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

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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.