 这些波形在不同频段的功率，例如众所周知，theta、alpha、beta 和 gamma 功率在早期发育的不同时间点出现，并与外部刺激、信息处理需求和行为的变化相关。 然而，在这个年龄范围内，大脑区域的主要振荡频率、功率和空间分布与年龄相关的变化尚未得到系统的表征。 将在相同类型的任务和无任务条件下分析约 200 名 3、6、9、12、18、24 和 36 个月龄正常发育婴儿的纵向高密度脑电图数据。 新颖的源分析方法将应用于 hdEEG，以提取和定位主要源，并将源信号分解为单独的振荡分量，并在各个年龄段进行比较。休息和功能网络以及已识别来源之间的定向连接也将被系统地量化并进行跨年龄比较。该项目预计将提供一种新的基于源代码的语言，用于利用脑电图研究人类大脑的发育，并揭示神经信号如何随时间、频率和大脑空间变化，从而使婴儿能够与世界交流并获得新技能。

The human brain undergoes rapid and profound changes during the first 3 years of life, which accompany the emergence of new cognitive skills, including remembering and recognizing faces and objects, acquiring vocabularies, and focusing attention on the task at hand, among others. While such a rich repertoire of functions requires the coordination of multiple brain regions, little is known about how changes in the brain's electrical activity across different brain regions correlate with changes in behavior. This knowledge gap is in part due to methodological limitations imposed by the predominant brain imaging method, functional magnetic resonance imaging (fMRI). fMRI not only requires infants or toddlers to remain still or asleep during the observation, but also cannot directly measure rapid changes in neuronal activity at a high temporal resolutions (millisecond). With recent technological and computational advances, it has become possible to overcome these technical barriers and obtain direct measurements of brain electrical activity in behaving infants. With the support of the National Science foundation, Dr Stamoulis and colleagues at the Laboratory of Cognitive Neuroscience at Boston Children's Hospital/Harvard Medical School, will have the rare opportunity to systematically characterize development-related changes in neural signals derived from longitudinal hdEEG data acquired in infants and toddlers repeatedly across 3 to 36 months of age using a number of advanced computational approaches. This study will provide fundamental information regarding how the brain changes across early development. Findings from this project are also expected to help educate families on how early experiences shape the brain and facilitate cognitive functions, and will inspire the development of new courses and instructional materials to educate students, researchers and clinicians on the relationships between behavioral and neural mechanisms of cognitive development.The project is an ambitious attempt at characterizing changes in the developing human brain by analysing high-density electroencephalography (hdEEG) data collected from the same infants across the first three years of life using source localization and frequency analysis of neural oscillations within and between different functional brain regions. The investigation will focus on characterizing oscillatory waveforms of brain electrical signals originating from different spatial locations across multiple time points during early development. The power of these waveforms in different frequency bands, e.g. theta, alpha, beta, and gamma power, are known to emerge at different time points during early development and to be associated with variations in external stimuli, information processing demands, and behaviors. However, age-related changes in the dominant oscillation frequency, power and spatial distribution among brain regions have not been systematically characterized during this age range. Longitudinal high-density EEG data from about 200 typically developing infants at 3, 6, 9, 12, 18, 24, and 36 months of age will be analyzed under the same type of tasks and no-task conditions. Novel source analysis methods will be applied to hdEEGs, to extract and localize dominant sources and to decompose source signals into individual oscillation components and compare them across ages. Resting and functional networks and directional connectivities between identified sources will also be systematically quantified and compared across ages. This project is expected to provide a new source-based language for investigating human brain development using EEG and to reveal how neural signals change in time, frequency and brain spaces to enable infants to communicate with the world and to acquire new skills.