BRAIN Initiative: Assessing development of event-related cortical network dynamics

BRAIN Initiative：评估事件相关皮层网络动态的发展

基本信息

批准号：
10190670
负责人：
Arnaud Delorme
金额：
$ 111.48万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10190670
关键词：
Adolescent Affective Age Archives Area BRAIN initiative Bayesian learning Behavioral Brain Brain imaging Child Cognitive Collection Communities Complex Computer Analysis Computer software Data Data Analyses Data Set Data Store Descriptor Development Developmental Course Diet Electroencephalography Electrophysiology (science)Environment Event Exhibits Eye Family Film Functional Magnetic Resonance Imaging Funding Genetic Goals Head High Performance Computing Human Image Individual Institutes Life Style Link Machine Learning Magnetic Resonance Imaging Measures Methods Mind Modeling Monoclonal Antibody R24 Neurosciences New York City Participant Pathway Analysis Pattern Phenotype Process Psychopathology Pythons Research Research Personnel Resources Rest Saccades Sampling Signal Transduction Source System Testing Time Video Recording Videotape Visualization Voice actigraphy animation archive data archived data base biobank brain health computer network computing resources data archive data portal data resource data standards data tools density digital experimental study fitness gaze independent component analysis innovation movie multimodality network models neuroimaging open source psychiatric symptom recruit response sample fixation secondary analysis signal processing source localization tool visual tracking

项目摘要

Project Summary The Child Mind Institute’s Healthy Brain Network (HBN) is an ongoing initiative focused on creating and sharing a biobank of brain and behavioral data now being collected from 10,000 New York City area children and adolescents (ages 5-21). In response to RFA-MH-120-20 (‘Integration and analysis of BRAIN Initiative data’), we will collect their high-density EEG and continuous eye tracking data, recorded from now over 3,000 young participants while they watch movies and rest, plus participant MR head images, to create a developmental EEG and eye tracking data and analysis resource within the BRAIN Initiative-funded NEMAR portal (NEMAR.org) to the OpenNeuro human neuroimaging resource (OpenNeuro.org). We will store the data in accord with the latest advances in the Brain Imaging Data Standard (BIDS), including innovative co-registration of eye-tracking information and EEG, extraction of anonymized Leadfield matrices for advanced participant-based source localization, and Hierarchical Event Descriptor (HED) annotation of movie and eye tracking events in the data, to enable sophisticated exploratory and model-based analyses by any user of the Neuroscience Gateway (nsgportal.org), which offers processing of complex tasks on the U.S. XSEDE computer network (XSEDE.org) for users of standard neuroscience software. We propose to build on the large existing and readily extensible set of tools for analysis, modeling, and visualization of human EEG data in the world leading EEGLAB software environment for electrophysiological signal processing we develop and maintain (5R01-NS047293-15) (sccn.ucsd.edu/eeglab), to process the large corpus of innovative EEG and synchronous eye tracking data recorded during movie viewing by the HBN project. We will then apply source-resolved event-related analysis models to cortical EEG network dynamics associated with movie watching, will model their changes across development, and will compare the time course of these changes with changes in eye gaze patterns at the same age levels. We will use cortical source-resolved, event-related functional EEG connectivity measures implemented in the Source Information Flow Toolbox (SIFT) to model event-related EEG dynamics of individual participants, and will apply advanced hierarchical Bayes-based methods and statistical assessment to perform statistical testing on subject group models. We will study the development of event-related cortical information flow during movie watching and rest across the large HBN developmental (5-21 years) sample, and compare it to the developmental trajectory of changes in eye gaze patterns during movie viewing.

项目概要儿童心灵研究所的健康大脑网络 (HBN) 是一项持续开展的计划，专注于创建和共享目前从纽约市地区 10,000 名儿童收集的大脑和行为数据生物库和青少年（5-21 岁）。响应 RFA-MH-120-20（“BRAIN Initiative 的整合和分析”）。数据”），我们将收集他们的高密度脑电图和连续眼动追踪数据，从现在开始记录超过 3,000 年轻参与者在看电影和休息时，加上参与者的 MR 头部图像，以创建 BRAIN Initiative 资助的 NEMAR 内的发育脑电图和眼动追踪数据和分析资源 OpenNeuro 人类神经影像资源 (OpenNeuro.org) 的门户 (NEMAR.org) 我们将存储数据。符合脑成像数据标准 (BIDS) 的最新进展，包括创新眼动追踪信息和脑电图的共同注册，提取匿名 Leadfield 矩阵以进行高级分析基于参与者的源定位以及电影和眼睛的分层事件描述符 (HED) 注释跟踪数据中的事件，以便任何用户能够进行复杂的探索性和基于模型的分析 Neuroscience Gateway (nsgportal.org)，提供美国 XSEDE 上复杂任务的处理面向标准神经科学软件用户的计算机网络（XSEDE.org）我们建议以大型为基础。现有且易于扩展的工具集，用于对人类脑电图数据进行分析、建模和可视化我们开发和开发世界领先的用于电生理信号处理的 EEGLAB 软件环境维护（5R01-NS047293-15）（sccn.ucsd.edu/eeglab），处理创新脑电图和然后我们将应用 HBN 项目在观看电影时记录的同步眼动追踪数据。与电影相关的皮层脑电图网络动态的源解析事件相关分析模型观察，将对他们在发展过程中的变化进行建模，并比较这些变化的时间进程我们将使用皮层源解析的、与事件相关的眼睛注视模式的变化。在源信息流工具箱（SIFT）中实施功能性脑电图连接措施来建模个体参与者的事件相关脑电图动态，并将应用基于高级分层贝叶斯的我们将研究对主题组模型进行统计测试的方法和统计评估。观看电影和休息期间，整个 HBN 中与事件相关的皮层信息流的发展发育（5-21岁）样本，并将其与眼睛注视变化的发育轨迹进行比较观看电影时的模式。